From 75c048a2fc14881082c790ed6146a929df64b4d1 Mon Sep 17 00:00:00 2001 From: Mateo Gianolio Date: Tue, 15 Sep 2020 16:44:18 +0200 Subject: [PATCH] 6.0.0 (#193) * upd dependencies * es5 => es2015 * prepare strides * Prepare iterator * Add strided iterator * Working strides for abs * Fix #153, #172 * Mega-cleanup commit (types, docs, tests, etc) * Fix examples * More cleanup * Tslint * Export statics globally and cleanup * Fix examples * Add NDMultiIter class * Fix * Export both iterators * Add todo note * Update version * Updates * Switch to jest * Add github actions workflow * Remove .travis.yml * Fix conflict * GH Actions fix * Fix * Skip code coverage for now * Fix bug --- .github/workflows/ci.yml | 46 + .gitignore | 1 + .travis.yml | 27 - dist/vectorious.min.js | 2 +- docs.tmpl | 69 + docs/assets/css/main.css | 2333 ----- docs/assets/images/icons.png | Bin 9615 -> 0 bytes docs/assets/images/icons@2x.png | Bin 28144 -> 0 bytes docs/assets/images/widgets.png | Bin 480 -> 0 bytes docs/assets/images/widgets@2x.png | Bin 855 -> 0 bytes docs/assets/js/main.js | 1 - docs/assets/js/search.js | 3 - docs/classes/ndarray.html | 7852 --------------- docs/index.html | 873 -- docs/interfaces/indarray.html | 312 - examples/blas.ts | 6 +- examples/logistic-regression.ts | 35 +- examples/neural-network.ts | 30 +- examples/solve.ts | 6 +- examples/text_raytracer.ts | 79 +- gulpfile.js | 16 +- jest.config.js | 195 + jsdoc.json | 37 + package-lock.json | 14359 +++++++++++++++++++--------- package.json | 68 +- src/bench.ts | 12 +- src/blas.ts | 114 + src/core/abs.bench.ts | 13 +- src/core/abs.spec.ts | 17 +- src/core/abs.ts | 38 +- src/core/acos.bench.ts | 13 +- src/core/acos.spec.ts | 17 +- src/core/acos.ts | 38 +- src/core/acosh.bench.ts | 13 +- src/core/acosh.spec.ts | 18 +- src/core/acosh.ts | 39 +- src/core/add.bench.ts | 13 +- src/core/add.spec.ts | 41 +- src/core/add.ts | 75 +- src/core/angle.bench.ts | 13 +- src/core/angle.spec.ts | 13 +- src/core/angle.ts | 35 +- src/core/array.bench.ts | 7 +- src/core/array.spec.ts | 15 +- src/core/array.ts | 16 +- src/core/asin.bench.ts | 13 +- src/core/asin.spec.ts | 17 +- src/core/asin.ts | 38 +- src/core/asinh.bench.ts | 13 +- src/core/asinh.spec.ts | 17 +- src/core/asinh.ts | 38 +- src/core/atan.bench.ts | 13 +- src/core/atan.spec.ts | 17 +- src/core/atan.ts | 38 +- src/core/atanh.bench.ts | 13 +- src/core/atanh.spec.ts | 17 +- src/core/atanh.ts | 38 +- src/core/augment.bench.ts | 13 +- src/core/augment.spec.ts | 27 +- src/core/augment.ts | 38 +- src/core/binOp.bench.ts | 14 +- src/core/binOp.spec.ts | 32 +- src/core/binOp.ts | 56 +- src/core/cbrt.bench.ts | 13 +- src/core/cbrt.spec.ts | 17 +- src/core/cbrt.ts | 38 +- src/core/ceil.bench.ts | 13 +- src/core/ceil.spec.ts | 17 +- src/core/ceil.ts | 38 +- src/core/check.bench.ts | 16 +- src/core/check.spec.ts | 10 +- src/core/check.ts | 31 +- src/core/combine.bench.ts | 14 +- src/core/combine.spec.ts | 14 +- src/core/combine.ts | 31 +- src/core/copy.bench.ts 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src/core/log10.bench.ts | 13 +- src/core/log10.spec.ts | 17 +- src/core/log10.ts | 38 +- src/core/log1p.bench.ts | 13 +- src/core/log1p.spec.ts | 17 +- src/core/log1p.ts | 38 +- src/core/log2.bench.ts | 13 +- src/core/log2.spec.ts | 17 +- src/core/log2.ts | 38 +- src/core/lu.bench.ts | 13 +- src/core/lu.spec.ts | 36 +- src/core/lu.ts | 61 +- src/core/lu_factor.bench.ts | 13 +- src/core/lu_factor.spec.ts | 20 +- src/core/lu_factor.ts | 57 +- src/core/magic.bench.ts | 7 +- src/core/magic.spec.ts | 9 +- src/core/magic.ts | 42 +- src/core/map.bench.ts | 13 +- src/core/map.spec.ts | 16 +- src/core/map.ts | 49 +- src/core/matrix.bench.ts | 7 +- src/core/matrix.spec.ts | 16 +- src/core/matrix.ts | 20 +- src/core/max.bench.ts | 13 +- src/core/max.spec.ts | 21 +- src/core/max.ts | 75 +- src/core/mean.bench.ts | 13 +- src/core/mean.spec.ts | 15 +- src/core/mean.ts | 34 +- src/core/min.bench.ts | 13 +- src/core/min.spec.ts | 21 +- src/core/min.ts | 43 +- src/core/multiply.bench.ts | 13 +- 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src/core/slice.spec.ts | 20 +- src/core/slice.ts | 70 +- src/core/solve.bench.ts | 13 +- src/core/solve.spec.ts | 24 +- src/core/solve.ts | 59 +- src/core/sqrt.bench.ts | 13 +- src/core/sqrt.spec.ts | 17 +- src/core/sqrt.ts | 38 +- src/core/square.bench.ts | 13 +- src/core/square.spec.ts | 9 +- src/core/square.ts | 29 +- src/core/subtract.bench.ts | 13 +- src/core/subtract.spec.ts | 38 +- src/core/subtract.ts | 33 +- src/core/sum.bench.ts | 13 +- src/core/sum.spec.ts | 15 +- src/core/sum.ts | 36 +- src/core/swap.bench.ts | 15 +- src/core/swap.spec.ts | 17 +- src/core/swap.ts | 33 +- src/core/tan.bench.ts | 13 +- src/core/tan.spec.ts | 17 +- src/core/tan.ts | 38 +- src/core/tanh.bench.ts | 13 +- src/core/tanh.spec.ts | 17 +- src/core/tanh.ts | 38 +- src/core/toArray.bench.ts | 14 +- src/core/toArray.spec.ts | 26 +- src/core/toArray.ts | 27 +- src/core/toString.bench.ts | 14 +- src/core/toString.spec.ts | 31 +- src/core/toString.ts | 31 +- src/core/trace.bench.ts | 13 +- src/core/trace.spec.ts | 15 +- src/core/trace.ts | 27 +- src/core/transpose.bench.ts | 13 +- src/core/transpose.spec.ts | 35 +- src/core/transpose.ts | 52 +- src/core/trunc.bench.ts | 13 +- src/core/trunc.spec.ts | 17 +- src/core/trunc.ts | 42 +- src/core/zeros.bench.ts | 7 +- src/core/zeros.spec.ts | 16 +- src/core/zeros.ts | 19 +- src/index.ts | 8 +- src/iterator/index.spec.ts | 122 + src/iterator/index.ts | 539 ++ src/lapack.ts | 54 + src/types.ts | 1 + src/util.ts | 52 +- tsconfig.json | 6 +- tslint.json | 38 - 294 files changed, 15684 insertions(+), 19352 deletions(-) create mode 100644 .github/workflows/ci.yml delete mode 100644 .travis.yml create mode 100644 docs.tmpl delete mode 100644 docs/assets/css/main.css delete mode 100644 docs/assets/images/icons.png delete mode 100644 docs/assets/images/icons@2x.png delete mode 100644 docs/assets/images/widgets.png delete mode 100644 docs/assets/images/widgets@2x.png delete mode 100644 docs/assets/js/main.js delete mode 100644 docs/assets/js/search.js delete mode 100644 docs/classes/ndarray.html delete mode 100644 docs/index.html delete mode 100644 docs/interfaces/indarray.html create mode 100644 jest.config.js create mode 100644 jsdoc.json create mode 100644 src/blas.ts create mode 100644 src/iterator/index.spec.ts create mode 100644 src/iterator/index.ts create mode 100644 src/lapack.ts delete mode 100644 tslint.json diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..041c2cab --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,46 @@ +name: CI + +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + +jobs: + test: + name: Test on node ${{ matrix.node_version }} and ${{ matrix.os }} + runs-on: ${{ matrix.os }} + + strategy: + matrix: + node_version: [14.x] + os: [ubuntu-latest, macOS-latest] + + steps: + - uses: actions/checkout@v2 + + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v1 + with: + node-version: ${{ matrix.node-version }} + + - name: Setup C++ environment on linux + uses: kurocha/setup-cpp@v1 + if: runner.os == 'Linux' + + - name: Install dependencies on linux + run: sudo apt-get install -yq libblas-dev liblapack-dev liblapacke-dev libpython-dev + if: runner.os == 'Linux' + + - name: Install dependencies on mac + run: brew install lapack + if: runner.os == 'macOS' + + - name: Install package dependencies + run: npm ci + + - name: Build + run: npm run build + + - name: Test + run: npm test diff --git a/.gitignore b/.gitignore index c1566fea..cdccac61 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ coverage .DS_Store built benchmarks +docs \ No newline at end of file diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 2e5eeae1..00000000 --- a/.travis.yml +++ /dev/null @@ -1,27 +0,0 @@ -language: node_js -node_js: - - "lts/*" -before_install: - - sudo apt-get install libblas-dev libpython-dev -install: - - npm install -env: - global: - - 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.tsd-navigation.primary a { - display: block; - padding-top: 6px; - padding-bottom: 6px; } - .tsd-navigation.primary ul li a { - padding-left: 5px; } - .tsd-navigation.primary ul li li a { - padding-left: 25px; } - .tsd-navigation.primary ul li li li a { - padding-left: 45px; } - .tsd-navigation.primary ul li li li li a { - padding-left: 65px; } - .tsd-navigation.primary ul li li li li li a { - padding-left: 85px; } - .tsd-navigation.primary ul li li li li li li a { - padding-left: 105px; } - .tsd-navigation.primary > ul { - border-bottom: 1px solid #eee; } - .tsd-navigation.primary li { - border-top: 1px solid #eee; } - .tsd-navigation.primary li.current > a { - font-weight: bold; } - .tsd-navigation.primary li.label span { - display: block; - padding: 20px 0 6px 5px; - color: #808080; } - .tsd-navigation.primary li.globals + li > span, - .tsd-navigation.primary li.globals + li > a { - padding-top: 20px; } - -.tsd-navigation.secondary ul { - transition: opacity 0.2s; } - 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    Class NDArray

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    Hierarchy

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        Returns string

        -
        • -
      -
      • -
    -
    -
    -
    - -

    abs

    -
    abs: function
    - -
    -
    -

    Returns the absolute value of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    acos

    -
    acos: function
    - -
    -
    -

    Returns the arccosine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    acosh

    -
    acosh: function
    - -
    -
    -

    Returns the hyperbolic arccosine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    add

    -
    add: function
    - -
    -
    -

    Adds x multiplied by alpha to the current array. - Accelerated with BLAS ?axpy.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T, alpha?: number): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        • -
          Optional alpha: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    angle

    -
    angle: function
    - -
    -
    -

    Determines the angle between the current vector and x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    asin

    -
    asin: function
    - -
    -
    -

    Returns the arcsine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    asinh

    -
    asinh: function
    - -
    -
    -

    Returns the hyperbolic arcsine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    atan

    -
    atan: function
    - -
    -
    -

    Returns the arctangent of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    atanh

    -
    atanh: function
    - -
    -
    -

    Returns the hyperbolic arctangent of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    augment

    -
    augment: function
    - -
    -
    -

    Augments x with current matrix.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    binOp

    -
    binOp: function
    - -
    -
    -

    Perform binary operation f on x in the current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T, f: function): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        • -
          f: function
          -
            -
          • -
              -
            • (a: number, b: number, index: number): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                a: number
                -
                • -
              • -
                b: number
                -
                • -
              • -
                index: number
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    cbrt

    -
    cbrt: function
    - -
    -
    -

    Returns the cube root of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    ceil

    -
    ceil: function
    - -
    -
    -

    Returns smallest integer greater than or equal to of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    check

    -
    check: function
    - -
    -
    -

    Asserts if indices i, j, ..., n are within the bounds of current array

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (...indices: number[]): void
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          Rest ...indices: number[]
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    combine

    -
    combine: function
    - -
    -
    -

    Combines the current vector with x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    copy

    -
    copy: function
    - -
    -
    -

    Makes a copy of the class and underlying data

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    cos

    -
    cos: function
    - -
    -
    -

    Returns the cosine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    cosh

    -
    cosh: function
    - -
    -
    -

    Returns the hyperbolic cosine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    cross

    -
    cross: function
    - -
    -
    -

    Computes the cross product of the current vector and the vector x - This operation can only be calculated for vectors with three components. - Otherwise it throws an exception. - The method returns a new (result) vector.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    data

    -
    data: TypedArray = new Float32Array(0)
    - -
    -
    - -

    det

    -
    det: function
    - -
    -
    -

    Gets the determinant of current matrix using LU factorization.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    diagonal

    -
    diagonal: function
    - -
    -
    -

    Gets the diagonal of current matrix.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    dot

    -
    dot: function
    - -
    -
    -

    Performs dot multiplication with x and current array - Accelerated with BLAS ?dot.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    dtype

    -
    dtype: DType = "float32"
    - -
    -
    - -

    eig

    -
    eig: function
    - -
    -
    -

    Gets eigenvalues and eigenvectors of the current matrix using the Jacobi method. - Accelerated with LAPACK ?geev.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(): [T, T]
        • -
      -
        -
      • -

        Type parameters

        - -

        Returns [T, T]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    equals

    -
    equals: function
    - -
    -
    -

    Checks if current array and x are equal.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): boolean
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns boolean

        -
        • -
      -
      • -
    -
    -
    -
    - -

    equidimensional

    -
    equidimensional: function
    - -
    -
    -

    Asserts if current array and x have the same shape

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    equilateral

    -
    equilateral: function
    - -
    -
    -

    Asserts if current array and x have the same length

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    exp

    -
    exp: function
    - -
    -
    -

    Returns e^x of each element of current array, where x is the argument, - and e is Euler's constant (2.718…), the base of the natural logarithm.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    expm1

    -
    expm1: function
    - -
    -
    -

    Returns subtracting 1 from exp(x) of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    fill

    -
    fill: function
    - -
    -
    -

    Fills the current array with a scalar value

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (value: number | function): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          value: number | function
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    floor

    -
    floor: function
    - -
    -
    -

    Returns the largest integer less than or equal to a number of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    forEach

    -
    forEach: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.forEach.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (f: function): void
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          f: function
          -
            -
          • -
              -
            • (value: number, i: number, src: TypedArray): void
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns void

              -
              • -
            -
            • -
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    fround

    -
    fround: function
    - -
    -
    -

    Returns the nearest single precision float representation of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    gauss

    -
    gauss: function
    - -
    -
    -

    Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of current matrix.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    get

    -
    get: function
    - -
    -
    -

    Gets the element at i, j, ..., n from current vector.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (...indices: number[]): number
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          Rest ...indices: number[]
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    inv

    -
    inv: function
    - -
    -
    -

    Determines the inverse of current matrix using Gaussian elimination. - Accelerated with LAPACK ?getri.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    length

    -
    length: number = 0
    - -
    -
    - -

    log

    -
    log: function
    - -
    -
    -

    Returns the natural logarithm (log_e, also ln) of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    log10

    -
    log10: function
    - -
    -
    -

    Returns the natural logarithm (log_e, also ln) of 1 + x for each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    log1p

    -
    log1p: function
    - -
    -
    -

    Returns the base 2 logarithm of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    log2

    -
    log2: function
    - -
    -
    -

    Returns the base 10 logarithm of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    lu

    -
    lu: function
    - -
    -
    -

    Performs full LU decomposition on current matrix. - Accelerated with LAPACK ?getrf.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(): [T, T, Int32Array]
        • -
      -
        -
      • -

        Type parameters

        - -

        Returns [T, T, Int32Array]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    lu_factor

    -
    lu_factor: function
    - -
    -
    -

    Performs LU factorization on current matrix. - Accelerated with LAPACK ?getrf.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): [this, Int32Array]
        • -
      -
        -
      • -

        Returns [this, Int32Array]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    map

    -
    map: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.map.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (f: function): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          f: function
          -
            -
          • -
              -
            • (value: number, i: number, src: TypedArray): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    max

    -
    max: function
    - -
    -
    -

    Gets the maximum value (smallest) element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    mean

    -
    mean: function
    - -
    -
    -

    Gets the arithmetic mean of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    min

    -
    min: function
    - -
    -
    -

    Gets the minimum value (smallest) element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    multiply

    -
    multiply: function
    - -
    -
    -

    Multiplies current matrix with x. - Accelerated with BLAS ?gemm.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    norm

    -
    norm: function
    - -
    -
    -

    Calculates the norm of current array (also called L2 norm or Euclidean length). - Accelerated with BLAS ?nrm2.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    normalize

    -
    normalize: function
    - -
    -
    -

    Normalizes current vector.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    pow

    -
    pow: function
    - -
    -
    -

    Returns each element of current array to the exponent power, that is, element^exponent.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (exponent: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          exponent: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    prod

    -
    prod: function
    - -
    -
    -

    Product of all elements of current array

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    product

    -
    product: function
    - -
    -
    -

    Hadamard product of current matrix and x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    project

    -
    project: function
    - -
    -
    -

    Projects the current vector onto x using the projection formula (y * (x * y / y * y)).

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    push

    -
    push: function
    - -
    -
    -

    Pushes a new value into current vector.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (value: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          value: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    rank

    -
    rank: function
    - -
    -
    -

    Finds the rank of current matrix using gaussian elimination.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    reciprocal

    -
    reciprocal: function
    - -
    -
    -

    Gets the element-wise reciprocal of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    reduce

    -
    reduce: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.reduce.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (f: function, initialValue?: number): number
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          f: function
          -
            -
          • -
              -
            • (acc: number, value: number, i: number, src: TypedArray): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                acc: number
                -
                • -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        • -
          Optional initialValue: number
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    reshape

    -
    reshape: function
    - -
    -
    -

    Reshapes current array

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (...shape: number[]): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          Rest ...shape: number[]
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    round

    -
    round: function
    - -
    -
    -

    Returns the value of each element of current array rounded to the nearest integer.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    row_add

    -
    row_add: function
    - -
    -
    -

    Adds a multiple of one row multiplied by scalar to another inside current matrix.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (dest: number, source: number, scalar?: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          dest: number
          -
          • -
        • -
          source: number
          -
          • -
        • -
          Optional scalar: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    scale

    -
    scale: function
    - -
    -
    -

    Multiplies all elements of current array with a specified scalar. - Accelerated with BLAS ?scal.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (scalar: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          scalar: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    set

    -
    set: function
    - -
    -
    -

    Sets the element at i, j, ..., n to value.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (...args: number[]): void
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          Rest ...args: number[]
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    shape

    -
    shape: number[] = [0]
    - -
    -
    - -

    sign

    -
    sign: function
    - -
    -
    -

    Returns the sign of each element of current array, indicating - whether it is positive, negative or zero.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    sin

    -
    sin: function
    - -
    -
    -

    Returns the sine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    sinh

    -
    sinh: function
    - -
    -
    -

    Returns the hyperbolic sine of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    slice

    -
    slice: function
    - -
    -
    -

    Slices the current array in the corresponding dimension

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (start?: number, step?: number, end?: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          Optional start: number
          -
          • -
        • -
          Optional step: number
          -
          • -
        • -
          Optional end: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    solve

    -
    solve: function
    - -
    -
    -

    Solves the equation AX = B (where A is current matrix and B is x). - Accelerated with LAPACK ?gesv.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    sqrt

    -
    sqrt: function
    - -
    -
    -

    Returns the positive square root of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    square

    -
    square: function
    - -
    -
    -

    Asserts if current matrix is square.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): void
        • -
      -
        -
      • -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    subtract

    -
    subtract: function
    - -
    -
    -

    Subtracts x from the current array. - Accelerated with BLAS ?axpy.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(y: T): this
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          y: T
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    sum

    -
    sum: function
    - -
    -
    -

    Sum of array elements

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    swap

    -
    swap: function
    - -
    -
    -

    Swaps two rows i and j in current matrix

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (i: number, j: number): this
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          i: number
          -
          • -
        • -
          j: number
          -
          • -
        -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    tan

    -
    tan: function
    - -
    -
    -

    Returns the tangent of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    tanh

    -
    tanh: function
    - -
    -
    -

    Returns the hyperbolic tangent of each element of current array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    toArray

    -
    toArray: function
    - -
    -
    -

    Converts current vector into a JavaScript array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number[]
        • -
      -
        -
      • -

        Returns number[]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    toString

    -
    toString: function
    - -
    -
    -

    Converts current vector into a readable formatted string.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): string
        • -
      -
        -
      • -

        Returns string

        -
        • -
      -
      • -
    -
    -
    -
    - -

    trace

    -
    trace: function
    - -
    -
    -

    Gets the trace of the matrix (the sum of all diagonal elements).

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): number
        • -
      -
        -
      • -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    transpose

    -
    transpose: function
    - -
    -
    -

    Transposes current matrix (mirror across the diagonal).

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    trunc

    -
    trunc: function
    - -
    -
    -

    Returns the integer part of each element of current array, - removing any fractional digits.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (): this
        • -
      -
        -
      • -

        Returns this

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static abs

    -
    abs: function
    - -
    -
    -

    Returns the absolute value of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static acos

    -
    acos: function
    - -
    -
    -

    Returns the arccosine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static acosh

    -
    acosh: function
    - -
    -
    -

    Returns the hyperbolic arccosine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static add

    -
    add: function
    - -
    -
    -

    Adds y multiplied by alpha to x. - Accelerated with BLAS ?axpy.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>, alpha?: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        • -
          Optional alpha: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static angle

    -
    angle: function
    - -
    -
    -

    Determines the angle between the x and y

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static array

    -
    array: function
    - -
    -
    -

    Constructor alias

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(...args: any[]): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          Rest ...args: any[]
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static asin

    -
    asin: function
    - -
    -
    -

    Returns the arcsine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static asinh

    -
    asinh: function
    - -
    -
    -

    Returns the hyperbolic arcsine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static atan

    -
    atan: function
    - -
    -
    -

    Returns the arctangent of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static atanh

    -
    atanh: function
    - -
    -
    -

    Returns the hyperbolic arctangent of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static augment

    -
    augment: function
    - -
    -
    -

    Augments x and y.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static binOp

    -
    binOp: function
    - -
    -
    -

    Perform binary operation f on y in x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>, f: function): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        • -
          f: function
          -
            -
          • -
              -
            • (a: number, b: number, index: number): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                a: number
                -
                • -
              • -
                b: number
                -
                • -
              • -
                index: number
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static cbrt

    -
    cbrt: function
    - -
    -
    -

    Returns the cube root of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static ceil

    -
    ceil: function
    - -
    -
    -

    Returns smallest integer greater than or equal to of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static check

    -
    check: function
    - -
    -
    -

    Asserts if indices i, j, ..., n are within the bounds of x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, ...indices: number[]): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          Rest ...indices: number[]
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static combine

    -
    combine: function
    - -
    -
    -

    Combines the vector x with y

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static copy

    -
    copy: function
    - -
    -
    -

    Makes a copy of x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static cos

    -
    cos: function
    - -
    -
    -

    Returns the cosine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static cosh

    -
    cosh: function
    - -
    -
    -

    Returns the hyperbolic cosine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static cross

    -
    cross: function
    - -
    -
    -

    Computes the cross product of the x and the vector y - This operation can only calculated for vectors with three components. - Otherwise it throws an exception. - The method returns a new (result) vector.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static det

    -
    det: function
    - -
    -
    -

    Gets the determinant of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static diagonal

    -
    diagonal: function
    - -
    -
    -

    Gets the diagonal of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static dot

    -
    dot: function
    - -
    -
    -

    Performs dot multiplication with x and y. - Accelerated with BLAS ?dot.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static eig

    -
    eig: function
    - -
    -
    -

    Gets eigenvalues and eigenvectors of x using the Jacobi method. - Accelerated with LAPACK ?geev.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): [T, T]
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns [T, T]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static equals

    -
    equals: function
    - -
    -
    -

    Checks if x and y are equal.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): boolean
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns boolean

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static equidimensional

    -
    equidimensional: function
    - -
    -
    -

    Asserts if x and y have the same shape

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static equilateral

    -
    equilateral: function
    - -
    -
    -

    Asserts if x and y have the same length

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static exp

    -
    exp: function
    - -
    -
    -

    Returns e^x of each element of x, where x is the argument, - and e is Euler's constant (2.718…), the base of the natural logarithm.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static expm1

    -
    expm1: function
    - -
    -
    -

    Returns subtracting 1 from exp(x) of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static eye

    -
    eye: function
    - -
    -
    -

    Creates an identity matrix of size n and type type.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(n: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          n: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static fill

    -
    fill: function
    - -
    -
    -

    Fills x with a scalar value

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, value: number | function): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          value: number | function
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static floor

    -
    floor: function
    - -
    -
    -

    Returns the largest integer less than or equal to a number of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static forEach

    -
    forEach: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.forEach.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T, f: function): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        • -
          f: function
          -
            -
          • -
              -
            • (value: number, i: number, src: TypedArray): void
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns void

              -
              • -
            -
            • -
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static fround

    -
    fround: function
    - -
    -
    -

    Returns the nearest single precision float representation of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static gauss

    -
    gauss: function
    - -
    -
    -

    Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static get

    -
    get: function
    - -
    -
    -

    Gets the element at i, j, ..., n from x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, ...indices: number[]): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          Rest ...indices: number[]
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static inv

    -
    inv: function
    - -
    -
    -

    Determines the inverse of x. - Accelerated with LAPACK ?getri.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static log

    -
    log: function
    - -
    -
    -

    Returns the natural logarithm (log_e, also ln) of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static log10

    -
    log10: function
    - -
    -
    -

    Returns the natural logarithm (log_e, also ln) of 1 + x for each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static log1p

    -
    log1p: function
    - -
    -
    -

    Returns the base 2 logarithm of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static log2

    -
    log2: function
    - -
    -
    -

    Returns the base 10 logarithm of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static lu

    -
    lu: function
    - -
    -
    -

    Performs full LU decomposition on x. - Accelerated with LAPACK ?getrf.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): [T, T, Int32Array]
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns [T, T, Int32Array]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static lu_factor

    -
    lu_factor: function
    - -
    -
    -

    Performs LU factorization on x. - Accelerated with LAPACK ?getrf.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): [T, Int32Array]
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns [T, Int32Array]

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static magic

    -
    magic: function
    - -
    -
    -

    Creates a magic square matrix of size

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(size: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          size: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static map

    -
    map: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.map.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, f: function): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          f: function
          -
            -
          • -
              -
            • (value: number, i: number, src: TypedArray): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static matrix

    -
    matrix: function
    - -
    -
    -

    Creates a matrix of r rows and c columns.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(r: number, c: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          r: number
          -
          • -
        • -
          c: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static max

    -
    max: function
    - -
    -
    -

    Gets the maximum value (largest) element of x. - Accelerated with BLAS i?amax.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static mean

    -
    mean: function
    - -
    -
    -

    Gets the arithmetic mean of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static min

    -
    min: function
    - -
    -
    -

    Gets the minimum value (smallest) element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
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      -
      • -
    -
    -
    -
    - -

    Static multiply

    -
    multiply: function
    - -
    -
    -

    Multiplies two matrices x and y of matching dimensions. - Accelerated with BLAS ?gemm.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static norm

    -
    norm: function
    - -
    -
    -

    Calculates the norm of current array (also called L2 norm or Euclidean length). - Accelerated with BLAS ?nrm2.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static normalize

    -
    normalize: function
    - -
    -
    -

    Normalizes x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static ones

    -
    ones: function
    - -
    -
    -

    Creates an array containing ones (1) of shape shape

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(...shape: number[]): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          Rest ...shape: number[]
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static pow

    -
    pow: function
    - -
    -
    -

    Returns each element of x to the exponent power, that is, element^exponent.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, exponent: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          exponent: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static prod

    -
    prod: function
    - -
    -
    -

    Product of all elements of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static product

    -
    product: function
    - -
    -
    -

    Hadamard product of x and y

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static project

    -
    project: function
    - -
    -
    -

    Projects the y onto x using the projection formula (y * (x * y / y * y)).

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static push

    -
    push: function
    - -
    -
    -

    Pushes a new value into x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, value: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          value: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static random

    -
    random: function
    - -
    -
    -

    Creates a vector containing random samples from a uniform distribution over [0, 1) of shape shape

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(...shape: number[]): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          Rest ...shape: number[]
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static range

    -
    range: function
    - -
    -
    -

    Creates an array containing a range (can be either ascending or descending) - of numbers specified by the arguments provided (e.g. NDArray.range(0, .5, 2) - gives an array containing all numbers in the interval [0, 2) separated by - steps of 0.5)

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(...args: number[]): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          Rest ...args: number[]
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static rank

    -
    rank: function
    - -
    -
    -

    Finds the rank of x using gaussian elimination.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static reciprocal

    -
    reciprocal: function
    - -
    -
    -

    Gets the element-wise reciprocal of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static reduce

    -
    reduce: function
    - -
    -
    -

    Equivalent to TypedArray.prototype.reduce.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • (x: NDArray, f: function, initialValue?: number): number
        • -
      -
        -
      • -

        Parameters

        -
          -
        • -
          x: NDArray
          -
          • -
        • -
          f: function
          -
            -
          • -
              -
            • (acc: number, value: number, i: number, src: TypedArray): number
              • -
            -
              -
            • -

              Parameters

              -
                -
              • -
                acc: number
                -
                • -
              • -
                value: number
                -
                • -
              • -
                i: number
                -
                • -
              • -
                src: TypedArray
                -
                • -
              -

              Returns number

              -
              • -
            -
            • -
          -
          • -
        • -
          Optional initialValue: number
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static reshape

    -
    reshape: function
    - -
    -
    -

    Reshapes x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, ...shape: number[]): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          Rest ...shape: number[]
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static round

    -
    round: function
    - -
    -
    -

    Returns the value of each element of x rounded to the nearest integer.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static row_add

    -
    row_add: function
    - -
    -
    -

    Adds a multiple of one row multiplied by scalar to another inside x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, dest: number, source: number, scalar?: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          dest: number
          -
          • -
        • -
          source: number
          -
          • -
        • -
          Optional scalar: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static scale

    -
    scale: function
    - -
    -
    -

    Multiplies all elements of x with a specified scalar. - Accelerated with BLAS ?scal.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, scalar: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          scalar: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static set

    -
    set: function
    - -
    -
    -

    Sets the element at i, j, ..., n to value.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T, ...args: number[]): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T
          -
          • -
        • -
          Rest ...args: number[]
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static sign

    -
    sign: function
    - -
    -
    -

    Returns the sign of each element of x, indicating - whether it is positive, negative or zero.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static sin

    -
    sin: function
    - -
    -
    -

    Returns the sine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static sinh

    -
    sinh: function
    - -
    -
    -

    Returns the hyperbolic sine of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static slice

    -
    slice: function
    - -
    -
    -

    Slices x in the corresponding dimension

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, start?: number, step?: number, end?: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          Optional start: number
          -
          • -
        • -
          Optional step: number
          -
          • -
        • -
          Optional end: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static solve

    -
    solve: function
    - -
    -
    -

    Solves the equation AX = B (where A is x and B is y). - Accelerated with LAPACK ?gesv.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static sqrt

    -
    sqrt: function
    - -
    -
    -

    Returns the positive square root of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static square

    -
    square: function
    - -
    -
    -

    Asserts if x is square.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): void
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns void

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static subtract

    -
    subtract: function
    - -
    -
    -

    Subtracts y from x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, y: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          y: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static sum

    -
    sum: function
    - -
    -
    -

    Sum of x

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns number

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static swap

    -
    swap: function
    - -
    -
    -

    Swaps two rows i and j in x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>, i: number, j: number): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        • -
          i: number
          -
          • -
        • -
          j: number
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static tan

    -
    tan: function
    - -
    -
    -

    Returns the tangent of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static tanh

    -
    tanh: function
    - -
    -
    -

    Returns the hyperbolic tangent of each element of x.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): T
        • -
      -
        -
      • -

        Type parameters

        - -

        Parameters

        -
          -
        • -
          x: T | ArrayLike<any>
          -
          • -
        -

        Returns T

        -
        • -
      -
      • -
    -
    -
    -
    - -

    Static toArray

    -
    toArray: function
    - -
    -
    -

    Converts x into a JavaScript array.

    -
    -
    -
    -

    Type declaration

    -
      -
    • -
        -
      • <T>(x: T | ArrayLike<any>): number[]
        • -
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    - - - - \ No newline at end of file diff --git a/examples/blas.ts b/examples/blas.ts index f6c7f2b3..246cb3e0 100644 --- a/examples/blas.ts +++ b/examples/blas.ts @@ -1,4 +1,4 @@ -import v = require('../src'); +import { array } from '../src/core/array'; let nblas: any; try { @@ -7,8 +7,8 @@ try { throw new Error('no blas support!'); } -const a: v = v.array([[1, 2, 3]]); -const b: v = v.array([[3], [2], [1]]); +const a = array([[1, 2, 3]]); +const b = array([[3], [2], [1]]); console.log(`a: ${a}`); console.log(`b: ${b}`); diff --git a/examples/logistic-regression.ts b/examples/logistic-regression.ts index 539a69b2..453f3ea5 100644 --- a/examples/logistic-regression.ts +++ b/examples/logistic-regression.ts @@ -1,15 +1,20 @@ // Logistic regression example based on https://github.com/junku901/dnn -import v = require('../src'); +import { NDArray } from '../src/core'; +import { array } from '../src/core/array'; +import { map } from '../src/core/map'; +import { ones } from '../src/core/ones'; +import { subtract } from '../src/core/subtract'; +import { zeros } from '../src/core/zeros'; // Perform row-wise softmax on matrix -const softmax: (x: v) => v = (x: v): v => { +const softmax = (x: NDArray) => { const { data: d1 } = x; const [, c] = x.shape; const max: number = x.max(); let sum: number; return x - .add(v.ones(...x.shape).scale(-max)) + .add(ones(...x.shape).scale(-max)) .exp() .map((value: number, index: number) => { const i: number = Math.floor(index / c); @@ -29,10 +34,10 @@ const softmax: (x: v) => v = (x: v): v => { }; // Get col-wise mean of matrix as vector -const mean: (x: v) => v = (x: v): v => { +const mean = (x: NDArray) => { const { data: d1 } = x; const [, c] = x.shape; - const vec: v = v.zeros(c); + const vec = zeros(c); return vec.map((_: number, index: number): number => { let sum: number = 0; @@ -46,11 +51,11 @@ const mean: (x: v) => v = (x: v): v => { }; // Row-wise add vector to matrix -const addMatVec: (x: v, y: v) => v = (x: v, y: v): v => { +const addMatVec = (x: NDArray, y: NDArray) => { const { data: d1 } = y; const [, c] = x.shape; - return x.map((value: number, index: number): number => { + return map(x, (value: number, index: number): number => { const j: number = index % c; return value + d1[j]; @@ -58,37 +63,37 @@ const addMatVec: (x: v, y: v) => v = (x: v, y: v): v => { }; ((): void => { - const x: v = v.array([ + const x = array([ [1, 1, 1, 0, 0], [0, 0, 1, 1, 1], ]); - const y: v = v.array([ + const y = array([ [1, 0], [0, 1], ]); - const w: v = v.zeros(x.shape[1], y.shape[1]); - const b: v = v.zeros(y.shape[1]); + const w = zeros(x.shape[1], y.shape[1]); + const b = zeros(y.shape[1]); // Learning rate const alpha: number = 0.01; - let prob: v; - let delta: v; + let prob: NDArray; + let delta: NDArray; // Train let i: number; for (i = 0; i < 800; i += 1) { prob = softmax(addMatVec(x.multiply(w), b)); - delta = v.subtract(y, prob); + delta = subtract(y, prob); w.add(x.T.multiply(delta).scale(alpha)); b.add(mean(delta).scale(alpha)); } // Predict - const z: v = v.array([ + const z = array([ [1, 1, 0, 0, 0], [0, 0, 0, 1, 1], [1, 1, 1, 1, 1], diff --git a/examples/neural-network.ts b/examples/neural-network.ts index d49aa1df..2ded83ec 100644 --- a/examples/neural-network.ts +++ b/examples/neural-network.ts @@ -1,4 +1,8 @@ -import v = require('../src'); +import { array } from '../src/core/array'; +import { random } from '../src/core/random'; +import { ones } from '../src/core/ones'; +import { subtract } from '../src/core/subtract'; +import { map } from '../src/core/map'; const sigmoid: (ddx: boolean) => (value: number) => number = (ddx: boolean): (value: number) => number => (value: number): number => ddx @@ -7,7 +11,7 @@ const sigmoid: (ddx: boolean) => (value: number) => number = (ddx: boolean): (va ((): void => { // Input - const x: v = v.array([ + const x = array([ [0, 0, 1], [0, 1, 1], [1, 0, 1], @@ -15,25 +19,25 @@ const sigmoid: (ddx: boolean) => (value: number) => number = (ddx: boolean): (va ]); // Output - const y: v = v.array([[0, 1, 1, 0]]).T; + const y = array([[0, 1, 1, 0]]).T; // Initialize weights with values in [-1, 1) - const syn0: v = v.random(3, 4).scale(2).subtract(v.ones(3, 4)); - const syn1: v = v.random(4, 1).scale(2).subtract(v.ones(4, 1)); + const syn0 = random(3, 4).scale(2).subtract(ones(3, 4)); + const syn1 = random(4, 1).scale(2).subtract(ones(4, 1)); // Layers and deltas - let l0: v = v.array(4, 4); - let l1: v = v.array(4, 1); - let l0_delta: v = v.array(4, 4); - let l1_delta: v = v.array(4, 1); + let l0 = random(4, 4); + let l1 = random(4, 1); + let l0_delta = random(4, 4); + let l1_delta = random(4, 1); let i: number; for (i = 0; i < 60000; i += 1) { - l0 = x.multiply(syn0).map(sigmoid(false)); - l1 = l0.multiply(syn1).map(sigmoid(false)); + l0 = map(x.multiply(syn0), sigmoid(false)); + l1 = map(l0.multiply(syn1), sigmoid(false)); - l1_delta = v.subtract(y, l1).product(l1.map(sigmoid(true))); - l0_delta = l1_delta.multiply(syn1.T).product(l0.map(sigmoid(true))); + l1_delta = subtract(y, l1).product(map(l1, sigmoid(true))); + l0_delta = l1_delta.multiply(syn1.T).product(map(l0, sigmoid(true))); syn1.add(l0.T.multiply(l1_delta)); syn0.add(x.T.multiply(l0_delta)); diff --git a/examples/solve.ts b/examples/solve.ts index eebde24f..7906c642 100644 --- a/examples/solve.ts +++ b/examples/solve.ts @@ -1,7 +1,7 @@ -import v = require('../src'); +import { random } from '../src/core/random'; -const a: v = v.random(3, 3).scale(10); -const b: v = v.random(3, 1).scale(10); +const a = random(3, 3).scale(10); +const b = random(3, 1).scale(10); console.log(`a: ${a}`); console.log(`b: ${b}`); diff --git a/examples/text_raytracer.ts b/examples/text_raytracer.ts index 0b4602e8..44ad272b 100644 --- a/examples/text_raytracer.ts +++ b/examples/text_raytracer.ts @@ -2,41 +2,46 @@ // URL: https://gist.github.com/joshmarinacci/c84d0979e100d107f685 // URL: http://joshondesign.com/2014/09/17/rustlang // tslint:disable: max-classes-per-file -import v = require('../src'); +import { NDArray } from '../src/core'; +import { array } from '../src/core/array'; +import { add } from '../src/core/add'; +import { dot } from '../src/core/dot'; +import { scale } from '../src/core/scale'; +import { subtract } from '../src/core/subtract'; const render: (pixel: string) => void = process.stdout.write.bind(process.stdout); class Ray { - public dir: v; - public orig: v; + public dir: NDArray; + public orig: NDArray; - public constructor(orig: v, dir: v) { + public constructor(orig: NDArray, dir: NDArray) { this.orig = orig; this.dir = dir; } } class Sphere { - public center: v; - public color: v; + public center: NDArray; + public color: NDArray; public radius: number; - public constructor(center: v, radius: number, color: v) { + public constructor(center: NDArray, radius: number, color: NDArray) { this.center = center; this.radius = radius; this.color = color; } - public normal(pt: v): v { - return v.subtract(pt, this.center).normalize(); + public normal(pt: NDArray) { + return subtract(pt, this.center).normalize(); } } class Light { - public color: v; - public position: v; + public color: NDArray; + public position: NDArray; - public constructor(position: v, color: v) { + public constructor(position: NDArray, color: NDArray) { this.position = position; this.color = color; } @@ -66,26 +71,22 @@ const clamp: return x; }; -const diffuseShading: -(pi: v, obj: Sphere, light: Light) => v = -(pi: v, obj: Sphere, light: Light): v => { - const n: v = obj.normal(pi); - const lam1: number = v.subtract(light.position, pi).normalize().dot(n); +const diffuseShading = (pi: NDArray, obj: Sphere, light: Light) => { + const n = obj.normal(pi); + const lam1: number = subtract(light.position, pi).normalize().dot(n); const lam2: number = clamp(lam1, 0, 1); - return v.scale(light.color, lam2 * 0.5).add(v.scale(obj.color, 0.3)); + return scale(light.color, lam2 * 0.5).add(scale(obj.color, 0.3)); }; -const intersectSphere: -(ray: Ray, center: v, radius: number) => number | undefined = -(ray: Ray, center: v, radius: number): number | undefined => { - const l: v = v.subtract(center, ray.orig); +const intersectSphere = (ray: Ray, center: NDArray, radius: number): number | undefined => { + const l = subtract(center, ray.orig); const tca: number = l.dot(ray.dir); if (tca < 0) { return undefined; } - const d2: number = v.dot(l, l) - tca * tca; + const d2: number = dot(l, l) - tca * tca; const r2: number = radius * radius; if (d2 > r2) { return undefined; @@ -100,9 +101,7 @@ const intersectSphere: return t0; }; -const range: -(lo: number, hi: number, cb: (value: number) => void) => void = -(lo: number, hi: number, cb: (value: number) => void): void => { +const range = (lo: number, hi: number, cb: (value: number) => void): void => { let i: number; for (i = lo; i < hi; i += 1) { cb(i); @@ -110,19 +109,17 @@ const range: }; // Declare colors -const WHITE: v = new v([1, 1, 1]); -const RED: v = new v([1, 0, 0]); -const GREEN: v = new v([0, 1, 0]); -const BLUE: v = new v([0, 0, 1]); +const WHITE = array([1, 1, 1]); +const RED = array([1, 0, 0]); +const GREEN = array([0, 1, 0]); +const BLUE = array([0, 0, 1]); // Declare lighting -const LIGHT1: Light = new Light(new v([0.7, -1, 1.7]), WHITE); +const LIGHT1: Light = new Light(array([0.7, -1, 1.7]), WHITE); -const shadePixel: -(ray: Ray, obj: Sphere, tval: number) => number = -(ray: Ray, obj: Sphere, tval: number): number => { - const pi: v = v.add(ray.orig, v.scale(ray.dir, tval)); - const color: v = diffuseShading(pi, obj, LIGHT1); +const shadePixel = (ray: Ray, obj: Sphere, tval: number): number => { + const pi = add(ray.orig, scale(ray.dir, tval)); + const color = diffuseShading(pi, obj, LIGHT1); const col: number = (color.data[0] + color.data[1] + color.data[2]) / 3; return Math.floor(col * 6); @@ -135,17 +132,17 @@ const h: number = 10 * 4; // Declare scene const scene: Sphere[] = [ - new Sphere(new v([-1, 0, 3]), 0.3, RED), - new Sphere(new v([0, 0, 3]), 0.8, GREEN), - new Sphere(new v([1, 0, 3]), 0.4, BLUE), + new Sphere(array([-1, 0, 3]), 0.3, RED), + new Sphere(array([0, 0, 3]), 0.8, GREEN), + new Sphere(array([1, 0, 3]), 0.4, BLUE), ]; // Render scene range(0, h, (j: number): void => { range(0, w, (i: number): void => { const ray: Ray = new Ray( - new v([0, 0, 0]), - new v([(i - w / 2) / w, (j - h / 2) / h, 1]).normalize() + array([0, 0, 0]), + array([(i - w / 2) / w, (j - h / 2) / h, 1]).normalize() ); let hit: Hit | undefined; diff --git a/gulpfile.js b/gulpfile.js index 1aaf0b41..9fb68d28 100644 --- a/gulpfile.js +++ b/gulpfile.js @@ -7,26 +7,12 @@ uglify = require('gulp-uglify'), browserify = require('browserify'), source = require('vinyl-source-stream'), - del = require('del'), - typedoc = require('gulp-typedoc'); + del = require('del'); gulp.task('clean', function () { return del(['built', 'dist']); }); - gulp.task('docs', function () { - return gulp - .src(['src/**/*.ts']) - .pipe(typedoc({ - module: 'commonjs', - target: 'es5', - mode: 'file', - out: 'docs/', - name: 'Vectorious', - readme: 'none', - })) - }); - gulp.task('build', function () { return browserify({ entries: ['src/index.ts'] }) .ignore('nblas') diff --git a/jest.config.js b/jest.config.js new file mode 100644 index 00000000..a0b8ddcb --- /dev/null +++ b/jest.config.js @@ -0,0 +1,195 @@ +// For a detailed explanation regarding each configuration property, visit: +// https://jestjs.io/docs/en/configuration.html + +module.exports = { + // All imported modules in your tests should be mocked automatically + // automock: false, + + // Stop running tests after `n` failures + // bail: 0, + + // The directory where Jest should store its cached dependency information + // cacheDirectory: "/private/var/folders/py/z8cd3knn5k9cm3qwxgcm5k5h0000gn/T/jest_dx", + + // Automatically clear mock calls and instances between every test + clearMocks: true, + + // Indicates whether the coverage information should be collected while executing the test + collectCoverage: true, + + // An array of glob patterns indicating a set of files for which coverage information should be collected + // collectCoverageFrom: undefined, + + // The directory where Jest should output its coverage files + coverageDirectory: "coverage", + + // An array of regexp pattern strings used to skip coverage collection + // coveragePathIgnorePatterns: [ + // "/node_modules/" + // ], + + // Indicates which provider should be used to instrument code for coverage + coverageProvider: "v8", + + // A list of reporter names that Jest uses when writing coverage reports + // coverageReporters: [ + // "json", + // "text", + // "lcov", + // "clover" + // ], + + // An object that configures minimum threshold enforcement for coverage results + // coverageThreshold: undefined, + + // A path to a custom dependency extractor + // dependencyExtractor: undefined, + + // Make calling deprecated APIs throw helpful error messages + // errorOnDeprecated: false, + + // Force coverage collection from ignored files using an array of glob patterns + // forceCoverageMatch: [], + + // A path to a module which exports an async function that is triggered once before all test suites + // globalSetup: undefined, + + // A path to a module which exports an async function that is triggered once after all test suites + // globalTeardown: undefined, + + // A set of global variables that need to be available in all test environments + // globals: {}, + + // The maximum amount of workers used to run your tests. Can be specified as % or a number. E.g. maxWorkers: 10% will use 10% of your CPU amount + 1 as the maximum worker number. maxWorkers: 2 will use a maximum of 2 workers. + // maxWorkers: "50%", + + // An array of directory names to be searched recursively up from the requiring module's location + // moduleDirectories: [ + // "node_modules" + // ], + + // An array of file extensions your modules use + // moduleFileExtensions: [ + // "js", + // "json", + // "jsx", + // "ts", + // "tsx", + // "node" + // ], + + // A map from regular expressions to module names or to arrays of module names that allow to stub out resources with a single module + // moduleNameMapper: {}, + + // An array of regexp pattern strings, matched against all module paths before considered 'visible' to the module loader + // modulePathIgnorePatterns: [], + + // Activates notifications for test results + // notify: false, + + // An enum that specifies notification mode. Requires { notify: true } + // notifyMode: "failure-change", + + // A preset that is used as a base for Jest's configuration + // preset: undefined, + + // Run tests from one or more projects + // projects: undefined, + + // Use this configuration option to add custom reporters to Jest + // reporters: undefined, + + // Automatically reset mock state between every test + // resetMocks: false, + + // Reset the module registry before running each individual test + // resetModules: false, + + // A path to a custom resolver + // resolver: undefined, + + // Automatically restore mock state between every test + // restoreMocks: false, + + // The root directory that Jest should scan for tests and modules within + // rootDir: undefined, + + // A list of paths to directories that Jest should use to search for files in + // roots: [ + // "" + // ], + + // Allows you to use a custom runner instead of Jest's default test runner + // runner: "jest-runner", + + // The paths to modules that run some code to configure or set up the testing environment before each test + // setupFiles: [], + + // A list of paths to modules that run some code to configure or set up the testing framework before each test + // setupFilesAfterEnv: [], + + // The number of seconds after which a test is considered as slow and reported as such in the results. + // slowTestThreshold: 5, + + // A list of paths to snapshot serializer modules Jest should use for snapshot testing + // snapshotSerializers: [], + + // The test environment that will be used for testing + testEnvironment: "node", + + // Options that will be passed to the testEnvironment + // testEnvironmentOptions: {}, + + // Adds a location field to test results + // testLocationInResults: false, + + // The glob patterns Jest uses to detect test files + // testMatch: [ + // "**/__tests__/**/*.[jt]s?(x)", + // "**/?(*.)+(spec|test).[tj]s?(x)" + // ], + + // An array of regexp pattern strings that are matched against all test paths, matched tests are skipped + // testPathIgnorePatterns: [ + // "/node_modules/" + // ], + + // The regexp pattern or array of patterns that Jest uses to detect test files + // testRegex: [], + + // This option allows the use of a custom results processor + // testResultsProcessor: undefined, + + // This option allows use of a custom test runner + // testRunner: "jasmine2", + + // This option sets the URL for the jsdom environment. It is reflected in properties such as location.href + // testURL: "http://localhost", + + // Setting this value to "fake" allows the use of fake timers for functions such as "setTimeout" + // timers: "real", + + // A map from regular expressions to paths to transformers + // transform: undefined, + transform: { + "\\.ts?$": "ts-jest", + } + + // An array of regexp pattern strings that are matched against all source file paths, matched files will skip transformation + // transformIgnorePatterns: [ + // "/node_modules/", + // "\\.pnp\\.[^\\/]+$" + // ], + + // An array of regexp pattern strings that are matched against all modules before the module loader will automatically return a mock for them + // unmockedModulePathPatterns: undefined, + + // Indicates whether each individual test should be reported during the run + // verbose: undefined, + + // An array of regexp patterns that are matched against all source file paths before re-running tests in watch mode + // watchPathIgnorePatterns: [], + + // Whether to use watchman for file crawling + // watchman: true, +}; diff --git a/jsdoc.json b/jsdoc.json new file mode 100644 index 00000000..d3a6ade6 --- /dev/null +++ b/jsdoc.json @@ -0,0 +1,37 @@ +{ + "tags": { + "allowUnknownTags": false, + "dictionaries": ["jsdoc"] + }, + "source": { + "include": ["built", "package.json", "README.md"], + "includePattern": "\\.(jsx|js)$", + "excludePattern": "(node_modules|benchmarks|coverage|dist|docs|examples)" + }, + "plugins": [ + "plugins/markdown" + ], + "templates": { + "cleverLinks": false, + "monospaceLinks": true, + "default": { + "layoutFile": "docs.tmpl" + } + }, + "opts": { + "destination": "./docs/", + "encoding": "utf8", + "private": true, + "recurse": true, + "template": "./node_modules/tidy-jsdoc", + "prism-theme": "prism-custom" + }, + "metadata": { + "title": "Vectorious" + }, + "styles": { + "primary-color": "#0E6EB8", + "secondary-color": "#000000", + "nav-title-size": "0" + } +} diff --git a/package-lock.json b/package-lock.json index 41b4d753..542463a3 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,49 +1,42 @@ { "name": "vectorious", - "version": "6.0.0-beta.11", + "version": "6.0.0", "lockfileVersion": 1, "requires": true, "dependencies": { "@babel/code-frame": { - "version": "7.0.0", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0.tgz", - "integrity": "sha512-OfC2uemaknXr87bdLUkWog7nYuliM9Ij5HUcajsVcMCpQrcLmtxRbVFTIqmcSkSeYRBFBRxs2FiUqFJDLdiebA==", + "version": "7.10.4", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz", + "integrity": "sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg==", "dev": true, "requires": { - "@babel/highlight": "^7.0.0" + "@babel/highlight": "^7.10.4" } }, "@babel/core": { - "version": "7.7.7", - "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.7.7.tgz", - "integrity": 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"^2.1.2", + "lodash": "^4.17.19", "resolve": "^1.3.2", "semver": "^5.4.1", "source-map": "^0.5.0" }, "dependencies": { - "@babel/code-frame": { - "version": "7.5.5", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.5.5.tgz", - "integrity": "sha512-27d4lZoomVyo51VegxI20xZPuSHusqbQag/ztrBC7wegWoQ1nLREPVSKSW8byhTlzTKyNE4ifaTA6lCp7JjpFw==", - "dev": true, - "requires": { - "@babel/highlight": "^7.0.0" - } - }, "convert-source-map": { "version": "1.7.0", "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-1.7.0.tgz", @@ -62,6 +55,12 @@ "ms": "^2.1.1" } }, + "lodash": { + "version": "4.17.20", + "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.20.tgz", + "integrity": "sha512-PlhdFcillOINfeV7Ni6oF1TAEayyZBoZ8bcshTHqOYJYlrqzRK5hagpagky5o4HfCzzd1TRkXPMFq6cKk9rGmA==", + "dev": true + }, "ms": { "version": "2.1.2", "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.2.tgz", @@ -71,111 +70,303 @@ } }, "@babel/generator": { - 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"sha1-bRX7qITAhnnA136I53WegR4H+kE=", "dev": true }, + "yallist": { + "version": "2.1.2", + "resolved": "https://registry.npmjs.org/yallist/-/yallist-2.1.2.tgz", + "integrity": "sha1-HBH5IY8HYImkfdUS+TxmmaaoHVI=", + "dev": true + }, "yargs": { "version": "7.1.0", "resolved": "https://registry.npmjs.org/yargs/-/yargs-7.1.0.tgz", @@ -7736,14 +12981,16 @@ } }, "yargs-unparser": { - "version": "1.6.0", - "resolved": "https://registry.npmjs.org/yargs-unparser/-/yargs-unparser-1.6.0.tgz", - "integrity": "sha512-W9tKgmSn0DpSatfri0nx52Joq5hVXgeLiqR/5G0sZNDoLZFOr/xjBUDcShCOGNsBnEMNo1KAMBkTej1Hm62HTw==", + "version": "1.6.1", + "resolved": "https://registry.npmjs.org/yargs-unparser/-/yargs-unparser-1.6.1.tgz", + "integrity": "sha512-qZV14lK9MWsGCmcr7u5oXGH0dbGqZAIxTDrWXZDo5zUr6b6iUmelNKO6x6R1dQT24AH3LgRxJpr8meWy2unolA==", "dev": true, "requires": { + "camelcase": "^5.3.1", + "decamelize": "^1.2.0", "flat": "^4.1.0", - "lodash": "^4.17.15", - "yargs": "^13.3.0" + "is-plain-obj": "^1.1.0", + "yargs": "^14.2.3" }, "dependencies": { "ansi-regex": { @@ -7790,6 +13037,40 @@ "integrity": "sha1-o7MKXE8ZkYMWeqq5O+764937ZU8=", "dev": true }, + "locate-path": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/locate-path/-/locate-path-3.0.0.tgz", + "integrity": "sha512-7AO748wWnIhNqAuaty2ZWHkQHRSNfPVIsPIfwEOWO22AmaoVrWavlOcMR5nzTLNYvp36X220/maaRsrec1G65A==", + "dev": true, + "requires": { + "p-locate": "^3.0.0", + "path-exists": "^3.0.0" + } + }, + "p-limit": { + "version": "2.3.0", + "resolved": "https://registry.npmjs.org/p-limit/-/p-limit-2.3.0.tgz", + "integrity": "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w==", + "dev": true, + "requires": { + "p-try": "^2.0.0" + } + }, + "p-locate": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/p-locate/-/p-locate-3.0.0.tgz", + "integrity": "sha512-x+12w/To+4GFfgJhBEpiDcLozRJGegY+Ei7/z0tSLkMmxGZNybVMSfWj9aJn8Z5Fc7dBUNJOOVgPv2H7IwulSQ==", + "dev": true, + "requires": { + "p-limit": "^2.0.0" + } + }, + "path-exists": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/path-exists/-/path-exists-3.0.0.tgz", + "integrity": "sha1-zg6+ql94yxiSXqfYENe1mwEP1RU=", + "dev": true + }, "require-main-filename": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/require-main-filename/-/require-main-filename-2.0.0.tgz", @@ -7840,12 +13121,13 @@ "dev": true }, "yargs": { - "version": "13.3.0", - "resolved": "https://registry.npmjs.org/yargs/-/yargs-13.3.0.tgz", - "integrity": "sha512-2eehun/8ALW8TLoIl7MVaRUrg+yCnenu8B4kBlRxj3GJGDKU1Og7sMXPNm1BYyM1DOJmTZ4YeN/Nwxv+8XJsUA==", + "version": "14.2.3", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-14.2.3.tgz", + "integrity": "sha512-ZbotRWhF+lkjijC/VhmOT9wSgyBQ7+zr13+YLkhfsSiTriYsMzkTUFP18pFhWwBeMa5gUc1MzbhrO6/VB7c9Xg==", "dev": true, "requires": { "cliui": "^5.0.0", + "decamelize": "^1.2.0", "find-up": "^3.0.0", "get-caller-file": "^2.0.1", "require-directory": "^2.1.1", @@ -7854,13 +13136,13 @@ "string-width": "^3.0.0", "which-module": "^2.0.0", "y18n": "^4.0.0", - "yargs-parser": "^13.1.1" + "yargs-parser": "^15.0.1" } }, "yargs-parser": { - "version": "13.1.1", - "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-13.1.1.tgz", - "integrity": "sha512-oVAVsHz6uFrg3XQheFII8ESO2ssAf9luWuAd6Wexsu4F3OtIW0o8IribPXYrD4WC24LWtPrJlGy87y5udK+dxQ==", + "version": "15.0.1", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-15.0.1.tgz", + "integrity": "sha512-0OAMV2mAZQrs3FkNpDQcBk1x5HXb8X4twADss4S0Iuk+2dGnLOE/fRHrsYm542GduMveyA77OF4wrNJuanRCWw==", "dev": true, "requires": { "camelcase": "^5.0.0", @@ -7870,18 +13152,19 @@ } }, "yauzl": { - "version": "2.4.1", - "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.4.1.tgz", - "integrity": "sha1-lSj0QtqxsihOWLQ3m7GU4i4MQAU=", + "version": "2.10.0", + "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz", + "integrity": "sha1-x+sXyT4RLLEIb6bY5R+wZnt5pfk=", "dev": true, "requires": { - "fd-slicer": "~1.0.1" + "buffer-crc32": "~0.2.3", + "fd-slicer": "~1.1.0" } }, "yn": { - "version": "3.1.0", - "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.0.tgz", - "integrity": "sha512-kKfnnYkbTfrAdd0xICNFw7Atm8nKpLcLv9AZGEt+kczL/WQVai4e2V6ZN8U/O+iI6WrNuJjNNOyu4zfhl9D3Hg==", + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz", + "integrity": "sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==", "dev": true } } diff --git a/package.json b/package.json index f779fd0d..8c1fb156 100644 --- a/package.json +++ b/package.json @@ -1,48 +1,20 @@ { "name": "vectorious", - "version": "6.0.0-beta.11", + "version": "6.0.0", "description": "A high performance linear algebra library.", "main": "built/index.js", "types": "built/index.d.ts", "scripts": { - "test:node": "nyc mocha -r ts-node/register 'src/**/*.spec.ts'", - "test:single": "mocha -r ts-node/register", - "test:browser": "mochify --plugin tsify 'src/**/*.spec.ts'", - "test": "npm run test:node && npm run test:browser", - "lint": "tslint -p . -c tslint.json 'src/*.ts'", + "test": "jest", "benchmark": "for file in $(ls src/**/*.bench.ts); do ts-node $file; done", - "docs": "gulp docs", - "build": "gulp clean && gulp build && gulp docs && tsc", + "docs": "jsdoc --configure jsdoc.json", + "build": "gulp clean && gulp build && tsc && npm run docs", "prepublishOnly": "npm run build" }, "browser": { "nblas": false, "nlapack": false }, - "nyc": { - "extension": [ - ".ts", - ".tsx" - ], - "exclude": [ - "gulpfile.js", - "**/*.d.ts", - "benchmarks/**", - "built/**", - "dist/**", - "docs/**", - "examples/**", - "coverage/**", - "src/**/*.spec.ts", - "src/**/*.bench.ts", - "src/bench.ts", - "src/types.ts" - ], - "reporter": [ - "lcov" - ], - "all": true - }, "repository": { "type": "git", "url": "https://github.com/mateogianolio/vectorious.git" @@ -67,30 +39,32 @@ }, "homepage": "https://github.com/mateogianolio/vectorious", "devDependencies": { - "@types/mocha": "^7.0.0", - "@types/node": "^14.0.0", + "@types/mocha": "^8.0.3", + "@types/node": "^14.10.1", "benchmark": "^2.1.4", - "browserify": "^16.3.0", + "better-docs": "^2.3.2", + "browserify": "^16.5.2", "del": "5.1.0", "gulp": "^4.0.2", "gulp-streamify": "^1.0.2", - "gulp-typedoc": "^2.2.2", "gulp-uglify": "^3.0.2", + "jest": "^26.4.2", + "jsdoc": "^3.6.5", "matplotnode": "^0.4.2", - "mocha": "^7.0.1", - "mochify": "^6.3.0", + "mocha": "^8.1.3", + "mochify": "^6.6.0", "nodemark": "^0.3.0", - "nyc": "^15.0.0", - "ts-node": "^8.3.0", - "tsify": "^4.0.1", - "tslint": "^5.18.0", - "typedoc": "^0.17.0", - "typescript": "^3.5.3", + "tidy-jsdoc": "^1.4.0", + "ts-jest": "^26.3.0", + "ts-node": "^9.0.0", + "tsify": "^5.0.2", + "typescript": "^4.0.2", "vinyl-source-stream": "^2.0.0" }, "optionalDependencies": { - "nblas": "^2.1.6", - "nlapack": "^2.0.2" + "nblas": "^2.1.9" }, - "dependencies": {} + "dependencies": { + "nlapack": "^2.0.4" + } } diff --git a/src/bench.ts b/src/bench.ts index 8c689d1e..60820889 100644 --- a/src/bench.ts +++ b/src/bench.ts @@ -2,12 +2,7 @@ const benchmark: any = require('nodemark'); const plt: any = require('matplotnode'); const { execSync } = require('child_process'); -export const bench: ( - group: string, - name: string, - setup: (n: number) => any[], - ...funcs: Array<(...args: any[]) => void> -) => typeof benchmark = ( +export const bench = ( group: string, name: string, setup: (n: number) => any[], @@ -15,7 +10,7 @@ export const bench: ( ): typeof benchmark => { execSync(`mkdir -p benchmarks/${group}`); const filename: string = `benchmarks/${group}/${name}.png`; - const xs: number[] = [4, 16, 64, 256, 1024]; + const xs: number[] = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]; let ys: number[] = []; plt.title(name); @@ -29,7 +24,7 @@ export const bench: ( const result: typeof benchmark = benchmark( (): void => { func(...args); }, (): void => { args = setup(x); }, - 100 + 250 ); ys.push(result.milliseconds(3)); @@ -46,7 +41,6 @@ export const bench: ( plt.xlabel('n'); plt.ylabel('ms'); - plt.xlim(0, 1024); plt.grid(true); plt.legend(); diff --git a/src/blas.ts b/src/blas.ts new file mode 100644 index 00000000..16cd54d3 --- /dev/null +++ b/src/blas.ts @@ -0,0 +1,114 @@ +import { + TypedArray, + DType, +} from './types'; + +let nblas: any; +try { + nblas = require('nblas'); +} catch (err) {} + +export const NoTrans = nblas.NoTrans; +export const Trans = nblas.Trans; + +export function axpy(dtype: DType, n: number, alpha: number, x: TypedArray, inc_x: number, y: TypedArray, inc_y: number) { + if (x.length / inc_x !== n || y.length / inc_y !== n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.daxpy(n, alpha, x, inc_x, y, inc_y); + case 'float32': + return nblas.saxpy(n, alpha, x, inc_x, y, inc_y); + default: + throw new Error('wrong dtype'); + } +} + +export function dot(dtype: DType, n: number, x: TypedArray, inc_x: number, y: TypedArray, inc_y: number) { + if (x.length / inc_x !== n || y.length / inc_y !== n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.ddot(n, x, inc_x, y, inc_y); + case 'float32': + return nblas.sdot(n, x, inc_x, y, inc_y); + default: + throw new Error('wrong dtype'); + } +} + +export function iamax(dtype: DType, n: number, x: TypedArray, inc_x: number) { + if (x.length / inc_x !== n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.idamax(n, x, inc_x); + case 'float32': + return nblas.isamax(n, x, inc_x); + default: + throw new Error('wrong dtype'); + } +} + +export function gemm(dtype: DType, transx: number, transy: number, m: number, n: number, k: number, alpha: number, x: TypedArray, ldx: number, y: TypedArray, ldy: number, beta: number, z: TypedArray, ldz: number) { + const { length: l1 } = x; + const { length: l2 } = y; + const { length: l3 } = z; + + if ((transx === nblas.NoTrans && l1 !== ldx * m) || (transx === nblas.Trans && l1 !== ldx * k)) { + throw new Error('lengths do not match'); + } + + if ((transy === nblas.NoTrans && l2 !== ldy * k) || (transy === nblas.Trans && l2 !== ldy * n)) { + throw new Error('lengths do not match'); + } + + if (l3 !== ldz * m) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.dgemm(transx, transy, m, n, k, alpha, x, ldx, y, ldy, beta, z, ldz); + case 'float32': + return nblas.sgemm(transx, transy, m, n, k, alpha, x, ldx, y, ldy, beta, z, ldz); + default: + throw new Error('wrong dtype'); + } +} + +export function nrm2(dtype: DType, n: number, x: TypedArray, inc_x: number) { + if (x.length / inc_x !== n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.dnrm2(n, x, inc_x); + case 'float32': + return nblas.snrm2(n, x, inc_x); + default: + throw new Error('wrong dtype'); + } +} + +export function scal(dtype: DType, n: number, alpha: number, x: TypedArray, inc_x: number) { + if (x.length / inc_x !== n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nblas.dscal(n, alpha, x, inc_x); + case 'float32': + return nblas.sscal(n, alpha, x, inc_x); + default: + throw new Error('wrong dtype'); + } +} diff --git a/src/core/abs.bench.ts b/src/core/abs.bench.ts index e2bb4587..d56c4ce1 100644 --- a/src/core/abs.bench.ts +++ b/src/core/abs.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { abs } from './abs'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'abs', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.abs(); }, - (x: v): void => { - v.abs(x); + (x): void => { + abs(x); } ); diff --git a/src/core/abs.spec.ts b/src/core/abs.spec.ts index 70b532ae..217c189a 100644 --- a/src/core/abs.spec.ts +++ b/src/core/abs.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { abs } from './abs'; +import { map } from './map'; +import { random } from './random'; describe('(v) abs', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3).scale(-1); - deepStrictEqual(x.map((value: number) => Math.abs(value)), x.abs()); + equals(map(x, (value: number) => Math.abs(value)), x.abs()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3, 3); - deepStrictEqual(x.map((value: number) => Math.abs(value)), v.abs(x)); + equals(map(x, (value: number) => Math.abs(value)), abs(x)); }); }); diff --git a/src/core/abs.ts b/src/core/abs.ts index 0efc3947..61e6baa4 100644 --- a/src/core/abs.ts +++ b/src/core/abs.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.abs = (x: T | ArrayLike): T => NDArray.array(x).abs(); +const { abs: f } = Math; -NDArray.prototype.abs = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function abs + * @description Returns the absolute value of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { abs } from 'vectorious/core/abs'; + * + * abs([-1, -2, -3]) // => array([1, 2, 3]) + */ +export const abs = (x: NDArray | ArrayLike): NDArray => array(x).abs(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.abs(d1[i]); +/** + * @function abs + * @memberof NDArray.prototype + * @description Returns the absolute value of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([-1, -2, -3]).abs() // <=> array([1, 2, 3]) + */ +export default function (this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/acos.bench.ts b/src/core/acos.bench.ts index 5e162076..5557514e 100644 --- a/src/core/acos.bench.ts +++ b/src/core/acos.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { acos } from './acos'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'acos', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.acos(); }, - (x: v): void => { - v.acos(x); + (x): void => { + acos(x); } ); diff --git a/src/core/acos.spec.ts b/src/core/acos.spec.ts index 2faa0bc2..cb55e7b7 100644 --- a/src/core/acos.spec.ts +++ b/src/core/acos.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { acos } from './acos'; +import { map } from './map'; +import { random } from './random'; describe('(v) acos', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.acos(value)), x.acos()); + equals(map(x, (value: number) => Math.acos(value)), x.acos()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.acos(value)), v.acos(x)); + equals(map(x, (value: number) => Math.acos(value)), acos(x)); }); }); diff --git a/src/core/acos.ts b/src/core/acos.ts index f7a45854..c015cdab 100644 --- a/src/core/acos.ts +++ b/src/core/acos.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.acos = (x: T | ArrayLike): T => NDArray.array(x).acos(); +const { acos: f } = Math; -NDArray.prototype.acos = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function acos + * @description Returns the arccosine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { acos } from 'vectorious/core/acos'; + * + * acos([-1, 0, 1]); // => array([3.141592653589793, 1.5707963267948966, 0]) + */ +export const acos = (x: NDArray | ArrayLike): NDArray => array(x).acos(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.acos(d1[i]); +/** + * @function acos + * @memberof NDArray.prototype + * @description Returns the arccosine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([-1, 0, 1]).acos(); // <=> array([3.141592653589793, 1.5707963267948966, 0]) + */ +export default function (this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/acosh.bench.ts b/src/core/acosh.bench.ts index a3e2f843..030ccabe 100644 --- a/src/core/acosh.bench.ts +++ b/src/core/acosh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { acosh } from './acosh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'acosh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.acosh(); }, - (x: v): void => { - v.acosh(x); + (x): void => { + acosh(x); } ); diff --git a/src/core/acosh.spec.ts b/src/core/acosh.spec.ts index 32d2cd48..44225f88 100644 --- a/src/core/acosh.spec.ts +++ b/src/core/acosh.spec.ts @@ -1,19 +1,19 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { acosh } from './acosh'; +import { map } from './map'; +import { ones } from './ones'; +import { random } from './random'; describe('(v) acosh', () => { it('should work as expected', () => { - const x: v = v.random(3).add(v.ones(3)); + const x = random(3).add(ones(3)); - deepStrictEqual(x.map((value: number) => Math.acosh(value)), x.acosh()); + equals(map(x, (value: number) => Math.acosh(value)), x.acosh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3).add(v.ones(3)); + const x = random(3).add(ones(3)); - deepStrictEqual(x.map((value: number) => Math.acosh(value)), v.acosh(x)); + equals(map(x, (value: number) => Math.acosh(value)), acosh(x)); }); }); diff --git a/src/core/acosh.ts b/src/core/acosh.ts index 2213a63c..2db273d2 100644 --- a/src/core/acosh.ts +++ b/src/core/acosh.ts @@ -1,13 +1,40 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.acosh = (x: T | ArrayLike): T => NDArray.array(x).acosh(); +const { acosh: f } = Math; -NDArray.prototype.acosh = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function acosh + * @description Returns the hyperbolic arccosine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { acosh } from 'vectorious/core/acosh'; + * + * acosh([1, 2, 3]); // => array([0, 1.316957950592041, 1.7627471685409546]) + */ +export const acosh = (x: NDArray | ArrayLike): NDArray => array(x).acosh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.acosh(d1[i]); +/** + * @function acosh + * @memberof NDArray.prototype + * @description Returns the hyperbolic arccosine of each element of current array. + * @param {NDArray} x + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).acosh(); // <=> array([0, 1.316957950592041, 1.7627471685409546]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/add.bench.ts b/src/core/add.bench.ts index 71ec1e55..a7ded891 100644 --- a/src/core/add.bench.ts +++ b/src/core/add.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { add } from './add'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'add', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.add(y); }, - (x: v, y: v): void => { - v.add(x, y); + (x, y): void => { + add(x, y); } ); diff --git a/src/core/add.spec.ts b/src/core/add.spec.ts index 39feee79..a8d6a08c 100644 --- a/src/core/add.spec.ts +++ b/src/core/add.spec.ts @@ -1,38 +1,47 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { add } from './add'; +import { array } from './array'; describe('(v) add', () => { it('should return empty vector if adding two empty vectors', () => { - const x: v = v.array(); - const y: v = v.array(); + const x = array(); + const y = array(); - deepStrictEqual(v.array(), x.add(y)); + equals(array(), x.add(y)); }); - it('should throw error if sizes do not match', () => { - const x: v = v.array([1]); - const y: v = v.array([1, 2]); + it('should throw error if shapes cannot be broadcast together', () => { + const x = array([1, 2, 3]); + const y = array([10, 11]); throws(() => { x.add(y); }, Error); }); it('should produce v([5, 7, 9]) from v([1, 2, 3]) and v([4, 5, 6])', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); - const z: v = v.array([5, 7, 9]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); + const z = array([5, 7, 9]); - deepStrictEqual(z, x.add(y)); + equals(z, x.add(y)); + }); + + it('should product v([11, 12, 13]) from v([1, 2, 3]) and v([10])', () => { + const x = array([1, 2, 3]); + const y = array([10]); + const z = array([11, 12, 13]); + + equals(z, x.add(y)) }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); - const z: v = v.array([5, 7, 9]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); + const z = array([5, 7, 9]); - deepStrictEqual(z, v.add(x, y)); + equals(z, add(x, y)); }); }); diff --git a/src/core/add.ts b/src/core/add.ts index 343ea541..dcfb6dad 100644 --- a/src/core/add.ts +++ b/src/core/add.ts @@ -1,41 +1,50 @@ -import { get_type } from '../util'; - import { NDArray } from './'; - -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.add = (x: T | ArrayLike, y: T | ArrayLike, alpha: number = 1): T => - NDArray.array(x).add(NDArray.array(y), alpha); - -NDArray.prototype.add = function(this: T, x: NDArray, alpha: number = 1): T { - this.equilateral(x); - this.equidimensional(x); - - const { length: l1 } = this; +import { NDMultiIter } from '../iterator'; +import { array } from './array'; +import * as blas from '../blas'; + +/** + * @static + * @memberof module:Globals + * @function add + * @description + * Adds `y` multiplied by `alpha` to `x`. + * Accelerated with BLAS `?axpy`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { add } from 'vectorious/core/add'; + * + * add([1, 2, 3], [4, 5, 6]); // => array([5, 7, 9]) + */ +export const add = (x: NDArray | ArrayLike, y: NDArray | ArrayLike, alpha: number = 1): NDArray => + array(x).add(array(y), alpha); + +/** + * @function add + * @memberof NDArray.prototype + * @description + * Adds `x` multiplied by `alpha` to the current array. + * Accelerated with BLAS `?axpy`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).add([4, 5, 6]); // <=> array([5, 7, 9]) + */ +export default function (this: NDArray, x: NDArray | ArrayLike, alpha: number = 1): NDArray { + const { data: d1, length: l1, dtype } = this; + const { data: d2 } = array(x); try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - const { data: d2 } = x; - if (this.dtype === 'float64') { - nblas.daxpy(l1, alpha, d2, 1, d1, 1); - } else if (this.dtype === 'float32') { - nblas.saxpy(l1, alpha, d2, 1, d1, 1); - } + blas.axpy(dtype, l1, alpha, d2, 1, d1, 1); } catch (err) { - const { data: d1 } = this; - const { data: d2 } = x; + const iter = new NDMultiIter(this, x); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] += alpha * d2[i]; + for (const [i, j] of iter) { + d1[i] += alpha * d2[j]; } } diff --git a/src/core/angle.bench.ts b/src/core/angle.bench.ts index 277f0aa1..b0cdf19f 100644 --- a/src/core/angle.bench.ts +++ b/src/core/angle.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { angle } from './angle'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'angle', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.angle(y); }, - (x: v, y: v): void => { - v.angle(x, y); + (x, y): void => { + angle(x, y); } ); diff --git a/src/core/angle.spec.ts b/src/core/angle.spec.ts index 26e1c7c9..5294b530 100644 --- a/src/core/angle.spec.ts +++ b/src/core/angle.spec.ts @@ -2,20 +2,21 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { angle } from './angle'; +import { array } from './array'; describe('(v) angle', () => { it('should work as expected', () => { - const x: v = v.array([1, 0]); - const y: v = v.array([0, 1]); + const x = array([1, 0]); + const y = array([0, 1]); strictEqual(Math.PI / 2, x.angle(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 0]); - const y: v = v.array([0, 1]); + const x = array([1, 0]); + const y = array([0, 1]); - strictEqual(Math.PI / 2, v.angle(x, y)); + strictEqual(Math.PI / 2, angle(x, y)); }); }); diff --git a/src/core/angle.ts b/src/core/angle.ts index d439ddd7..d69a58a4 100644 --- a/src/core/angle.ts +++ b/src/core/angle.ts @@ -1,8 +1,35 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.angle = (x: T | ArrayLike, y: T | ArrayLike): number => - NDArray.array(x).angle(NDArray.array(y)); +const { acos: f } = Math; -NDArray.prototype.angle = function(this: T, x: T): number { - return Math.acos(this.dot(x) / this.norm() / x.norm()); +/** + * @static + * @memberof module:Globals + * @function angle + * @description Determines the angle between the `x` and `y` + * @param {NDArray} x + * @param {NDArray} y + * @returns {number} + * @example + * import { angle } from 'vectorious/core/angle'; + * + * angle([1, 2, 3], [4, 5, 6]); // => 0.22572622788897287 + */ +export const angle = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): number => + array(x).angle(array(y)); + +/** + * @function angle + * @memberof NDArray.prototype + * @description Determines the angle between the current vector and `x`. + * @param {NDArray} x + * @returns {number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).angle([4, 5, 6]); // <=> 0.22572622788897287 + */ +export default function(this: NDArray, x: NDArray | ArrayLike): number { + return f(this.dot(array(x)) / this.norm() / array(x).norm()); }; diff --git a/src/core/array.bench.ts b/src/core/array.bench.ts index 096a51a5..ef8cd1f1 100644 --- a/src/core/array.bench.ts +++ b/src/core/array.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { array } from './array'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'array', (n: number): [number] => [n], (n: number): void => { - v.array(new Array(n)); + array(new Array(n)); } ); diff --git a/src/core/array.spec.ts b/src/core/array.spec.ts index e9f8be89..4582477d 100644 --- a/src/core/array.spec.ts +++ b/src/core/array.spec.ts @@ -1,19 +1,16 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { array } from './array'; describe('(v) array', () => { it('should work as expected', () => { - const x: v = v.array([0, 0, 0]); + const x = array([0, 0, 0]); - deepStrictEqual(x, v.array([0, 0, 0])); + equals(x, array([0, 0, 0])); }); it('should work as expected in two dimensions', () => { - const x: v = v.array([0, 0]); + const x = array([0, 0]); - deepStrictEqual(x, v.array([0, 0])); + equals(x, array([0, 0])); }); }); diff --git a/src/core/array.ts b/src/core/array.ts index 3a5d452b..bd9ce9f3 100644 --- a/src/core/array.ts +++ b/src/core/array.ts @@ -1,5 +1,15 @@ import { NDArray } from './'; -NDArray.array = function(this: new(...args: any[]) => T, ...args: any[]): T { - return new this(...args); -}; +/** + * @static + * @memberof module:Globals + * @function array + * @description `array(...args)` is an alias for `new v(...args)` + * @param {} ...args + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]); // => array([1, 2, 3]) + */ +export const array = (...args: any[]): NDArray => new NDArray(...args); diff --git a/src/core/asin.bench.ts b/src/core/asin.bench.ts index 797d5b0c..24acd275 100644 --- a/src/core/asin.bench.ts +++ b/src/core/asin.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { asin } from './asin'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'asin', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.asin(); }, - (x: v): void => { - v.asin(x); + (x): void => { + asin(x); } ); diff --git a/src/core/asin.spec.ts b/src/core/asin.spec.ts index 6b61b535..77d23824 100644 --- a/src/core/asin.spec.ts +++ b/src/core/asin.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { asin } from './asin'; +import { map } from './map'; +import { random } from './random'; describe('(v) asin', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.asin(value)), x.asin()); + equals(map(x, (value: number) => Math.asin(value)), x.asin()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.asin(value)), v.asin(x)); + equals(map(x, (value: number) => Math.asin(value)), asin(x)); }); }); diff --git a/src/core/asin.ts b/src/core/asin.ts index 5a1226f8..01c36daf 100644 --- a/src/core/asin.ts +++ b/src/core/asin.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.asin = (x: T | ArrayLike): T => NDArray.array(x).asin(); +const { asin: f } = Math; -NDArray.prototype.asin = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function asin + * @description Returns the arcsine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { asin } from 'vectorious/core/asin'; + * + * asin([-1, 0, 1]) // => array([-1.5707963705062866, 0, 1.5707963705062866]) + */ +export const asin = (x: NDArray | ArrayLike): NDArray => array(x).asin(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.asin(d1[i]); +/** + * @function asin + * @memberof NDArray.prototype + * @description Returns the arcsine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([-1, 0, 1]).asin() // <=> array([-1.5707963705062866, 0, 1.5707963705062866]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/asinh.bench.ts b/src/core/asinh.bench.ts index 33125c8e..03c35ca8 100644 --- a/src/core/asinh.bench.ts +++ b/src/core/asinh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { asinh } from './asinh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'asinh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.asinh(); }, - (x: v): void => { - v.asinh(x); + (x): void => { + asinh(x); } ); diff --git a/src/core/asinh.spec.ts b/src/core/asinh.spec.ts index 5177346e..c7fe6556 100644 --- a/src/core/asinh.spec.ts +++ b/src/core/asinh.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { asinh } from './asinh'; +import { map } from './map'; +import { random } from './random'; describe('(v) asinh', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.asinh(value)), x.asinh()); + equals(map(x, (value: number) => Math.asinh(value)), x.asinh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.asinh(value)), v.asinh(x)); + equals(map(x, (value: number) => Math.asinh(value)), asinh(x)); }); }); diff --git a/src/core/asinh.ts b/src/core/asinh.ts index b86a32c0..40c89d4a 100644 --- a/src/core/asinh.ts +++ b/src/core/asinh.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.asinh = (x: T | ArrayLike): T => NDArray.array(x).asinh(); +const { asinh: f } = Math; -NDArray.prototype.asinh = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function asinh + * @description Returns the hyperbolic arcsine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { asinh } from 'vectorious/core/asinh'; + * + * asinh([0, 1, 2]) // => array([0, 0.8813735842704773, 1.4436354637145996]) + */ +export const asinh = (x: NDArray | ArrayLike): NDArray => array(x).asinh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.asinh(d1[i]); +/** + * @function asinh + * @memberof NDArray.prototype + * @description Returns the hyperbolic arcsine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0, 1, 2]).asinh() // <=> array([0, 0.8813735842704773, 1.4436354637145996]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/atan.bench.ts b/src/core/atan.bench.ts index 3d3a631b..6d64565a 100644 --- a/src/core/atan.bench.ts +++ b/src/core/atan.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { atan } from './atan'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'atan', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.atan(); }, - (x: v): void => { - v.atan(x); + (x): void => { + atan(x); } ); diff --git a/src/core/atan.spec.ts b/src/core/atan.spec.ts index bc83e5cb..85a4aa19 100644 --- a/src/core/atan.spec.ts +++ b/src/core/atan.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { atan } from './atan'; +import { map } from './map'; +import { random } from './random'; describe('(v) atan', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.atan(value)), x.atan()); + equals(map(x, (value: number) => Math.atan(value)), x.atan()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.atan(value)), v.atan(x)); + equals(map(x, (value: number) => Math.atan(value)), atan(x)); }); }); diff --git a/src/core/atan.ts b/src/core/atan.ts index 15e2d465..af338a09 100644 --- a/src/core/atan.ts +++ b/src/core/atan.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.atan = (x: T | ArrayLike): T => NDArray.array(x).atan(); +const { atan: f } = Math; -NDArray.prototype.atan = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function atan + * @description Returns the arctangent of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { atan } from 'vectorious/core/atan'; + * + * atan([1, 2, 3]); // => array([0.7853981852531433, 1.1071487665176392, 1.249045729637146]) + */ +export const atan = (x: NDArray | ArrayLike): NDArray => array(x).atan(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.atan(d1[i]); +/** + * @function atan + * @memberof NDArray.prototype + * @description Returns the arctangent of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).atan() // <=> array([0.7853981852531433, 1.1071487665176392, 1.249045729637146]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/atanh.bench.ts b/src/core/atanh.bench.ts index c00d9f35..57941a6b 100644 --- a/src/core/atanh.bench.ts +++ b/src/core/atanh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { atanh } from './atanh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'atanh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.atanh(); }, - (x: v): void => { - v.atanh(x); + (x): void => { + atanh(x); } ); diff --git a/src/core/atanh.spec.ts b/src/core/atanh.spec.ts index d7c0ce7c..7c9d3439 100644 --- a/src/core/atanh.spec.ts +++ b/src/core/atanh.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { atanh } from './atanh'; +import { map } from './map'; +import { random } from './random'; describe('(v) atanh', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.atanh(value)), x.atanh()); + equals(map(x, (value: number) => Math.atanh(value)), x.atanh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.atanh(value)), v.atanh(x)); + equals(map(x, (value: number) => Math.atanh(value)), atanh(x)); }); }); diff --git a/src/core/atanh.ts b/src/core/atanh.ts index d3f53410..a5206074 100644 --- a/src/core/atanh.ts +++ b/src/core/atanh.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.atanh = (x: T | ArrayLike): T => NDArray.array(x).atanh(); +const { atanh: f } = Math; -NDArray.prototype.atanh = function(): NDArray { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function atanh + * @description Returns the hyperbolic arctangent of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { atanh } from 'vectorious/core/atanh'; + * + * atanh([0, -0.5]); // => array([0, -0.5493061542510986]) + */ +export const atanh = (x: NDArray | ArrayLike): NDArray => array(x).atanh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.atanh(d1[i]); +/** + * @function atanh + * @memberof NDArray.prototype + * @description Returns the hyperbolic arctangent of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0, -0.5]).atanh(); // <=> array([0, -0.5493061542510986]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/augment.bench.ts b/src/core/augment.bench.ts index 1160d8aa..2e2a0a6b 100644 --- a/src/core/augment.bench.ts +++ b/src/core/augment.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { augment } from './augment'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'augment', - (n: number): [v, v] => [v.random(floor(sqrt(n)), floor(sqrt(n))), v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v, y: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n))), random(floor(sqrt(n)), floor(sqrt(n)))], + (x, y): void => { x.augment(y); }, - (x: v, y: v): void => { - v.augment(x, y); + (x, y): void => { + augment(x, y); } ); diff --git a/src/core/augment.spec.ts b/src/core/augment.spec.ts index 18eb30e9..15f54a58 100644 --- a/src/core/augment.spec.ts +++ b/src/core/augment.spec.ts @@ -1,35 +1,36 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { augment } from './augment'; +import { array } from './array'; describe('(v) augment', () => { it('should return current matrix when combined with empty matrix', () => { - const x: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2], [3, 4]]); - deepStrictEqual(x, x.augment(v.array())); + equals(x, x.augment(array())); }); it('should work as expected', () => { - const x: v = v.array([[1, 2], [3, 4]]); - const y: v = v.array([[5, 6], [7, 8]]); - const z: v = v.array([[1, 2, 5, 6], [3, 4, 7, 8]]); + const x = array([[1, 2], [3, 4]]); + const y = array([[5, 6], [7, 8]]); + const z = array([[1, 2, 5, 6], [3, 4, 7, 8]]); - deepStrictEqual(z, x.augment(y)); + equals(z, x.augment(y)); }); it('should throw error when rows do not match', () => { - const x: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2], [3, 4]]); - throws(() => { x.augment(v.array([[1]])); }, Error); + throws(() => { x.augment(array([[1]])); }, Error); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 1, 1]]); - const y: v = v.array([[1, 2, 3]]); + const x = array([[1, 1, 1]]); + const y = array([[1, 2, 3]]); - deepStrictEqual(x.copy().augment(y), v.augment(x, y)); + equals(x.copy().augment(y), augment(x, y)); }); }); diff --git a/src/core/augment.ts b/src/core/augment.ts index 0055e6a9..fc1679c6 100644 --- a/src/core/augment.ts +++ b/src/core/augment.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { zeros } from './zeros'; -NDArray.augment = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).augment(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function augment + * @description Augments `x` and `y`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { augment } from 'vectorious/core/augment'; + * + * augment([[1, 2], [3, 4]], [[1], [2]]); // => array([[1, 2, 1], [3, 4, 2]]) + */ +export const augment = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).augment(array(y)); -NDArray.prototype.augment = function(this: T, x: T): T { +/** + * @function augment + * @memberof NDArray.prototype + * @description Augments `x` with current matrix. + * @param {NDArray} x + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2], [3, 4]]).augment(array([[1], [2]])); // <=> array([[1, 2, 1], [3, 4, 2]]) + */ +export default function(this: NDArray, x: NDArray | ArrayLike): NDArray { const [r1, c1] = this.shape; - const [r2, c2] = x.shape; + const [r2, c2] = array(x).shape; const { data: d1 } = this; - const { data: d2 } = x; + const { data: d2 } = array(x); if (r2 === 0 || c2 === 0) { return this; @@ -17,7 +43,7 @@ NDArray.prototype.augment = function(this: T, x: T): T { throw new Error('rows do not match'); } - const y: T = NDArray.zeros(r1, c1 + c2); + const y = zeros(r1, c1 + c2); const { data: d3 } = y; let i: number; diff --git a/src/core/binOp.bench.ts b/src/core/binOp.bench.ts index bd1f4f03..9729bc6d 100644 --- a/src/core/binOp.bench.ts +++ b/src/core/binOp.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { binOp } from './binOp'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'binOp', - (n: number): [v, v, (a: number, b: number) => number] => - [v.random(n), v.random(n), (a: number, b: number): number => a + b], - (x: v, y: v, op: (a: number, b: number) => number): void => { + (n: number) => [random(n), random(n), (a: number, b: number): number => a + b], + (x, y, op: (a: number, b: number) => number): void => { x.binOp(y, op); }, - (x: v, y: v, op: (a: number, b: number) => number): void => { - v.binOp(x, y, op); + (x, y, op: (a: number, b: number) => number): void => { + binOp(x, y, op); } ); diff --git a/src/core/binOp.spec.ts b/src/core/binOp.spec.ts index 219ca149..05edc268 100644 --- a/src/core/binOp.spec.ts +++ b/src/core/binOp.spec.ts @@ -1,33 +1,37 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { binOp } from './binOp'; +import { array } from './array'; describe('(v) binOp', () => { it('should work as expected', () => { - const x: v = v.array([1, 1, 1]); - const y: v = v.array([1, 2, 3]); - const z: v = v.array([2, 3, 4]); + const x = array([1, 1, 1]); + const y = array([1, 2, 3]); + const z = array([2, 3, 4]); const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; - deepStrictEqual(x.binOp(y, sum), z); + equals(x.binOp(y, sum), z); }); it('should throw error when sizes do not match', () => { - const x: v = v.array([1, 1, 1]); - const y: v = v.array([1, 2, 3, 4]); + const x = array([1, 1, 1]); + const y = array([1, 2, 3, 4]); const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; throws(() => { x.binOp(y, sum); }, Error); }); - it('should work as the static equivalent of a.binOp(b, (a, b) => a + b)', () => { - const x: v = v.array([1, 1, 1]); - const y: v = v.array([1, 2, 3]); - const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; + it( + 'should work as the static equivalent of a.binOp(b, (a, b) => a + b)', + () => { + const x = array([1, 1, 1]); + const y = array([1, 2, 3]); + const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; - deepStrictEqual(x.copy().binOp(y, sum), v.binOp(x, y, sum)); - }); + equals(x.copy().binOp(y, sum), binOp(x, y, sum)); + } + ); }); diff --git a/src/core/binOp.ts b/src/core/binOp.ts index 429e07f9..17eb15c9 100644 --- a/src/core/binOp.ts +++ b/src/core/binOp.ts @@ -1,33 +1,57 @@ + import { NDArray } from './'; +import { NDMultiIter } from '../iterator'; +import { array } from './array'; -NDArray.binOp = ( - x: T | ArrayLike, - y: T | ArrayLike, +/** + * @static + * @memberof module:Globals + * @function binOp + * @description Perform binary operation `f` on `y` in `x`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { binOp } from 'vectorious/core/binOp'; + * + * binOp([1, 2, 3], [4, 5, 6], (a, b) => a + b); // => array([[5, 7, 9]) + */ +export const binOp = ( + x: NDArray | ArrayLike, + y: NDArray | ArrayLike, f: ( a: number, b: number, index: number ) => number -): T => NDArray.array(x).binOp(NDArray.array(y), f); +): NDArray => array(x).binOp(array(y), f); -NDArray.prototype.binOp = function( - this: T, - x: T, +/** + * @function binOp + * @memberof NDArray.prototype + * @description Perform binary operation `f` on `x` in the current array. + * @param {NDArray} x + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).binOp([4, 5, 6], (a, b) => a + b); // => array([[5, 7, 9]) + */ +export default function( + this: NDArray, + x: NDArray | ArrayLike, f: ( a: number, b: number, index: number ) => number -): T { - this.equilateral(x); - this.equidimensional(x); - - const { data: d1, length: l1 } = this; - const { data: d2 } = x; +): NDArray { + const { data: d1 } = this; + const { data: d2 } = array(x); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = f(d1[i], d2[i], i); + const iter = new NDMultiIter(this, x); + for (const [i, j] of iter) { + d1[i] = f(d1[i], d2[j], i); } return this; diff --git a/src/core/cbrt.bench.ts b/src/core/cbrt.bench.ts index 3639a5bd..a00bc735 100644 --- a/src/core/cbrt.bench.ts +++ b/src/core/cbrt.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { cbrt } from './cbrt'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'cbrt', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.cbrt(); }, - (x: v): void => { - v.cbrt(x); + (x): void => { + cbrt(x); } ); diff --git a/src/core/cbrt.spec.ts b/src/core/cbrt.spec.ts index 3a61bcab..623188dd 100644 --- a/src/core/cbrt.spec.ts +++ b/src/core/cbrt.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { cbrt } from './cbrt'; +import { map } from './map'; +import { random } from './random'; describe('(v) cbrt', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cbrt(value)), x.cbrt()); + equals(map(x, (value: number) => Math.cbrt(value)), x.cbrt()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cbrt(value)), v.cbrt(x)); + equals(map(x, (value: number) => Math.cbrt(value)), cbrt(x)); }); }); diff --git a/src/core/cbrt.ts b/src/core/cbrt.ts index 665a676a..b9fbe24d 100644 --- a/src/core/cbrt.ts +++ b/src/core/cbrt.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.cbrt = (x: T | ArrayLike): T => NDArray.array(x).cbrt(); +const { cbrt: f } = Math; -NDArray.prototype.cbrt = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function cbrt + * @description Returns the cube root of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { cbrt } from 'vectorious/core/cbrt'; + * + * cbrt([1, 8, 27]); // => array([1, 2, 3]) + */ +export const cbrt = (x: NDArray | ArrayLike): NDArray => array(x).cbrt(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.cbrt(d1[i]); +/** + * @function cbrt + * @memberof NDArray.prototype + * @description Returns the cube root of each element of current array. + * @returns {this} + * @example + * import { cbrt } from 'vectorious/core/cbrt'; + * + * cbrt([1, 8, 27]); // => array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/ceil.bench.ts b/src/core/ceil.bench.ts index c7809448..005bb908 100644 --- a/src/core/ceil.bench.ts +++ b/src/core/ceil.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { ceil } from './ceil'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'ceil', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.ceil(); }, - (x: v): void => { - v.ceil(x); + (x): void => { + ceil(x); } ); diff --git a/src/core/ceil.spec.ts b/src/core/ceil.spec.ts index ae04c6d4..0bc42e0c 100644 --- a/src/core/ceil.spec.ts +++ b/src/core/ceil.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { ceil } from './ceil'; +import { map } from './map'; +import { random } from './random'; describe('(v) ceil', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.ceil(value)), x.ceil()); + equals(map(x, (value: number) => Math.ceil(value)), x.ceil()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.ceil(value)), v.ceil(x)); + equals(map(x, (value: number) => Math.ceil(value)), ceil(x)); }); }); diff --git a/src/core/ceil.ts b/src/core/ceil.ts index 0a4e42fd..f2a7a0de 100644 --- a/src/core/ceil.ts +++ b/src/core/ceil.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.ceil = (x: T | ArrayLike): T => NDArray.array(x).ceil(); +const { ceil: f } = Math; -NDArray.prototype.ceil = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function ceil + * @description Returns smallest integer greater than or equal to of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { ceil } from 'vectorious/core/ceil'; + * + * ceil([0.5, 1.5, 2.5]); // => array([1, 2, 3]) + */ +export const ceil = (x: NDArray | ArrayLike): NDArray => array(x).ceil(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.ceil(d1[i]); +/** + * @function ceil + * @memberof NDArray.prototype + * @description Returns smallest integer greater than or equal to of each element of current array. + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0.5, 1.5, 2.5]).ceil(); // <=> array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/check.bench.ts b/src/core/check.bench.ts index d35f57b8..aaeb67e4 100644 --- a/src/core/check.bench.ts +++ b/src/core/check.bench.ts @@ -1,17 +1,15 @@ -import v = require('..'); +import { check } from './check'; +import { random } from './random'; import { bench } from '../bench'; -const { random, floor } = Math; -const r: (n: number) => v = (n: number): v => - v.array(new Float32Array(n)).fill(random); bench( - 'v', + 'NDArray', 'check', - (n: number): [v, number] => [v.random(n), floor(random() * n)], - (x: v, i: number): void => { + (n: number) => [random(n), Math.floor(Math.random() * n)], + (x, i: number): void => { x.check(i); }, - (x: v, i: number): void => { - v.check(x, i); + (x, i: number): void => { + check(x, i); } ); diff --git a/src/core/check.spec.ts b/src/core/check.spec.ts index 025d34f5..e6501c50 100644 --- a/src/core/check.spec.ts +++ b/src/core/check.spec.ts @@ -3,18 +3,20 @@ import { throws, } from 'assert'; -import v = require('..'); +import { check } from './check'; +import { array } from './array'; +import { random } from './random'; describe('(v) check', () => { it('should throw error if the index is NaN', () => { - const x: v = v.array([1, 2, 3, 4]); + const x = array([1, 2, 3, 4]); throws(() => { x.check(NaN); }, Error); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - doesNotThrow(() => { x.check(0); }, Error); + doesNotThrow(() => { check(x, 0); }, Error); }); }); diff --git a/src/core/check.ts b/src/core/check.ts index e4669625..785d7e08 100644 --- a/src/core/check.ts +++ b/src/core/check.ts @@ -1,10 +1,35 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.check = (x: T | ArrayLike, ...indices: number[]): void => { - NDArray.array(x).check(...indices); +/** + * @static + * @memberof module:Globals + * @function check + * @description Asserts if indices `i, j, ..., n` are within the bounds of `x` + * @param {NDArray} x + * @param {Number[]} ...indices + * @throws {Error} index out of bounds + * @example + * import { check } from 'vectorious/core/check'; + * + * check([0.5, 1.5, 2.5], 3); // Error: index out of bounds + */ +export const check = (x: NDArray | ArrayLike, ...indices: number[]): void => { + array(x).check(...indices); }; -NDArray.prototype.check = function(this: T, ...indices: number[]): void { +/** + * @function check + * @memberof NDArray.prototype + * @description Asserts if indices `i, j, ..., n` are within the bounds of current array + * @param {Number[]} ...indices + * @throws {Error} index out of bounds + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0.5, 1.5, 2.5]).check(3); // Error: index out of bounds + */ +export default function(this: NDArray, ...indices: number[]): void { const { shape: s1, length: l1 } = this; if (indices.length === 1) { diff --git a/src/core/combine.bench.ts b/src/core/combine.bench.ts index 6c0bce8c..3a2e5348 100644 --- a/src/core/combine.bench.ts +++ b/src/core/combine.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { combine } from './combine'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'combine', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.combine(y); }, - (x: v, y: v): void => { - v.combine(x, y); + (x, y): void => { + combine(x, y); } ); diff --git a/src/core/combine.spec.ts b/src/core/combine.spec.ts index d9d175a2..667f2e6f 100644 --- a/src/core/combine.spec.ts +++ b/src/core/combine.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { combine } from './combine'; +import { array } from './array'; describe('(v) combine', () => { it('should return current vector if combined with empty vector', () => { - deepStrictEqual(v.array([1, 2, 3]), v.array([1, 2, 3]).combine(v.array())); + equals(array([1, 2, 3]), array([1, 2, 3]).combine(array())); }); it('should work as expected', () => { - deepStrictEqual(v.array([1, 2, 3, 0, 1]), v.array([1, 2, 3]).combine(v.array([0, 1]))); + equals(array([1, 2, 3, 0, 1]), array([1, 2, 3]).combine(array([0, 1]))); }); it('should work as the static equivalent', () => { - deepStrictEqual(v.array([1, 2, 3, 0, 1]), v.combine(v.array([1, 2, 3]), v.array([0, 1]))); + equals(array([1, 2, 3, 0, 1]), combine(array([1, 2, 3]), array([0, 1]))); }); }); diff --git a/src/core/combine.ts b/src/core/combine.ts index 2c866c8f..2822df59 100644 --- a/src/core/combine.ts +++ b/src/core/combine.ts @@ -2,11 +2,36 @@ import { TypedArray } from '../types'; import { get_type } from '../util'; import { NDArray } from './'; +import { array } from './array'; -NDArray.combine = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).combine(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function combine + * @description Combines the vector `x` with `y` + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { combine } from 'vectorious/core/combine'; + * + * combine([1, 2, 3], [4, 5, 6]); // => array([1, 2, 3, 4, 5, 6]) + */ +export const combine = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).combine(array(y)); -NDArray.prototype.combine = function(this: T, x: T): T { +/** + * @function combine + * @memberof NDArray.prototype + * @description Combines the current vector with `x` + * @param {NDArray} x + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).combine([4, 5, 6]); // => array([1, 2, 3, 4, 5, 6]) + */ +export default function(this: NDArray, x: NDArray): NDArray { if (this.shape.length !== 1 && x.shape.length !== 1) { throw new Error('combine operation not permitted for multidimensional arrays'); } diff --git a/src/core/copy.bench.ts b/src/core/copy.bench.ts index 157eed69..1064ff37 100644 --- a/src/core/copy.bench.ts +++ b/src/core/copy.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { copy } from './copy'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'copy', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.copy(); }, - (x: v): void => { - v.copy(x); + (x): void => { + copy(x); } ); diff --git a/src/core/copy.spec.ts b/src/core/copy.spec.ts index c688088b..ee4855b9 100644 --- a/src/core/copy.spec.ts +++ b/src/core/copy.spec.ts @@ -1,28 +1,29 @@ import { - deepStrictEqual, notStrictEqual, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { copy } from './copy'; +import { array } from './array'; describe('(v) copy', () => { it('should create an immutable copy of class', () => { - const f32: Float32Array = new Float32Array([1, 2, 3, 4]); - const original: v = v.array(f32); - const copy: v = original.copy(); + const f32 = new Float64Array([1, 2, 3, 4]); + const original = array(f32); + const cp = original.copy(); - notStrictEqual(original, copy); - notStrictEqual(original.data, copy.data); - deepStrictEqual(original, copy); + notStrictEqual(original, cp); + notStrictEqual(original.data, cp.data); + equals(original, cp); }); it('should work as the static equivalent', () => { - const f32: Float32Array = new Float32Array([1, 2, 3, 4]); - const original: v = v.array(f32); - const copy: v = v.copy(original); + const f32 = new Float64Array([1, 2, 3, 4]); + const original = array(f32); + const cp = copy(original); - notStrictEqual(original, copy); - notStrictEqual(original.data, copy.data); - deepStrictEqual(original, copy); + notStrictEqual(original, cp); + notStrictEqual(original.data, cp.data); + equals(original, cp); }); }); diff --git a/src/core/copy.ts b/src/core/copy.ts index 810ce444..c334a8e1 100644 --- a/src/core/copy.ts +++ b/src/core/copy.ts @@ -1,14 +1,37 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { array } from './array'; -NDArray.copy = (x: T | ArrayLike): T => NDArray.array(x).copy(); +/** + * @static + * @memberof module:Globals + * @function copy + * @description Makes a copy of `x` + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { copy } from 'vectorious/core/copy'; + * + * copy([1, 2, 3]); // => array([1, 2, 3]) + */ +export const copy = (x: NDArray | ArrayLike): NDArray => array(x).copy(); -NDArray.prototype.copy = function(this: T): T { - const copy: T = Object.assign(Object.create(Object.getPrototypeOf(this)), this); +/** + * @function copy + * @memberof NDArray.prototype + * @description Makes a copy of the class and underlying data + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).copy(); // => array([1, 2, 3]) + * @todo Should make new array C-contiguous in addition to just copying + */ +export default function(this: NDArray): NDArray { + const copy: NDArray = Object.assign(Object.create(Object.getPrototypeOf(this)), this); - copy.data = new (get_type(this.dtype))(this.data); - copy.shape = this.shape; + copy.data = this.data.slice(); + copy.shape = this.shape.slice(); + copy.strides = this.strides.slice(); copy.length = this.length; copy.dtype = this.dtype; diff --git a/src/core/cos.bench.ts b/src/core/cos.bench.ts index c2e6a77e..0e5c4943 100644 --- a/src/core/cos.bench.ts +++ b/src/core/cos.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { cos } from './cos'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'cos', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.cos(); }, - (x: v): void => { - v.cos(x); + (x): void => { + cos(x); } ); diff --git a/src/core/cos.spec.ts b/src/core/cos.spec.ts index 1e0a7234..dfea75d8 100644 --- a/src/core/cos.spec.ts +++ b/src/core/cos.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { cos } from './cos'; +import { map } from './map'; +import { random } from './random'; describe('(v) cos', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cos(value)), x.cos()); + equals(map(x, (value: number) => Math.cos(value)), x.cos()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cos(value)), v.cos(x)); + equals(map(x, (value: number) => Math.cos(value)), cos(x)); }); }); diff --git a/src/core/cos.ts b/src/core/cos.ts index 9806cee6..52179afd 100644 --- a/src/core/cos.ts +++ b/src/core/cos.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.cos = (x: T | ArrayLike): T => NDArray.array(x).cos(); +const { cos: f } = Math; -NDArray.prototype.cos = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function cos + * @description Returns the cosine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { cos } from 'vectorious/core/cos'; + * + * cos([0, Math.PI / 2, Math.PI]); // => array([1, 0, -1]) + */ +export const cos = (x: NDArray | ArrayLike): NDArray => array(x).cos(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.cos(d1[i]); +/** + * @function cos + * @memberof NDArray.prototype + * @description Returns the cosine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0, Math.PI / 2, Math.PI]).cos(); // => array([1, 0, -1]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/cosh.bench.ts b/src/core/cosh.bench.ts index 3591f414..042d1d9f 100644 --- a/src/core/cosh.bench.ts +++ b/src/core/cosh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { cosh } from './cosh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'cosh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.cosh(); }, - (x: v): void => { - v.cosh(x); + (x): void => { + cosh(x); } ); diff --git a/src/core/cosh.spec.ts b/src/core/cosh.spec.ts index 5710d81d..eedb71c3 100644 --- a/src/core/cosh.spec.ts +++ b/src/core/cosh.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { cosh } from './cosh'; +import { map } from './map'; +import { random } from './random'; describe('(v) cosh', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cosh(value)), x.cosh()); + equals(map(x, (value: number) => Math.cosh(value)), x.cosh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.cosh(value)), v.cosh(x)); + equals(map(x, (value: number) => Math.cosh(value)), cosh(x)); }); }); diff --git a/src/core/cosh.ts b/src/core/cosh.ts index 810d77ea..0179bacf 100644 --- a/src/core/cosh.ts +++ b/src/core/cosh.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.cosh = (x: T | ArrayLike): T => NDArray.array(x).cosh(); +const { cosh: f } = Math; -NDArray.prototype.cosh = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function cosh + * @description Returns the hyperbolic cosine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { cosh } from 'vectorious/core/cosh'; + * + * cosh([0, 1, 2]); // => array([1, 1.5430806875228882, 3.762195587158203]) + */ +export const cosh = (x: NDArray | ArrayLike): NDArray => array(x).cosh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.cosh(d1[i]); +/** + * @function cosh + * @memberof NDArray.prototype + * @description Returns the hyperbolic cosine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0, 1, 2]).cosh(); // => array([1, 1.5430806875228882, 3.762195587158203]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/cross.bench.ts b/src/core/cross.bench.ts index d2c03b43..7ee846ca 100644 --- a/src/core/cross.bench.ts +++ b/src/core/cross.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { cross } from './cross'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'cross', - (): [v, v] => [v.random(3), v.random(3)], - (x: v, y: v): void => { + () => [random(3), random(3)], + (x, y): void => { x.cross(y); }, - (x: v, y: v): void => { - v.cross(x, y); + (x, y): void => { + cross(x, y); } ); diff --git a/src/core/cross.spec.ts b/src/core/cross.spec.ts index fcdf1228..11a14a90 100644 --- a/src/core/cross.spec.ts +++ b/src/core/cross.spec.ts @@ -1,29 +1,30 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { cross } from './cross'; +import { array } from './array'; describe('(v) cross', () => { it('should work as expected', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([2, 3, 4]); + const x = array([1, 2, 3]); + const y = array([2, 3, 4]); - deepStrictEqual(v.array([-1, 2, -1]), x.cross(y)); + equals(array([-1, 2, -1]), x.cross(y)); }); it('should throw an exception when lengths do not match', () => { - const x: v = v.array([1, 2, 3, 4]); - const y: v = v.array([5, 6, 7]); + const x = array([1, 2, 3, 4]); + const y = array([5, 6, 7]); throws(() => { x.cross(y); }, Error); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([2, 3, 4]); + const x = array([1, 2, 3]); + const y = array([2, 3, 4]); - deepStrictEqual(v.array([-1, 2, -1]), v.cross(x, y)); + equals(array([-1, 2, -1]), cross(x, y)); }); }); diff --git a/src/core/cross.ts b/src/core/cross.ts index b89b0211..4e70bd30 100644 --- a/src/core/cross.ts +++ b/src/core/cross.ts @@ -1,9 +1,40 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.cross = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).cross(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function cross + * @description + * Computes the cross product of the `x` and the vector `y` + * This operation can only calculated for vectors with three components. + * Otherwise it throws an exception. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { cross } from 'vectorious/core/cross'; + * + * cross([1, 2, 3], [4, 5, 6]); // => array([-3, 6, -3]) + */ +export const cross = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).cross(array(y)); -NDArray.prototype.cross = function(this: T, x: T): T { +/** + * @function cross + * @memberof NDArray.prototype + * @description + * Computes the cross product of the current vector and the vector `x` + * This operation can only calculated for vectors with three components. + * Otherwise it throws an exception. + * @param {NDArray} x + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).cross([4, 5, 6]); // <=> array([-3, 6, -3]) + */ +export default function(this: NDArray, x: NDArray): NDArray { const { length: l1 } = this; const { length: l2 } = x; diff --git a/src/core/det.bench.ts b/src/core/det.bench.ts index 7acbe3c9..68c6d5a8 100644 --- a/src/core/det.bench.ts +++ b/src/core/det.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { det } from './det'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'det', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.det(); }, - (x: v): void => { - v.det(x); + (x): void => { + det(x); } ); diff --git a/src/core/det.spec.ts b/src/core/det.spec.ts index e801b4cc..5da492be 100644 --- a/src/core/det.spec.ts +++ b/src/core/det.spec.ts @@ -1,21 +1,21 @@ import { - deepStrictEqual, strictEqual, throws, } from 'assert'; -import v = require('..'); +import { det } from './det'; +import { array } from './array'; describe('(v) det', () => { it('should throw error if matrix is not square', () => { - const x: v = v.array([[0, 0]]); + const x = array([[0, 0]]); throws(() => { x.det(); }, Error); }); it('should work as expected', () => { - const x: v = v.array([[1, 2], [3, 4]]); - const y: v = v.array([[1, 5, 6], [3.3, 9, 10], [7, 9, 3.2]]); - const z: v = v.array([[2, -1, 1], [-1, -2, 1], [-1, -1, -1]]); + const x = array([[1, 2], [3, 4]]); + const y = array([[1, 5, 6], [3.3, 9, 10], [7, 9, 3.2]]); + const z = array([[2, -1, 1], [-1, -2, 1], [-1, -1, -1]]); strictEqual(-2, Number(x.det().toFixed(2))); strictEqual(36.2, Number(y.det().toFixed(2))); @@ -23,8 +23,8 @@ describe('(v) det', () => { }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2], [3, 4]]); - deepStrictEqual(x.copy().det(), v.det(x)); + strictEqual(x.copy().det(), det(x)); }); }); diff --git a/src/core/det.ts b/src/core/det.ts index d5b158f6..76e2589c 100644 --- a/src/core/det.ts +++ b/src/core/det.ts @@ -1,8 +1,31 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.det = (x: T | ArrayLike): number => NDArray.array(x).det(); +/** + * @static + * @memberof module:Globals + * @function det + * @description Gets the determinant of `x`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { det } from 'vectorious/core/det'; + * + * det([[0, 1], [2, 3]]); // => -2 + */ +export const det = (x: NDArray | ArrayLike): number => array(x).det(); -NDArray.prototype.det = function(this: T): number { +/** + * @function det + * @memberof NDArray.prototype + * @description Gets the determinant of current matrix using LU factorization. + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[0, 1], [2, 3]]).det(); // => -2 + */ +export default function(this: NDArray): number { this.square(); const [n] = this.shape; diff --git a/src/core/diagonal.bench.ts b/src/core/diagonal.bench.ts index e74f12c2..dbd5f9da 100644 --- a/src/core/diagonal.bench.ts +++ b/src/core/diagonal.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { diagonal } from './diagonal'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'diag', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.diagonal(); }, - (x: v): void => { - v.diagonal(x); + (x): void => { + diagonal(x); } ); diff --git a/src/core/diagonal.spec.ts b/src/core/diagonal.spec.ts index a6c2e9fd..71efc882 100644 --- a/src/core/diagonal.spec.ts +++ b/src/core/diagonal.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { diagonal } from './diagonal'; +import { array } from './array'; describe('(v) diagonal', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - deepStrictEqual(v.array([1, 5, 9]), x.diagonal()); + equals(array([1, 5, 9]), x.diagonal()); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - deepStrictEqual(x.copy().diagonal(), v.diagonal(x)); + equals(x.copy().diagonal(), diagonal(x)); }); }); diff --git a/src/core/diagonal.ts b/src/core/diagonal.ts index a3c459d3..dfa6fa92 100644 --- a/src/core/diagonal.ts +++ b/src/core/diagonal.ts @@ -1,9 +1,32 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.diagonal = (x: T | ArrayLike): T => - NDArray.array(x).diagonal(); +/** + * @static + * @memberof module:Globals + * @function diagonal + * @description Gets the diagonal of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { diagonal } from 'vectorious/core/diagonal'; + * + * diagonal([[1, 2], [3, 4]]); // => array([1, 4]) + */ +export const diagonal = (x: NDArray | ArrayLike): NDArray => + array(x).diagonal(); -NDArray.prototype.diagonal = function(this: T): T { +/** + * @function diagonal + * @memberof NDArray.prototype + * @description Gets the diagonal of current matrix. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).diagonal(); // => array([1, 4]) + */ +export default function(this: NDArray): NDArray { this.square(); const { length: l1 } = this; diff --git a/src/core/dot.bench.ts b/src/core/dot.bench.ts index d967ae70..b07ecd12 100644 --- a/src/core/dot.bench.ts +++ b/src/core/dot.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { dot } from './dot'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'dot', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.dot(y); }, - (x: v, y: v): void => { - v.dot(x, y); + (x, y): void => { + dot(x, y); } ); diff --git a/src/core/dot.spec.ts b/src/core/dot.spec.ts index dbb596f2..7a589b78 100644 --- a/src/core/dot.spec.ts +++ b/src/core/dot.spec.ts @@ -1,29 +1,22 @@ import { strictEqual, - throws, } from 'assert'; -import v = require('..'); +import { dot } from './dot'; +import { array } from './array'; describe('(v) dot', () => { - it('should throw error if sizes do not match', () => { - const x: v = v.array([1]); - const y: v = v.array([1, 2]); - - throws(() => { x.dot(y); }, Error); - }); - it('should work as expected', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); strictEqual(32, x.dot(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); - strictEqual(32, v.dot(x, y)); + strictEqual(32, dot(x, y)); }); }); diff --git a/src/core/dot.ts b/src/core/dot.ts index bffff67f..00f3ce8b 100644 --- a/src/core/dot.ts +++ b/src/core/dot.ts @@ -1,44 +1,51 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { NDMultiIter } from '../iterator'; +import { array } from './array'; +import * as blas from '../blas'; + +/** + * @static + * @memberof module:Globals + * @function dot + * @description + * Performs dot multiplication with `x` and `y`. + * Accelerated with BLAS `?dot`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {Number} + * @example + * import { dot } from 'vectorious/core/dot'; + * + * dot([1, 2, 3], [4, 5, 6]); // => 32 + */ +export const dot = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): number => + array(x).dot(array(y)); + +/** + * @function dot + * @memberof NDArray.prototype + * @description + * Performs dot multiplication with `x` and current array + * Accelerated with BLAS `?dot`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).dot([4, 5, 6]); // => 32 + */ +export default function(this: NDArray, x: NDArray): number { + const { data: d1, length: l1, dtype } = this; + const { data: d2 } = x; -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.dot = (x: T | ArrayLike, y: T | ArrayLike): number => - NDArray.array(x).dot(NDArray.array(y)); - -NDArray.prototype.dot = function(this: T, x: T): number { - this.equilateral(x); - this.equidimensional(x); - - const { length: l1 } = this; let result: number = 0; - try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - const { data: d2 } = x; - if (this.dtype === 'float64') { - result = nblas.ddot(l1, d2, 1, d1, 1); - } - - if (this.dtype === 'float32') { - result = nblas.sdot(l1, d2, 1, d1, 1); - } + result = blas.dot(dtype, l1, d2, 1, d1, 1); } catch (err) { - const { data: d1 } = this; - const { data: d2 } = x; + const iter = new NDMultiIter(this, x); - let i: number; - for (i = 0; i < l1; i += 1) { - result += d1[i] * d2[i]; + for (const [i, j] of iter) { + result += d1[i] * d2[j]; } } diff --git a/src/core/eig.bench.ts b/src/core/eig.bench.ts index f490844c..4c989feb 100644 --- a/src/core/eig.bench.ts +++ b/src/core/eig.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { eig } from './eig'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'eig', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.eig(); }, - (x: v): void => { - v.eig(x); + (x): void => { + eig(x); } ); diff --git a/src/core/eig.spec.ts b/src/core/eig.spec.ts index 13a01808..4cce3037 100644 --- a/src/core/eig.spec.ts +++ b/src/core/eig.spec.ts @@ -1,25 +1,26 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { eig } from './eig'; +import { array } from './array'; describe('(v) eig', () => { it('should throw error if matrix is not square', () => { - const x: v = v.array([[1, 2]]); + const x = array([[1, 2]]); throws(() => { x.eig(); }, Error); }); it('should work as expected', () => { - const x: v = v.array([ + const x = array([ [1, 0, 0], [0, 2, 0], [0, 0, 3], ]); - const y: v = v.array([1, 2, 3]); - const z: v = v.array([ + const y = array([1, 2, 3]); + const z = array([ [1, 0, 0], [0, 1, 0], [0, 0, 1], @@ -27,17 +28,17 @@ describe('(v) eig', () => { const [e, E] = x.eig(); - deepStrictEqual(y, e); - deepStrictEqual(z, E); + equals(y, e); + equals(z, E); }); it('should work as the static equivalent', () => { - const x: v = v.array([ + const x = array([ [1, 0, 0], [0, 2, 0], [0, 0, 3], ]); - deepStrictEqual(x.copy().eig(), v.eig(x)); + equals(x.copy().eig(), eig(x)); }); }); diff --git a/src/core/eig.ts b/src/core/eig.ts index 1bf2a803..b0c7e73d 100644 --- a/src/core/eig.ts +++ b/src/core/eig.ts @@ -1,6 +1,9 @@ import { get_type } from '../util'; import { NDArray } from './'; +import { array } from './array'; +import { zeros } from './zeros'; +import { eye } from './eye'; let nlapack: any; try { @@ -8,6 +11,7 @@ try { } catch (err) {} /** + * @ignore * ┌ ┐ ┌ ┐┌ ┐ * │Skl│ │c −s││Skl│ * │ │ := │ ││ │ @@ -15,8 +19,8 @@ try { * └ ┘ └ ┘└ ┘ */ const rotate: - (x: T, c: number, s: number, k: number, l: number, i: number, j: number) => void = - (x: T, c: number, s: number, k: number, l: number, i: number, j: number): void => { + (x: NDArray, c: number, s: number, k: number, l: number, i: number, j: number) => void = + (x: NDArray, c: number, s: number, k: number, l: number, i: number, j: number): void => { const [n] = x.shape; const { data: d1 } = x; const temp: number = d1[k * n + l]; @@ -26,9 +30,35 @@ const rotate: d1[i * n + j] += s * (temp - tau * d1[i * n + j]); }; -NDArray.eig = (x: T | ArrayLike): [T, T] => NDArray.array(x).eig(); +/** + * @static + * @memberof module:Globals + * @function eig + * @description + * Gets eigenvalues and eigenvectors of `x` using the Jacobi method. + * Accelerated with LAPACK `?geev`. + * @param {NDArray} x + * @returns {Array} + * @example + * import { eig } from 'vectorious/core/eig'; + * + * eig([[1, 0, 0], [0, 2, 0], [0, 0, 3]]); // => [array([1, 2, 3]), array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])] + */ +export const eig = (x: NDArray | ArrayLike): [NDArray, NDArray] => array(x).eig(); -NDArray.prototype.eig = function(this: T): [T, T] { +/** + * @function eig + * @memberof NDArray.prototype + * @description + * Gets eigenvalues and eigenvectors of the current matrix using the Jacobi method. + * Accelerated with LAPACK `?geev`. + * @returns {Array} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 0, 0], [0, 2, 0], [0, 0, 3]]).eig(); // => [array([1, 2, 3]), array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])] + */ +export default function(this: NDArray): [NDArray, NDArray] { this.square(); const [n] = this.shape; @@ -42,11 +72,11 @@ NDArray.prototype.eig = function(this: T): [T, T] { const jobvl: typeof nlapack.NDArrayEigenvector = nlapack.NoEigenvector; const jobvr: typeof nlapack.NDArrayEigenvector = nlapack.Eigenvector; - const wr: T = NDArray.zeros(n); - const wi: T = NDArray.zeros(n); + const wr = zeros(n); + const wi = zeros(n); - const vl: T = NDArray.zeros(n, n); - const vr: T = NDArray.zeros(n, n); + const vl = zeros(n, n); + const vr = zeros(n, n); const { data: d1 } = this; const { data: d2 } = wr; @@ -64,13 +94,13 @@ NDArray.prototype.eig = function(this: T): [T, T] { return [wr, vr]; } catch (err) { const { data: d1 } = this; - const p: T = NDArray.eye(n); + const p = eye(n); - let max: number = 0; - let i: number = 0; - let j: number = 0; - let k: number = 0; - let l: number = 0; + let max = 0; + let i = 0; + let j = 0; + let k = 0; + let l = 0; do { // Find maximum off-diagonal element @@ -85,18 +115,18 @@ NDArray.prototype.eig = function(this: T): [T, T] { } // Find c and s - let t: number; + let t; if (Math.abs(d1[k * n + l]) < Math.abs(d1[l * n + l]) * 1e-36) { t = d1[k * n + l] / d1[l * n + l]; } else { - const phi: number = d1[l * n + l] / 2 * d1[k * n + l]; + const phi = d1[l * n + l] / 2 * d1[k * n + l]; t = 1 / (Math.abs(phi) + Math.sqrt(phi * phi + 1)); } - const c: number = 1 / Math.sqrt(t * t + 1); - const s: number = t * c; + const c = 1 / Math.sqrt(t * t + 1); + const s = t * c; - const e: number = d1[k * n + l]; + const e = d1[k * n + l]; d1[k * n + l] = 0; d1[k * n + k] -= t * e; d1[l * n + l] += t * e; diff --git a/src/core/equals.bench.ts b/src/core/equals.bench.ts index 841587c5..5c923ad6 100644 --- a/src/core/equals.bench.ts +++ b/src/core/equals.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { equals } from './equals'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'equals', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.equals(y); }, - (x: v, y: v): void => { - v.equals(x, y); + (x, y): void => { + equals(x, y); } ); diff --git a/src/core/equals.spec.ts b/src/core/equals.spec.ts index 9aef29b5..1132e689 100644 --- a/src/core/equals.spec.ts +++ b/src/core/equals.spec.ts @@ -2,18 +2,19 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { array } from './array'; describe('(v) equals', () => { it('should work as expected', () => { - strictEqual(true, v.array([1, 3, 2]).equals(v.array([1, 3, 2]))); - strictEqual(true, v.array().equals(v.array())); - strictEqual(false, v.array([1, 2, 3]).equals(v.array([1, 3, 2]))); + strictEqual(true, array([1, 3, 2]).equals(array([1, 3, 2]))); + strictEqual(true, array().equals(array())); + strictEqual(false, array([1, 2, 3]).equals(array([1, 3, 2]))); }); it('should work as the static equivalent', () => { - strictEqual(true, v.equals(v.array([1, 3, 2]), v.array([1, 3, 2]))); - strictEqual(true, v.equals(v.array(), v.array())); - strictEqual(false, v.equals(v.array([1, 2, 3]), v.array([1, 3, 2]))); + strictEqual(true, equals(array([1, 3, 2]), array([1, 3, 2]))); + strictEqual(true, equals(array(), array())); + strictEqual(false, equals(array([1, 2, 3]), array([1, 3, 2]))); }); }); diff --git a/src/core/equals.ts b/src/core/equals.ts index eee2502d..09e8fa76 100644 --- a/src/core/equals.ts +++ b/src/core/equals.ts @@ -1,19 +1,44 @@ import { NDArray } from './'; +import { NDMultiIter } from '../iterator'; +import { array } from './array'; -NDArray.equals = (x: T | ArrayLike, y: T | ArrayLike): boolean => - NDArray.array(x).equals(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function equals + * @description Checks if `x` and `y` are equal. + * @param {NDArray} x + * @param {NDArray} y + * @param {Number} tolerance + * @returns {Boolean} + * @example + * import { equals } from 'vectorious/core/equals'; + * + * equals([1, 2, 3], [1, 2, 3]); // => true + */ +export const equals = (x: NDArray | ArrayLike, y: NDArray | ArrayLike, tolerance: number = 1e-6): boolean => + array(x).equals(array(y), tolerance); -NDArray.prototype.equals = function(this: T, x: T): boolean { - this.equilateral(x); - this.equidimensional(x); - - const { data: d1, length: l1 } = this; +/** + * @function equals + * @memberof NDArray.prototype + * @description Checks if current array and `x` are equal. + * @param {NDArray} x + * @param {Number} tolerance + * @returns {Boolean} + * @example + * import { equals } from 'vectorious/core/equals'; + * + * array([1, 2, 3]).equals([1, 2, 3]); // => true + */ +export default function(this: NDArray, x: NDArray, tolerance: number = 1e-6): boolean { + const { data: d1 } = this; const { data: d2 } = x; - let i: number; + const iter = new NDMultiIter(this, x); - for (i = 0; i < l1; i += 1) { - if (d1[i] !== d2[i]) { + for (const [i, j] of iter) { + if (Math.abs(d1[i] - d2[j]) > tolerance) { return false; } } diff --git a/src/core/equidimensional.bench.ts b/src/core/equidimensional.bench.ts index 645175e5..9807e7e3 100644 --- a/src/core/equidimensional.bench.ts +++ b/src/core/equidimensional.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { equidimensional } from './equidimensional'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'equidimensional', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.equidimensional(y); }, - (x: v, y: v): void => { - v.equidimensional(x, y); + (x, y): void => { + equidimensional(x, y); } ); diff --git a/src/core/equidimensional.spec.ts b/src/core/equidimensional.spec.ts index 4b160d1d..75d58b32 100644 --- a/src/core/equidimensional.spec.ts +++ b/src/core/equidimensional.spec.ts @@ -2,33 +2,34 @@ import { throws, } from 'assert'; -import v = require('..'); +import { equidimensional } from './equidimensional'; +import { array } from './array'; describe('(v) equidimensional', () => { it('should pass if shapes match', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32x); - const y: v = v.array(f32y); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4]); + const x = array(f32x); + const y = array(f32y); x.equidimensional(y); }); it('should throw error if lengths do not match', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32x); - const y: v = v.array(f32y, { shape: [2, 2] }); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4]); + const x = array(f32x); + const y = array(f32y, { shape: [2, 2] }); throws(() => { x.equidimensional(y); }, Error); }); it('should work as the static equivalent', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32x); - const y: v = v.array(f32y); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4]); + const x = array(f32x); + const y = array(f32y); - v.equidimensional(x, y); + equidimensional(x, y); }); }); diff --git a/src/core/equidimensional.ts b/src/core/equidimensional.ts index 674da614..a73bb9ed 100644 --- a/src/core/equidimensional.ts +++ b/src/core/equidimensional.ts @@ -1,10 +1,37 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.equidimensional = (x: T | ArrayLike, y: T | ArrayLike): void => { - NDArray.array(x).equidimensional(NDArray.array(y)); +/** + * @deprecated + * @static + * @memberof module:Globals + * @function equidimensional + * @description Asserts if `x` and `y` have the same shape + * @param {NDArray} x + * @param {NDArray} y + * @throws {Error} shapes x and y do not match + * @example + * import { equidimensional } from 'vectorious/core/equidimensional'; + * + * equidimensional([1, 2, 3], [1, 2]); // Error: shapes 3 and 2 do not match + */ +export const equidimensional = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): void => { + array(x).equidimensional(array(y)); }; -NDArray.prototype.equidimensional = function(this: T, x: T): void { +/** + * @deprecated + * @function equidimensional + * @memberof NDArray.prototype + * @description Asserts if current array and `x` have the same shape + * @param {NDArray} x + * @throws {Error} shapes x and y do not match + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).equidimensional([1, 2]); // Error: shapes 3 and 2 do not match + */ +export default function(this: NDArray, x: NDArray): void { const { shape: s1 } = this; const { shape: s2 } = x; diff --git a/src/core/equilateral.bench.ts b/src/core/equilateral.bench.ts index 4ded89f0..19c370ea 100644 --- a/src/core/equilateral.bench.ts +++ b/src/core/equilateral.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { equilateral } from './equilateral'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'equilateral', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.equilateral(y); }, - (x: v, y: v): void => { - v.equilateral(x, y); + (x, y): void => { + equilateral(x, y); } ); diff --git a/src/core/equilateral.spec.ts b/src/core/equilateral.spec.ts index 92bf42e2..acd897c7 100644 --- a/src/core/equilateral.spec.ts +++ b/src/core/equilateral.spec.ts @@ -2,33 +2,34 @@ import { throws, } from 'assert'; -import v = require('..'); +import { equilateral } from './equilateral'; +import { array } from './array'; describe('(v) equilateral', () => { it('should pass if lengths match', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32x); - const y: v = v.array(f32y); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4]); + const x = array(f32x); + const y = array(f32y); x.equilateral(y); }); it('should throw error if lengths do not match', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4, 5]); - const x: v = v.array(f32x); - const y: v = v.array(f32y); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4, 5]); + const x = array(f32x); + const y = array(f32y); throws(() => { x.equilateral(y); }, Error); }); it('should work as the static equivalent', () => { - const f32x: Float32Array = new Float32Array([1, 2, 3, 4]); - const f32y: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32x); - const y: v = v.array(f32y); + const f32x = new Float64Array([1, 2, 3, 4]); + const f32y = new Float64Array([1, 2, 3, 4]); + const x = array(f32x); + const y = array(f32y); - v.equilateral(x, y); + equilateral(x, y); }); }); diff --git a/src/core/equilateral.ts b/src/core/equilateral.ts index f797f6ca..fb62f2e0 100644 --- a/src/core/equilateral.ts +++ b/src/core/equilateral.ts @@ -1,10 +1,37 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.equilateral = (x: T | ArrayLike, y: T | ArrayLike): void => { - NDArray.array(x).equilateral(NDArray.array(y)); +/** + * @deprecated + * @static + * @memberof module:Globals + * @function equilateral + * @description Asserts if `x` and `y` have the same length + * @param {NDArray} x + * @param {NDArray} y + * @throws {Error} lengths x and y do not match + * @example + * import { equilateral } from 'vectorious/core/equilateral'; + * + * equilateral([1, 2, 3], [1, 2]); // Error: lengths 3 and 2 do not match + */ +export const equilateral = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): void => { + array(x).equilateral(array(y)); }; -NDArray.prototype.equilateral = function(this: T, x: T): void { +/** + * @deprecated + * @function equilateral + * @memberof NDArray.prototype + * @description Asserts if current array and `x` have the same length + * @param {NDArray} x + * @throws {Error} lengths x and y do not match + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).equilateral([1, 2]); // Error: lengths 3 and 2 do not match + */ +export default function(this: NDArray, x: NDArray): void { const { length: l1 } = this; const { length: l2 } = x; diff --git a/src/core/exp.bench.ts b/src/core/exp.bench.ts index b708b7bd..c9d5e8ae 100644 --- a/src/core/exp.bench.ts +++ b/src/core/exp.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { exp } from './exp'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'exp', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.exp(); }, - (x: v): void => { - v.exp(x); + (x): void => { + exp(x); } ); diff --git a/src/core/exp.spec.ts b/src/core/exp.spec.ts index 7d965f6c..0a3503cf 100644 --- a/src/core/exp.spec.ts +++ b/src/core/exp.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { exp } from './exp'; +import { map } from './map'; +import { random } from './random'; describe('(v) exp', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.exp(value)), x.exp()); + equals(map(x, (value: number) => Math.exp(value)), x.exp()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.exp(value)), v.exp(x)); + equals(map(x, (value: number) => Math.exp(value)), exp(x)); }); }); diff --git a/src/core/exp.ts b/src/core/exp.ts index 7dfbb066..e3cf0036 100644 --- a/src/core/exp.ts +++ b/src/core/exp.ts @@ -1,13 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.exp = (x: T | ArrayLike): T => NDArray.array(x).exp(); +const { exp: f } = Math; -NDArray.prototype.exp = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function exp + * @description + * Returns e^x of each element of `x`, where x is the argument, + * and e is Euler's constant (2.718…), the base of the natural logarithm. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { exp } from 'vectorious/core/exp'; + * + * exp([1, 2, 3]); // => array([2.7182817459106445, 7.389056205749512, 20.08553695678711]) + */ +export const exp = (x: NDArray | ArrayLike): NDArray => array(x).exp(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.exp(d1[i]); +/** + * @function exp + * @memberof NDArray.prototype + * @description + * Returns e^x of each element of current array, where x is the argument, + * and e is Euler's constant (2.718…), the base of the natural logarithm. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).exp(); // <=> array([2.7182817459106445, 7.389056205749512, 20.08553695678711]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/expm1.bench.ts b/src/core/expm1.bench.ts index c0eb2e4c..bbb1ade8 100644 --- a/src/core/expm1.bench.ts +++ b/src/core/expm1.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { expm1 } from './expm1'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'expm1', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.expm1(); }, - (x: v): void => { - v.expm1(x); + (x): void => { + expm1(x); } ); diff --git a/src/core/expm1.spec.ts b/src/core/expm1.spec.ts index 515d3a0d..e579d10f 100644 --- a/src/core/expm1.spec.ts +++ b/src/core/expm1.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { expm1 } from './expm1'; +import { map } from './map'; +import { random } from './random'; describe('(v) expm1', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.expm1(value)), x.expm1()); + equals(map(x, (value: number) => Math.expm1(value)), x.expm1()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.expm1(value)), v.expm1(x)); + equals(map(x, (value: number) => Math.expm1(value)), expm1(x)); }); }); diff --git a/src/core/expm1.ts b/src/core/expm1.ts index e182b21e..a55d4872 100644 --- a/src/core/expm1.ts +++ b/src/core/expm1.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.expm1 = (x: T | ArrayLike): T => NDArray.array(x).expm1(); +const { expm1: f } = Math; -NDArray.prototype.expm1 = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function expm1 + * @description Returns subtracting 1 from exp(x) of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { expm1 } from 'vectorious/core/expm1'; + * + * expm1([1, 2, 3]); // => array([1.7182817459106445, 6.389056205749512, 19.08553695678711]) + */ +export const expm1 = (x: NDArray | ArrayLike): NDArray => array(x).expm1(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.expm1(d1[i]); +/** + * @function expm1 + * @memberof NDArray.prototype + * @description Returns subtracting 1 from exp(x) of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).expm1(); // <=> array([1.7182817459106445, 6.389056205749512, 19.08553695678711]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/eye.bench.ts b/src/core/eye.bench.ts index 9a9de1f6..af1a79e7 100644 --- a/src/core/eye.bench.ts +++ b/src/core/eye.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { eye } from './eye'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'eye', (n: number): [number] => [n], (n: number): void => { - v.eye(n); + eye(n); } ); diff --git a/src/core/eye.spec.ts b/src/core/eye.spec.ts index 13a6873c..35287550 100644 --- a/src/core/eye.spec.ts +++ b/src/core/eye.spec.ts @@ -1,13 +1,11 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { eye } from './eye'; +import { array } from './array'; describe('(v) eye', () => { it('should work as expected', () => { - const x: v = v.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]); + const x = array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]); - deepStrictEqual(x, v.eye(3)); + equals(x, eye(3)); }); }); diff --git a/src/core/eye.ts b/src/core/eye.ts index 6f8daa9b..37a9556f 100644 --- a/src/core/eye.ts +++ b/src/core/eye.ts @@ -1,15 +1,24 @@ import { NDArray } from './'; -NDArray.eye = function(this: new(...args: any[]) => T, n: number): T { - const x: T = new this(new Float32Array(n * n), { shape: [n, n] }); +/** + * @static + * @memberof module:Globals + * @function eye + * @description Creates an identity matrix of size `n` and type `type`. + * @param {Number} n + * @returns {NDArray} + * @example + * import { eye } from 'vectorious/core/eye'; + * + * eye(2); // => array([[1, 0], [0, 1]]) + */ +export const eye = (n: number): NDArray => { + const x = new NDArray(new Float64Array(n * n), { shape: [n, n] }); const { data: d1 } = x; let i: number; - let j: number; for (i = 0; i < n; i += 1) { - for (j = 0; j < n; j += 1) { - d1[i * n + j] = i === j ? 1 : 0; - } + d1[i * n + i] = 1; } return x; diff --git a/src/core/fill.bench.ts b/src/core/fill.bench.ts index 2b9619e4..c5e50468 100644 --- a/src/core/fill.bench.ts +++ b/src/core/fill.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { fill } from './fill'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'fill', - (n: number): [v, number] => [v.random(n), Math.random()], - (x: v, value: number) => { + (n: number) => [random(n), Math.random()], + (x, value: number) => { x.fill(value); }, - (x: v, value: number) => { - v.fill(x, value); + (x, value: number) => { + fill(x, value); } ); diff --git a/src/core/fill.spec.ts b/src/core/fill.spec.ts index 5121c8f4..d78d8032 100644 --- a/src/core/fill.spec.ts +++ b/src/core/fill.spec.ts @@ -1,28 +1,26 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { fill } from './fill'; +import { array } from './array'; describe('(v) fill', () => { it('should work as expected', () => { - const x: v = v.array([1, 1, 1]); - const y: v = v.array([0, 0, 0]); + const x = array([1, 1, 1]); + const y = array([0, 0, 0]); - deepStrictEqual(x, y.fill(1)); + equals(x, y.fill(1)); }); it('should work as expected with function argument', () => { - const x: v = v.array([0, 1, 2]); - const y: v = v.array([0, 0, 0]); + const x = array([0, 1, 2]); + const y = array([0, 0, 0]); - deepStrictEqual(x, y.fill((index: number) => index)); + equals(x, y.fill((index: number) => index)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 1, 1]); - const y: v = v.array([0, 0, 0]); + const x = array([1, 1, 1]); + const y = array([0, 0, 0]); - deepStrictEqual(x, v.fill(y, 1)); + equals(x, fill(y, 1)); }); }); diff --git a/src/core/fill.ts b/src/core/fill.ts index 0e046399..bd29eae2 100644 --- a/src/core/fill.ts +++ b/src/core/fill.ts @@ -1,17 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.fill = ( - x: T | ArrayLike, +/** + * @static + * @memberof module:Globals + * @function fill + * @description Fills `x` with a scalar value + * @param {NDArray} x + * @param {Number} value + * @returns {NDArray} + * @example + * import { fill } from 'vectorious/core/fill'; + * + * fill([1, 2, 3], 0); // => array([0, 0, 0]) + */ +export const fill = ( + x: NDArray | ArrayLike, value: number | ((index: number) => number) = 0 -): T => - NDArray.array(x).fill(value); +): NDArray => + array(x).fill(value); -NDArray.prototype.fill = function(this: T, value: number | ((index: number) => number) = 0): T { - const { data: d1, length: l1 } = this; +/** + * @function fill + * @memberof NDArray.prototype + * @description Fills the current array with a scalar value + * @param {Number} value + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).fill(0); // <=> array([0, 0, 0]) + */ +export default function(this: NDArray, value: number | ((index: number) => number) = 0): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = value instanceof Function ? value(i) : value; + for (const i of iter) { + d1[i!] = value instanceof Function ? value(iter.pos) : value; } return this; diff --git a/src/core/floor.bench.ts b/src/core/floor.bench.ts index e25b11a4..da2822aa 100644 --- a/src/core/floor.bench.ts +++ b/src/core/floor.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { floor } from './floor'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'floor', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.floor(); }, - (x: v): void => { - v.floor(x); + (x): void => { + floor(x); } ); diff --git a/src/core/floor.spec.ts b/src/core/floor.spec.ts index febac1a1..785ea7da 100644 --- a/src/core/floor.spec.ts +++ b/src/core/floor.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { floor } from './floor'; +import { map } from './map'; +import { random } from './random'; describe('(v) floor', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.floor(value)), x.floor()); + equals(map(x, (value: number) => Math.floor(value)), x.floor()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.floor(value)), v.floor(x)); + equals(map(x, (value: number) => Math.floor(value)), floor(x)); }); }); diff --git a/src/core/floor.ts b/src/core/floor.ts index 23037688..02c2df1b 100644 --- a/src/core/floor.ts +++ b/src/core/floor.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.floor = (x: T | ArrayLike): T => NDArray.array(x).floor(); +const { floor: f } = Math; -NDArray.prototype.floor = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function floor + * @description Returns the largest integer less than or equal to a number of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { floor } from 'vectorious/core/floor'; + * + * floor([1.5, 2.5, 3.5]); // => array([1, 2, 3]) + */ +export const floor = (x: NDArray | ArrayLike): NDArray => array(x).floor(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.floor(d1[i]); +/** + * @function floor + * @memberof NDArray.prototype + * @description Returns the largest integer less than or equal to a number of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1.5, 2.5, 3.5]).floor(); // <=> array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/forEach.bench.ts b/src/core/forEach.bench.ts index 6f576ed3..0992e479 100644 --- a/src/core/forEach.bench.ts +++ b/src/core/forEach.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { forEach } from './forEach'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'forEach', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.forEach((value: number) => value); }, - (x: v): void => { - v.forEach(x, (value: number) => value); + (x): void => { + forEach(x, (value: number) => value); } ); diff --git a/src/core/forEach.spec.ts b/src/core/forEach.spec.ts index 2d095748..77ecc185 100644 --- a/src/core/forEach.spec.ts +++ b/src/core/forEach.spec.ts @@ -1,26 +1,24 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { forEach } from './forEach'; +import { array } from './array'; import { TypedArray } from '../types'; describe('(v) forEach', () => { it('should work as expected', () => { - const x: v = v.array([1, 2, 3]); + const x = array([1, 2, 3]); x.forEach((value: number, index: number, src: TypedArray) => { src[index] = value * 2; }); - deepStrictEqual(v.array([2, 4, 6]), x); + equals(array([2, 4, 6]), x); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - v.forEach(x, (value: number, index: number, src: TypedArray) => { + const x = array([1, 2, 3]); + forEach(x, (value: number, index: number, src: TypedArray) => { src[index] = value * 2; }); - deepStrictEqual(v.array([2, 4, 6]), x); + equals(array([2, 4, 6]), x); }); }); diff --git a/src/core/forEach.ts b/src/core/forEach.ts index 96281d39..4cbfe42f 100644 --- a/src/core/forEach.ts +++ b/src/core/forEach.ts @@ -1,21 +1,51 @@ import { TypedArray } from '../types'; import { NDArray } from './'; +import { NDIter } from '../iterator'; -NDArray.forEach = ( - x: T, +/** + * @static + * @memberof module:Globals + * @function forEach + * @description Equivalent to `TypedArray.prototype.forEach`. + * @param {NDArray} x + * @param {Function} f + * @example + * import { forEach } from 'vectorious/core/forEach'; + * + * forEach([1, 2, 3], console.log); + * // 1 0 [ 1, 2, 3 ] + * // 2 1 [ 1, 2, 3 ] + * // 3 2 [ 1, 2, 3 ] + */ +export const forEach = ( + x: NDArray, f: (value: number, i: number, src: TypedArray) => void ): void => { x.forEach(f); }; -NDArray.prototype.forEach = function( - this: T, +/** + * @function forEach + * @memberof NDArray.prototype + * @description Equivalent to `TypedArray.prototype.forEach`. + * @param {Function} f + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).forEach(console.log); + * // 1 0 [ 1, 2, 3 ] + * // 2 1 [ 1, 2, 3 ] + * // 3 2 [ 1, 2, 3 ] + */ +export default function( + this: NDArray, f: (value: number, i: number, src: TypedArray) => void ): void { - const { data: d1, length: l1 } = this; - let i: number; - for (i = 0; i < l1; i += 1) { - f.call(this, d1[i], i, d1); + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + f.call(this, d1[i!], i!, d1); } }; diff --git a/src/core/fround.bench.ts b/src/core/fround.bench.ts index aa6401de..9fc7b03c 100644 --- a/src/core/fround.bench.ts +++ b/src/core/fround.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { fround } from './fround'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'fround', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.fround(); }, - (x: v): void => { - v.fround(x); + (x): void => { + fround(x); } ); diff --git a/src/core/fround.spec.ts b/src/core/fround.spec.ts index fdf88e00..01baa7fa 100644 --- a/src/core/fround.spec.ts +++ b/src/core/fround.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { fround } from './fround'; +import { map } from './map'; +import { random } from './random'; describe('(v) fround', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.fround(value)), x.fround()); + equals(map(x, (value: number) => Math.fround(value)), x.fround()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.fround(value)), v.fround(x)); + equals(map(x, (value: number) => Math.fround(value)), fround(x)); }); }); diff --git a/src/core/fround.ts b/src/core/fround.ts index 7f8c71ca..63d6752e 100644 --- a/src/core/fround.ts +++ b/src/core/fround.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.fround = (x: T | ArrayLike): T => NDArray.array(x).fround(); +const { fround: f } = Math; -NDArray.prototype.fround = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function fround + * @description Returns the nearest single precision float representation of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { fround } from 'vectorious/core/fround'; + * + * fround([-5.05, 5.05]); // => array([-5.050000190734863, 5.050000190734863]) + */ +export const fround = (x: NDArray | ArrayLike): NDArray => array(x).fround(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.fround(d1[i]); +/** + * @function fround + * @memberof NDArray.prototype + * @description Returns the nearest single precision float representation of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([-5.05, 5.05]).fround(); // <=> array([-5.050000190734863, 5.050000190734863]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/gauss.bench.ts b/src/core/gauss.bench.ts index 088c0164..63f63def 100644 --- a/src/core/gauss.bench.ts +++ b/src/core/gauss.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { gauss } from './gauss'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'gauss', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.gauss(); }, - (x: v): void => { - v.gauss(x); + (x): void => { + gauss(x); } ); diff --git a/src/core/gauss.spec.ts b/src/core/gauss.spec.ts index 2643010f..c50afcb9 100644 --- a/src/core/gauss.spec.ts +++ b/src/core/gauss.spec.ts @@ -1,27 +1,25 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { gauss } from './gauss'; +import { array } from './array'; describe('(v) gauss', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2, 3], [3, 4, 5]]); - const y: v = v.array([[1, 0, -1], [-0, 1, 2]]); + const x = array([[1, 2, 3], [3, 4, 5]]); + const y = array([[1, 0, -1], [0, 1, 2]]); - deepStrictEqual(y, x.gauss()); + equals(y, x.gauss()); }); it('should work as expected', () => { - const x: v = v.array([[1, 2, -1, -4], [2, 3, -1, -11], [-2, 0, -3, 22]]); - const y: v = v.array([[1, 0, 0, -8], [-0, 1, 0, 1], [-0, -0, 1, -2]]); + const x = array([[1, 2, -1, -4], [2, 3, -1, -11], [-2, 0, -3, 22]]); + const y = array([[1, 0, 0, -8], [0, 1, 0, 1], [0, 0, 1, -2]]); - deepStrictEqual(y, x.gauss()); + equals(y, x.gauss()); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2, 3], [3, 4, 5]]); + const x = array([[1, 2, 3], [3, 4, 5]]); - deepStrictEqual(x.copy().gauss(), v.gauss(x)); + equals(x.copy().gauss(), gauss(x)); }); }); diff --git a/src/core/gauss.ts b/src/core/gauss.ts index 89d47550..b5cf56a5 100644 --- a/src/core/gauss.ts +++ b/src/core/gauss.ts @@ -1,76 +1,118 @@ import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; +// import { diagonal } from './diagonal'; +import * as lapack from '../lapack'; /** + * @static + * @memberof module:Globals + * @function gauss + * @description * Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of `x`. + * Accelerated with LAPACK `?getrf`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { gauss } from 'vectorious/core/gauss'; + * + * gauss([[1, 2, 3], [4, 5, 6]]); // => array([[1, 0, -1], [-0, 1, 2]]) */ -NDArray.gauss = (x: T | ArrayLike): T => NDArray.array(x).gauss(); +export const gauss = (x: NDArray | ArrayLike): NDArray => array(x).gauss(); /** + * @function gauss + * @memberof NDArray.prototype + * @description * Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of the current matrix. + * Accelerated with LAPACK `?getrf`. + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2, 3], [4, 5, 6]]).gauss(); // <=> array([[1, 0, -1], [-0, 1, 2]]) */ -NDArray.prototype.gauss = function(this: T): T { - const [r, c] = this.shape; - const { data: d1 } = this; - - let lead: number = 0; - let leadValue: number; - let pivot: number; - - let i: number; - let j: number; - let k: number; - for (i = 0; i < r; i += 1) { - if (c <= lead) { - return this; - } +export default function(this: NDArray): NDArray { + const { shape: [r, c], data: d1, dtype } = this; - j = i; - while (d1[j * c + lead] === 0) { - j += 1; - if (r === j) { - j = i; - lead += 1; + try { + const { data: d1 } = this; + const ipiv = new Int32Array(Math.min(r, c)); - if (c === lead) { - return this; - } + lapack.getrf(dtype, r, c, d1, c, ipiv); + + const iter = new NDIter(this); + + // Zero out lower triangular part of matrix + let [ci, cj] = iter.coords; + for (const i of iter) { + if (cj < ci) { + d1[i!] = 0; } - } - if (i !== j) { - this.swap(i, j); + [ci, cj] = iter.coords; } + } catch (err) { + let lead: number = 0; + let leadValue: number; + let pivot: number; - pivot = d1[i * c + lead]; - if (pivot !== 0) { - for (k = 0; k < c; k += 1) { - d1[i * c + k] /= pivot; + let i: number; + let j: number; + let k: number; + for (i = 0; i < r; i += 1) { + if (c <= lead) { + return this; } - } - for (j = 0; j < r; j += 1) { - leadValue = d1[j * c + lead]; - if (j !== i) { - for (k = 0; k < c; k += 1) { - d1[j * c + k] -= d1[i * c + k] * leadValue; + j = i; + while (d1[j * c + lead] === 0) { + j += 1; + if (r === j) { + j = i; + lead += 1; + + if (c === lead) { + return this; + } } } - } - lead += 1; - } + if (i !== j) { + this.swap(i, j); + } - for (i = 0; i < r; i += 1) { - pivot = 0; - for (j = 0; j < c; j += 1) { - if (pivot === 0) { - pivot = d1[i * c + j]; + pivot = d1[i * c + lead]; + if (pivot !== 0) { + for (k = 0; k < c; k += 1) { + d1[i * c + k] /= pivot; + } + } + + for (j = 0; j < r; j += 1) { + leadValue = d1[j * c + lead]; + if (j !== i) { + for (k = 0; k < c; k += 1) { + d1[j * c + k] -= d1[i * c + k] * leadValue; + } + } } + + lead += 1; } - if (pivot === 0) { - for (k = 0; k < c; k += 1) { - d1[i * c + k] /= pivot; + for (i = 0; i < r; i += 1) { + pivot = 0; + for (j = 0; j < c; j += 1) { + if (pivot === 0) { + pivot = d1[i * c + j]; + } + } + + if (pivot === 0) { + for (k = 0; k < c; k += 1) { + d1[i * c + k] /= pivot; + } } } } diff --git a/src/core/get.bench.ts b/src/core/get.bench.ts index f0d4f397..d089d3f1 100644 --- a/src/core/get.bench.ts +++ b/src/core/get.bench.ts @@ -1,17 +1,15 @@ -import v = require('..'); +import { get } from './get'; +import { random } from './random'; import { bench } from '../bench'; -const { random, floor } = Math; -const r: (n: number) => v = (n: number): v => - v.array(new Float32Array(n)).fill(random); bench( - 'v', + 'NDArray', 'get', - (n: number): [v, number] => [v.random(n), floor(random() * n)], - (x: v, i: number) => { + (n: number) => [random(n), Math.floor(Math.random() * n)], + (x, i: number) => { x.get(i); }, - (x: v, i: number) => { - v.get(x, i); + (x, i: number) => { + get(x, i); } ); diff --git a/src/core/get.spec.ts b/src/core/get.spec.ts index 8dcd9985..6947044c 100644 --- a/src/core/get.spec.ts +++ b/src/core/get.spec.ts @@ -2,11 +2,13 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { get } from './get'; +import { array } from './array'; +import { random } from './random'; describe('(v) get', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); strictEqual(x.data[0], x.get(0)); strictEqual(x.data[1], x.get(1)); @@ -14,7 +16,7 @@ describe('(v) get', () => { }); it('should work as expected', () => { - const x: v = v.array([[[-1]], [[-2]], [[3]]]); + const x = array([[[-1]], [[-2]], [[3]]]); strictEqual(x.data[0], x.get(0, 0, 0)); strictEqual(x.data[1], x.get(1, 0, 0)); @@ -22,10 +24,10 @@ describe('(v) get', () => { }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - strictEqual(x.data[0], v.get(x, 0)); - strictEqual(x.data[1], v.get(x, 1)); - strictEqual(x.data[2], v.get(x, 2)); + strictEqual(x.data[0], get(x, 0)); + strictEqual(x.data[1], get(x, 1)); + strictEqual(x.data[2], get(x, 2)); }); }); diff --git a/src/core/get.ts b/src/core/get.ts index b7af81cd..917b775f 100644 --- a/src/core/get.ts +++ b/src/core/get.ts @@ -1,9 +1,34 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.get = (x: T | ArrayLike, ...indices: number[]): number => - NDArray.array(x).get(...indices); +/** + * @static + * @memberof module:Globals + * @function get + * @description Gets the element at `i, j, ..., n` from `x` + * @param {NDArray} x + * @param {Number[]} ...indices + * @returns {Number} + * @example + * import { get } from 'vectorious/core/get'; + * + * get([1, 2, 3], 2); // 3 + */ +export const get = (x: NDArray | ArrayLike, ...indices: number[]): number => + array(x).get(...indices); -NDArray.prototype.get = function(this: T, ...indices: number[]): number { +/** + * @function get + * @memberof NDArray.prototype + * @description Gets the element at `i, j, ..., n` from current vector. + * @param {Number[]} ...indices + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).get(2); // 3 + */ +export default function(this: NDArray, ...indices: number[]): number { this.check(...indices); const { data: d1, shape: s1 } = this; diff --git a/src/core/index.ts b/src/core/index.ts index 395c5883..b534cf6b 100644 --- a/src/core/index.ts +++ b/src/core/index.ts @@ -6,1079 +6,437 @@ import { import { flatten, get_dtype, + get_length, get_shape, + get_strides, get_type, is_typed_array, } from '../util'; - -export const inspectSymbol: unique symbol = Symbol.for('nodejs.util.inspect.custom'); - +import { + NDIter, +} from '../iterator'; + +import { default as abs } from './abs'; +import { default as acos } from './acos'; +import { default as acosh } from './acosh'; +import { default as add } from './add'; +import { default as angle } from './angle'; +import { default as asin } from './asin'; +import { default as asinh } from './asinh'; +import { default as atan } from './atan'; +import { default as atanh } from './atanh'; +import { default as augment } from './augment'; +import { default as binOp } from './binOp'; +import { default as cbrt } from './cbrt'; +import { default as ceil } from './ceil'; +import { default as check } from './check'; +import { default as combine } from './combine'; +import { default as copy } from './copy'; +import { default as cos } from './cos'; +import { default as cosh } from './cosh'; +import { default as cross } from './cross'; +import { default as det } from './det'; +import { default as diagonal } from './diagonal'; +import { default as dot } from './dot'; +import { default as eig } from './eig'; +import { default as equals } from './equals'; +import { default as equidimensional } from './equidimensional'; +import { default as equilateral } from './equilateral'; +import { default as exp } from './exp'; +import { default as expm1 } from './expm1'; +import { default as fill } from './fill'; +import { default as floor } from './floor'; +import { default as forEach } from './forEach'; +import { default as fround } from './fround'; +import { default as gauss } from './gauss'; +import { default as get } from './get'; +import { default as inv } from './inv'; +import { default as log } from './log'; +import { default as log10 } from './log10'; +import { default as log1p } from './log1p'; +import { default as log2 } from './log2'; +import { default as lu } from './lu'; +import { default as lu_factor } from './lu_factor'; +import { default as map } from './map'; +import { default as max } from './max'; +import { default as mean } from './mean'; +import { default as min } from './min'; +import { default as multiply } from './multiply'; +import { default as norm } from './norm'; +import { default as normalize } from './normalize'; +import { default as pow } from './pow'; +import { default as prod } from './prod'; +import { default as product } from './product'; +import { default as project } from './project'; +import { default as push } from './push'; +import { default as rank } from './rank'; +import { default as reciprocal } from './reciprocal'; +import { default as reduce } from './reduce'; +import { default as reshape } from './reshape'; +import { default as round } from './round'; +import { default as row_add } from './row_add'; +import { default as scale } from './scale'; +import { default as set } from './set'; +import { default as sign } from './sign'; +import { default as sin } from './sin'; +import { default as sinh } from './sinh'; +import { default as slice } from './slice'; +import { default as solve } from './solve'; +import { default as sqrt } from './sqrt'; +import { default as square } from './square'; +import { default as subtract } from './subtract'; +import { default as sum } from './sum'; +import { default as swap } from './swap'; +import { default as tan } from './tan'; +import { default as tanh } from './tanh'; +import { default as toArray } from './toArray'; +import { default as toString } from './toString'; +import { default as trace } from './trace'; +import { default as transpose } from './transpose'; +import { default as trunc } from './trunc'; + +const inspectSymbol: unique symbol = Symbol.for('nodejs.util.inspect.custom'); + +/** + * @class NDArray + * @description Constructs or copies an NDArray instance. + * @param data + * @param {Object} [options] + * @param {Number[]} [options.shape] + * @param {Number} [options.length] + * @param {Number[]} [options.strides] + * @param {string} [options.dtype] + * @example + * import { NDArray } from 'vectorious'; + * + * new NDArray() // => array([], dtype=float64) + * new NDArray([]) // => array([], dtype=float64) + * new NDArray([1, 2, 3]) // => array([1, 2, 3], dtype=float64) + * new NDArray([[1, 2], [3, 4]]) // => array([ [ 1, 2 ], [ 3, 4 ] ], dtype=float64) + * new NDArray(new Int32Array([1, 2, 3])) // => array([ 1, 2, 3 ], dtype=int32) + * new NDArray([1, 2, 3, 4], { + * shape: [2, 2], + * dtype: 'uint32' + * }) // => array([ [ 1, 2 ], [ 3, 4 ] ], dtype=uint32) + */ export class NDArray implements INDArray { /** - * Returns the absolute value of each element of `x`. - */ - public static abs: (x: T | ArrayLike) => T; - - /** - * Returns the arccosine of each element of `x`. - */ - public static acos: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic arccosine of each element of `x`. - */ - public static acosh: (x: T | ArrayLike) => T; - - /** - * Adds `y` multiplied by `alpha` to `x`. - * Accelerated with BLAS `?axpy`. - */ - public static add: (x: T | ArrayLike, y: T | ArrayLike, alpha?: number) => T; - - /** - * Determines the angle between the `x` and `y` - */ - public static angle: (x: T | ArrayLike, y: T | ArrayLike) => number; - - /** - * Constructor alias - */ - public static array: (...args: any[]) => T; - - /** - * Returns the arcsine of each element of `x`. - */ - public static asin: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic arcsine of each element of `x`. - */ - public static asinh: (x: T | ArrayLike) => T; - - /** - * Returns the arctangent of each element of `x`. - */ - public static atan: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic arctangent of each element of `x`. - */ - public static atanh: (x: T | ArrayLike) => T; - - /** - * Augments `x` and `y`. - */ - public static augment: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Perform binary operation `f` on `y` in `x`. - */ - public static binOp: ( - x: T | ArrayLike, - y: T | ArrayLike, - f: (a: number, b: number, index: number) => number - ) => T; - - /** - * Returns the cube root of each element of `x`. - */ - public static cbrt: (x: T | ArrayLike) => T; - - /** - * Returns smallest integer greater than or equal to of each element of `x`. - */ - public static ceil: (x: T | ArrayLike) => T; - - /** - * Asserts if indices `i, j, ..., n` are within the bounds of `x` - */ - public static check: (x: T | ArrayLike, ...indices: number[]) => void; - - /** - * Combines the vector `x` with `y` - */ - public static combine: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Makes a copy of `x` - */ - public static copy: (x: T | ArrayLike) => T; - - /** - * Returns the cosine of each element of `x`. - */ - public static cos: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic cosine of each element of `x`. - */ - public static cosh: (x: T | ArrayLike) => T; - - /** - * Computes the cross product of the `x` and the vector `y` - * This operation can only calculated for vectors with three components. - * Otherwise it throws an exception. - * The method returns a new (result) vector. - */ - public static cross: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Gets the determinant of `x`. - */ - public static det: (x: T | ArrayLike) => number; - - /** - * Gets the diagonal of `x`. - */ - public static diagonal: (x: T | ArrayLike) => T; - - /** - * Performs dot multiplication with `x` and `y`. - * Accelerated with BLAS `?dot`. - */ - public static dot: (x: T | ArrayLike, y: T | ArrayLike) => number; - - /** - * Gets eigenvalues and eigenvectors of `x` using the Jacobi method. - * Accelerated with LAPACK `?geev`. - */ - public static eig: (x: T | ArrayLike) => [T, T]; - - /** - * Checks if `x` and `y` are equal. - */ - public static equals: (x: T | ArrayLike, y: T | ArrayLike) => boolean; - - /** - * Asserts if `x` and `y` have the same shape - */ - public static equidimensional: (x: T | ArrayLike, y: T | ArrayLike) => void; - - /** - * Asserts if `x` and `y` have the same length - */ - public static equilateral: (x: T | ArrayLike, y: T | ArrayLike) => void; - - /** - * Returns e^x of each element of `x`, where x is the argument, - * and e is Euler's constant (2.718…), the base of the natural logarithm. - */ - public static exp: (x: T | ArrayLike) => T; - - /** - * Returns subtracting 1 from exp(x) of each element of `x`. - */ - public static expm1: (x: T | ArrayLike) => T; - - /** - * Creates an identity matrix of size `n` and type `type`. - */ - public static eye: (n: number) => T; - - /** - * Fills `x` with a scalar value - */ - public static fill: ( - x: T | ArrayLike, - value: number | ((index: number) => number) - ) => T; - - /** - * Returns the largest integer less than or equal to a number of each element of `x`. - */ - public static floor: (x: T | ArrayLike) => T; - - /** - * Equivalent to `TypedArray.prototype.forEach`. - */ - public static forEach: ( - x: T, - f: (value: number, i: number, src: TypedArray) => void - ) => void; - - /** - * Returns the nearest single precision float representation of each element of `x`. - */ - public static fround: (x: T | ArrayLike) => T; - - /** - * Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of `x`. - */ - public static gauss: (x: T | ArrayLike) => T; - - /** - * Gets the element at `i, j, ..., n` from `x` - */ - public static get: (x: T | ArrayLike, ...indices: number[]) => number; - - /** - * Determines the inverse of `x`. - * Accelerated with LAPACK `?getri`. - */ - public static inv: (x: T | ArrayLike) => T; - - /** - * Returns the natural logarithm (log_e, also ln) of each element of `x`. - */ - public static log: (x: T | ArrayLike) => T; - - /** - * Returns the natural logarithm (log_e, also ln) of 1 + x for each element of `x`. - */ - public static log10: (x: T | ArrayLike) => T; - - /** - * Returns the base 2 logarithm of each element of `x`. - */ - public static log1p: (x: T | ArrayLike) => T; - - /** - * Returns the base 10 logarithm of each element of `x`. - */ - public static log2: (x: T | ArrayLike) => T; - - /** - * Performs full LU decomposition on `x`. - * Accelerated with LAPACK `?getrf`. + * @name data + * @memberof NDArray.prototype + * @type TypedArray + * @default new Float64Array(0) */ - public static lu: (x: T | ArrayLike) => [T, T, Int32Array]; - - /** - * Performs LU factorization on `x`. - * Accelerated with LAPACK `?getrf`. - */ - public static lu_factor: (x: T | ArrayLike) => [T, Int32Array]; - - /** - * Creates a magic square matrix of `size` - */ - public static magic: (size: number) => T; - - /** - * Equivalent to `TypedArray.prototype.map`. - */ - public static map: ( - x: T | ArrayLike, - f: (value: number, i: number, src: TypedArray) => number - ) => T; - - /** - * Creates a matrix of `r` rows and `c` columns. - */ - public static matrix: (r: number, c: number) => T; - - /** - * Gets the maximum value (largest) element of `x`. - * Accelerated with BLAS `i?amax`. - */ - public static max: (x: T | ArrayLike) => number; - - /** - * Gets the arithmetic mean of `x`. - */ - public static mean: (x: T | ArrayLike) => number; - - /** - * Gets the minimum value (smallest) element of `x`. - */ - public static min: (x: T | ArrayLike) => number; - - /** - * Multiplies two matrices `x` and `y` of matching dimensions. - * Accelerated with BLAS `?gemm`. - */ - public static multiply: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Calculates the norm of current array (also called L2 norm or Euclidean length). - * Accelerated with BLAS `?nrm2`. - */ - public static norm: (x: T | ArrayLike) => number; - - /** - * Normalizes `x`. - */ - public static normalize: (x: T | ArrayLike) => T; - - /** - * Creates an array containing ones (`1`) of shape `shape` - */ - public static ones: (...shape: number[]) => T; - - /** - * Returns each element of `x` to the exponent power, that is, element^exponent. - */ - public static pow: (x: T | ArrayLike, exponent: number) => T; - - /** - * Product of all elements of `x`. - */ - public static prod: (x: T | ArrayLike) => number; - - /** - * Hadamard product of `x` and `y` - */ - public static product: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Projects the `y` onto `x` using the projection formula `(y * (x * y / y * y))`. - */ - public static project: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Pushes a new `value` into `x`. - */ - public static push: (x: T | ArrayLike, value: number) => T; - - /** - * Creates a vector containing random samples from a uniform distribution over `[0, 1)` of shape `shape` - */ - public static random: (...shape: number[]) => T; - - /** - * Creates an array containing a range (can be either ascending or descending) - * of numbers specified by the arguments provided (e.g. `NDArray.range(0, .5, 2)` - * gives an array containing all numbers in the interval `[0, 2)` separated by - * steps of `0.5`) - */ - public static range: (...args: number[]) => T; - - /** - * Finds the rank of `x` using gaussian elimination. - */ - public static rank: (x: T | ArrayLike) => number; - - /** - * Gets the element-wise reciprocal of `x`. - */ - public static reciprocal: (x: T | ArrayLike) => T; - - /** - * Equivalent to `TypedArray.prototype.reduce`. - */ - public static reduce: ( - x: NDArray, - f: (acc: number, value: number, i: number, src: TypedArray) => number, - initialValue?: number - ) => number; - - /** - * Reshapes `x` - */ - public static reshape: (x: T | ArrayLike, ...shape: number[]) => T; - - /** - * Returns the value of each element of `x` rounded to the nearest integer. - */ - public static round: (x: T | ArrayLike) => T; - - /** - * Adds a multiple of one row multiplied by `scalar` to another inside `x`. - */ - public static row_add: ( - x: T | ArrayLike, - dest: number, - source: number, - scalar?: number - ) => T; - - /** - * Multiplies all elements of `x` with a specified `scalar`. - * Accelerated with BLAS `?scal`. - */ - public static scale: (x: T | ArrayLike, scalar: number) => T; - - /** - * Sets the element at `i, j, ..., n` to `value`. - */ - public static set: (x: T, ...args: number[]) => void; - + public data: TypedArray = new Float64Array(0); + /** - * Returns the sign of each element of `x`, indicating - * whether it is positive, negative or zero. + * @name dtype + * @memberof NDArray.prototype + * @type String + * @default 'float64' */ - public static sign: (x: T | ArrayLike) => T; + public dtype: DType = 'float64'; /** - * Returns the sine of each element of `x`. + * @name length + * @memberof NDArray.prototype + * @type Number + * @default 0 */ - public static sin: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic sine of each element of `x`. - */ - public static sinh: (x: T | ArrayLike) => T; - - /** - * Slices `x` in the corresponding dimension - */ - public static slice: ( - x: T | ArrayLike, - start?: number, - step?: number, - end?: number - ) => T; - - /** - * Solves the equation AX = B (where A is `x` and B is `y`). - * Accelerated with LAPACK `?gesv`. - */ - public static solve: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Returns the positive square root of each element of `x`. - */ - public static sqrt: (x: T | ArrayLike) => T; - - /** - * Asserts if `x` is square. - */ - public static square: (x: T | ArrayLike) => void; - - /** - * Subtracts `y` from `x`. - */ - public static subtract: (x: T | ArrayLike, y: T | ArrayLike) => T; - - /** - * Sum of `x` - */ - public static sum: (x: T | ArrayLike) => number; - - /** - * Swaps two rows `i` and `j` in `x`. - */ - public static swap: (x: T | ArrayLike, i: number, j: number) => T; - - /** - * Returns the tangent of each element of `x`. - */ - public static tan: (x: T | ArrayLike) => T; - - /** - * Returns the hyperbolic tangent of each element of `x`. - */ - public static tanh: (x: T | ArrayLike) => T; - - /** - * Converts `x` into a JavaScript array. - */ - public static toArray: (x: T | ArrayLike) => number[]; - - /** - * Converts `x` into a readable formatted string. - */ - public static toString: (x: T | ArrayLike) => string; - - /** - * Gets the trace of `x` (the sum of all diagonal elements). - */ - public static trace: (x: T | ArrayLike) => number; - - /** - * Transposes `x` (mirror across the diagonal). - */ - public static transpose: (x: T | ArrayLike) => T; - - /** - * Returns the integer part of each element of `x`, - * removing any fractional digits. - */ - public static trunc: (x: T | ArrayLike) => T; - - /** - * Creates an array containing zeros (`0`) of shape `shape` - */ - public static zeros: (...shape: number[]) => T; - - public [inspectSymbol]!: () => string; - - /** - * Returns the absolute value of each element of current array. - */ - public abs!: () => this; - - /** - * Returns the arccosine of each element of current array. - */ - public acos!: () => this; - - /** - * Returns the hyperbolic arccosine of each element of current array. - */ - public acosh!: () => this; - - /** - * Adds `x` multiplied by `alpha` to the current array. - * Accelerated with BLAS `?axpy`. - */ - public add!: (y: T, alpha?: number) => this; - - /** - * Determines the angle between the current vector and `x`. - */ - public angle!: (x: T) => number; - - /** - * Returns the arcsine of each element of current array. - */ - public asin!: () => this; - - /** - * Returns the hyperbolic arcsine of each element of current array. - */ - public asinh!: () => this; - - /** - * Returns the arctangent of each element of current array. - */ - public atan!: () => this; - - /** - * Returns the hyperbolic arctangent of each element of current array. - */ - public atanh!: () => this; - - /** - * Augments `x` with current matrix. - */ - public augment!: (x: T) => this; - - /** - * Perform binary operation `f` on `x` in the current array. - */ - public binOp!: (y: T, f: (a: number, b: number, index: number) => number) => this; - - /** - * Returns the cube root of each element of current array. - */ - public cbrt!: () => this; - - /** - * Returns smallest integer greater than or equal to of each element of current array. - */ - public ceil!: () => this; - - /** - * Asserts if indices `i, j, ..., n` are within the bounds of current array - */ - public check!: (...indices: number[]) => void; - - /** - * Combines the current vector with `x` - */ - public combine!: (x: T) => this; - - /** - * Makes a copy of the class and underlying data - */ - public copy!: () => this; - - /** - * Returns the cosine of each element of current array. - */ - public cos!: () => this; - - /** - * Returns the hyperbolic cosine of each element of current array. - */ - public cosh!: () => this; - - /** - * Computes the cross product of the current vector and the vector `x` - * This operation can only be calculated for vectors with three components. - * Otherwise it throws an exception. - * The method returns a new (result) vector. - */ - public cross!: (x: T) => this; - - public data: TypedArray = new Float32Array(0); - - /** - * Gets the determinant of current matrix using LU factorization. - */ - public det!: () => number; - - /** - * Gets the diagonal of current matrix. - */ - public diagonal!: () => this; - - /** - * Performs dot multiplication with `x` and current array - * Accelerated with BLAS `?dot`. - */ - public dot!: (y: T) => number; - - public dtype: DType = 'float32'; - - /** - * Gets eigenvalues and eigenvectors of the current matrix using the Jacobi method. - * Accelerated with LAPACK `?geev`. - */ - public eig!: () => [T, T]; - - /** - * Checks if current array and `x` are equal. - */ - public equals!: (y: T) => boolean; - - /** - * Asserts if current array and `x` have the same shape - */ - public equidimensional!: (y: T) => void; - - /** - * Asserts if current array and `x` have the same length - */ - public equilateral!: (y: T) => void; - - /** - * Returns e^x of each element of current array, where x is the argument, - * and e is Euler's constant (2.718…), the base of the natural logarithm. - */ - public exp!: () => this; - - /** - * Returns subtracting 1 from exp(x) of each element of current array. - */ - public expm1!: () => this; - - /** - * Fills the current array with a scalar value - */ - public fill!: (value: number | ((index: number) => number)) => this; - - /** - * Returns the largest integer less than or equal to a number of each element of current array. - */ - public floor!: () => this; - - /** - * Equivalent to `TypedArray.prototype.forEach`. - */ - public forEach!: (f: (value: number, i: number, src: TypedArray) => void) => void; - - /** - * Returns the nearest single precision float representation of each element of current array. - */ - public fround!: () => this; - - /** - * Gauss-Jordan elimination (i.e. returns the reduced row echelon form) of current matrix. - */ - public gauss!: () => this; - - /** - * Gets the element at `i, j, ..., n` from current vector. - */ - public get!: (...indices: number[]) => number; - - /** - * Determines the inverse of current matrix using Gaussian elimination. - * Accelerated with LAPACK `?getri`. - */ - public inv!: () => this; - public length: number = 0; /** - * Returns the natural logarithm (log_e, also ln) of each element of current array. + * @name shape + * @memberof NDArray.prototype + * @type Number[] + * @default [0] */ - public log!: () => this; - - /** - * Returns the natural logarithm (log_e, also ln) of 1 + x for each element of current array. - */ - public log10!: () => this; - - /** - * Returns the base 2 logarithm of each element of current array. - */ - public log1p!: () => this; - - /** - * Returns the base 10 logarithm of each element of current array. - */ - public log2!: () => this; - - /** - * Performs full LU decomposition on current matrix. - * Accelerated with LAPACK `?getrf`. - */ - public lu!: () => [T, T, Int32Array]; - - /** - * Performs LU factorization on current matrix. - * Accelerated with LAPACK `?getrf`. - */ - public lu_factor!: () => [this, Int32Array]; - - /** - * Equivalent to `TypedArray.prototype.map`. - */ - public map!: (f: (value: number, i: number, src: TypedArray) => number) => this; - - /** - * Gets the maximum value (smallest) element of current array. - */ - public max!: () => number; - - /** - * Gets the arithmetic mean of current array. - */ - public mean!: () => number; - - /** - * Gets the minimum value (smallest) element of current array. - */ - public min!: () => number; - - /** - * Multiplies current matrix with `x`. - * Accelerated with BLAS `?gemm`. - */ - public multiply!: (x: T) => this; - - /** - * Calculates the norm of current array (also called L2 norm or Euclidean length). - * Accelerated with BLAS `?nrm2`. - */ - public norm!: () => number; - - /** - * Normalizes current vector. - */ - public normalize!: () => this; - - /** - * Returns each element of current array to the exponent power, that is, element^exponent. - */ - public pow!: (exponent: number) => this; - - /** - * Product of all elements of current array - */ - public prod!: () => number; - - /** - * Hadamard product of current matrix and `x` - */ - public product!: (y: T) => this; - - /** - * Projects the current vector onto `x` using the projection formula `(y * (x * y / y * y))`. - */ - public project!: (x: T) => this; - - /** - * Pushes a new `value` into current vector. - */ - public push!: (value: number) => this; - - /** - * Finds the rank of current matrix using gaussian elimination. - */ - public rank!: () => number; - - /** - * Gets the element-wise reciprocal of current array. - */ - public reciprocal!: () => this; - - /** - * Equivalent to `TypedArray.prototype.reduce`. - */ - public reduce!: ( - f: (acc: number, value: number, i: number, src: TypedArray) => number, - initialValue?: number - ) => number; - - /** - * Reshapes current array - */ - public reshape!: (...shape: number[]) => this; - - /** - * Returns the value of each element of current array rounded to the nearest integer. - */ - public round!: () => this; - - /** - * Adds a multiple of one row multiplied by `scalar` to another inside current matrix. - */ - public row_add!: (dest: number, source: number, scalar?: number) => this; - - /** - * Multiplies all elements of current array with a specified `scalar`. - * Accelerated with BLAS `?scal`. - */ - public scale!: (scalar: number) => this; - - /** - * Sets the element at `i, j, ..., n` to `value`. - */ - public set!: (...args: number[]) => void; - public shape: number[] = [0]; /** - * Returns the sign of each element of current array, indicating - * whether it is positive, negative or zero. - */ - public sign!: () => this; - - /** - * Returns the sine of each element of current array. - */ - public sin!: () => this; - - /** - * Returns the hyperbolic sine of each element of current array. - */ - public sinh!: () => this; - - /** - * Slices the current array in the corresponding dimension - */ - public slice!: (start?: number, step?: number, end?: number) => this; - - /** - * Solves the equation AX = B (where A is current matrix and B is `x`). - * Accelerated with LAPACK `?gesv`. - */ - public solve!: (x: T) => this; - - /** - * Returns the positive square root of each element of current array. - */ - public sqrt!: () => this; - - /** - * Asserts if current matrix is square. - */ - public square!: () => void; - - /** - * Subtracts `x` from the current array. - * Accelerated with BLAS `?axpy`. - */ - public subtract!: (y: T) => this; - - /** - * Sum of array elements - */ - public sum!: () => number; - - /** - * Swaps two rows `i` and `j` in current matrix - */ - public swap!: (i: number, j: number) => this; - - /** - * Returns the tangent of each element of current array. - */ - public tan!: () => this; - - /** - * Returns the hyperbolic tangent of each element of current array. - */ - public tanh!: () => this; - - /** - * Converts current vector into a JavaScript array. - */ - public toArray!: () => number[]; - - /** - * Converts current vector into a readable formatted string. - */ - public toString!: () => string; - - /** - * Gets the trace of the matrix (the sum of all diagonal elements). - */ - public trace!: () => number; - - /** - * Transposes current matrix (mirror across the diagonal). - */ - public transpose!: () => this; - - /** - * Returns the integer part of each element of current array, - * removing any fractional digits. - */ - public trunc!: () => this; + * @name strides + * @memberof NDArray.prototype + * @type Number[] + * @default [0] + */ + public strides: number[] = [0]; + + public [inspectSymbol]: () => string = toString; + public abs = abs; + public acos = acos; + public acosh = acosh; + public add = add; + public angle = angle; + public asin = asin; + public asinh = asinh; + public atan = atan; + public atanh = atanh; + public augment = augment; + public binOp = binOp; + public cbrt = cbrt; + public ceil = ceil; + public check = check; + public combine = combine; + public copy = copy; + public cos = cos; + public cosh = cosh; + public cross = cross; + public det = det; + public diagonal = diagonal; + public dot = dot; + public eig = eig; + public equals = equals; + public equidimensional = equidimensional; + public equilateral = equilateral; + public exp = exp; + public expm1 = expm1; + public fill = fill; + public floor = floor; + public forEach = forEach; + public fround = fround; + public gauss = gauss; + public get = get; + public inv = inv; + public log = log; + public log10 = log10; + public log1p = log1p; + public log2 = log2; + public lu = lu; + public lu_factor = lu_factor; + public map = map; + public max = max; + public mean = mean; + public min = min; + public multiply = multiply; + public norm = norm; + public normalize = normalize; + public pow = pow; + public prod = prod; + public product = product; + public project = project; + public push = push; + public rank = rank; + public reciprocal = reciprocal; + public reduce = reduce; + public reshape = reshape; + public round = round; + public row_add = row_add; + public scale = scale; + public set = set; + public sign = sign; + public sin = sin; + public sinh = sinh; + public slice = slice; + public solve = solve; + public sqrt = sqrt; + public square = square; + public subtract = subtract; + public sum = sum; + public swap = swap; + public tan = tan; + public tanh = tanh; + public toArray = toArray; + public toString = toString; + public trace = trace; + public transpose = transpose; + public trunc = trunc; public constructor( data?: any, - options?: any + options?: { + shape?: number[]; + length?: number; + strides?: number[]; + dtype?: DType; + } ) { - if (is_typed_array(data)) { - this.data = data as TypedArray; - this.shape = typeof options === 'object' && options.hasOwnProperty('shape') ? options.shape : [this.data.length]; - this.length = this.data.length; - this.dtype = typeof options === 'object' && options.hasOwnProperty('dtype') ? options.dtype : get_dtype(data); - } else if (data instanceof Array) { - this.data = new (get_type(this.dtype))(flatten(data)); - this.shape = get_shape(data); - this.length = this.data.length; - } else if (data instanceof NDArray) { + if (!data) { + return; + } + + if (data instanceof NDArray) { return data.copy(); } + + if (data instanceof NDIter) { + if (!options || !options.dtype) { + throw new Error('dtype is missing'); + } + + if (data.shape) { + options.shape = data.shape; + } + + const length = data.length; + data = new (get_type(options.dtype))(length); + } + + const { + shape = get_shape(data), + length = get_length(shape), + strides = get_strides(shape), + dtype = get_dtype(data), + } = options || {}; + + this.data = is_typed_array(data) ? data : new (get_type(dtype))(flatten(data)); + this.shape = shape; + this.length = length; + this.dtype = dtype; + this.strides = strides; } /** - * Equivalent to this.get(0) + * @name x + * @memberof NDArray.prototype + * @description Gets or sets the value at index 0 + * @type Number */ public get x(): number { return this.get(0); } - /** - * Equivalent to this.set(0, value) - */ public set x(value: number) { this.set(0, value); } /** - * Equivalent to this.get(1) + * @name y + * @memberof NDArray.prototype + * @description Gets or sets the value at index 1 + * @type Number */ public get y(): number { return this.get(1); } - /** - * Equivalent to this.set(1, value) - */ public set y(value: number) { this.set(1, value); } /** - * Equivalent to this.get(2) + * @name z + * @memberof NDArray.prototype + * @description Gets or sets the value at index 2 + * @type Number */ public get z(): number { return this.get(2); } - /** - * Equivalent to this.set(2, value) - */ public set z(value: number) { this.set(2, value); } /** - * Equivalent to this.get(3) + * @name w + * @memberof NDArray.prototype + * @description Gets or sets the value at index 3 + * @type Number */ public get w(): number { return this.get(3); } - /** - * Equivalent to this.set(3, value) - */ public set w(value: number) { this.set(3, value); } /** - * Transposes current matrix (mirror across the diagonal). + * @name T + * @memberof NDArray.prototype + * @description Short for this.copy().transpose() + * @type NDArray */ - public get T(): this { - return this.transpose(); + public get T() { + return this.copy().transpose(); } } -import './abs'; -import './acos'; -import './acosh'; -import './add'; -import './angle'; -import './array'; -import './asin'; -import './asinh'; -import './atan'; -import './atanh'; -import './augment'; -import './binOp'; -import './cbrt'; -import './ceil'; -import './check'; -import './combine'; -import './copy'; -import './cos'; -import './cosh'; -import './cross'; -import './det'; -import './diagonal'; -import './dot'; -import './eig'; -import './equals'; -import './equidimensional'; -import './equilateral'; -import './exp'; -import './expm1'; -import './eye'; -import './fill'; -import './floor'; -import './forEach'; -import './fround'; -import './gauss'; -import './get'; -import './inv'; -import './log'; -import './log10'; -import './log1p'; -import './log2'; -import './lu'; -import './lu_factor'; -import './magic'; -import './map'; -import './matrix'; -import './max'; -import './mean'; -import './min'; -import './multiply'; -import './norm'; -import './normalize'; -import './ones'; -import './pow'; -import './prod'; -import './product'; -import './project'; -import './push'; -import './random'; -import './range'; -import './rank'; -import './reciprocal'; -import './reduce'; -import './reshape'; -import './round'; -import './row_add'; -import './scale'; -import './set'; -import './sign'; -import './sin'; -import './sinh'; -import './slice'; -import './solve'; -import './sqrt'; -import './square'; -import './subtract'; -import './sum'; -import './swap'; -import './tan'; -import './tanh'; -import './toArray'; -import './toString'; -import './trace'; -import './transpose'; -import './trunc'; -import './zeros'; +export { abs } from './abs'; +export { acos } from './acos'; +export { acosh } from './acosh'; +export { add } from './add'; +export { angle } from './angle'; +export { array } from './array'; +export { asin } from './asin'; +export { asinh } from './asinh'; +export { atan } from './atan'; +export { atanh } from './atanh'; +export { augment } from './augment'; +export { binOp } from './binOp'; +export { cbrt } from './cbrt'; +export { ceil } from './ceil'; +export { check } from './check'; +export { combine } from './combine'; +export { copy } from './copy'; +export { cos } from './cos'; +export { cosh } from './cosh'; +export { cross } from './cross'; +export { det } from './det'; +export { diagonal } from './diagonal'; +export { dot } from './dot'; +export { eig } from './eig'; +export { equals } from './equals'; +export { equidimensional } from './equidimensional'; +export { equilateral } from './equilateral'; +export { exp } from './exp'; +export { expm1 } from './expm1'; +export { eye } from './eye'; +export { fill } from './fill'; +export { floor } from './floor'; +export { forEach } from './forEach'; +export { fround } from './fround'; +export { gauss } from './gauss'; +export { get } from './get'; +export { inv } from './inv'; +export { log } from './log'; +export { log10 } from './log10'; +export { log1p } from './log1p'; +export { log2 } from './log2'; +export { lu } from './lu'; +export { lu_factor } from './lu_factor'; +export { magic } from './magic'; +export { map } from './map'; +export { matrix } from './matrix'; +export { max } from './max'; +export { mean } from './mean'; +export { min } from './min'; +export { multiply } from './multiply'; +export { norm } from './norm'; +export { normalize } from './normalize'; +export { ones } from './ones'; +export { pow } from './pow'; +export { prod } from './prod'; +export { product } from './product'; +export { project } from './project'; +export { push } from './push'; +export { random } from './random'; +export { range } from './range'; +export { rank } from './rank'; +export { reciprocal } from './reciprocal'; +export { reduce } from './reduce'; +export { reshape } from './reshape'; +export { round } from './round'; +export { row_add } from './row_add'; +export { scale } from './scale'; +export { set } from './set'; +export { sign } from './sign'; +export { sin } from './sin'; +export { sinh } from './sinh'; +export { slice } from './slice'; +export { solve } from './solve'; +export { sqrt } from './sqrt'; +export { square } from './square'; +export { subtract } from './subtract'; +export { sum } from './sum'; +export { swap } from './swap'; +export { tan } from './tan'; +export { tanh } from './tanh'; +export { toArray } from './toArray'; +export { toString } from './toString'; +export { trace } from './trace'; +export { transpose } from './transpose'; +export { trunc } from './trunc'; +export { zeros } from './zeros'; try { (window as any).v = NDArray; diff --git a/src/core/inv.bench.ts b/src/core/inv.bench.ts index e901f673..ade3d9fe 100644 --- a/src/core/inv.bench.ts +++ b/src/core/inv.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { inv } from './inv'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'inv', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.inv(); }, - (x: v): void => { - v.inv(x); + (x): void => { + inv(x); } ); diff --git a/src/core/inv.spec.ts b/src/core/inv.spec.ts index 043162a6..54c6721a 100644 --- a/src/core/inv.spec.ts +++ b/src/core/inv.spec.ts @@ -1,40 +1,41 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { inv } from './inv'; +import { array } from './array'; describe('(v) inv', () => { it('should throw error if matrix is not square', () => { - const x: v = v.array([[1, 2]]); + const x = array([[1, 2]]); throws(() => { x.inv(); }, Error); }); it('should work as expected', () => { - const x: v = v.array([ + const x = array([ [2, -1, 0], [-1, 2, -1], [0, -1, 2], ]); - const y: v = v.array([ + const y = array([ [3 / 4, 1 / 2, 1 / 4], [1 / 2, 1, 1 / 2], [1 / 4, 1 / 2, 3 / 4], ]); // Need to round result to avoid floating point rounding errors, e.g. 0.99999999994 - deepStrictEqual(y, x.inv().map((value: number) => Number(value.toFixed(2)))); + equals(y, x.inv()); }); it('should work as the static equivalent', () => { - const x: v = v.array([ + const x = array([ [2, -1, 0], [-1, 2, -1], [0, -1, 2], ]); - deepStrictEqual(x.copy().inv(), v.inv(x)); + equals(x.copy().inv(), inv(x)); }); }); diff --git a/src/core/inv.ts b/src/core/inv.ts index e46f3b8f..17b53008 100644 --- a/src/core/inv.ts +++ b/src/core/inv.ts @@ -1,60 +1,75 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; +import { eye } from './eye'; +import { augment } from './augment'; +import { zeros } from './zeros'; +import * as lapack from '../lapack'; -let nlapack: any; -try { - nlapack = require('nlapack'); -} catch (err) {} - -NDArray.inv = (x: T | ArrayLike): T => NDArray.array(x).inv(); +/** + * @static + * @memberof module:Globals + * @function inv + * @description + * Determines the inverse of `x`. + * Accelerated with LAPACK `?getrf` and `?getri`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { inv } from 'vectorious/core/inv'; + * + * inv([[2, -1, 0], [-1, 2, -1], [0, -1, 2]]); // => array([[0.75, 0.5, 0.25], [0.5, 1, 0.5], [0.25, 0.5, 0.75]]) + */ +export const inv = (x: NDArray | ArrayLike): NDArray => array(x).inv(); -NDArray.prototype.inv = function(this: T): T { +/** + * @function inv + * @memberof NDArray.prototype + * @description + * Determines the inverse of current matrix using Gaussian elimination. + * Accelerated with LAPACK `?getri`. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[2, -1, 0], [-1, 2, -1], [0, -1, 2]]).inv(); // <=> array([[0.75, 0.5, 0.25], [0.5, 1, 0.5], [0.25, 0.5, 0.75]]) + */ +export default function(this: NDArray): NDArray { this.square(); - const [n] = this.shape; + const { shape: [n], dtype } = this; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - const { data: d1 } = this; - const ipiv: Int32Array = new Int32Array(n); - if (this.dtype === 'float64') { - nlapack.dgetrf(n, n, d1, n, ipiv); - nlapack.dgetri(n, d1, n, ipiv); - } else if (this.dtype === 'float32') { - nlapack.sgetrf(n, n, d1, n, ipiv); - nlapack.sgetri(n, d1, n, ipiv); - } + const ipiv = new Int32Array(n); + + lapack.getrf(dtype, n, n, d1, n, ipiv); + lapack.getri(dtype, n, d1, n, ipiv); return this; } catch (err) { - const eye: T = NDArray.eye(n); - const rref: T = NDArray.augment(this, eye).gauss(); - const left: T = NDArray.zeros(n, n); - const right: T = NDArray.zeros(n, n); + const identity = eye(n); + const rref = augment(this, identity).gauss(); + const left = zeros(n, n); + const right = zeros(n, n); const { data: d1 } = rref; const { data: d2 } = left; const { data: d3 } = right; - let i: number; - let j: number; - - for (i = 0; i < n; i += 1) { - for (j = 0; j < n + n; j += 1) { - if (j < n) { - d2[i * n + j] = d1[i * (n + n) + j]; - } else { - d3[i * n + (j - n)] = d1[i * (n + n) + j]; - } + const iter = new NDIter(rref); + let [ci, cj] = iter.coords; + for (const i of iter) { + if (cj < n) { + d2[ci * n + cj] = d1[i!]; + } else { + d3[ci * n + (cj - n)] = d1[i!]; } + + [ci, cj] = iter.coords; } - if (!left.equals(eye)) { + if (!left.equals(identity)) { throw new Error('matrix is not invertible'); } diff --git a/src/core/log.bench.ts b/src/core/log.bench.ts index 8d0319ac..562f177e 100644 --- a/src/core/log.bench.ts +++ b/src/core/log.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { log } from './log'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'log', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.log(); }, - (x: v): void => { - v.log(x); + (x): void => { + log(x); } ); diff --git a/src/core/log.spec.ts b/src/core/log.spec.ts index dddace13..f907133f 100644 --- a/src/core/log.spec.ts +++ b/src/core/log.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { log } from './log'; +import { map } from './map'; +import { random } from './random'; describe('(v) log', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log(value)), x.log()); + equals(map(x, (value: number) => Math.log(value)), x.log()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log(value)), v.log(x)); + equals(map(x, (value: number) => Math.log(value)), log(x)); }); }); diff --git a/src/core/log.ts b/src/core/log.ts index e8964cd4..cfb70704 100644 --- a/src/core/log.ts +++ b/src/core/log.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.log = (x: T | ArrayLike): T => NDArray.array(x).log(); +const { log: f } = Math; -NDArray.prototype.log = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function log + * @description Returns the natural logarithm (log_e, also ln) of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { log } from 'vectorious/core/log'; + * + * log([1, 2, 3]); // => array([0, 0.6931471824645996, 1.0986123085021973]) + */ +export const log = (x: NDArray | ArrayLike): NDArray => array(x).log(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.log(d1[i]); +/** + * @function log + * @memberof NDArray.prototype + * @description Returns the natural logarithm (log_e, also ln) of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).log(); // <=> array([0, 0.6931471824645996, 1.0986123085021973]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/log10.bench.ts b/src/core/log10.bench.ts index b097b3cc..a8f3b4bb 100644 --- a/src/core/log10.bench.ts +++ b/src/core/log10.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { log10 } from './log10'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'log10', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.log10(); }, - (x: v): void => { - v.log10(x); + (x): void => { + log10(x); } ); diff --git a/src/core/log10.spec.ts b/src/core/log10.spec.ts index d93d027b..f39cdd0b 100644 --- a/src/core/log10.spec.ts +++ b/src/core/log10.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { log10 } from './log10'; +import { map } from './map'; +import { random } from './random'; describe('(v) log10', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log10(value)), x.log10()); + equals(map(x, (value: number) => Math.log10(value)), x.log10()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log10(value)), v.log10(x)); + equals(map(x, (value: number) => Math.log10(value)), log10(x)); }); }); diff --git a/src/core/log10.ts b/src/core/log10.ts index 10e9d7d2..0cbac76d 100644 --- a/src/core/log10.ts +++ b/src/core/log10.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.log10 = (x: T | ArrayLike): T => NDArray.array(x).log10(); +const { log10: f } = Math; -NDArray.prototype.log10 = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function log10 + * @description Returns the base 10 logarithm of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { log10 } from 'vectorious/core/log10'; + * + * log10([10, 100, 1000]); // => array([1, 2, 3]) + */ +export const log10 = (x: NDArray | ArrayLike): NDArray => array(x).log10(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.log10(d1[i]); +/** + * @function log10 + * @memberof NDArray.prototype + * @description Returns the base 10 logarithm of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([10, 100, 1000]).log10(); // <=> array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/log1p.bench.ts b/src/core/log1p.bench.ts index 57dbd09c..db5e0753 100644 --- a/src/core/log1p.bench.ts +++ b/src/core/log1p.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { log1p } from './log1p'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'log1p', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.log1p(); }, - (x: v): void => { - v.log1p(x); + (x): void => { + log1p(x); } ); diff --git a/src/core/log1p.spec.ts b/src/core/log1p.spec.ts index 286d69e5..7c8c51de 100644 --- a/src/core/log1p.spec.ts +++ b/src/core/log1p.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { log1p } from './log1p'; +import { map } from './map'; +import { random } from './random'; describe('(v) log1p', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log1p(value)), x.log1p()); + equals(map(x, (value: number) => Math.log1p(value)), x.log1p()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log1p(value)), v.log1p(x)); + equals(map(x, (value: number) => Math.log1p(value)), log1p(x)); }); }); diff --git a/src/core/log1p.ts b/src/core/log1p.ts index c379c449..35be0168 100644 --- a/src/core/log1p.ts +++ b/src/core/log1p.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.log1p = (x: T | ArrayLike): T => NDArray.array(x).log1p(); +const { log1p: f } = Math; -NDArray.prototype.log1p = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function log1p + * @description Returns the natural logarithm (log_e, also ln) of 1 + x for each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { log1p } from 'vectorious/core/log1p'; + * + * log1p([1, 2, 3]); // => array([0.6931471824645996, 1.0986123085021973, 1.3862943649291992]) + */ +export const log1p = (x: NDArray | ArrayLike): NDArray => array(x).log1p(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.log1p(d1[i]); +/** + * @function log1p + * @memberof NDArray.prototype + * @description Returns the natural logarithm (log_e, also ln) of 1 + x for each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]); // <=> array([0.6931471824645996, 1.0986123085021973, 1.3862943649291992]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/log2.bench.ts b/src/core/log2.bench.ts index a5d0c6ea..6cbdb7ce 100644 --- a/src/core/log2.bench.ts +++ b/src/core/log2.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { log2 } from './log2'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'log2', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.log2(); }, - (x: v): void => { - v.log2(x); + (x): void => { + log2(x); } ); diff --git a/src/core/log2.spec.ts b/src/core/log2.spec.ts index cdf0af43..ec038ac2 100644 --- a/src/core/log2.spec.ts +++ b/src/core/log2.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { log2 } from './log2'; +import { map } from './map'; +import { random } from './random'; describe('(v) log2', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log2(value)), x.log2()); + equals(map(x, (value: number) => Math.log2(value)), x.log2()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.log2(value)), v.log2(x)); + equals(map(x, (value: number) => Math.log2(value)), log2(x)); }); }); diff --git a/src/core/log2.ts b/src/core/log2.ts index 0672ef46..951a9226 100644 --- a/src/core/log2.ts +++ b/src/core/log2.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.log2 = (x: T | ArrayLike): T => NDArray.array(x).log2(); +const { log2: f } = Math; -NDArray.prototype.log2 = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function log2 + * @description Returns the base 2 logarithm of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { log2 } from 'vectorious/core/log2'; + * + * log2([1, 2, 4]); // => array([0, 1, 2]) + */ +export const log2 = (x: NDArray | ArrayLike): NDArray => array(x).log2(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.log2(d1[i]); +/** + * @function log2 + * @memberof NDArray.prototype + * @description Returns the base 2 logarithm of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 4]).log2(); // => array([0, 1, 2]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/lu.bench.ts b/src/core/lu.bench.ts index f507c56f..503ea2e4 100644 --- a/src/core/lu.bench.ts +++ b/src/core/lu.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { lu } from './lu'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'lu', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.lu(); }, - (x: v): void => { - v.lu(x); + (x): void => { + lu(x); } ); diff --git a/src/core/lu.spec.ts b/src/core/lu.spec.ts index d7556555..5be71311 100644 --- a/src/core/lu.spec.ts +++ b/src/core/lu.spec.ts @@ -1,35 +1,33 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { lu } from './lu'; +import { array } from './array'; describe('(v) lu', () => { it('should work as expected', () => { - const x: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - const ys: v[] = [ - v.array([[1, 0, 0], [0.5, 1, 0], [0.5, -1, 1]]), - v.array([[2, 4, 7], [0, 1, 1.5], [0, 0, -2]]), + const x = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const ys = [ + array([[1, 0, 0], [0.5, 1, 0], [0.5, -1, 1]]), + array([[2, 4, 7], [0, 1, 1.5], [0, 0, -2]]), ]; - deepStrictEqual(ys, x.lu().slice(0, 2)); + equals(ys, x.lu().slice(0, 2)); }); it('should work as expected', () => { - const x: v = v.array([[11, 9, 24, 2], [1, 5, 2, 6], [3, 17, 18, 1], [2, 5, 7, 1]]); - const ys: v[] = [ - v.array([[1, 0, 0, 0], [0.27273, 1, 0, 0], [0.09091, 0.2875, 1, 0], [0.18182, 0.23125, 0.0036, 1]]), - v.array([[11, 9, 24, 2], [0, 14.54545, 11.45455, 0.45455], [0, 0, -3.475, 5.6875], [0, 0, 0, 0.51079]]), + const x = array([[11, 9, 24, 2], [1, 5, 2, 6], [3, 17, 18, 1], [2, 5, 7, 1]]); + const ys = [ + array([[1, 0, 0, 0], [0.27273, 1, 0, 0], [0.09091, 0.2875, 1, 0], [0.18182, 0.23125, 0.0036, 1]]), + array([[11, 9, 24, 2], [0, 14.54545, 11.45455, 0.45455], [0, 0, -3.475, 5.6875], [0, 0, 0, 0.51079]]), ]; - const [lower, upper]: [v, v, Int32Array] = x.lu(); - deepStrictEqual(ys[0], lower.map((value: number) => Number(value.toFixed(5)))); - deepStrictEqual(ys[1], upper.map((value: number) => Number(value.toFixed(5)))); + const [lower, upper] = x.lu(); + equals(ys[0], lower.map((value: number) => Number(value.toFixed(5)))); + equals(ys[1], upper.map((value: number) => Number(value.toFixed(5)))); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 1, 1]]); + const x = array([[1, 1, 1]]); - deepStrictEqual(x.copy().lu(), v.lu(x)); + equals(x.copy().lu(), lu(x)); }); }); diff --git a/src/core/lu.ts b/src/core/lu.ts index 371bc010..4a68b383 100644 --- a/src/core/lu.ts +++ b/src/core/lu.ts @@ -1,32 +1,55 @@ import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; /** - * Performs full LU decomposition on a matrix. + * @static + * @memberof module:Globals + * @function lu + * @description + * Performs full LU decomposition on `x`. + * Accelerated with LAPACK `?getrf`. + * @param {NDArray} x + * @returns {Array} + * @example + * import { lu } from 'vectorious/core/lu'; + * + * lu([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); // => [array([[1, 0, 0], [0.5, 1, 0], [0.5, -1, 1]]), array([[2, 4, 7], [0, 1, 1.5], [0, 0, -2]]), Int32Array([2, 2, 3])] */ -NDArray.lu = (x: T | ArrayLike): [T, T, Int32Array] => - NDArray.array(x).lu(); +export const lu = (x: NDArray | ArrayLike): [NDArray, NDArray, Int32Array] => + array(x).lu(); -NDArray.prototype.lu = function(this: T): [T, T, Int32Array] { - const [r, c] = this.shape; +/** + * @function lu + * @memberof NDArray.prototype + * @description + * Performs full LU decomposition on current matrix. + * Accelerated with LAPACK `?getrf`. + * @returns {Array} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]).lu(); // => [array([[1, 0, 0], [0.5, 1, 0], [0.5, -1, 1]]), array([[2, 4, 7], [0, 1, 1.5], [0, 0, -2]]), Int32Array([2, 2, 3])] + */ +export default function (this: NDArray): [NDArray, NDArray, Int32Array] { const [LU, ipiv] = this.copy().lu_factor(); - const L: T = LU.copy(); - const T: T = LU.copy(); + const L = LU.copy(); + const U = LU.copy(); const { data: d1 } = L; - const { data: d2 } = T; + const { data: d2 } = U; - let i: number; - let j: number; - for (i = 0; i < r; i += 1) { - for (j = i; j < c; j += 1) { - d1[i * c + j] = i === j ? 1 : 0; - } - } + const iter = new NDIter(LU); - for (i = 0; i < r; i += 1) { - for (j = 0; j < i && j < c; j += 1) { - d2[i * c + j] = 0; + let [ci, cj] = iter.coords; + for (const i of iter) { + if (cj < ci) { + d2[i!] = 0; + } else { + d1[i!] = ci === cj ? 1 : 0; } + + [ci, cj] = iter.coords; } - return [L, T, ipiv]; + return [L, U, ipiv]; }; diff --git a/src/core/lu_factor.bench.ts b/src/core/lu_factor.bench.ts index 3fd10bc2..f32ef17e 100644 --- a/src/core/lu_factor.bench.ts +++ b/src/core/lu_factor.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { lu_factor } from './lu_factor'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'lu_factor', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.lu_factor(); }, - (x: v): void => { - v.lu_factor(x); + (x): void => { + lu_factor(x); } ); diff --git a/src/core/lu_factor.spec.ts b/src/core/lu_factor.spec.ts index 43326285..3c8e5704 100644 --- a/src/core/lu_factor.spec.ts +++ b/src/core/lu_factor.spec.ts @@ -1,23 +1,21 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { lu_factor } from './lu_factor'; +import { array } from './array'; describe('(v) lu_factor', () => { it('should work as expected', () => { - const x: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - const y: v = v.array([[2, 4, 7], [0.5, 1, 1.5], [0.5, -1, -2]]); + const x = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const y = array([[2, 4, 7], [0.5, 1, 1.5], [0.5, -1, -2]]); const ipiv: Int32Array = new Int32Array([2, 2, 3]); const [LU, piv] = x.lu_factor(); - deepStrictEqual(ipiv, piv); - deepStrictEqual(y, LU); + equals(ipiv, piv); + equals(y, LU); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const x = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - deepStrictEqual(x.copy().lu_factor(), v.lu_factor(x)); + equals(x.copy().lu_factor(), lu_factor(x)); }); }); diff --git a/src/core/lu_factor.ts b/src/core/lu_factor.ts index 24c85b87..1fb45be0 100644 --- a/src/core/lu_factor.ts +++ b/src/core/lu_factor.ts @@ -1,34 +1,43 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { array } from './array'; +import * as lapack from '../lapack'; -let nlapack: any; -try { - nlapack = require('nlapack'); -} catch (err) {} - -NDArray.lu_factor = (x: T | ArrayLike): [T, Int32Array] => - NDArray.array(x).lu_factor(); +/** + * @static + * @memberof module:Globals + * @function lu_factor + * @description + * Performs LU factorization on `x`. + * Accelerated with LAPACK `?getrf`. + * @param {NDArray} x + * @returns {Array} + * @example + * import { lu_factor } from 'vectorious/core/lu_factor'; + * + * lu_factor([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); // => [array([[2, 4, 7], [0.5, 1, 1.5], [0.5, -1, -2]]), Int32Array([2, 2, 3])] + */ +export const lu_factor = (x: NDArray | ArrayLike): [NDArray, Int32Array] => + array(x).lu_factor(); -NDArray.prototype.lu_factor = function(this: T): [T, Int32Array] { - const [n] = this.shape; +/** + * @function lu_factor + * @memberof NDArray.prototype + * @description + * Performs LU factorization on current matrix. + * Accelerated with LAPACK `?getrf`. + * @returns {Array} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]).lu_factor(); // <=> [array([[2, 4, 7], [0.5, 1, 1.5], [0.5, -1, -2]]), Int32Array([2, 2, 3])] + */ +export default function(this: NDArray): [NDArray, Int32Array] { + const { data: d1, shape: [n], dtype } = this; const ipiv: Int32Array = new Int32Array(n); try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - if (this.dtype === 'float64') { - nlapack.dgetrf(n, n, d1, n, ipiv); - } else if (this.dtype === 'float32') { - nlapack.sgetrf(n, n, d1, n, ipiv); - } + lapack.getrf(dtype, n, n, d1, n, ipiv); } catch (err) { - const { data: d1 } = this; - let max: number; let abs: number; let diag: number; diff --git a/src/core/magic.bench.ts b/src/core/magic.bench.ts index 359e26a5..0e8b5af7 100644 --- a/src/core/magic.bench.ts +++ b/src/core/magic.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { magic } from './magic'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'magic', (n: number): [number] => [n], (n: number): void => { - v.magic(n); + magic(n); } ); diff --git a/src/core/magic.spec.ts b/src/core/magic.spec.ts index 05f49cf3..1025bf33 100644 --- a/src/core/magic.spec.ts +++ b/src/core/magic.spec.ts @@ -1,16 +1,17 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { magic } from './magic'; +import { array } from './array'; describe('(v) magic', () => { it('should throw error if invalid size', () => { - throws(() => { v.magic(-1); }, Error); + throws(() => { magic(-1); }, Error); }); it('should work as expected', () => { - deepStrictEqual(v.array([[8, 1, 6], [3, 5, 7], [4, 9, 2]]), v.magic(3)); + equals(array([[8, 1, 6], [3, 5, 7], [4, 9, 2]]), magic(3)); }); }); diff --git a/src/core/magic.ts b/src/core/magic.ts index 7b33a2e4..ee981952 100644 --- a/src/core/magic.ts +++ b/src/core/magic.ts @@ -1,25 +1,37 @@ import { TypedArray } from '../types'; import { NDArray } from './'; +import { NDIter } from '../iterator'; -const magicHelper: (n: number, x: number, y: number) => number = (n: number, x: number, y: number): number => - (x + y * 2 + 1) % n; - -NDArray.magic = function(this: new(...args: any[]) => T, size: number): T { - if (size < 0) { - throw new Error('invalid size'); +/** + * @static + * @memberof module:Globals + * @function magic + * @description Creates a magic square matrix of size `n` + * @param {Number} n + * @returns {NDArray} + * @example + * import { magic } from 'vectorious/core/magic'; + * + * magic(3); // => array([[8, 1, 6], [3, 5, 7], [4, 9, 2]]) + */ +export const magic = (n: number): NDArray => { + if (n < 0) { + throw new Error('invalid n'); } - const d1: TypedArray = new Float32Array(size * size); + const d1: TypedArray = new Float64Array(n * n); + const magic = new NDArray(d1, { shape: [n, n] }); + const iter = new NDIter(magic); + + let [ci, cj] = iter.coords; + for (const i of iter) { + const a = n - ci - 1; + const b = n - cj - 1; - let i: number; - let j: number; - for (i = 0; i < size; i += 1) { - for (j = 0; j < size; j += 1) { - d1[(size - i - 1) * size + (size - j - 1)] = - magicHelper(size, size - j - 1, i) * size + magicHelper(size, j, i) + 1; - } + d1[i!] = ((cj + a * 2 + 1) % n) * n + ((b + a * 2 + 1) % n) + 1; + [ci, cj] = iter.coords; } - return new this(d1, { shape: [size, size] }); + return magic; }; diff --git a/src/core/map.bench.ts b/src/core/map.bench.ts index 9c8f9e37..094a6ae8 100644 --- a/src/core/map.bench.ts +++ b/src/core/map.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { map } from './map'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'map', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.map((value: number) => value); }, - (x: v): void => { - v.map(x, (value: number) => value); + (x): void => { + map(x, (value: number) => value); } ); diff --git a/src/core/map.spec.ts b/src/core/map.spec.ts index 46f85660..2546510b 100644 --- a/src/core/map.spec.ts +++ b/src/core/map.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { map } from './map'; +import { array } from './array'; describe('(v) map', () => { it('should work as expected', () => { - const x: v = v.array([1, 2, 3]); + const x = array([1, 2, 3]); - deepStrictEqual(v.array([1, 4, 9]), x.map((value: number) => value * value)); + equals(array([1, 4, 9]), x.map((value: number) => value * value)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); + const x = array([1, 2, 3]); - deepStrictEqual(v.array([1, 4, 9]), v.map(x, (value: number) => value * value)); + equals(array([1, 4, 9]), map(x, (value: number) => value * value)); }); }); diff --git a/src/core/map.ts b/src/core/map.ts index e8bbd1a6..7d424265 100644 --- a/src/core/map.ts +++ b/src/core/map.ts @@ -1,24 +1,47 @@ import { TypedArray } from '../types'; import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; -NDArray.map = ( - x: T | ArrayLike, +/** + * @static + * @memberof module:Globals + * @function map + * @description Equivalent to `TypedArray.prototype.map`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { map } from 'vectorious/core/map'; + * + * map([1, 2, 3], value => -value); // => array([-1, -2, -3]) + */ +export const map = ( + x: NDArray | ArrayLike, f: (value: number, i: number, src: TypedArray) => number -): T => NDArray.array(x).map(f); +): NDArray => array(x).map(f); -NDArray.prototype.map = function( - this: T, +/** + * @function map + * @memberof NDArray.prototype + * @description Equivalent to `TypedArray.prototype.map`. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).map(value => -value); // => array([-1, -2, -3]) + */ +export default function( + this: NDArray, f: (value: number, i: number, src: TypedArray) => number -): T { - const { data: d1, length: l1 } = this; - const mapped: T = this.copy(); - const { data: d2 } = mapped; +): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + const map = f.bind(this); - let i: number; - for (i = 0; i < l1; i += 1) { - d2[i] = f.call(mapped, d1[i], i, d1); + for (const i of iter) { + map(d1[i!], i!, d1); } - return mapped; + return this; }; diff --git a/src/core/matrix.bench.ts b/src/core/matrix.bench.ts index 5936cc93..00dde542 100644 --- a/src/core/matrix.bench.ts +++ b/src/core/matrix.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { matrix } from './matrix'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'matrix', (n: number): [number] => [n], (n: number): void => { - v.matrix(n, n); + matrix(n, n); } ); diff --git a/src/core/matrix.spec.ts b/src/core/matrix.spec.ts index 8c69b7b1..cb7420ce 100644 --- a/src/core/matrix.spec.ts +++ b/src/core/matrix.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { matrix } from './matrix'; +import { array } from './array'; describe('(v) matrix', () => { it('should work as expected', () => { - const x: v = v.array(new Float32Array([0, 0, 0]), { shape: [3, 1] }); + const x = array(new Float64Array([0, 0, 0]), { shape: [3, 1] }); - deepStrictEqual(x, v.matrix(3, 1)); + equals(x, matrix(3, 1)); }); it('should work as expected in two dimensions', () => { - const x: v = v.array([[0, 0], [0, 0]]); + const x = array([[0, 0], [0, 0]]); - deepStrictEqual(x, v.matrix(2, 2)); + equals(x, matrix(2, 2)); }); }); diff --git a/src/core/matrix.ts b/src/core/matrix.ts index 779db347..5fba2bda 100644 --- a/src/core/matrix.ts +++ b/src/core/matrix.ts @@ -1,8 +1,20 @@ import { NDArray } from './'; -NDArray.matrix = function(this: new(...args: any[]) => T, r: number, c: number): T { - return new this( - new Float32Array(r * c), +/** + * @static + * @memberof module:Globals + * @function matrix + * @description Creates a matrix of `r` rows and `c` columns. + * @param {Number} r + * @param {Number} c + * @returns {NDArray} + * @example + * import { matrix } from 'vectorious/core/matrix'; + * + * matrix(2, 2); // => array([[0, 0], [0, 0]]) + */ +export const matrix = (r: number, c: number): NDArray => + new NDArray( + new Float64Array(r * c), { shape: [r, c] } ); -}; diff --git a/src/core/max.bench.ts b/src/core/max.bench.ts index 841e1ab5..5107d18a 100644 --- a/src/core/max.bench.ts +++ b/src/core/max.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { max } from './max'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'max', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.max(); }, - (x: v): void => { - v.max(x); + (x): void => { + max(x); } ); diff --git a/src/core/max.spec.ts b/src/core/max.spec.ts index 8af8a48c..910b053e 100644 --- a/src/core/max.spec.ts +++ b/src/core/max.spec.ts @@ -2,13 +2,14 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { max } from './max'; +import { array } from './array'; describe('(v) max', () => { it('should find the maximum number in arrays', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([3, -1, 1]); - const z: v = v.array([2, 5, 1]); + const x = array([1, 2, 3]); + const y = array([3, -1, 1]); + const z = array([2, 5, 1]); strictEqual(3, x.max()); strictEqual(3, y.max()); @@ -16,12 +17,12 @@ describe('(v) max', () => { }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([3, -1, 1]); - const z: v = v.array([2, 5, 1]); + const x = array([1, 2, 3]); + const y = array([3, -1, 1]); + const z = array([2, 5, 1]); - strictEqual(3, v.max(x)); - strictEqual(3, v.max(y)); - strictEqual(5, v.max(z)); + strictEqual(3, max(x)); + strictEqual(3, max(y)); + strictEqual(5, max(z)); }); }); diff --git a/src/core/max.ts b/src/core/max.ts index 2e7adbd1..decec26a 100644 --- a/src/core/max.ts +++ b/src/core/max.ts @@ -1,41 +1,52 @@ -import { get_type } from '../util'; - import { NDArray } from './'; - -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.max = (x: T | ArrayLike): number => NDArray.array(x).max(); - -NDArray.prototype.max = function(this: T): number { - const { length: l1 } = this; - let result: number = Number.NEGATIVE_INFINITY; +import { array } from './array'; +import { NDIter } from '../iterator'; +import * as blas from '../blas'; + +/** + * @static + * @memberof module:Globals + * @function max + * @description + * Gets the maximum value (largest) element of `x`. + * Accelerated with BLAS `i?amax`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { max } from 'vectorious/core/max'; + * + * max([1, 2, 3]); // => 3 + */ +export const max = (x: NDArray | ArrayLike): number => array(x).max(); + +/** + * @function max + * @memberof NDArray.prototype + * @description + * Gets the maximum value (smallest) element of current array. + * Accelerated with BLAS `i?amax`. + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).max(); // => 3 + */ +export default function(this: NDArray): number { + const { data: d1, length: l1, dtype } = this; + let max: number = Number.NEGATIVE_INFINITY; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - if (this.dtype === 'float64') { - result = d1[nblas.idamax(l1, d1, 1)]; - } - - if (this.dtype === 'float32') { - result = d1[nblas.isamax(l1, d1, 1)]; - } + max = d1[blas.iamax(dtype, l1, d1, 1)]; } catch (err) { - const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; - for (i = 0; i < l1; i += 1) { - const value: number = d1[i]; - result = result < value ? value : result; + for (const i of iter) { + const value = d1[i!]; + if (max < value) { + max = value; + } } } - return result; + return max; }; diff --git a/src/core/mean.bench.ts b/src/core/mean.bench.ts index 7983832d..6b4a6a7d 100644 --- a/src/core/mean.bench.ts +++ b/src/core/mean.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { mean } from './mean'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'mean', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.mean(); }, - (x: v): void => { - v.mean(x); + (x): void => { + mean(x); } ); diff --git a/src/core/mean.spec.ts b/src/core/mean.spec.ts index 463601f9..e4a7769e 100644 --- a/src/core/mean.spec.ts +++ b/src/core/mean.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; +import { strictEqual } from 'assert'; -import v = require('..'); +import { mean } from './mean'; +import { random } from './random'; describe('(v) mean', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc + value, 0) / x.length, x.mean()); + strictEqual(x.reduce((acc: number, value: number) => acc + value, 0) / x.length, x.mean()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc + value, 0) / x.length, v.mean(x)); + strictEqual(x.reduce((acc: number, value: number) => acc + value, 0) / x.length, mean(x)); }); }); diff --git a/src/core/mean.ts b/src/core/mean.ts index 86c8eb02..4e4eb5df 100644 --- a/src/core/mean.ts +++ b/src/core/mean.ts @@ -1,14 +1,38 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.mean = (x: T | ArrayLike): number => NDArray.array(x).mean(); +/** + * @static + * @memberof module:Globals + * @function mean + * @description Gets the arithmetic mean of `x`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { mean } from 'vectorious/core/mean'; + * + * mean([1, 2, 3]); // => 2 + */ +export const mean = (x: NDArray | ArrayLike): number => array(x).mean(); -NDArray.prototype.mean = function(this: T): number { +/** + * @function mean + * @memberof NDArray.prototype + * @description Gets the arithmetic mean of current array. + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).mean(); // => 2 + */ +export default function(this: NDArray): number { const { data: d1, length: l1 } = this; + const iter = new NDIter(this); - let i: number; let mean: number = 0; - for (i = 0; i < l1; i += 1) { - mean += d1[i]; + for (const i of iter) { + mean += d1[i!]; } return mean / l1; diff --git a/src/core/min.bench.ts b/src/core/min.bench.ts index b6055425..84e7dcb4 100644 --- a/src/core/min.bench.ts +++ b/src/core/min.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { min } from './min'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'min', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.min(); }, - (x: v): void => { - v.min(x); + (x): void => { + min(x); } ); diff --git a/src/core/min.spec.ts b/src/core/min.spec.ts index b16ef312..b9821728 100644 --- a/src/core/min.spec.ts +++ b/src/core/min.spec.ts @@ -2,13 +2,14 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { min } from './min'; +import { array } from './array'; describe('(v) min', () => { it('should find the minimum number in arrays', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([3, -1, 1]); - const z: v = v.array([2, 5, 1]); + const x = array([1, 2, 3]); + const y = array([3, -1, 1]); + const z = array([2, 5, 1]); strictEqual(1, x.min()); strictEqual(-1, y.min()); @@ -16,12 +17,12 @@ describe('(v) min', () => { }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([3, -1, 1]); - const z: v = v.array([2, 5, 1]); + const x = array([1, 2, 3]); + const y = array([3, -1, 1]); + const z = array([2, 5, 1]); - strictEqual(1, v.min(x)); - strictEqual(-1, v.min(y)); - strictEqual(1, v.min(z)); + strictEqual(1, min(x)); + strictEqual(-1, min(y)); + strictEqual(1, min(z)); }); }); diff --git a/src/core/min.ts b/src/core/min.ts index 99a1d6a3..1dc40d0d 100644 --- a/src/core/min.ts +++ b/src/core/min.ts @@ -1,16 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.min = (x: T | ArrayLike): number => NDArray.array(x).min(); +/** + * @static + * @memberof module:Globals + * @function min + * @description Gets the minimum value (smallest) element of `x`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { min } from 'vectorious/core/min'; + * + * min([1, 2, 3]); // => 1 + */ +export const min = (x: NDArray | ArrayLike): number => array(x).min(); -NDArray.prototype.min = function(this: T): number { - const { data, length } = this; +/** + * @function min + * @memberof NDArray.prototype + * @description Gets the minimum value (smallest) element of current array. + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).min(); // 1 + */ +export default function(this: NDArray): number { + const { data: d1 } = this; - let result: number = Number.POSITIVE_INFINITY; + const iter = new NDIter(this); - let i: number; - for (i = 0; i < length; i += 1) { - result = result < data[i] ? result : data[i]; + let min: number = Number.POSITIVE_INFINITY; + for (const i of iter) { + const value = d1[i!]; + if (min > value) { + min = value; + } } - return result; + return min; }; diff --git a/src/core/multiply.bench.ts b/src/core/multiply.bench.ts index 89a079bd..bac501de 100644 --- a/src/core/multiply.bench.ts +++ b/src/core/multiply.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { multiply } from './multiply'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'multiply', - (n: number): [v, v] => [v.random(floor(sqrt(n)), floor(sqrt(n))), v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v, y: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n))), random(floor(sqrt(n)), floor(sqrt(n)))], + (x, y): void => { x.multiply(y); }, - (x: v, y: v): void => { - v.multiply(x, y); + (x, y): void => { + multiply(x, y); } ); diff --git a/src/core/multiply.spec.ts b/src/core/multiply.spec.ts index 8f1dcf89..cdd955c1 100644 --- a/src/core/multiply.spec.ts +++ b/src/core/multiply.spec.ts @@ -1,47 +1,48 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { multiply } from './multiply'; +import { array } from './array'; describe('(v) multiply', () => { it('should throw error if sizes do not match', () => { - const x: v = v.array([[1, 2], [3, 4]]); - const y: v = v.array([[1, 2]]); + const x = array([[1, 2], [3, 4]]); + const y = array([[1, 2]]); throws(() => { x.multiply(y); }, Error); }); it('should work as expected', () => { - const x: v = v.array([[1, 2]]); - const y: v = v.array([[1], [2]]); - const z: v = v.array([[5]]); - const u: v = v.array([[1, 2], [2, 4]]); + const x = array([[1, 2]]); + const y = array([[1], [2]]); + const z = array([[5]]); + const u = array([[1, 2], [2, 4]]); - deepStrictEqual(z, x.copy().multiply(y)); - deepStrictEqual(u, y.copy().multiply(x)); + equals(z, x.copy().multiply(y)); + equals(u, y.copy().multiply(x)); }); it('should work as expected', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - const y: v = v.array([[ 30, 36, 42], [ 66, 81, 96], [102, 126, 150]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const y = array([[30, 36, 42], [66, 81, 96], [102, 126, 150]]); - deepStrictEqual(y, x.multiply(x)); + equals(y, x.multiply(x)); }); it('should work as expected', () => { - const x: v = v.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]); - const y: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - const z: v = v.array([[2, 4, 7], [1, 3, 5], [1, 1, 0]]); + const x = array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]); + const y = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const z = array([[2, 4, 7], [1, 3, 5], [1, 1, 0]]); - deepStrictEqual(z, x.multiply(y)); + equals(z, x.multiply(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1], [2], [3]]); - const y: v = v.array([[1, 1, 1]]); + const x = array([[1], [2], [3]]); + const y = array([[1, 1, 1]]); - deepStrictEqual(x.copy().multiply(y), v.multiply(x, y)); + equals(x.copy().multiply(y), multiply(x, y)); }); }); diff --git a/src/core/multiply.ts b/src/core/multiply.ts index 1ef9bb98..3c6085f7 100644 --- a/src/core/multiply.ts +++ b/src/core/multiply.ts @@ -1,59 +1,68 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; +import { matrix } from './matrix'; +import * as blas from '../blas'; -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.multiply = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).multiply(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function multiply + * @description + * Multiplies two matrices `x` and `y` of matching dimensions. + * Accelerated with BLAS `?gemm`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { multiply } from 'vectorious/core/multiply'; + * + * multiply([[1, 2]], [[1], [2]]); // => array([[5]]) + */ +export const multiply = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).multiply(array(y)); -NDArray.prototype.multiply = function(this: T, x: T): T { - const [r1, c1] = this.shape; - const [r2, c2] = x.shape; +/** + * @function multiply + * @memberof NDArray.prototype + * @description + * Multiplies current matrix with `x`. + * Accelerated with BLAS `?gemm`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2]]).multiply([[1], [2]]); // <=> array([[5]]) + */ +export default function(this: NDArray, x: NDArray): NDArray { + const { shape: [r1, c1], data: d1, dtype } = this; + const { shape: [r2, c2], data: d2 } = x; if (c1 !== r2) { throw new Error('sizes do not match'); } - const y: NDArray = NDArray.matrix(r1, c2); + const y = matrix(r1, c2); + const { data: d3 } = y; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - const { data: d2 } = x; - const { data: d3 } = y; - if (this.dtype === 'float64') { - nblas.dgemm(nblas.NoTrans, nblas.NoTrans, r1, c2, c1, 1, d1, c1, d2, c2, 0, d3, c2); - } else if (this.dtype === 'float32') { - nblas.sgemm(nblas.NoTrans, nblas.NoTrans, r1, c2, c1, 1, d1, c1, d2, c2, 0, d3, c2); - } + blas.gemm(dtype, blas.NoTrans, blas.NoTrans, r1, c2, c1, 1, d1, c1, d2, c2, 0, d3, c2); } catch (err) { - const { data: d1 } = this; - const { data: d2 } = x; - const { data: d3 } = y; - - let i: number; - let j: number; - let k: number; - let sum: number; - for (i = 0; i < r1; i += 1) { - for (j = 0; j < c2; j += 1) { - sum = 0; - for (k = 0; k < c1; k += 1) { - sum += d1[i * c1 + k] * d2[k * c2 + j]; - } - - d3[i * c2 + j] = sum; + const iter = new NDIter(y); + + let k; + let [ci, cj] = iter.coords; + for (const i of iter) { + let sum = 0; + for (k = 0; k < c1; k += 1) { + sum += d1[ci * c1 + k] * d2[k * c2 + cj]; } + + d3[i!] = sum; + [ci, cj] = iter.coords; } } - return y as T; + return y; }; diff --git a/src/core/norm.bench.ts b/src/core/norm.bench.ts index 902ef933..bf5d56fd 100644 --- a/src/core/norm.bench.ts +++ b/src/core/norm.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { norm } from './norm'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'norm', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.norm(); }, - (x: v): void => { - v.norm(x); + (x): void => { + norm(x); } ); diff --git a/src/core/norm.spec.ts b/src/core/norm.spec.ts index c09b1e22..72ea5ac7 100644 --- a/src/core/norm.spec.ts +++ b/src/core/norm.spec.ts @@ -2,18 +2,19 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { norm } from './norm'; +import { array } from './array'; describe('(v) norm', () => { it('should return 0 if empty vector', () => { - strictEqual(0, v.array().norm()); + strictEqual(0, array().norm()); }); it('should work as expected', () => { - strictEqual(4, v.array([1, 1, 1, 2, 3]).norm()); + strictEqual(4, array([1, 1, 1, 2, 3]).norm()); }); it('should work as the static equivalent', () => { - strictEqual(4, v.norm(v.array([1, 1, 1, 2, 3]))); + strictEqual(4, norm(array([1, 1, 1, 2, 3]))); }); }); diff --git a/src/core/norm.ts b/src/core/norm.ts index 912850eb..4e4c1505 100644 --- a/src/core/norm.ts +++ b/src/core/norm.ts @@ -1,34 +1,45 @@ -import { get_type } from '../util'; - import { NDArray } from './'; - -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.norm = (x: T | ArrayLike): number => NDArray.array(x).norm(); - -NDArray.prototype.norm = function(this: T): number { - const { length: l1 } = this; +import { array } from './array'; +import * as blas from '../blas'; + +const { sqrt: f } = Math; + +/** + * @static + * @memberof module:Globals + * @function norm + * @description + * Calculates the norm of current array (also called L2 norm or Euclidean length). + * Accelerated with BLAS `?nrm2`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { norm } from 'vectorious/core/norm'; + * + * norm([1, 2, 3]); // => 3.7416574954986572 + */ +export const norm = (x: NDArray | ArrayLike): number => array(x).norm(); + +/** + * @function norm + * @memberof NDArray.prototype + * @description + * Calculates the norm of current array (also called L2 norm or Euclidean length). + * Accelerated with BLAS `?nrm2`. + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).norm(); // => 3.7416574954986572 + */ +export default function(this: NDArray): number { + const { data: d1, length: l1, dtype } = this; let result: number = 0; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - if (this.dtype === 'float64') { - result = nblas.dnrm2(l1, d1, 1); - } - - if (this.dtype === 'float32') { - result = nblas.snrm2(l1, d1, 1); - } + result = blas.nrm2(dtype, l1, d1, 1); } catch (err) { - result = Math.sqrt(this.dot(this)); + result = f(this.dot(this)); } return result; diff --git a/src/core/normalize.bench.ts b/src/core/normalize.bench.ts index 3d8b6534..012b3727 100644 --- a/src/core/normalize.bench.ts +++ b/src/core/normalize.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { normalize } from './normalize'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'normalize', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.normalize(); }, - (x: v): void => { - v.normalize(x); + (x): void => { + normalize(x); } ); diff --git a/src/core/normalize.spec.ts b/src/core/normalize.spec.ts index 8cf23e21..d4d72396 100644 --- a/src/core/normalize.spec.ts +++ b/src/core/normalize.spec.ts @@ -1,21 +1,19 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { normalize } from './normalize'; +import { array } from './array'; describe('(v) normalize', () => { it('should work as expected', () => { - const x: v = v.array([1, 1]); - const y: v = v.array([1 / Math.sqrt(2), 1 / Math.sqrt(2)]); + const x = array([1, 1]); + const y = array([1 / Math.sqrt(2), 1 / Math.sqrt(2)]); - deepStrictEqual(y, x.normalize()); + equals(y, x.normalize()); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 1]); - const y: v = v.array([1 / Math.sqrt(2), 1 / Math.sqrt(2)]); + const x = array([1, 1]); + const y = array([1 / Math.sqrt(2), 1 / Math.sqrt(2)]); - deepStrictEqual(y, v.normalize(x)); + equals(y, normalize(x)); }); }); diff --git a/src/core/normalize.ts b/src/core/normalize.ts index 7c74c809..a22cbea0 100644 --- a/src/core/normalize.ts +++ b/src/core/normalize.ts @@ -1,8 +1,31 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.normalize = (x: T | ArrayLike): T => - NDArray.array(x).normalize(); +/** + * @static + * @memberof module:Globals + * @function normalize + * @description Normalizes `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { normalize } from 'vectorious/core/normalize'; + * + * normalize([1, 2, 3]); // => array([0.26726123690605164, 0.5345224738121033, 0.8017836809158325]) + */ +export const normalize = (x: NDArray | ArrayLike): NDArray => + array(x).normalize(); -NDArray.prototype.normalize = function(this: T): NDArray { +/** + * @function normalize + * @memberof NDArray.prototype + * @description Normalizes current vector. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).normalize(); // => array([0.26726123690605164, 0.5345224738121033, 0.8017836809158325]) + */ +export default function(this: NDArray): NDArray { return this.scale(1 / this.norm()); }; diff --git a/src/core/ones.bench.ts b/src/core/ones.bench.ts index 8b82fe2c..23d18fc7 100644 --- a/src/core/ones.bench.ts +++ b/src/core/ones.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { ones } from './ones'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'ones', (n: number): [number] => [n], (n: number): void => { - v.ones(n); + ones(n); } ); diff --git a/src/core/ones.spec.ts b/src/core/ones.spec.ts index ce7f5b6f..6cf9438d 100644 --- a/src/core/ones.spec.ts +++ b/src/core/ones.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { ones } from './ones'; +import { array } from './array'; describe('(v) ones', () => { it('should work as expected', () => { - const x: v = v.array([1, 1, 1]); + const x = array([1, 1, 1]); - deepStrictEqual(x, v.ones(3)); + equals(x, ones(3)); }); it('should work as expected in two dimensions', () => { - const x: v = v.array([[1, 1], [1, 1]]); + const x = array([[1, 1], [1, 1]]); - deepStrictEqual(x, v.ones(2, 2)); + equals(x, ones(2, 2)); }); }); diff --git a/src/core/ones.ts b/src/core/ones.ts index 8af51901..170da50d 100644 --- a/src/core/ones.ts +++ b/src/core/ones.ts @@ -1,8 +1,19 @@ import { NDArray } from './'; -NDArray.ones = function(this: new(...args: any[]) => T, ...shape: number[]): T { - return new this( - new Float32Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), +/** + * @static + * @memberof module:Globals + * @function ones + * @description Creates an array containing ones (`1`) of shape `shape` + * @param {Number[]} ...shape + * @returns {NDArray} + * @example + * import { ones } from 'vectorious/core/ones'; + * + * ones(3); // => array([1, 1, 1]) + */ +export const ones = (...shape: number[]): NDArray => + new NDArray( + new Float64Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), { shape } ).fill(1); -}; diff --git a/src/core/pow.bench.ts b/src/core/pow.bench.ts index 9b979d67..a99a3a2e 100644 --- a/src/core/pow.bench.ts +++ b/src/core/pow.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { pow } from './pow'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'pow', - (n: number): [v, number] => [v.random(n), Math.random()], - (x: v, exponent: number): void => { + (n: number) => [random(n), Math.random()], + (x, exponent: number): void => { x.pow(exponent); }, - (x: v, exponent: number): void => { - v.pow(x, exponent); + (x, exponent: number): void => { + pow(x, exponent); } ); diff --git a/src/core/pow.spec.ts b/src/core/pow.spec.ts index abb399a3..834b58e9 100644 --- a/src/core/pow.spec.ts +++ b/src/core/pow.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { pow } from './pow'; +import { map } from './map'; +import { random } from './random'; describe('(v) pow', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.pow(value, 2)), x.pow(2)); + equals(map(x, (value: number) => Math.pow(value, 2)), x.pow(2)); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.pow(value, 2)), v.pow(x, 2)); + equals(map(x, (value: number) => Math.pow(value, 2)), pow(x, 2)); }); }); diff --git a/src/core/pow.ts b/src/core/pow.ts index 4e32e91f..a89c923f 100644 --- a/src/core/pow.ts +++ b/src/core/pow.ts @@ -1,14 +1,42 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.pow = (x: T | ArrayLike, exponent: number): T => - NDArray.array(x).pow(exponent); +const { pow: f } = Math; -NDArray.prototype.pow = function(this: T, exponent: number): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function pow + * @description Returns each element of `x` to the exponent power, that is, element^exponent. + * @param {NDArray} x + * @param {Number} exponent + * @returns {NDArray} + * @example + * import { pow } from 'vectorious/core/pow'; + * + * pow([1, 2, 3], 2); // => array([1, 4, 9]) + */ +export const pow = (x: NDArray | ArrayLike, exponent: number): NDArray => + array(x).pow(exponent); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.pow(d1[i], exponent); +/** + * @function pow + * @memberof NDArray.prototype + * @description Returns each element of current array to the exponent power, that is, element^exponent. + * @param {Number} exponent + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).pow(2); // <=> array([1, 4, 9]) + */ +export default function(this: NDArray, exponent: number): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!], exponent); } return this; diff --git a/src/core/prod.bench.ts b/src/core/prod.bench.ts index 2f1f442d..eeb340b0 100644 --- a/src/core/prod.bench.ts +++ b/src/core/prod.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { prod } from './prod'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'prod', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.prod(); }, - (x: v): void => { - v.prod(x); + (x): void => { + prod(x); } ); diff --git a/src/core/prod.spec.ts b/src/core/prod.spec.ts index 22ca2e9a..fab7d59a 100644 --- a/src/core/prod.spec.ts +++ b/src/core/prod.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; +import { strictEqual } from 'assert'; -import v = require('..'); +import { prod } from './prod'; +import { random } from './random'; describe('(v) prod', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc * value, 1), x.prod()); + strictEqual(x.reduce((acc: number, value: number) => acc * value, 1), x.prod()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc * value, 1), v.prod(x)); + strictEqual(x.reduce((acc: number, value: number) => acc * value, 1), prod(x)); }); }); diff --git a/src/core/prod.ts b/src/core/prod.ts index 9e2a937e..d2e025d7 100644 --- a/src/core/prod.ts +++ b/src/core/prod.ts @@ -1,14 +1,38 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.prod = (x: T | ArrayLike): number => NDArray.array(x).prod(); +/** + * @static + * @memberof module:Globals + * @function prod + * @description Product of all elements of `x`. + * @param {NDArray} x + * @returns {Number} + * @example + * import { prod } from 'vectorious/core/prod'; + * + * prod([1, 2, 3]); // => 6 + */ +export const prod = (x: NDArray | ArrayLike): number => array(x).prod(); -NDArray.prototype.prod = function(this: T): number { - const { data: d1, length: l1 } = this; +/** + * @function prod + * @memberof NDArray.prototype + * @description Product of all elements of current array + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).prod(); // => 6 + */ +export default function(this: NDArray): number { + const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; let prod: number = 1; - for (i = 0; i < l1; i += 1) { - prod *= d1[i]; + for (const i of iter) { + prod *= d1[i!]; } return prod; diff --git a/src/core/product.bench.ts b/src/core/product.bench.ts index 1e5e18a5..227898b1 100644 --- a/src/core/product.bench.ts +++ b/src/core/product.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { product } from './product'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'product', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.product(y); }, - (x: v, y: v): void => { - v.product(x, y); + (x, y): void => { + product(x, y); } ); diff --git a/src/core/product.spec.ts b/src/core/product.spec.ts index c8be3638..0f6adaa3 100644 --- a/src/core/product.spec.ts +++ b/src/core/product.spec.ts @@ -1,21 +1,19 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { product } from './product'; +import { array } from './array'; describe('(v) product', () => { it('should work as expected', () => { - const x: v = v.array([[3, 2, 1]]); - const y: v = v.array([[1, 2, 3]]); + const x = array([[3, 2, 1]]); + const y = array([[1, 2, 3]]); - deepStrictEqual(v.array([[3, 4, 3]]), x.product(y)); + equals(array([[3, 4, 3]]), x.product(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([[3, 2, 1]]); - const y: v = v.array([[1, 2, 3]]); + const x = array([[3, 2, 1]]); + const y = array([[1, 2, 3]]); - deepStrictEqual(v.array([[3, 4, 3]]), v.product(x, y)); + equals(array([[3, 4, 3]]), product(x, y)); }); }); diff --git a/src/core/product.ts b/src/core/product.ts index d51f3e7c..eda1acba 100644 --- a/src/core/product.ts +++ b/src/core/product.ts @@ -1,18 +1,40 @@ import { NDArray } from './'; +import { NDMultiIter } from '../iterator'; +import { array } from './array'; -NDArray.product = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).product(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function product + * @description Hadamard product of `x` and `y` + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { product } from 'vectorious/core/product'; + * + * product([1, 2, 3], [4, 5, 6]); // => array([4, 10, 18]) + */ +export const product = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).product(array(y)); -NDArray.prototype.product = function(this: T, x: T): T { - this.equilateral(x); - this.equidimensional(x); - - const { data: d1, length: l1 } = this; +/** + * @function product + * @memberof NDArray.prototype + * @description Hadamard product of current matrix and `x` + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).product([4, 5, 6]); // <=> array([4, 10, 18]) + */ +export default function(this: NDArray, x: NDArray): NDArray { + const { data: d1 } = this; const { data: d2 } = x; - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] *= d2[i]; + const iter = new NDMultiIter(this, x); + for (const [i, j] of iter) { + d1[i!] *= d2[j!]; } return this; diff --git a/src/core/project.bench.ts b/src/core/project.bench.ts index 9d12ec62..96fd4c50 100644 --- a/src/core/project.bench.ts +++ b/src/core/project.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { project } from './project'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'project', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.project(y); }, - (x: v, y: v): void => { - v.project(x, y); + (x, y): void => { + project(x, y); } ); diff --git a/src/core/project.spec.ts b/src/core/project.spec.ts index a26650bc..804b13db 100644 --- a/src/core/project.spec.ts +++ b/src/core/project.spec.ts @@ -1,31 +1,21 @@ -import { - deepStrictEqual, - throws, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { project } from './project'; +import { array } from './array'; describe('(v) project', () => { - it('should throw error if sizes do not match', () => { - const x: v = v.array([1]); - const y: v = v.array([1, 2]); - - throws(() => { x.project(y); }, Error); - }); - it('should work as expected', () => { - const x: v = v.array([2, 1]); - const y: v = v.array([-3, 4]); - const z: v = v.array([6 / 25, -8 / 25]); + const x = array([2, 1]); + const y = array([-3, 4]); + const z = array([6 / 25, -8 / 25]); - deepStrictEqual(z, x.project(y)); + equals(z, x.project(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([2, 1]); - const y: v = v.array([-3, 4]); - const z: v = v.array([6 / 25, -8 / 25]); + const x = array([2, 1]); + const y = array([-3, 4]); + const z = array([6 / 25, -8 / 25]); - deepStrictEqual(z, v.project(x, y)); + equals(z, project(x, y)); }); }); diff --git a/src/core/project.ts b/src/core/project.ts index 47ecc776..06456878 100644 --- a/src/core/project.ts +++ b/src/core/project.ts @@ -1,8 +1,32 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.project = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).project(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function project + * @description Projects the `y` onto `x` using the projection formula `(y * (x * y / y * y))`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { project } from 'vectorious/core/project'; + * + * project([1, 2, 3], [4, 5, 6]); // => array([1.6623376607894897, 2.0779221057891846, 2.49350643157959]) + */ +export const project = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).project(array(y)); -NDArray.prototype.project = function(this: T, x: T): T { +/** + * @function project + * @memberof NDArray.prototype + * @description Projects the current vector onto `x` using the projection formula `(y * (x * y / y * y))`. + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).project([4, 5, 6]); // <=> array([1.6623376607894897, 2.0779221057891846, 2.49350643157959]) + */ +export default function(this: NDArray, x: NDArray): NDArray { return x.scale(this.dot(x) / x.dot(x)); }; diff --git a/src/core/push.bench.ts b/src/core/push.bench.ts index 4fd82157..bfa29ebb 100644 --- a/src/core/push.bench.ts +++ b/src/core/push.bench.ts @@ -1,16 +1,15 @@ -import v = require('..'); +import { push } from './push'; +import { random } from './random'; import { bench } from '../bench'; -const { random } = Math; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'push', - (n: number): [v, number] => [v.random(n), random()], - (x: v, value: number): void => { + (n: number) => [random(n), Math.random()], + (x, value: number): void => { x.push(value); }, - (x: v, value: number): void => { - v.push(x, value); + (x, value: number): void => { + push(x, value); } ); diff --git a/src/core/push.spec.ts b/src/core/push.spec.ts index 7d9eb4b9..54c2d2c2 100644 --- a/src/core/push.spec.ts +++ b/src/core/push.spec.ts @@ -1,15 +1,13 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { push } from './push'; +import { array } from './array'; describe('(v) push', () => { it('should start with v(1, 2), push(3) to get v(1, 2, 3)', () => { - deepStrictEqual(v.array([1, 2, 3]), v.array([1, 2]).push(3)); + equals(array([1, 2, 3]), array([1, 2]).push(3)); }); it('should work as the static equivalent', () => { - deepStrictEqual(v.array([1, 2, 3]), v.push(v.array([1, 2]), 3)); + equals(array([1, 2, 3]), push(array([1, 2]), 3)); }); }); diff --git a/src/core/push.ts b/src/core/push.ts index 7d26421b..31baaf1b 100644 --- a/src/core/push.ts +++ b/src/core/push.ts @@ -2,11 +2,36 @@ import { TypedArray } from '../types'; import { get_type } from '../util'; import { NDArray } from './'; +import { array } from './array'; -NDArray.push = (x: T | ArrayLike, value: number): T => - NDArray.array(x).push(value); +/** + * @static + * @memberof module:Globals + * @function push + * @description Pushes a new `value` into `x`. + * @param {NDArray} x + * @param {Number} value + * @returns {NDArray} + * @example + * import { push } from 'vectorious/core/push'; + * + * push([1, 2, 3], 4); // => array([1, 2, 3, 4]) + */ +export const push = (x: NDArray | ArrayLike, value: number): NDArray => + array(x).push(value); -NDArray.prototype.push = function(this: T, value: number): T { +/** + * @function push + * @memberof NDArray.prototype + * @description Pushes a new `value` into current vector. + * @param {Number} value + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).push(4); // => array([1, 2, 3, 4]) + */ +export default function(this: NDArray, value: number): NDArray { if (this.shape.length !== 1) { throw new Error('push operation not permitted for multidimensional arrays'); } diff --git a/src/core/random.bench.ts b/src/core/random.bench.ts index 152cce81..23839465 100644 --- a/src/core/random.bench.ts +++ b/src/core/random.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { random } from './random'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'random', (n: number): [number] => [n], (n: number): void => { - v.random(n); + random(n); } ); diff --git a/src/core/random.spec.ts b/src/core/random.spec.ts index 6df30027..2ffbf688 100644 --- a/src/core/random.spec.ts +++ b/src/core/random.spec.ts @@ -1,22 +1,22 @@ import { - deepStrictEqual, strictEqual, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { random } from './random'; describe('(v) random', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); strictEqual(3, x.length); - deepStrictEqual([3], x.shape); + equals([3], x.shape); }); it('should work as expected', () => { - const x: v = v.random(3, 3); + const x = random(3, 3); strictEqual(9, x.length); - deepStrictEqual([3, 3], x.shape); + equals([3, 3], x.shape); }); }); diff --git a/src/core/random.ts b/src/core/random.ts index 9f0762aa..5fa7c471 100644 --- a/src/core/random.ts +++ b/src/core/random.ts @@ -1,8 +1,19 @@ import { NDArray } from './'; -NDArray.random = function(this: new(...args: any[]) => T, ...shape: number[]): T { - return new this( - new Float32Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), +/** + * @static + * @memberof module:Globals + * @function random + * @description Creates a vector containing random samples from a uniform distribution over `[0, 1)` of shape `shape` + * @param {Number[]} ...shape + * @returns {NDArray} + * @example + * import { random } from 'vectorious/core/random'; + * + * random(3); // => array([0.27496153116226196, 0.7581521272659302, 0.3682245910167694]) + */ +export const random = (...shape: number[]): NDArray => + new NDArray( + new Float64Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), { shape } ).map(() => Math.random()); -}; diff --git a/src/core/range.bench.ts b/src/core/range.bench.ts index 66aa6eea..d1829664 100644 --- a/src/core/range.bench.ts +++ b/src/core/range.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { range } from './range'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'range', (n: number): [number, number] => [0, n], (start: number, end: number): void => { - v.range(start, end); + range(start, end); } ); diff --git a/src/core/range.spec.ts b/src/core/range.spec.ts index 8621ca7e..3618938a 100644 --- a/src/core/range.spec.ts +++ b/src/core/range.spec.ts @@ -1,25 +1,23 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { range } from './range'; +import { array } from './array'; describe('(v) range', () => { it('should work as expected', () => { - const x: v = v.array([0, 1, 2]); + const x = array([0, 1, 2]); - deepStrictEqual(x, v.range(0, 3)); + equals(x, range(0, 3)); }); it('should work as expected', () => { - const x: v = v.array([2, 1, 0]); + const x = array([2, 1, 0]); - deepStrictEqual(x, v.range(2, -1)); + equals(x, range(2, -1)); }); it('should work as expected', () => { - const x: v = v.array([0, 0.5, 1, 1.5]); + const x = array([0, 0.5, 1, 1.5]); - deepStrictEqual(x, v.range(0, 0.5, 2)); + equals(x, range(0, 0.5, 2)); }); }); diff --git a/src/core/range.ts b/src/core/range.ts index 70995db7..7a019640 100644 --- a/src/core/range.ts +++ b/src/core/range.ts @@ -2,7 +2,25 @@ import { TypedArray, TypedArrayConstructor } from '../types'; import { NDArray } from './'; -NDArray.range = function(this: new(...args: any[]) => T, ...args: number[]): T { +/** + * @static + * @memberof module:Globals + * @function range + * @description + * Creates an array containing a range (can be either ascending or descending) + * of numbers specified by the arguments provided (e.g. `NDArray.range(0, .5, 2)` + * gives an array containing all numbers in the interval `[0, 2)` separated by + * steps of `0.5`) + * @param {Number} start + * @param {Number} step + * @param {Number} stop + * @returns {NDArray} + * @example + * import { range } from 'vectorious/core/range'; + * + * range(1, 2, 9); // => array([1, 3, 5, 7]) + */ +export const range = (...args: number[]): NDArray => { const type: TypedArrayConstructor = Float32Array; let backwards: boolean = false; let start: number; @@ -50,5 +68,5 @@ NDArray.range = function(this: new(...args: any[]) => T, ...a } } - return new this(data); + return new NDArray(data); }; diff --git a/src/core/rank.bench.ts b/src/core/rank.bench.ts index 1d66f08e..4b6fb777 100644 --- a/src/core/rank.bench.ts +++ b/src/core/rank.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { rank } from './rank'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'rank', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.rank(); }, - (x: v): void => { - v.rank(x); + (x): void => { + rank(x); } ); diff --git a/src/core/rank.spec.ts b/src/core/rank.spec.ts index bab8c313..ad305795 100644 --- a/src/core/rank.spec.ts +++ b/src/core/rank.spec.ts @@ -1,15 +1,15 @@ import { - deepStrictEqual, strictEqual, } from 'assert'; -import v = require('..'); +import { rank } from './rank'; +import { array } from './array'; describe('(v) rank', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2, 1], [-2, -3, 1], [3, 5, 0]]); - const y: v = v.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]); - const z: v = v.array([[0, 0, 0], [0, 0, 0]]); + const x = array([[1, 2, 1], [-2, -3, 1], [3, 5, 0]]); + const y = array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]); + const z = array([[0, 0, 0], [0, 0, 0]]); strictEqual(x.rank(), 2); strictEqual(y.rank(), 1); @@ -17,8 +17,8 @@ describe('(v) rank', () => { }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 1, 1]]); + const x = array([[1, 1, 1]]); - deepStrictEqual(x.copy().rank(), v.rank(x)); + strictEqual(x.copy().rank(), rank(x)); }); }); diff --git a/src/core/rank.ts b/src/core/rank.ts index cf3ae85e..26996c08 100644 --- a/src/core/rank.ts +++ b/src/core/rank.ts @@ -1,30 +1,47 @@ import { NDArray } from './'; +import { NDIter } from '../iterator'; +import { array } from './array'; /** - * Finds the rank of the matrix using gaussian elimination. + * @static + * @memberof module:Globals + * @function rank + * @description Finds the rank of `x` using gaussian elimination. + * @param {NDArray} x + * @param {Number} tolerance + * @returns {Number} + * @example + * import { rank } from 'vectorious/core/rank'; + * + * rank([[1, 1, 1], [2, 2, 2], [3, 3, 3]]); // => 1 */ -NDArray.rank = (x: T | ArrayLike): number => NDArray.array(x).rank(); +export const rank = (x: NDArray | ArrayLike, tolerance: number = 1e-6): number => array(x).rank(tolerance); /** - * Finds the rank of the matrix using gaussian elimination. + * @function rank + * @memberof NDArray.prototype + * @description Finds the rank of current matrix using gaussian elimination. + * @param {Number} tolerance + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]).rank(); // => 1 + * @todo Switch to SVD algorithm */ -NDArray.prototype.rank = function(this: T): number { - this.gauss(); +export default function(this: NDArray, tolerance: number = 1e-6): number { + const { data: d1 } = this.copy().gauss(); - const [r, c] = this.shape; - const { data: d1 } = this; + const iter = new NDIter(this); let rk: number = 0; - let i: number; - let j: number; - - for (i = 0; i < r; i += 1) { - for (j = i; j < c; j += 1) { - if (rk <= i && d1[i * c + j] !== 0) { - rk += 1; - continue; - } + let [ci, cj] = iter.coords; + for (const i of iter) { + if (rk <= ci && cj >= ci && d1[i!] > tolerance) { + rk += 1; } + + [ci, cj] = iter.coords; } return rk; diff --git a/src/core/reciprocal.bench.ts b/src/core/reciprocal.bench.ts index a3f134a0..e4d50000 100644 --- a/src/core/reciprocal.bench.ts +++ b/src/core/reciprocal.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { reciprocal } from './reciprocal'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'reciprocal', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.reciprocal(); }, - (x: v): void => { - v.reciprocal(x); + (x): void => { + reciprocal(x); } ); diff --git a/src/core/reciprocal.spec.ts b/src/core/reciprocal.spec.ts index 9958f7d1..6ca36943 100644 --- a/src/core/reciprocal.spec.ts +++ b/src/core/reciprocal.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { reciprocal } from './reciprocal'; +import { map } from './map'; +import { random } from './random'; describe('(v) reciprocal', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => 1 / value), x.reciprocal()); + equals(map(x, (value: number) => 1 / value), x.reciprocal()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => 1 / value), v.reciprocal(x)); + equals(map(x, (value: number) => 1 / value), reciprocal(x)); }); }); diff --git a/src/core/reciprocal.ts b/src/core/reciprocal.ts index 0f622569..e2b53130 100644 --- a/src/core/reciprocal.ts +++ b/src/core/reciprocal.ts @@ -1,14 +1,38 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.reciprocal = (x: T | ArrayLike): T => - NDArray.array(x).reciprocal(); +/** + * @static + * @memberof module:Globals + * @function reciprocal + * @description Gets the element-wise reciprocal of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { reciprocal } from 'vectorious/core/reciprocal'; + * + * reciprocal([1, 2, 3]); // => array([1, 0.5, 0.3333333432674408]) + */ +export const reciprocal = (x: NDArray | ArrayLike): NDArray => + array(x).reciprocal(); -NDArray.prototype.reciprocal = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @function reciprocal + * @memberof NDArray.prototype + * @description Gets the element-wise reciprocal of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]); // => array([1, 0.5, 0.3333333432674408]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = 1 / d1[i]; + for (const i of iter) { + d1[i!] = 1 / d1[i!]; } return this; diff --git a/src/core/reduce.bench.ts b/src/core/reduce.bench.ts index 7f476785..e26f87ea 100644 --- a/src/core/reduce.bench.ts +++ b/src/core/reduce.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { reduce } from './reduce'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'reduce', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.reduce((value: number) => value); }, - (x: v): void => { - v.reduce(x, (value: number) => value); + (x): void => { + reduce(x, (value: number) => value); } ); diff --git a/src/core/reduce.spec.ts b/src/core/reduce.spec.ts index 47338dc5..b230315a 100644 --- a/src/core/reduce.spec.ts +++ b/src/core/reduce.spec.ts @@ -1,32 +1,33 @@ import { - deepStrictEqual, + strictEqual, throws, } from 'assert'; -import v = require('..'); +import { reduce } from './reduce'; +import { array } from './array'; describe('(v) reduce', () => { it('should work as expected', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([1, 2, 3, 4, 5, 6]); + const x = array([1, 2, 3]); + const y = array([1, 2, 3, 4, 5, 6]); const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; - deepStrictEqual(6, x.reduce(sum, 0)); - deepStrictEqual(21, y.reduce(sum)); + strictEqual(6, x.reduce(sum, 0)); + strictEqual(21, y.reduce(sum)); }); it('should throw error if empty vector with no initial value', () => { - const x: v = v.array(); + const x = array(); const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; throws(() => { x.reduce(sum); }, Error); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([1, 2, 3, 4, 5, 6]); + const x = array([1, 2, 3]); + const y = array([1, 2, 3, 4, 5, 6]); const sum: (a: number, b: number) => number = (a: number, b: number): number => a + b; - deepStrictEqual(6, v.reduce(x, sum, 0)); - deepStrictEqual(21, v.reduce(y, sum)); + strictEqual(6, reduce(x, sum, 0)); + strictEqual(21, reduce(y, sum)); }); }); diff --git a/src/core/reduce.ts b/src/core/reduce.ts index 327d8860..18b89205 100644 --- a/src/core/reduce.ts +++ b/src/core/reduce.ts @@ -1,36 +1,65 @@ import { TypedArray } from '../types'; import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.reduce = ( - x: T, +/** + * @static + * @memberof module:Globals + * @function reduce + * @description Equivalent to `TypedArray.prototype.reduce`. + * @param {NDArray} x + * @param {Function} f + * @param {Number} initialValue + * @returns {Number} + * @example + * import { reduce } from 'vectorious/core/reduce'; + * + * reduce([1, 2, 3], (a, b) => a + b, 0); // => 6 + */ +export const reduce = ( + x: NDArray, f: (acc: number, value: number, i: number, src: TypedArray) => number, initialValue?: number -): number => NDArray.array(x).reduce(f, initialValue); +): number => array(x).reduce(f, initialValue); -NDArray.prototype.reduce = function( - this: T, +/** + * @function reduce + * @memberof NDArray.prototype + * @description Equivalent to `TypedArray.prototype.reduce`. + * @param {Function} f + * @param {Number} initialValue + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).reduce((a, b) => a + b, 0); // => 6 + */ +export default function( + this: NDArray, f: (acc: number, value: number, i: number, src: TypedArray) => number, initialValue?: number ): number { const { data: d1, length: l1 } = this; if (l1 === 0 && typeof initialValue === 'undefined') { - throw new Error('Reduce of empty matrix with no initial value.'); + throw new Error('Reduce of empty array with no initial value.'); } - let i: number; + const iter = new NDIter(this); + const reduce = f.bind(this); + let value: number; if (typeof initialValue === 'undefined') { value = d1[0]; - i = 1; + iter.next(); } else { value = initialValue; - i = 0; } - for (; i < l1; i += 1) { - value = f.call(this, value, d1[i], i, d1); + for (const i of iter) { + value = reduce(value, d1[i!], i!, d1); } return value; diff --git a/src/core/reshape.bench.ts b/src/core/reshape.bench.ts index cdf9adce..edc6238b 100644 --- a/src/core/reshape.bench.ts +++ b/src/core/reshape.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { reshape } from './reshape'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'reshape', - (n: number): [v, number] => [v.random(n), n], - (x: v, n: number): void => { + (n: number) => [random(n), n], + (x, n: number): void => { x.reshape(n); }, - (x: v, n: number): void => { - v.reshape(x, n); + (x, n: number): void => { + reshape(x, n); } ); diff --git a/src/core/reshape.spec.ts b/src/core/reshape.spec.ts index c69d77dc..de74dc66 100644 --- a/src/core/reshape.spec.ts +++ b/src/core/reshape.spec.ts @@ -1,32 +1,33 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { reshape } from './reshape'; +import { array } from './array'; describe('(v) reshape', () => { it('should throw error if new shape does not match length', () => { - const f32: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32); + const f32 = new Float64Array([1, 2, 3, 4]); + const x = array(f32); throws(() => { x.reshape(1, 2); }, Error); }); it('should be able to create row vector of column vector', () => { - const f32: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32, { shape: [1, 4] }); + const f32 = new Float64Array([1, 2, 3, 4]); + const x = array(f32, { shape: [1, 4] }); - deepStrictEqual(x.shape, [1, 4]); + equals(x.shape, [1, 4]); x.reshape(4, 1); - deepStrictEqual(x.shape, [4, 1]); + equals(x.shape, [4, 1]); }); it('should work as the static equivalent', () => { - const f32: Float32Array = new Float32Array([1, 2, 3, 4]); - const x: v = v.array(f32, { shape: [1, 4] }); + const f32 = new Float64Array([1, 2, 3, 4]); + const x = array(f32, { shape: [1, 4] }); - deepStrictEqual(x.shape, [1, 4]); - deepStrictEqual(v.reshape(x, 4, 1).shape, [4, 1]); + equals(x.shape, [1, 4]); + equals(reshape(x, 4, 1).shape, [4, 1]); }); }); diff --git a/src/core/reshape.ts b/src/core/reshape.ts index b215115c..36a12c9c 100644 --- a/src/core/reshape.ts +++ b/src/core/reshape.ts @@ -1,15 +1,43 @@ +import { get_strides } from '../util'; + import { NDArray } from './'; +import { array } from './array'; -NDArray.reshape = (x: T | ArrayLike, ...shape: number[]): T => - NDArray.array(x).reshape(...shape); +/** + * @static + * @memberof module:Globals + * @function reshape + * @description Reshapes `x` + * @param {NDArray} x + * @param {Number[]} ...shape + * @returns {NDArray} + * @example + * import { reshape } from 'vectorious/core/reshape'; + * + * reshape([1, 2, 3, 4], 2, 2); // => array([[1, 2], [3, 4]]) + */ +export const reshape = (x: NDArray | ArrayLike, ...shape: number[]): NDArray => + array(x).reshape(...shape); -NDArray.prototype.reshape = function(this: T, ...shape: number[]): T { +/** + * @function reshape + * @memberof NDArray.prototype + * @description Reshapes current array + * @param {Number[]} ...shape + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3, 4]).reshape(2, 2); // <=> array([[1, 2], [3, 4]]) + */ +export default function(this: NDArray, ...shape: number[]): NDArray { const { length } = this; if (shape.reduce((sum: number, dim: number) => sum * dim, 1) !== length) { throw new Error(`shape ${shape} does not match length ${length}`); } this.shape = shape; + this.strides = get_strides(shape); return this; }; diff --git a/src/core/round.bench.ts b/src/core/round.bench.ts index d7119579..1cfd1612 100644 --- a/src/core/round.bench.ts +++ b/src/core/round.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { round } from './round'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'round', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.round(); }, - (x: v): void => { - v.round(x); + (x): void => { + round(x); } ); diff --git a/src/core/round.spec.ts b/src/core/round.spec.ts index be7936ff..1d31de9b 100644 --- a/src/core/round.spec.ts +++ b/src/core/round.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { round } from './round'; +import { map } from './map'; +import { random } from './random'; describe('(v) round', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.round(value)), x.round()); + equals(map(x, (value: number) => Math.round(value)), x.round()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.round(value)), v.round(x)); + equals(map(x, (value: number) => Math.round(value)), round(x)); }); }); diff --git a/src/core/round.ts b/src/core/round.ts index 93892c84..47018212 100644 --- a/src/core/round.ts +++ b/src/core/round.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.round = (x: T | ArrayLike): T => NDArray.array(x).round(); +const { round: f } = Math; -NDArray.prototype.round = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function round + * @description Returns the value of each element of `x` rounded to the nearest integer. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { round } from 'vectorious/core/round'; + * + * round([1.2, 2.8, 3.5]); // => array([1, 3, 4]) + */ +export const round = (x: NDArray | ArrayLike): NDArray => array(x).round(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.round(d1[i]); +/** + * @function round + * @memberof NDArray.prototype + * @description Returns the value of each element of current array rounded to the nearest integer. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1.2, 2.8, 3.5]).round(); // <=> array([1, 3, 4]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/row_add.bench.ts b/src/core/row_add.bench.ts index 921ac33f..b06a2493 100644 --- a/src/core/row_add.bench.ts +++ b/src/core/row_add.bench.ts @@ -1,16 +1,15 @@ -import v = require('..'); +import { row_add } from './row_add'; +import { random } from './random'; import { bench } from '../bench'; -const { floor, random, sqrt } = Math; - bench( - 'v', + 'NDArray', 'row_add', - (n: number): [v, number, number] => [v.random(n), floor(random() * floor(sqrt(n))), floor(random() * floor(sqrt(n)))], - (x: v, dest: number, source: number): void => { + (n: number) => [random(n), Math.floor(Math.random() * Math.floor(Math.sqrt(n))), Math.floor(Math.random() * Math.floor(Math.sqrt(n)))], + (x, dest: number, source: number): void => { x.row_add(dest, source); }, - (x: v, dest: number, source: number): void => { - v.row_add(x, dest, source); + (x, dest: number, source: number): void => { + row_add(x, dest, source); } ); diff --git a/src/core/row_add.spec.ts b/src/core/row_add.spec.ts index c558199c..dec9d49f 100644 --- a/src/core/row_add.spec.ts +++ b/src/core/row_add.spec.ts @@ -1,23 +1,21 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { row_add } from './row_add'; +import { array } from './array'; describe('(v) row_add', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2], [3, 4]]); - const y: v = v.array([[31, 42], [3, 4]]); + const x = array([[1, 2], [3, 4]]); + const y = array([[31, 42], [3, 4]]); x.row_add(0, 1, 10); - deepStrictEqual(y, x); + equals(y, x); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2], [3, 4]]); - const y: v = v.array([[31, 42], [3, 4]]); + const x = array([[1, 2], [3, 4]]); + const y = array([[31, 42], [3, 4]]); - deepStrictEqual(y, v.row_add(x, 0, 1, 10)); + equals(y, row_add(x, 0, 1, 10)); }); }); diff --git a/src/core/row_add.ts b/src/core/row_add.ts index d5b3a949..28e2eea2 100644 --- a/src/core/row_add.ts +++ b/src/core/row_add.ts @@ -1,14 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.row_add = ( - x: T | ArrayLike, +/** + * @static + * @memberof module:Globals + * @function row_add + * @description Adds a multiple of one row multiplied by `scalar` to another inside `x`. + * @param {NDArray} x + * @param {Number} dest + * @param {Number} source + * @param {Number} scalar + * @returns {NDArray} + * @example + * import { row_add } from 'vectorious/core/row_add'; + * + * row_add([[1, 2], [3, 4]], 1, 0, 2); // => array([[1, 2], [5, 8]]) + */ +export const row_add = ( + x: NDArray | ArrayLike, dest: number, source: number, scalar: number = 1 -): T => - NDArray.array(x).row_add(dest, source, scalar); +): NDArray => + array(x).row_add(dest, source, scalar); -NDArray.prototype.row_add = function(this: T, dest: number, source: number, scalar: number = 1): T { +/** + * @function row_add + * @memberof NDArray.prototype + * @description Adds a multiple of one row multiplied by `scalar` to another inside current matrix. + * @param {Number} dest + * @param {Number} source + * @param {Number} scalar + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2], [3, 4]]).row_add(1, 0, 2); // <=> array([[1, 2], [5, 8]]) + */ +export default function(this: NDArray, dest: number, source: number, scalar: number = 1): NDArray { this.check(dest, 0); this.check(source, 0); diff --git a/src/core/scale.bench.ts b/src/core/scale.bench.ts index baae55ff..df0f02fd 100644 --- a/src/core/scale.bench.ts +++ b/src/core/scale.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { scale } from './scale'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'scale', - (n: number): [v, number] => [v.random(n), Math.random()], - (x: v, alpha: number) => { + (n: number) => [random(n), Math.random()], + (x, alpha: number) => { x.scale(alpha); }, - (x: v, alpha: number) => { - v.scale(x, alpha); + (x, alpha: number) => { + scale(x, alpha); } ); diff --git a/src/core/scale.spec.ts b/src/core/scale.spec.ts index 4293fc5c..ba2dd6a1 100644 --- a/src/core/scale.spec.ts +++ b/src/core/scale.spec.ts @@ -1,21 +1,19 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { scale } from './scale'; +import { array } from './array'; describe('(v) scale', () => { it('should scale v(1, 2, 3) by 2 to v(2, 4, 6)', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([2, 4, 6]); + const x = array([1, 2, 3]); + const y = array([2, 4, 6]); - deepStrictEqual(y, x.scale(2)); + equals(y, x.scale(2)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([2, 4, 6]); + const x = array([1, 2, 3]); + const y = array([2, 4, 6]); - deepStrictEqual(y, v.scale(x, 2)); + equals(y, scale(x, 2)); }); }); diff --git a/src/core/scale.ts b/src/core/scale.ts index 6ac2b979..e841aa34 100644 --- a/src/core/scale.ts +++ b/src/core/scale.ts @@ -1,36 +1,49 @@ -import { get_type } from '../util'; - import { NDArray } from './'; - -let nblas: any; -try { - nblas = require('nblas'); -} catch (err) {} - -NDArray.scale = (x: T | ArrayLike, scalar: number): T => - NDArray.array(x).scale(scalar); - -NDArray.prototype.scale = function(this: T, scalar: number): T { - const { length: l1 } = this; +import { array } from './array'; +import { NDIter } from '../iterator'; +import * as blas from '../blas'; + +/** + * @static + * @memberof module:Globals + * @function scale + * @description + * Multiplies all elements of `x` with a specified `scalar`. + * Accelerated with BLAS `?scal`. + * @param {NDArray} x + * @param {Number} scalar + * @returns {NDArray} + * @example + * import { scale } from 'vectorious/core/scale'; + * + * scale([1, 2, 3], 2); // => array([2, 4, 6]) + */ +export const scale = (x: NDArray | ArrayLike, scalar: number): NDArray => + array(x).scale(scalar); + +/** + * @function scale + * @memberof NDArray.prototype + * @description + * Multiplies all elements of current array with a specified `scalar`. + * Accelerated with BLAS `?scal`. + * @param {Number} scalar + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).scale(2); // <=> array([2, 4, 6]) + */ +export default function(this: NDArray, scalar: number): NDArray { + const { data: d1, length: l1, dtype } = this; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - if (this.dtype === 'float64') { - nblas.dscal(l1, scalar, d1, 1); - } else if (this.dtype === 'float32') { - nblas.sscal(l1, scalar, d1, 1); - } + blas.scal(dtype, l1, scalar, d1, 1); } catch (err) { - const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] *= scalar; + for (const i of iter) { + d1[i!] *= scalar; } } diff --git a/src/core/set.bench.ts b/src/core/set.bench.ts index fe458bb0..6523090e 100644 --- a/src/core/set.bench.ts +++ b/src/core/set.bench.ts @@ -1,17 +1,15 @@ -import v = require('..'); +import { set } from './set'; +import { random } from './random'; import { bench } from '../bench'; -const { random, floor } = Math; -const r: (n: number) => v = (n: number): v => - v.array(new Float32Array(n)).fill(random); bench( - 'v', + 'NDArray', 'set', - (n: number): [v, number, number] => [v.random(n), floor(random() * n), random()], - (x: v, i: number, alpha: number) => { + (n: number) => [random(n), Math.floor(Math.random() * n), Math.random()], + (x, i: number, alpha: number) => { x.set(i, alpha); }, - (x: v, i: number, alpha: number) => { - v.set(x, i, alpha); + (x, i: number, alpha: number) => { + set(x, i, alpha); } ); diff --git a/src/core/set.spec.ts b/src/core/set.spec.ts index f3857ba6..36db7806 100644 --- a/src/core/set.spec.ts +++ b/src/core/set.spec.ts @@ -2,11 +2,12 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { set } from './set'; +import { array } from './array'; describe('(v) set', () => { it('should set properties as expected', () => { - const x: v = v.array([-1, -1, -1, -1]); + const x = array([-1, -1, -1, -1]); x.set(0, 0); x.set(1, 1); @@ -20,12 +21,12 @@ describe('(v) set', () => { }); it('should work as the static equivalent', () => { - const x: v = v.array([-1, -1, -1, -1]); + const x = array([-1, -1, -1, -1]); - v.set(x, 0, 0); - v.set(x, 1, 1); - v.set(x, 2, 2); - v.set(x, 3, 3); + set(x, 0, 0); + set(x, 1, 1); + set(x, 2, 2); + set(x, 3, 3); strictEqual(0, x.data[0]); strictEqual(1, x.data[1]); diff --git a/src/core/set.ts b/src/core/set.ts index dd594266..2278e9df 100644 --- a/src/core/set.ts +++ b/src/core/set.ts @@ -1,10 +1,36 @@ import { NDArray } from './'; -NDArray.set = (x: T, ...args: number[]): void => { +/** + * @static + * @memberof module:Globals + * @function set + * @description Sets the element at `i, j, ..., n` to `value`. + * @param {NDArray} x + * @param {Number[]} ...indices + * @param {Number} value + * @returns {NDArray} + * @example + * import { set } from 'vectorious/core/set'; + * + * set([1, 2, 3], 1, 0); // => array([1, 0, 3]) + */ +export const set = (x: NDArray, ...args: number[]): void => { x.set(...args); }; -NDArray.prototype.set = function(this: T, ...args: number[]): void { +/** + * @function set + * @memberof NDArray.prototype + * @description Sets the element at `i, j, ..., n` to `value`. + * @param {Number[]} ...indices + * @param {Number} value + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).set(1, 0); // <=> array([1, 0, 3]) + */ +export default function(this: NDArray, ...args: number[]): void { const indices: number[] = args.slice(0, -1); const value: number = args[args.length - 1]; diff --git a/src/core/sign.bench.ts b/src/core/sign.bench.ts index 660b79fe..83b4899b 100644 --- a/src/core/sign.bench.ts +++ b/src/core/sign.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { sign } from './sign'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'sign', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.sign(); }, - (x: v): void => { - v.sign(x); + (x): void => { + sign(x); } ); diff --git a/src/core/sign.spec.ts b/src/core/sign.spec.ts index e234bd8b..3f55415b 100644 --- a/src/core/sign.spec.ts +++ b/src/core/sign.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { sign } from './sign'; +import { map } from './map'; +import { random } from './random'; describe('(v) sign', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sign(value)), x.sign()); + equals(map(x, (value: number) => Math.sign(value)), x.sign()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sign(value)), v.sign(x)); + equals(map(x, (value: number) => Math.sign(value)), sign(x)); }); }); diff --git a/src/core/sign.ts b/src/core/sign.ts index d780a38a..05b8d690 100644 --- a/src/core/sign.ts +++ b/src/core/sign.ts @@ -1,13 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.sign = (x: T | ArrayLike): T => NDArray.array(x).sign(); +const { sign: f } = Math; -NDArray.prototype.sign = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function sign + * @description + * Returns the sign of each element of `x`, indicating + * whether it is positive, negative or zero. + * @param {Number} x + * @returns {NDArray} + * @example + * import { sign } from 'vectorious/core/sign'; + * + * sign([1, 2, 3]); // => array([1, 1, 1]) + */ +export const sign = (x: NDArray | ArrayLike): NDArray => array(x).sign(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.sign(d1[i]); +/** + * @function sign + * @memberof NDArray.prototype + * @description + * Returns the sign of each element of current array, indicating + * whether it is positive, negative or zero. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).sign(); // <=> array([1, 1, 1]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/sin.bench.ts b/src/core/sin.bench.ts index 0e45bf9e..6fdb6ce2 100644 --- a/src/core/sin.bench.ts +++ b/src/core/sin.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { sin } from './sin'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'sin', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.sin(); }, - (x: v): void => { - v.sin(x); + (x): void => { + sin(x); } ); diff --git a/src/core/sin.spec.ts b/src/core/sin.spec.ts index 634b828d..5d7a68ee 100644 --- a/src/core/sin.spec.ts +++ b/src/core/sin.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { sin } from './sin'; +import { map } from './map'; +import { random } from './random'; describe('(v) sin', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sin(value)), x.sin()); + equals(map(x, (value: number) => Math.sin(value)), x.sin()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sin(value)), v.sin(x)); + equals(map(x, (value: number) => Math.sin(value)), sin(x)); }); }); diff --git a/src/core/sin.ts b/src/core/sin.ts index e8934937..5062ba6f 100644 --- a/src/core/sin.ts +++ b/src/core/sin.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.sin = (x: T | ArrayLike): T => NDArray.array(x).sin(); +const { sin: f } = Math; -NDArray.prototype.sin = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function sin + * @description Returns the sine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { sin } from 'vectorious/core/sin'; + * + * sin([0, Math.PI / 2, Math.PI]); // => array([0, 1, 0]) + */ +export const sin = (x: NDArray | ArrayLike): NDArray => array(x).sin(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.sin(d1[i]); +/** + * @function sin + * @memberof NDArray.prototype + * @description Returns the sine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([0, Math.PI / 2, Math.PI]).sin(); // <=> array([0, 1, 0]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/sinh.bench.ts b/src/core/sinh.bench.ts index 005b67d8..0f65292b 100644 --- a/src/core/sinh.bench.ts +++ b/src/core/sinh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { sinh } from './sinh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'sinh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.sinh(); }, - (x: v): void => { - v.sinh(x); + (x): void => { + sinh(x); } ); diff --git a/src/core/sinh.spec.ts b/src/core/sinh.spec.ts index 79a2b920..17936f3c 100644 --- a/src/core/sinh.spec.ts +++ b/src/core/sinh.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { sinh } from './sinh'; +import { map } from './map'; +import { random } from './random'; describe('(v) sinh', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sinh(value)), x.sinh()); + equals(map(x, (value: number) => Math.sinh(value)), x.sinh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sinh(value)), v.sinh(x)); + equals(map(x, (value: number) => Math.sinh(value)), sinh(x)); }); }); diff --git a/src/core/sinh.ts b/src/core/sinh.ts index b832b81e..816a8af4 100644 --- a/src/core/sinh.ts +++ b/src/core/sinh.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.sinh = (x: T | ArrayLike): T => NDArray.array(x).sinh(); +const { sinh: f } = Math; -NDArray.prototype.sinh = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function sinh + * @description Returns the hyperbolic sine of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { sinh } from 'vectorious/core/sinh'; + * + * sinh([1, 2, 3]); // => array([1.175201177597046, 3.6268603801727295, 10.017874717712402]) + */ +export const sinh = (x: NDArray | ArrayLike): NDArray => array(x).sinh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.sinh(d1[i]); +/** + * @function sinh + * @memberof NDArray.prototype + * @description Returns the hyperbolic sine of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).sinh(); // <=> array([1.175201177597046, 3.6268603801727295, 10.017874717712402]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/slice.bench.ts b/src/core/slice.bench.ts index 3bb0764d..993b5161 100644 --- a/src/core/slice.bench.ts +++ b/src/core/slice.bench.ts @@ -1,17 +1,15 @@ -import v = require('..'); +import { slice } from './slice'; +import { random } from './random'; import { bench } from '../bench'; -const { random, floor } = Math; -const r: (n: number) => v = (n: number): v => - v.array(new Float32Array(n)).fill(random); bench( - 'v', + 'NDArray', 'copy', - (n: number): [v, number, number, number] => [v.random(n), 0, floor(random() * n), n], - (x: v, start: number, step: number, end: number): void => { + (n: number) => [random(n), 0, Math.floor(Math.random() * n), n], + (x, start: number, step: number, end: number): void => { x.slice(start, step, end); }, - (x: v, start: number, step: number, end: number): void => { - v.slice(x, start, step, end); + (x, start: number, step: number, end: number): void => { + slice(x, start, step, end); } ); diff --git a/src/core/slice.spec.ts b/src/core/slice.spec.ts index 87916143..5c98a1c6 100644 --- a/src/core/slice.spec.ts +++ b/src/core/slice.spec.ts @@ -1,25 +1,23 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { slice } from './slice'; +import { array } from './array'; describe('(v) slice', () => { it('should work as expected', () => { - const x: v = v.array([-1, -2, 3, 4]); + const x = array([-1, -2, 3, 4]); - deepStrictEqual(v.array([-1, 3]), x.slice(0, 4, 2)); + equals(array([-1, 3]), x.slice(0, 4, 2)); }); it('should work as expected', () => { - const x: v = v.array([-1, -2, 3, 4]); + const x = array([-1, -2, 3, 4]); - deepStrictEqual(v.array([-2, 3]), x.slice(1, 3)); + equals(array([-2, 3]), x.slice(1, 3)); }); it('should work as the static equivalent', () => { - const x: v = v.array([-1, -2, 3, 4]); + const x = array([-1, -2, 3, 4]); - deepStrictEqual(v.array([-1, 3]), v.slice(x, 0, 4, 2)); + equals(array([-1, 3]), slice(x, 0, 4, 2)); }); }); diff --git a/src/core/slice.ts b/src/core/slice.ts index 006a39c7..34ad634f 100644 --- a/src/core/slice.ts +++ b/src/core/slice.ts @@ -1,27 +1,54 @@ -import { TypedArray } from '../types'; -import { get_type } from '../util'; - +import { get_strides } from '../util'; import { NDArray } from './'; +import { array } from './array'; -NDArray.slice = ( - x: T | ArrayLike, - start?: number, - step?: number, - end?: number -): T => - NDArray.array(x).slice(start, step, end); +/** + * @static + * @memberof module:Globals + * @function slice + * @description Slices `x` in the corresponding dimension + * @param {NDArray} x + * @param {Number} begin + * @param {Number} end + * @param {Number} step + * @returns {NDArray} + * @example + * import { slice } from 'vectorious/core/slice'; + * + * slice([1, 2, 3, 4], 0, 4, 2); // => array([1, 3]) + */ +export const slice = ( + x: NDArray | ArrayLike, + begin?: number, + end?: number, + step?: number +): NDArray => + array(x).slice(begin, end, step); -NDArray.prototype.slice = function( - this: T, +/** + * @function slice + * @memberof NDArray.prototype + * @description Slices the current array in the corresponding dimension + * @param {Number} begin + * @param {Number} end + * @param {Number} step + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3, 4]).slice(0, 4, 2); // => array([1, 3]) + */ +export default function( + this: NDArray, begin: number = 0, end: number = this.shape[0], step: number = 1 -): T { - const { data: d1, shape: s1 } = this; - const { length: ndim } = s1; +): NDArray { + const { shape: s1 } = this; + const nd = s1.length; if (begin < 0 || end < 0) { - return this.slice(begin < 0 ? ndim + begin : begin, end < 0 ? ndim + end : end); + return this.slice(begin < 0 ? nd + begin : begin, end < 0 ? nd + end : end); } if (step === 0) { @@ -30,16 +57,11 @@ NDArray.prototype.slice = function( const s2: number[] = [Math.ceil((end - begin) / step), ...s1.slice(1)]; const l2: number = s2.reduce((sum: number, dim: number) => sum * dim, 1); - const d2: TypedArray = new (get_type(this.dtype))(l2); - - let i: number; - for (i = 0; i < l2; i += 1) { - d2[i] = d1[begin + i * step]; - } + const st2: number[] = get_strides(s2); + this.shape = s2; this.length = l2; - this.data = d2; - this.reshape(...s2); + this.strides = st2; return this; }; diff --git a/src/core/solve.bench.ts b/src/core/solve.bench.ts index 42ab4838..4af8a580 100644 --- a/src/core/solve.bench.ts +++ b/src/core/solve.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { solve } from './solve'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'solve', - (n: number): [v, v] => [v.random(floor(sqrt(n)), floor(sqrt(n))), v.random(floor(sqrt(n)), 1)], - (x: v, y: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n))), random(floor(sqrt(n)), 1)], + (x, y): void => { x.solve(y); }, - (x: v, y: v): void => { - v.solve(x, y); + (x, y): void => { + solve(x, y); } ); diff --git a/src/core/solve.spec.ts b/src/core/solve.spec.ts index bb20c8f5..a7951efe 100644 --- a/src/core/solve.spec.ts +++ b/src/core/solve.spec.ts @@ -1,23 +1,21 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { solve } from './solve'; +import { array } from './array'; describe('(v) solve', () => { it('should work as expected', () => { - const x: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - const y: v = v.array([[1], [3], [5]]); - const z: v = v.array([[3.25], [1.75], [-1.5]]); + const x = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const y = array([[1], [3], [5]]); + const z = array([[3.25], [1.75], [-1.5]]); - deepStrictEqual(z, x.solve(y)); + equals(z, x.solve(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); - const y: v = v.array([[1], [3], [5]]); - const z: v = v.array([[3.25], [1.75], [-1.5]]); + const x = array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]); + const y = array([[1], [3], [5]]); + const z = array([[3.25], [1.75], [-1.5]]); - deepStrictEqual(z, v.solve(x, y)); + equals(z, solve(x, y)); }); }); diff --git a/src/core/solve.ts b/src/core/solve.ts index 43bbbbf3..459552d9 100644 --- a/src/core/solve.ts +++ b/src/core/solve.ts @@ -1,32 +1,45 @@ -import { get_type } from '../util'; - import { NDArray } from './'; +import { array } from './array'; +import * as lapack from '../lapack'; -let nlapack: any; -try { - nlapack = require('nlapack'); -} catch (err) {} - -NDArray.solve = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).solve(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function solve + * @description + * Solves the equation AX = B (where A is `x` and B is `y`). + * Accelerated with LAPACK `?gesv`. + * @param {NDArray} x + * @param {NDArray} y + * @returns {NDArray} + * @example + * import { solve } from 'vectorious/core/solve'; + * + * solve([[1, 3, 5], [2, 4, 7], [1, 1, 0]], [[1], [3], [5]]); // => array([[3.25], [1.75], [-1.5]]) + */ +export const solve = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).solve(array(y)); -NDArray.prototype.solve = function(this: T, x: T): T { - const [n, nrhs] = x.shape; +/** + * @function solve + * @memberof NDArray.prototype + * @description + * Solves the equation AX = B (where A is current matrix and B is `x`). + * Accelerated with LAPACK `?gesv`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 3, 5], [2, 4, 7], [1, 1, 0]]).solve([[1], [3], [5]]); // => array([[3.25], [1.75], [-1.5]]) + */ +export default function(this: NDArray, x: NDArray): NDArray { + const { data: d1, dtype } = this; + const { data: d2, shape: [n, nrhs] } = x; try { - if (!['float32', 'float64'].includes(this.dtype)) { - this.dtype = 'float32'; - this.data = get_type(this.dtype).from(this.data); - } - - const { data: d1 } = this; - const { data: d2 } = x; const ipiv: Int32Array = new Int32Array(n); - if (this.dtype === 'float64') { - nlapack.dgesv(n, nrhs, d1, n, ipiv, d2, nrhs); - } else if (this.dtype === 'float32') { - nlapack.sgesv(n, nrhs, d1, n, ipiv, d2, nrhs); - } + lapack.gesv(dtype, n, nrhs, d1, n, ipiv, d2, nrhs); } catch (err) { const [LU, ipiv] = this.lu_factor(); const { data: d1 } = LU; diff --git a/src/core/sqrt.bench.ts b/src/core/sqrt.bench.ts index 507013f0..509417e0 100644 --- a/src/core/sqrt.bench.ts +++ b/src/core/sqrt.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { sqrt } from './sqrt'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'sqrt', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.sqrt(); }, - (x: v): void => { - v.sqrt(x); + (x): void => { + sqrt(x); } ); diff --git a/src/core/sqrt.spec.ts b/src/core/sqrt.spec.ts index 0b681ac9..7dfa01ed 100644 --- a/src/core/sqrt.spec.ts +++ b/src/core/sqrt.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { sqrt } from './sqrt'; +import { map } from './map'; +import { random } from './random'; describe('(v) sqrt', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sqrt(value)), x.sqrt()); + equals(map(x, (value: number) => Math.sqrt(value)), x.sqrt()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.sqrt(value)), v.sqrt(x)); + equals(map(x, (value: number) => Math.sqrt(value)), sqrt(x)); }); }); diff --git a/src/core/sqrt.ts b/src/core/sqrt.ts index 8527f020..9bc6442a 100644 --- a/src/core/sqrt.ts +++ b/src/core/sqrt.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.sqrt = (x: T | ArrayLike): T => NDArray.array(x).sqrt(); +const { sqrt: f } = Math; -NDArray.prototype.sqrt = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function sqrt + * @description Returns the positive square root of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { sqrt } from 'vectorious/core/sqrt'; + * + * sqrt([1, 4, 9]); // => array([1, 2, 3]) + */ +export const sqrt = (x: NDArray | ArrayLike): NDArray => array(x).sqrt(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.sqrt(d1[i]); +/** + * @function sqrt + * @memberof NDArray.prototype + * @description Returns the positive square root of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 4, 9]); // <=> array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/square.bench.ts b/src/core/square.bench.ts index e866db1c..0a3a2475 100644 --- a/src/core/square.bench.ts +++ b/src/core/square.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { square } from './square'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'square', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.square(); }, - (x: v): void => { - v.square(x); + (x): void => { + square(x); } ); diff --git a/src/core/square.spec.ts b/src/core/square.spec.ts index 0402b727..7dce094c 100644 --- a/src/core/square.spec.ts +++ b/src/core/square.spec.ts @@ -2,16 +2,17 @@ import { throws, } from 'assert'; -import v = require('..'); +import { square } from './square'; +import { array } from './array'; describe('(v) square', () => { it('should throw error if matrix is not square', () => { - const x: v = v.array([[0, 0]]); + const x = array([[0, 0]]); throws(() => { x.square(); }, Error); }); it('should work as the static equivalent', () => { - const x: v = v.array([[0, 0]]); - throws(() => { v.square(x); }, Error); + const x = array([[0, 0]]); + throws(() => { square(x); }, Error); }); }); diff --git a/src/core/square.ts b/src/core/square.ts index 79bf1aa3..26ed25a0 100644 --- a/src/core/square.ts +++ b/src/core/square.ts @@ -1,10 +1,33 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.square = (x: T | ArrayLike): void => { - NDArray.array(x).square(); +/** + * @static + * @memberof module:Globals + * @function square + * @description Asserts if `x` is square. + * @param {NDArray} x + * @throws {Error} matrix is not square + * @example + * import { square } from 'vectorious/core/square'; + * + * square([1, 2, 3]); // Error: matrix is not square + */ +export const square = (x: NDArray | ArrayLike): void => { + array(x).square(); }; -NDArray.prototype.square = function(this: T): void { +/** + * @function square + * @memberof NDArray.prototype + * @description Asserts if current matrix is square. + * @throws {Error} matrix is not square + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).square(); // Error: matrix is not square + */ +export default function(this: NDArray): void { const { length } = this.shape; const [r, c] = this.shape; diff --git a/src/core/subtract.bench.ts b/src/core/subtract.bench.ts index 53b1fe43..97f3b0a4 100644 --- a/src/core/subtract.bench.ts +++ b/src/core/subtract.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { subtract } from './subtract'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'subtract', - (n: number): [v, v] => [v.random(n), v.random(n)], - (x: v, y: v): void => { + (n: number) => [random(n), random(n)], + (x, y): void => { x.subtract(y); }, - (x: v, y: v): void => { - v.subtract(x, y); + (x, y): void => { + subtract(x, y); } ); diff --git a/src/core/subtract.spec.ts b/src/core/subtract.spec.ts index 9becc013..a0e642e2 100644 --- a/src/core/subtract.spec.ts +++ b/src/core/subtract.spec.ts @@ -1,38 +1,28 @@ -import { - deepStrictEqual, - throws, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { subtract } from './subtract'; +import { array } from './array'; describe('(v) subtract', () => { it('should return empty vector if subtracting two empty vectors', () => { - const x: v = v.array(); - const y: v = v.array(); - - deepStrictEqual(v.array(), x.subtract(y)); - }); - - it('should throw error if sizes do not match', () => { - const x: v = v.array([1]); - const y: v = v.array([1, 2]); + const x = array(); + const y = array(); - throws(() => { x.subtract(y); }, Error); + equals(array(), x.subtract(y)); }); it('should produce v(-3, -3, -3) from v(1, 2, 3) and v(4, 5, 6)', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); - const z: v = v.array([-3, -3, -3]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); + const z = array([-3, -3, -3]); - deepStrictEqual(z, x.subtract(y)); + equals(z, x.subtract(y)); }); it('should work as the static equivalent', () => { - const x: v = v.array([1, 2, 3]); - const y: v = v.array([4, 5, 6]); - const z: v = v.array([-3, -3, -3]); + const x = array([1, 2, 3]); + const y = array([4, 5, 6]); + const z = array([-3, -3, -3]); - deepStrictEqual(z, v.subtract(x, y)); + equals(z, subtract(x, y)); }); }); diff --git a/src/core/subtract.ts b/src/core/subtract.ts index 8710a54a..c6aeeb88 100644 --- a/src/core/subtract.ts +++ b/src/core/subtract.ts @@ -1,8 +1,35 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.subtract = (x: T | ArrayLike, y: T | ArrayLike): T => - NDArray.array(x).subtract(NDArray.array(y)); +/** + * @static + * @memberof module:Globals + * @function subtract + * @description + * Subtracts `y` from `x`. + * Accelerated with BLAS `?axpy`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { subtract } from 'vectorious/core/subtract'; + * + * subtract([1, 2, 3], [1, 1, 1]); // => array([0, 1, 2]) + */ +export const subtract = (x: NDArray | ArrayLike, y: NDArray | ArrayLike): NDArray => + array(x).subtract(array(y)); -NDArray.prototype.subtract = function(this: T, x: T): T { +/** + * @function subtract + * @memberof NDArray.prototype + * @description + * Subtracts `x` from the current array. + * Accelerated with BLAS `?axpy`. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).subtract([1, 1, 1]); // <=> array([0, 1, 2]) + */ +export default function (this: NDArray, x: NDArray): NDArray { return this.add(x, -1); }; diff --git a/src/core/sum.bench.ts b/src/core/sum.bench.ts index 30815962..beb1b0c8 100644 --- a/src/core/sum.bench.ts +++ b/src/core/sum.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { sum } from './sum'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'sum', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.sum(); }, - (x: v): void => { - v.sum(x); + (x): void => { + sum(x); } ); diff --git a/src/core/sum.spec.ts b/src/core/sum.spec.ts index ea950d89..00abe698 100644 --- a/src/core/sum.spec.ts +++ b/src/core/sum.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; +import { strictEqual } from 'assert'; -import v = require('..'); +import { sum } from './sum'; +import { random } from './random'; describe('(v) sum', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc + value, 0), x.sum()); + strictEqual(x.reduce((acc: number, value: number) => acc + value, 0), x.sum()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.reduce((acc: number, value: number) => acc + value, 0), v.sum(x)); + strictEqual(x.reduce((acc: number, value: number) => acc + value, 0), sum(x)); }); }); diff --git a/src/core/sum.ts b/src/core/sum.ts index 9859d8bf..f5d51327 100644 --- a/src/core/sum.ts +++ b/src/core/sum.ts @@ -1,14 +1,38 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.sum = (x: T | ArrayLike): number => NDArray.array(x).sum(); +/** + * @static + * @memberof module:Globals + * @function sum + * @description Sum of `x` + * @param {NDArray} x + * @returns {Number} + * @example + * import { sum } from 'vectorious/core/sum'; + * + * sum([1, 2, 3]); // => 6 + */ +export const sum = (x: NDArray | ArrayLike): number => array(x).sum(); -NDArray.prototype.sum = function(this: T): number { - const { data: d1, length: l1 } = this; +/** + * @function sum + * @memberof NDArray.prototype + * @description Sum of array elements + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).sum(); // => 6 + */ +export default function(this: NDArray): number { + const { data: d1 } = this; + const iter = new NDIter(this); - let i: number; let sum: number = 0; - for (i = 0; i < l1; i += 1) { - sum += d1[i]; + for (const i of iter) { + sum += d1[i!]; } return sum; diff --git a/src/core/swap.bench.ts b/src/core/swap.bench.ts index 9f3a1a9e..8791cdfe 100644 --- a/src/core/swap.bench.ts +++ b/src/core/swap.bench.ts @@ -1,16 +1,15 @@ -import v = require('..'); +import { swap } from './swap'; +import { random } from './random'; import { bench } from '../bench'; -const { floor, random, sqrt } = Math; - bench( - 'v', + 'NDArray', 'swap', - (n: number): [v, number, number] => [v.random(n), floor(random() * sqrt(n)), floor(random() * sqrt(n))], - (x: v, i: number, j: number): void => { + (n: number) => [random(n), Math.floor(Math.random() * Math.sqrt(n)), Math.floor(Math.random() * Math.sqrt(n))], + (x, i: number, j: number): void => { x.swap(i, j); }, - (x: v, i: number, j: number): void => { - v.swap(x, i, j); + (x, i: number, j: number): void => { + swap(x, i, j); } ); diff --git a/src/core/swap.spec.ts b/src/core/swap.spec.ts index d6860482..93ffe6a5 100644 --- a/src/core/swap.spec.ts +++ b/src/core/swap.spec.ts @@ -1,13 +1,14 @@ import { - deepStrictEqual, throws, } from 'assert'; -import v = require('..'); +import { equals } from './equals'; +import { swap } from './swap'; +import { array } from './array'; describe('(v) swap', () => { it('should throw error if index out of bounds', () => { - const x: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2], [3, 4]]); throws(() => { x.swap(-1, 0); }, Error); throws(() => { x.swap(0, -1); }, Error); @@ -16,17 +17,17 @@ describe('(v) swap', () => { }); it('should work as expected', () => { - const x: v = v.array([[1, 2], [3, 4], [5, 6]]); + const x = array([[1, 2], [3, 4], [5, 6]]); x.swap(0, 1); - deepStrictEqual(v.array([[3, 4], [1, 2], [5, 6]]), x); + equals(array([[3, 4], [1, 2], [5, 6]]), x); x.swap(1, 2); - deepStrictEqual(v.array([[3, 4], [5, 6], [1, 2]]), x); + equals(array([[3, 4], [5, 6], [1, 2]]), x); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2], [3, 4], [5, 6]]); + const x = array([[1, 2], [3, 4], [5, 6]]); - deepStrictEqual(v.array([[3, 4], [1, 2], [5, 6]]), v.swap(x, 0, 1)); + equals(array([[3, 4], [1, 2], [5, 6]]), swap(x, 0, 1)); }); }); diff --git a/src/core/swap.ts b/src/core/swap.ts index e730c408..fb47ca96 100644 --- a/src/core/swap.ts +++ b/src/core/swap.ts @@ -1,11 +1,38 @@ import { TypedArray } from '../types'; import { NDArray } from './'; +import { array } from './array'; -NDArray.swap = (x: T | ArrayLike, i: number, j: number): T => - NDArray.array(x).swap(i, j); +/** + * @static + * @memberof module:Globals + * @function swap + * @description Swaps two rows `i` and `j` in `x`. + * @param {NDArray} x + * @param {Number} i + * @param {Number} j + * @returns {NDArray} + * @example + * import { swap } from 'vectorious/core/swap'; + * + * swap([[1, 2], [3, 4]], 0, 1); // => array([[3, 4], [1, 2]]) + */ +export const swap = (x: NDArray | ArrayLike, i: number, j: number): NDArray => + array(x).swap(i, j); -NDArray.prototype.swap = function(this: T, i: number, j: number): T { +/** + * @function swap + * @memberof NDArray.prototype + * @description Swaps two rows `i` and `j` in current matrix + * @param {Number} i + * @param {Number} j + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2], [3, 4]]); // <=> array([[3, 4], [1, 2]]) + */ +export default function(this: NDArray, i: number, j: number): NDArray { this.check(i, 0); this.check(j, 0); diff --git a/src/core/tan.bench.ts b/src/core/tan.bench.ts index 165e8ced..0806378e 100644 --- a/src/core/tan.bench.ts +++ b/src/core/tan.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { tan } from './tan'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'tan', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.tan(); }, - (x: v): void => { - v.tan(x); + (x): void => { + tan(x); } ); diff --git a/src/core/tan.spec.ts b/src/core/tan.spec.ts index 034bbe06..48ae8257 100644 --- a/src/core/tan.spec.ts +++ b/src/core/tan.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { tan } from './tan'; +import { map } from './map'; +import { random } from './random'; describe('(v) tan', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.tan(value)), x.tan()); + equals(map(x, (value: number) => Math.tan(value)), x.tan()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.tan(value)), v.tan(x)); + equals(map(x, (value: number) => Math.tan(value)), tan(x)); }); }); diff --git a/src/core/tan.ts b/src/core/tan.ts index e692f897..8d4938cc 100644 --- a/src/core/tan.ts +++ b/src/core/tan.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.tan = (x: T | ArrayLike): T => NDArray.array(x).tan(); +const { tan: f } = Math; -NDArray.prototype.tan = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function tan + * @description Returns the tangent of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { tan } from 'vectorious/core/tan'; + * + * tan([1, 2, 3]); // => array([1.5574077367782593, -2.185039758682251, -0.14254654943943024]) + */ +export const tan = (x: NDArray | ArrayLike): NDArray => array(x).tan(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.tan(d1[i]); +/** + * @function tan + * @memberof NDArray.prototype + * @description Returns the tangent of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).tan(); // <=> array([1.5574077367782593, -2.185039758682251, -0.14254654943943024]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/tanh.bench.ts b/src/core/tanh.bench.ts index 582bc3cd..10923f24 100644 --- a/src/core/tanh.bench.ts +++ b/src/core/tanh.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { tanh } from './tanh'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'tanh', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.tanh(); }, - (x: v): void => { - v.tanh(x); + (x): void => { + tanh(x); } ); diff --git a/src/core/tanh.spec.ts b/src/core/tanh.spec.ts index 14162fd9..ec0f9118 100644 --- a/src/core/tanh.spec.ts +++ b/src/core/tanh.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { tanh } from './tanh'; +import { map } from './map'; +import { random } from './random'; describe('(v) tanh', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.tanh(value)), x.tanh()); + equals(map(x, (value: number) => Math.tanh(value)), x.tanh()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.tanh(value)), v.tanh(x)); + equals(map(x, (value: number) => Math.tanh(value)), tanh(x)); }); }); diff --git a/src/core/tanh.ts b/src/core/tanh.ts index f00049b4..1e701c68 100644 --- a/src/core/tanh.ts +++ b/src/core/tanh.ts @@ -1,13 +1,39 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.tanh = (x: T | ArrayLike): T => NDArray.array(x).tanh(); +const { tanh: f } = Math; -NDArray.prototype.tanh = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function tanh + * @description Returns the hyperbolic tangent of each element of `x`. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { tanh } from 'vectorious/core/tanh'; + * + * tanh([1, 2, 3]); // => array([0.7615941762924194, 0.9640275835990906, 0.9950547814369202]) + */ +export const tanh = (x: NDArray | ArrayLike): NDArray => array(x).tanh(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.tanh(d1[i]); +/** + * @function tanh + * @memberof NDArray.prototype + * @description Returns the hyperbolic tangent of each element of current array. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).tanh(); // <=> array([0.7615941762924194, 0.9640275835990906, 0.9950547814369202]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/toArray.bench.ts b/src/core/toArray.bench.ts index 38c008d6..c86b367a 100644 --- a/src/core/toArray.bench.ts +++ b/src/core/toArray.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { toArray } from './toArray'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'toArray', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.toArray(); }, - (x: v): void => { - v.toArray(x); + (x): void => { + toArray(x); } ); diff --git a/src/core/toArray.spec.ts b/src/core/toArray.spec.ts index cf22819b..6b404f36 100644 --- a/src/core/toArray.spec.ts +++ b/src/core/toArray.spec.ts @@ -1,24 +1,22 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { array } from './array'; +import { toArray } from './toArray'; describe('(v) toArray', () => { it('should work as expected', () => { - deepStrictEqual([], v.array().toArray()); - deepStrictEqual([0, 1, 2, 3, 4, 5, 6, 7], v.array([0, 1, 2, 3, 4, 5, 6, 7]).toArray()); - deepStrictEqual([[0, 1, 2, 3], [4, 5, 6, 7]], v.array([[0, 1, 2, 3], [4, 5, 6, 7]]).toArray()); - deepStrictEqual([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], v.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).toArray()); + equals([], array().toArray()); + equals([0, 1, 2, 3, 4, 5, 6, 7], array([0, 1, 2, 3, 4, 5, 6, 7]).toArray()); + equals([[0, 1, 2, 3], [4, 5, 6, 7]], array([[0, 1, 2, 3], [4, 5, 6, 7]]).toArray()); + equals([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).toArray()); }); it('should work as the static equivalent', () => { - deepStrictEqual([], v.array().toArray()); - deepStrictEqual([0, 1, 2, 3, 4, 5, 6, 7], v.toArray(v.array([0, 1, 2, 3, 4, 5, 6, 7]))); - deepStrictEqual([[0, 1, 2, 3], [4, 5, 6, 7]], v.toArray(v.array([[0, 1, 2, 3], [4, 5, 6, 7]]))); - deepStrictEqual( + equals([], array().toArray()); + equals([0, 1, 2, 3, 4, 5, 6, 7], toArray(array([0, 1, 2, 3, 4, 5, 6, 7]))); + equals([[0, 1, 2, 3], [4, 5, 6, 7]], toArray(array([[0, 1, 2, 3], [4, 5, 6, 7]]))); + equals( [[[0, 1], [2, 3]], [[4, 5], [6, 7]]], - v.toArray(v.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])) + toArray(array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])) ); }); }); diff --git a/src/core/toArray.ts b/src/core/toArray.ts index 3a1407d5..bfb6d743 100644 --- a/src/core/toArray.ts +++ b/src/core/toArray.ts @@ -1,8 +1,31 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.toArray = (x: T | ArrayLike): number[] => NDArray.array(x).toArray(); +/** + * @static + * @memberof module:Globals + * @function toArray + * @description Converts `x` into a JavaScript array. + * @param {NDArray} x + * @returns {Array} + * @example + * import { toArray } from 'vectorious/core/toArray'; + * + * toArray([1, 2, 3]); // => [1, 2, 3] + */ +export const toArray = (x: NDArray | ArrayLike): number[] => array(x).toArray(); -NDArray.prototype.toArray = function(this: T): number[] { +/** + * @function toArray + * @memberof NDArray.prototype + * @description Converts current vector into a JavaScript array. + * @returns {Array} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).toArray(); // => [1, 2, 3] + */ +export default function(this: NDArray): number[] { const { length: l1, shape: s1 } = this; const { length: ndim } = s1; diff --git a/src/core/toString.bench.ts b/src/core/toString.bench.ts index 8f72aaf4..40ad531b 100644 --- a/src/core/toString.bench.ts +++ b/src/core/toString.bench.ts @@ -1,15 +1,15 @@ -import v = require('..'); +import { toString } from './toString'; +import { random } from './random'; import { bench } from '../bench'; -const r: (n: number) => v = (n: number): v => v.random(n); bench( - 'v', + 'NDArray', 'toString', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.toString(); }, - (x: v): void => { - v.toString(x); + (x): void => { + toString(x); } ); diff --git a/src/core/toString.spec.ts b/src/core/toString.spec.ts index d51fb612..c8008eec 100644 --- a/src/core/toString.spec.ts +++ b/src/core/toString.spec.ts @@ -2,38 +2,39 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { toString } from './toString'; +import { array } from './array'; describe('(v) toString', () => { it('should work as expected', () => { - strictEqual('array([],dtype=float32)', v.array().toString().replace(/\s/g, '')); + strictEqual('array([],dtype=float64)', array().toString().replace(/\s/g, '')); strictEqual( - 'array([0,1,2,3,4,5,6,7],dtype=float32)', - v.array([0, 1, 2, 3, 4, 5, 6, 7]).toString().replace(/\s/g, '') + 'array([0,1,2,3,4,5,6,7],dtype=float64)', + array([0, 1, 2, 3, 4, 5, 6, 7]).toString().replace(/\s/g, '') ); strictEqual( - 'array([[0,1,2,3],[4,5,6,7]],dtype=float32)', - v.array([[0, 1, 2, 3], [4, 5, 6, 7]]).toString().replace(/\s/g, '') + 'array([[0,1,2,3],[4,5,6,7]],dtype=float64)', + array([[0, 1, 2, 3], [4, 5, 6, 7]]).toString().replace(/\s/g, '') ); strictEqual( - 'array([[[0,1],[2,3]],[[4,5],[6,7]]],dtype=float32)', - v.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).toString().replace(/\s/g, '') + 'array([[[0,1],[2,3]],[[4,5],[6,7]]],dtype=float64)', + array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).toString().replace(/\s/g, '') ); }); it('should work as the static equivalent', () => { - strictEqual('array([],dtype=float32)', v.toString(v.array()).replace(/\s/g, '')); + strictEqual('array([],dtype=float64)', toString(array()).replace(/\s/g, '')); strictEqual( - 'array([0,1,2,3,4,5,6,7],dtype=float32)', - v.toString(v.array([0, 1, 2, 3, 4, 5, 6, 7])).replace(/\s/g, '') + 'array([0,1,2,3,4,5,6,7],dtype=float64)', + toString(array([0, 1, 2, 3, 4, 5, 6, 7])).replace(/\s/g, '') ); strictEqual( - 'array([[0,1,2,3],[4,5,6,7]],dtype=float32)', - v.toString(v.array([[0, 1, 2, 3], [4, 5, 6, 7]])).replace(/\s/g, '') + 'array([[0,1,2,3],[4,5,6,7]],dtype=float64)', + toString(array([[0, 1, 2, 3], [4, 5, 6, 7]])).replace(/\s/g, '') ); strictEqual( - 'array([[[0,1],[2,3]],[[4,5],[6,7]]],dtype=float32)', - v.toString(v.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])).replace(/\s/g, '') + 'array([[[0,1],[2,3]],[[4,5],[6,7]]],dtype=float64)', + toString(array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])).replace(/\s/g, '') ); }); }); diff --git a/src/core/toString.ts b/src/core/toString.ts index 726b6b35..77e68e8d 100644 --- a/src/core/toString.ts +++ b/src/core/toString.ts @@ -1,11 +1,32 @@ import { inspect } from 'util'; -import { inspectSymbol, NDArray } from './'; +import { NDArray } from './'; +import { array } from './array'; -NDArray.toString = (x: T | ArrayLike): string => x.toString(); +/** + * @static + * @memberof module:Globals + * @function toString + * @description Converts `x` into a readable formatted string. + * @param {NDArray} x + * @returns {String} + * @example + * import { toString } from 'vectorious/core/toString'; + * + * toString([1, 2, 3]); // => '1,2,3' + */ +export const toString = (x: NDArray | ArrayLike): string => array(x).toString(); -NDArray.prototype.toString = function(this: T): string { +/** + * @function toString + * @memberof NDArray.prototype + * @description Converts current vector into a readable formatted string. + * @returns {String} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).toString(); // => '1,2,3' + */ +export default function (this: NDArray): string { return `array(${inspect(this.toArray(), { depth: 10, breakLength: 40 })}, dtype=${this.dtype})`; }; - -NDArray.prototype[inspectSymbol] = NDArray.prototype.toString; diff --git a/src/core/trace.bench.ts b/src/core/trace.bench.ts index 580a4708..6a37d23f 100644 --- a/src/core/trace.bench.ts +++ b/src/core/trace.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { trace } from './trace'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'trace', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.trace(); }, - (x: v): void => { - v.trace(x); + (x): void => { + trace(x); } ); diff --git a/src/core/trace.spec.ts b/src/core/trace.spec.ts index 59897b1a..9223e0b0 100644 --- a/src/core/trace.spec.ts +++ b/src/core/trace.spec.ts @@ -2,22 +2,23 @@ import { strictEqual, } from 'assert'; -import v = require('..'); +import { trace } from './trace'; +import { array } from './array'; describe('(v) trace', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - const y: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const y = array([[1, 2], [3, 4]]); strictEqual(15, x.trace()); strictEqual(5, y.trace()); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - const y: v = v.array([[1, 2], [3, 4]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const y = array([[1, 2], [3, 4]]); - strictEqual(15, v.trace(x)); - strictEqual(5, v.trace(y)); + strictEqual(15, trace(x)); + strictEqual(5, trace(y)); }); }); diff --git a/src/core/trace.ts b/src/core/trace.ts index 8a5d20e6..4e5c1e76 100644 --- a/src/core/trace.ts +++ b/src/core/trace.ts @@ -1,8 +1,31 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.trace = (x: T | ArrayLike): number => NDArray.array(x).trace(); +/** + * @static + * @memberof module:Globals + * @function trace + * @description Gets the trace of `x` (the sum of all diagonal elements). + * @param {NDArray} x + * @returns {Number} + * @example + * import { trace } from 'vectorious/core/trace'; + * + * trace([[1, 2], [3, 4]]); // => 5 + */ +export const trace = (x: NDArray | ArrayLike): number => array(x).trace(); -NDArray.prototype.trace = function(this: T): number { +/** + * @function trace + * @memberof NDArray.prototype + * @description Gets the trace of the matrix (the sum of all diagonal elements). + * @returns {Number} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1, 2, 3]).trace(); // => 5 + */ +export default function(this: NDArray): number { const [r, c] = this.shape; const { data: d1 } = this; const n: number = Math.min(r, c); diff --git a/src/core/transpose.bench.ts b/src/core/transpose.bench.ts index 7e78ef78..c7c380fa 100644 --- a/src/core/transpose.bench.ts +++ b/src/core/transpose.bench.ts @@ -1,16 +1,17 @@ -import v = require('..'); +import { transpose } from './transpose'; +import { random } from './random'; import { bench } from '../bench'; const { floor, sqrt } = Math; bench( - 'v', + 'NDArray', 'transpose', - (n: number): [v] => [v.random(floor(sqrt(n)), floor(sqrt(n)))], - (x: v): void => { + (n: number) => [random(floor(sqrt(n)), floor(sqrt(n)))], + (x): void => { x.transpose(); }, - (x: v): void => { - v.transpose(x); + (x): void => { + transpose(x); } ); diff --git a/src/core/transpose.spec.ts b/src/core/transpose.spec.ts index e551247d..765128b1 100644 --- a/src/core/transpose.spec.ts +++ b/src/core/transpose.spec.ts @@ -1,36 +1,35 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { array } from './array'; +import { random } from './random'; +import { transpose } from './transpose'; describe('(v) transpose', () => { it('should work as expected', () => { - const x: v = v.array([[1, 2, 3]]); - const y: v = v.array([[1], [2], [3]]); + const x = array([[1, 2, 3]]); + const y = array([[1], [2], [3]]); - deepStrictEqual(x, y.copy().T); - deepStrictEqual(y, x.copy().T); + equals(x, y.copy().T); + equals(y, x.copy().T); }); it('should work as expected', () => { - const x: v = v.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); - const y: v = v.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]); + const x = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); + const y = array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]); - deepStrictEqual(x, y.copy().T); - deepStrictEqual(y, x.copy().T); + equals(x, y.copy().T); + equals(y, x.copy().T); }); it('should work as expected', () => { - const x: v = v.random(2, 2); - const y: v = x.copy().T.T; + const x = random(2, 2); + const y = x.copy().T.T; - deepStrictEqual(x, y); + equals(x, y); }); it('should work as the static equivalent', () => { - const x: v = v.array([[1, 1, 1]]); + const x = array([[1, 1, 1]]); - deepStrictEqual(x.copy().T, v.transpose(x)); + equals(x.copy().T, transpose(x)); }); }); diff --git a/src/core/transpose.ts b/src/core/transpose.ts index de642bf0..3877ddb8 100644 --- a/src/core/transpose.ts +++ b/src/core/transpose.ts @@ -1,21 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; -NDArray.transpose = (x: T | ArrayLike): T => - NDArray.array(x).transpose(); +/** + * @static + * @memberof module:Globals + * @function transpose + * @description Transposes `x` (mirror across the diagonal). + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { transpose } from 'vectorious/core/transpose'; + * + * transpose([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); // => array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]) + */ +export const transpose = (x: NDArray | ArrayLike): NDArray => + array(x).transpose(); -NDArray.prototype.transpose = function(this: T): T { - const [r, c] = this.shape; - const { data: d1 } = this; - const x: T = this.copy().reshape(c, r); - const { data: d2 } = x; - - let i: number; - let j: number; - for (i = 0; i < r; i += 1) { - for (j = 0; j < c; j += 1) { - d2[j * r + i] = d1[i * c + j]; - } +/** + * @function transpose + * @memberof NDArray.prototype + * @description Transposes current matrix (mirror across the diagonal). + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]); // <=> array([[1, 4, 7], [2, 5, 8], [3, 6, 9]]) + */ +export default function(this: NDArray): NDArray { + if (this.shape.length < 2) { + return this; } - return x; + let tmp = this.shape[0]; + this.shape[0] = this.shape[1]; + this.shape[1] = tmp; + + tmp = this.strides[0]; + this.strides[0] = this.strides[1]; + this.strides[1] = tmp; + + return this; }; diff --git a/src/core/trunc.bench.ts b/src/core/trunc.bench.ts index c1c19a48..3369be3c 100644 --- a/src/core/trunc.bench.ts +++ b/src/core/trunc.bench.ts @@ -1,14 +1,15 @@ -import v = require('..'); +import { trunc } from './trunc'; +import { random } from './random'; import { bench } from '../bench'; bench( - 'v', + 'NDArray', 'trunc', - (n: number): [v] => [v.random(n)], - (x: v): void => { + (n: number) => [random(n)], + (x): void => { x.trunc(); }, - (x: v): void => { - v.trunc(x); + (x): void => { + trunc(x); } ); diff --git a/src/core/trunc.spec.ts b/src/core/trunc.spec.ts index 40273927..cb84504e 100644 --- a/src/core/trunc.spec.ts +++ b/src/core/trunc.spec.ts @@ -1,19 +1,18 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { trunc } from './trunc'; +import { map } from './map'; +import { random } from './random'; describe('(v) trunc', () => { it('should work as expected', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.trunc(value)), x.trunc()); + equals(map(x, (value: number) => Math.trunc(value)), x.trunc()); }); it('should work as the static equivalent', () => { - const x: v = v.random(3); + const x = random(3); - deepStrictEqual(x.map((value: number) => Math.trunc(value)), v.trunc(x)); + equals(map(x, (value: number) => Math.trunc(value)), trunc(x)); }); }); diff --git a/src/core/trunc.ts b/src/core/trunc.ts index fc89d61d..e357a1ab 100644 --- a/src/core/trunc.ts +++ b/src/core/trunc.ts @@ -1,13 +1,43 @@ import { NDArray } from './'; +import { array } from './array'; +import { NDIter } from '../iterator'; -NDArray.trunc = (x: T | ArrayLike): T => NDArray.array(x).trunc(); +const { trunc: f } = Math; -NDArray.prototype.trunc = function(this: T): T { - const { data: d1, length: l1 } = this; +/** + * @static + * @memberof module:Globals + * @function trunc + * @description + * Returns the integer part of each element of `x`, + * removing any fractional digits. + * @param {NDArray} x + * @returns {NDArray} + * @example + * import { trunc } from 'vectorious/core/trunc'; + * + * trunc([1.2, 2.8, 3.5]); // => array([1, 2, 3]) + */ +export const trunc = (x: NDArray | ArrayLike): NDArray => array(x).trunc(); - let i: number; - for (i = 0; i < l1; i += 1) { - d1[i] = Math.trunc(d1[i]); +/** + * @function trunc + * @memberof NDArray.prototype + * @description + * Returns the integer part of each element of current array, + * removing any fractional digits. + * @returns {this} + * @example + * import { array } from 'vectorious/core/array'; + * + * array([1.2, 2.8, 3.5]).trunc(); // => array([1, 2, 3]) + */ +export default function(this: NDArray): NDArray { + const { data: d1 } = this; + const iter = new NDIter(this); + + for (const i of iter) { + d1[i!] = f(d1[i!]); } return this; diff --git a/src/core/zeros.bench.ts b/src/core/zeros.bench.ts index 943a7b66..ec240cb6 100644 --- a/src/core/zeros.bench.ts +++ b/src/core/zeros.bench.ts @@ -1,10 +1,11 @@ -import v = require('..'); +import { zeros } from './zeros'; import { bench } from '../bench'; + bench( - 'v', + 'NDArray', 'zeros', (n: number): [number] => [n], (n: number): void => { - v.zeros(n); + zeros(n); } ); diff --git a/src/core/zeros.spec.ts b/src/core/zeros.spec.ts index 7c00ee52..c3a92cb5 100644 --- a/src/core/zeros.spec.ts +++ b/src/core/zeros.spec.ts @@ -1,19 +1,17 @@ -import { - deepStrictEqual, -} from 'assert'; - -import v = require('..'); +import { equals } from './equals'; +import { zeros } from './zeros'; +import { array } from './array'; describe('(v) zeros', () => { it('should work as expected', () => { - const x: v = v.array([0, 0, 0]); + const x = array([0, 0, 0]); - deepStrictEqual(x, v.zeros(3)); + equals(x, zeros(3)); }); it('should work as expected in two dimensions', () => { - const x: v = v.array([[0, 0], [0, 0]]); + const x = array([[0, 0], [0, 0]]); - deepStrictEqual(x, v.zeros(2, 2)); + equals(x, zeros(2, 2)); }); }); diff --git a/src/core/zeros.ts b/src/core/zeros.ts index 540657db..cc162564 100644 --- a/src/core/zeros.ts +++ b/src/core/zeros.ts @@ -1,8 +1,19 @@ import { NDArray } from './'; -NDArray.zeros = function(this: new(...args: any[]) => T, ...shape: number[]): T { - return new this( - new Float32Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), +/** + * @static + * @memberof module:Globals + * @function zeros + * @description Creates an array containing zeros (`0`) of shape `shape` + * @param {Number[]} ...shape + * @returns {NDArray} + * @example + * import { zeros } from 'vectorious/core/zeros'; + * + * zeros(3); // => array([0, 0, 0]) + */ +export const zeros = (...shape: number[]): NDArray => + new NDArray( + new Float64Array(shape.reduce((sum: number, dim: number) => sum * dim, 1)), { shape } ).fill(0); -}; diff --git a/src/index.ts b/src/index.ts index b265f86c..0fa22477 100644 --- a/src/index.ts +++ b/src/index.ts @@ -1,3 +1,5 @@ -import { NDArray } from './core'; - -export = NDArray; +/** + * @module Globals + */ +export * from './core'; +export * from './iterator'; diff --git a/src/iterator/index.spec.ts b/src/iterator/index.spec.ts new file mode 100644 index 00000000..bdf6f32f --- /dev/null +++ b/src/iterator/index.spec.ts @@ -0,0 +1,122 @@ +import { + deepStrictEqual, strictEqual, +} from 'assert'; + +import { array } from '../core/array'; +import { NDIter } from '.'; +import { NDMultiIter } from '.'; + +describe('(NDIter) constructor', () => { + it('should work as expected', () => { + const x = array([0, 1, 2]); + const iter = new NDIter(x); + + deepStrictEqual(iter.next(), { + value: 0, + done: false, + }); + deepStrictEqual(iter.next(), { + value: 1, + done: false, + }); + deepStrictEqual(iter.next(), { + value: 2, + done: false, + }); + deepStrictEqual(iter.next(), { + value: undefined, + done: true, + }); + }); + + it('should work as expected in two dimensions', () => { + const x = array([[0, 1], [2, 3]]); + const iter = new NDIter(x); + + deepStrictEqual(iter.next(), { + value: 0, + done: false, + }); + deepStrictEqual(iter.next(), { + value: 1, + done: false, + }); + deepStrictEqual(iter.next(), { + value: 2, + done: false, + }); + deepStrictEqual(iter.next(), { + value: 3, + done: false, + }); + deepStrictEqual(iter.next(), { + value: undefined, + done: true, + }); + }); + + it('should work in for...of loops', () => { + const x = array([[0, 1], [2, 3]]); + const iter = new NDIter(x); + + let i = 0; + for (const value of iter) { + strictEqual(value, i++); + } + }); + + it('should work in do...while loops', () => { + const x = array([[0, 1], [2, 3]]); + const iter = new NDIter(x); + + let i = 0; + do { + strictEqual(iter.next().value, i++); + } while (!iter.done()); + }); +}); + +describe('(NDMultiIter) constructor', () => { + it('should work as expected', () => { + const x = array([0, 1, 2, 3]); + const y = array([4, 5, 6, 7]); + + const iter = new NDMultiIter(x, y); + + deepStrictEqual(iter.next(), { + value: [0, 0], + done: false, + }); + deepStrictEqual(iter.next(), { + value: [1, 1], + done: false, + }); + deepStrictEqual(iter.next(), { + value: [2, 2], + done: false, + }); + deepStrictEqual(iter.next(), { + value: [3, 3], + done: false, + }); + deepStrictEqual(iter.next(), { + value: undefined, + done: true, + }); + }); + + it('should work with mixed input', () => { + const x = array([[0, 1], [2, 3]]); + const y = array([1, 2]); + const z = array([[1, 3], [3, 5]]); + + const iter = new NDMultiIter(x, y); + + do { + const [i, j] = iter.next().value; + x.data[i] += y.data[j]; + } while (!iter.done()); + + deepStrictEqual(z, x); + }); +}); diff --git a/src/iterator/index.ts b/src/iterator/index.ts new file mode 100644 index 00000000..cf17eb90 --- /dev/null +++ b/src/iterator/index.ts @@ -0,0 +1,539 @@ +import { NDArray } from '../'; +import { array } from '../core/array'; + +export const V_MAXDIMS = 32; + +/** + * @class NDIter + * @description Constructs an NDIter instance. + * @param {NDArray} x + */ +export class NDIter { + /** + * @name x + * @memberof NDIter.prototype + * @type NDArray + */ + public x: NDArray; + + /** + * @name shape + * @memberof NDIter.prototype + * @type Number[] + */ + public shape: number[]; + + /** + * @name shapem1 + * @memberof NDIter.prototype + * @type Number[] + */ + public shapem1: number[]; + + /** + * @name strides + * @memberof NDIter.prototype + * @type Number[] + */ + public strides: number[]; + + /** + * @name backstrides + * @memberof NDIter.prototype + * @type Number[] + */ + public backstrides: number[]; + + /** + * @name length + * @memberof NDIter.prototype + * @type Number + */ + public length: number; + + /** + * @name lengthm1 + * @memberof NDIter.prototype + * @type Number + */ + public lengthm1: number; + + /** + * @name nd + * @memberof NDIter.prototype + * @type Number + */ + public nd: number; + + /** + * @name ndm1 + * @memberof NDIter.prototype + * @type Number + */ + public ndm1: number; + + /** + * @name index + * @memberof NDIter.prototype + * @type Number + */ + public index: number; + + /** + * @name coords + * @memberof NDIter.prototype + * @type Number[] + */ + public coords: number[]; + + /** + * @name pos + * @memberof NDIter.prototype + * @type Number + */ + public pos: number; + + /** + * @name factors + * @memberof NDIter.prototype + * @type Number[] + */ + public factors: number[]; + + /** + * @name contiguous + * @memberof NDIter.prototype + * @type Boolean + */ + public contiguous: boolean; + + constructor(x: NDArray | ArrayLike) { + const { + shape, + strides, + length, + } = array(x); + + this.x = array(x); + this.length = length; + this.lengthm1 = length - 1; + this.nd = shape.length; + this.ndm1 = this.nd - 1; + + this.shape = Array(V_MAXDIMS).fill(0); + this.strides = Array(V_MAXDIMS).fill(0); + this.shapem1 = Array(V_MAXDIMS).fill(0); + this.coords = Array(V_MAXDIMS).fill(0); + this.backstrides = Array(V_MAXDIMS).fill(0); + this.factors = Array(V_MAXDIMS).fill(0); + + if (this.nd !== 0) { + this.factors[this.nd - 1] = 1; + } + + this.contiguous = true; + + let stride = 1; + let i; + for (i = 0; i < this.nd; i += 1) { + this.shape[i] = shape[i]; + this.shapem1[i] = shape[i] - 1; + this.strides[i] = strides[i]; + this.backstrides[i] = strides[i] * this.shapem1[i]; + this.coords[i] = 0; + + // Check if C-contiguous + if (shape[this.nd - i - 1] !== 1) { + if (strides[i] !== stride) { + this.contiguous = false; + } + + stride *= shape[this.nd - i - 1]; + } + + if (i > 0) { + this.factors[this.nd - i - 1] = this.factors[this.nd - i] * shape[this.nd - i]; + } + } + + this.index = 0; + this.pos = 0; + } + + /** + * @function done + * @memberof NDIter.prototype + * @description Returns true if the iterator is done, false otherwise + * @returns {Boolean} + * @example + * import { array } from 'vectorious/core/array'; + * import { NDIter } from 'vectorious/iterator'; + * + * const iter = new NDIter(array([1, 2, 3])); + * iter.done(); // false + */ + done() { + return this.index > this.lengthm1; + } + + /** + * @function current + * @memberof NDIter.prototype + * @description Returns the current element of the iterator + * @returns {Object} current + * @returns {Number} [current.value] + * @returns {Boolean} current.done + * @example + * import { array } from 'vectorious/core/array'; + * import { NDIter } from 'vectorious/iterator'; + * + * const iter = new NDIter(array([1, 2, 3])); + * iter.current(); // { value: 1, done: false } + */ + current() { + const done = this.done(); + return { + value: done ? undefined : this.pos, + done, + } + } + + /** + * @function next1d + * @memberof NDIter.prototype + * @description Steps to the next position in the iterator, assuming it is 1 dimensional. + */ + next1d() { + const { + strides, + coords, + } = this; + + this.pos += strides[0]; + coords[0] += 1; + } + + /** + * @function nextcontiguous + * @memberof NDIter.prototype + * @description Steps to the next position in the iterator, assuming its data is contiguous. + */ + nextcontiguous() { + this.pos += 1; + } + + /** + * @function next2d + * @memberof NDIter.prototype + * @description Steps to the next position in the iterator, assuming it is 2 dimensional. + */ + next2d() { + const { + coords, + strides, + shapem1, + backstrides, + } = this; + + if (coords[1] < shapem1[1]) { + coords[1] += 1; + this.pos += strides[1]; + } else { + coords[1] = 0; + coords[0] += 1; + this.pos += strides[0] - backstrides[1]; + } + } + + /** + * @function next2d + * @memberof NDIter.prototype + * @description Steps to the next position in the iterator + */ + nextnd() { + const { + ndm1, + shapem1, + coords, + strides, + backstrides, + } = this; + + let i; + for (i = ndm1; i >= 0; i -= 1) { + if (coords[i] < shapem1[i]) { + coords[i] += 1; + this.pos += strides[i]; + break; + } else { + coords[i] = 0; + this.pos -= backstrides[i]; + } + } + } + + /** + * @function next + * @memberof NDIter.prototype + * @description + * Steps to the next position in the iterator. + * Returns the current index of the iterator, or undefined if done. + * @returns {Object} + * @example + * import { array } from 'vectorious/core/array'; + * import { NDIter } from 'vectorious/iterator'; + * + * const iter = new NDIter(array([1, 2, 3])); + * iter.next(); // { value: 2, done: false } + * iter.next(); // { value: 3, done: false } + * iter.next(); // { done: true } + */ + next() { + const current = this.current(); + + this.index += 1; + + const { + ndm1, + contiguous, + } = this; + + if (ndm1 === 0) { + this.next1d(); + } else if (contiguous) { + this.nextcontiguous(); + } else if (ndm1 === 1) { + this.next2d(); + } else { + this.nextnd(); + } + + return current; + } + + [Symbol.iterator]() { + return this; + } +} + +/** + * @class NDMultiIter + * @description Constructs an NDMultiIter instance. + * @param {NDArray[]} ...args + */ +export class NDMultiIter { + /** + * @name iters + * @memberof NDMultiIter.prototype + * @type NDIter[] + */ + public iters: NDIter[]; + + /** + * @name shape + * @memberof NDMultiIter.prototype + * @type Number[] + */ + public shape: number[]; + + /** + * @name nd + * @memberof NDMultiIter.prototype + * @type Number + */ + public nd: number; + + /** + * @name length + * @memberof NDMultiIter.prototype + * @type Number + */ + public length: number; + + /** + * @name lengthm1 + * @memberof NDMultiIter.prototype + * @type Number + */ + public lengthm1: number; + + /** + * @name numiter + * @memberof NDMultiIter.prototype + * @type Number + */ + public numiter: number; + + /** + * @name index + * @memberof NDMultiIter.prototype + * @type Number + */ + public index: number; + + /** + * @name pos + * @memberof NDMultiIter.prototype + * @type Number[] + */ + public pos: number[]; + + constructor(...args: (NDArray | ArrayLike)[]) { + this.iters = args.map(arg => new NDIter(arg)); + this.numiter = args.length; + + let i; + let nd; + for (i = 0, nd = 0; i < this.numiter; i += 1) { + nd = Math.max(nd, this.iters[i].x.shape.length); + } + + this.nd = nd; + this.shape = Array(nd).fill(0); + + let it; + let j; + let k; + let tmp; + for (i = 0; i < nd; i += 1) { + this.shape[i] = 1; + for (j = 0; j < this.numiter; j += 1) { + it = this.iters[j]; + k = i + it.x.shape.length - nd; + if (k >= 0) { + tmp = it.x.shape[k]; + if (tmp == 1) { + continue; + } + if (this.shape[i] == 1) { + this.shape[i] = tmp; + + } else if (this.shape[i] !== tmp) { + throw new Error('shape mismatch'); + } + } + } + } + + tmp = this.shape.reduce((acc, dim) => acc * dim, 1); + + this.length = tmp; + this.lengthm1 = tmp - 1; + + for (i = 0; i < this.numiter; i += 1) { + it = this.iters[i]; + it.nd = this.nd; + it.ndm1 = this.nd - 1; + it.length = tmp; + it.lengthm1 = tmp - 1; + + nd = it.x.shape.length; + if (nd !== 0) { + it.factors[this.nd - 1] = 1; + } + + for (j = 0; j < this.nd; j += 1) { + it.shape[j] = this.shape[j]; + it.shapem1[j] = this.shape[j] - 1; + k = j + nd - this.nd; + + if ((k < 0) || it.x.shape[k] !== this.shape[j]) { + it.contiguous = false; + it.strides[j] = 0; + } else { + it.strides[j] = it.x.strides[k]; + } + + it.backstrides[j] = it.strides[j] * it.shapem1[j]; + + if (j > 0) { + it.factors[this.nd - j - 1] = it.factors[this.nd - j] * this.shape[this.nd - j] + } + } + } + + this.index = 0; + this.pos = Array(this.numiter).fill(0); + } + + /** + * @function done + * @memberof NDMultiIter + * @description Returns true if the iterator is done, false otherwise + * @returns {Boolean} + * @example + * import { array } from 'vectorious/core/array'; + * import { NDMultiIter } from 'vectorious/iterator'; + * + * const iter = new NDMultiIter(array([1, 2, 3]), array([4, 5, 6])); + * iter.done(); // false + */ + done() { + return this.index > this.lengthm1; + } + + /** + * @function current + * @memberof NDMultiIter + * @description Returns the current indices of the iterators + * @returns {Object} current + * @returns {Number[]} [current.value] + * @returns {Boolean} current.done + * @example + * import { array } from 'vectorious/core/array'; + * import { NDMultiIter } from 'vectorious/iterator'; + * + * const iter = new NDMultiIter(array([1, 2, 3]), array([4, 5, 6])); + * iter.current(); // { value: [0, 0], done: false } + */ + current() { + const done = this.done(); + return { + value: done ? undefined : this.pos, + done, + } + } + + /** + * @function next + * @memberof NDMultiIter + * @description + * Steps to the next position in the iterator. + * Returns the current indices of the iterators, or undefined if done. + * @returns {Object} current + * @returns {Number[]} [current.value] + * @returns {Boolean} current.done + * @example + * import { array } from 'vectorious/core/array'; + * import { NDMultiIter } from 'vectorious/iterator'; + * + * const iter = new NDMultiIter(array([1, 2, 3]), array([4, 5, 6])); + * iter.next(); // { value: [0, 0], done: false } + * iter.next(); // { value: [1, 1], done: false } + * iter.next(); // { value: [2, 2], done: false }, + * iter.next(); // { value: undefined, done: true }, + */ + next() { + const current = this.current(); + + this.index += 1; + + const { + numiter, + } = this; + + let it; + let i; + for (i = 0; i < numiter; i += 1) { + it = this.iters[i]; + this.pos[i] = it.pos; + it.next(); + } + + return current; + } + + [Symbol.iterator]() { + return this; + } +} diff --git a/src/lapack.ts b/src/lapack.ts new file mode 100644 index 00000000..e5ed7b3d --- /dev/null +++ b/src/lapack.ts @@ -0,0 +1,54 @@ +import { + TypedArray, + DType, +} from './types'; + +let nlapack: any; +try { + nlapack = require('nlapack'); +} catch (err) {} + +export function getrf(dtype: DType, m: number, n: number, x: TypedArray, ldx: number, ipiv: Int32Array) { + if (x.length !== m * n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nlapack.dgetrf(m, n, x, ldx, ipiv); + case 'float32': + return nlapack.sgetrf(m, n, x, ldx, ipiv); + default: + throw new Error('wrong dtype'); + } +} + +export function getri(dtype: DType, n: number, x: TypedArray, ldx: number, ipiv: Int32Array) { + if (x.length !== n * n) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nlapack.dgetri(n, x, ldx, ipiv); + case 'float32': + return nlapack.sgetri(n, x, ldx, ipiv); + default: + throw new Error('wrong dtype'); + } +} + +export function gesv(dtype: DType, n: number, nrhs: number, x: TypedArray, ldx: number, ipiv: Int32Array, y: TypedArray, ldy: number) { + if (x.length !== ldx * n || y.length !== ldy * nrhs) { + throw new Error('lengths do not match'); + } + + switch (dtype) { + case 'float64': + return nlapack.dgesv(n, nrhs, x, ldx, ipiv, y, ldy); + case 'float32': + return nlapack.sgesv(n, nrhs, x, ldx, ipiv, y, ldy); + default: + throw new Error('wrong dtype'); + } +} diff --git a/src/types.ts b/src/types.ts index 7467a2ab..74e64829 100644 --- a/src/types.ts +++ b/src/types.ts @@ -38,4 +38,5 @@ export interface INDArray { dtype: DType; length: number; shape: number[]; + strides: number[]; } diff --git a/src/util.ts b/src/util.ts index 4728cdfa..689a485d 100644 --- a/src/util.ts +++ b/src/util.ts @@ -4,18 +4,41 @@ import { TypedArrayConstructor, } from './types'; -export const flatten: (input: any[]) => number[] = (input: any[]): number[] => - input.reduce( - (acc: any[], next: any) => acc.concat(Array.isArray(next) ? flatten(next) : next), +export const flatten: (array: any[]) => number[] = (array: any[]): number[] => + array.reduce( + (acc: any[], next: any): any[] => acc.concat(Array.isArray(next) ? flatten(next) : next), [] ); -export const get_shape: (input: any) => number[] = (input: any): number[] => Array.isArray(input) - ? [input.length].concat(get_shape(input[0])) +export const is_typed_array: (array: any) => boolean = (array: any): boolean => + ArrayBuffer.isView(array) && !(array instanceof DataView); + +export const get_length: (shape: number[]) => number = (shape: number[]): number => + shape.reduce((a: number, b: number): number => a * b, 1); + +export const get_shape: (array: any) => number[] = (array: any): number[] => Array.isArray(array) || is_typed_array(array) + ? [array.length].concat(get_shape(array[0])) : []; -export const get_dtype: (input: TypedArray) => DType = (input: TypedArray): DType => { - switch (input.constructor.name) { +export const get_strides: (shape: number[]) => number[] = (shape: number[]): number[] => + [ + ...shape + .slice(1) + .map((_: number, i: number): number => shape + .slice(i + 1) + .reduce((a: number, b: number): number => a * b, 1) + ), + 1, +]; + +export const get_dtype: (array: TypedArray) => DType = (array: TypedArray): DType => { + const { + constructor: { + name = 'Float32Array', + } = {}, + } = array || {}; + + switch (name) { case 'Int8Array': return 'int8'; case 'Uint8Array': return 'uint8'; case 'Int16Array': return 'int16'; @@ -23,13 +46,14 @@ export const get_dtype: (input: TypedArray) => DType = (input: TypedArray): DTyp case 'Int32Array': return 'int32'; case 'Uint32Array': return 'uint32'; case 'Uint8ClampedArray': return 'uint8c'; + case 'Float32Array': return 'float32'; case 'Float64Array': return 'float64'; - default: return 'float32'; + default: return 'float64'; } }; -export const get_type: (input: DType) => TypedArrayConstructor = (input: DType): TypedArrayConstructor => { - switch (input) { +export const get_type: (dtype: DType) => TypedArrayConstructor = (dtype: DType): TypedArrayConstructor => { + switch (dtype) { case 'int8': return Int8Array; case 'uint8': return Uint8Array; case 'int16': return Int16Array; @@ -37,10 +61,8 @@ export const get_type: (input: DType) => TypedArrayConstructor = (input: DType): case 'int32': return Int32Array; case 'uint32': return Uint32Array; case 'uint8c': return Uint8ClampedArray; + case 'float32': return Float32Array; case 'float64': return Float64Array; - default: return Float32Array; + default: return Float64Array; } -}; - -export const is_typed_array: (input: any) => boolean = (input: any): boolean => - !!(input && input.buffer instanceof ArrayBuffer && input.BYTES_PER_ELEMENT); +}; \ No newline at end of file diff --git a/tsconfig.json b/tsconfig.json index 086fa269..1dbc2986 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -6,7 +6,7 @@ "downlevelIteration": true, "strict": true, "declaration": true, - "removeComments": true, + "removeComments": false, "esModuleInterop": true, "allowSyntheticDefaultImports": true, "forceConsistentCasingInFileNames": true, @@ -15,7 +15,7 @@ "noUnusedLocals": true, "noUnusedParameters": true, "lib": [ - "es5", + "es2015", "dom" ], "types": [ @@ -29,6 +29,6 @@ "exclude": [ "src/**/*.spec.ts", "src/**/*.bench.ts", - "bench.ts" + "src/bench.ts" ] } diff --git a/tslint.json b/tslint.json deleted file mode 100644 index aa51ab38..00000000 --- a/tslint.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "defaultSeverity": "error", - "extends": [ - "tslint:all" - ], - "jsRules": {}, - "rules": { - "quotemark": [true, "single"], - "trailing-comma": [ - true, - { - "multiline": { - "objects": "always", - "arrays": "always", - "functions": "never", - "typeLiterals": "ignore" - }, - "esSpecCompliant": true - } - ], - "variable-name": false, - "no-unused-expression": false, - "no-empty": false, - "no-var-requires": false, - "file-name-casing": [false, "pascal-case"], - "completed-docs": false, - "no-magic-numbers": false, - "no-inferrable-types": false, - "no-any": false, - "no-require-imports": false, - "no-this-assignment": false, - "no-implicit-dependencies": false, - "newline-per-chained-call": false, - "no-unsafe-any": false, - "no-import-side-effect": false - }, - "rulesDirectory": [] -}