09ex_Numba.ipynb

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    "## Exercise 1\n",
    "\n",
    "Create a vectorized version of log and exp math function for 1D array A = [2, 5, 10, 3, 8]\n",
    "\n",
    "Results should be: \n",
    "+ [0.6931472 1.609438  2.3025851 1.0986123 2.0794415]\n",
    "+ [7.3890562e+00 1.4841316e+02 2.2026465e+04 2.0085537e+01 2.9809580e+03]"
   ]
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   "source": [
    "## Exerice 2\n",
    "Compute the value of a Gaussian probability density function at $x$ with $mean = 1$, $\\sigma = 1$, lower and upper bound in $(-3, 3)$ and $size = 100000$"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "## Exercise 3\n",
    "\n",
    "Create a \"zero suppression\" function. A common operation when working with waveforms is to force all samples values below a certain absolute magnitude to be zero, as a way to eliminate low amplitude noise. \n",
    "Plot the data before and after the application of the zero_suppress function.\n",
    "\n",
    "$thresold = 15$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "n = 100000\n",
    "noise = np.random.normal(size=n) * 3\n",
    "pulses = np.maximum(np.sin(np.arange(n) / (n / 23)) - 0.3, 0.0)\n",
    "data = ((pulses * 300) + noise).astype(np.int16)\n",
    "\n",
    "#put your code here\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "\n",
    "Calculate the Sigmoid kernel between the matrix X and Y defined as below. The Sigmoid kernel is defined as:\n",
    "\n",
    "$k(x,y) = \\tanh(\\alpha x^T y + c) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.rand(3,3)\n",
    "Y = np.random.rand(3,3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 5\n",
    "\n",
    "Create a kernel function similar to the ```double_kernel``` see during the lecture and create a new function that takes a 3 dimensional matrix as input calculating the $cos$ for each element and then returns the result. The shape of the matrix must be $256X256X256$. The matrix can be randomly generated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 6\n",
    "\n",
    "Create a matrix multiplication kernel function, called ```matmul``` that takes as input two 2D matrices:\n",
    "+ A of shape $24x12$\n",
    "+ B of shape $12x22$\n",
    "and that computes the multiplication and put the results into a third matrix C of shape $24x12$\n",
    "\n",
    "A and B must be randomly generated and only int values are allowed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numba import cuda\n",
    "import numpy\n",
    "import math\n",
    "\n",
    "# complete the code\n",
    "@cuda.jit\n",
    "def matmul(A, B, C):\n",
    "    \"\"\"Perform matrix multiplication of C = A * B\n",
    "    \"\"\"\n",
    "    row, col = cuda.grid(2)\n",
    "    if row < C.shape[0] and col < C.shape[1]:\n",
    "        tmp = 0.\n",
    "        for k in range(A.shape[1]):\n",
    "            tmp += A[row, k] * B[k, col]\n",
    "        C[row, col] = tmp\n",
    "\n",
    "# Initialize the data arrays\n",
    "A = \n",
    "B = \n",
    "\n",
    "# Configure the blocks\n",
    "threadsperblock = \n",
    "blockspergrid_x = \n",
    "blockspergrid_y = \n",
    "blockspergrid = \n",
    "\n"
   ]
  }
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