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revert apache#15309 apache#15256 apache#15219 apache#15161
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@haojin2
haojin2 committed Aug 2, 2019
1 parent 96a0cbb commit 1136e00
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48 changes: 0 additions & 48 deletions python/mxnet/_numpy_op_doc.py
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Expand Up @@ -114,54 +114,6 @@ def _np_repeat(a, repeats, axis=None):
pass


def _np_cumsum(a, axis=None, dtype=None, out=None):
"""cumsum(a, axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along a given axis.
Parameters
----------
a : ndarray
Input array.
axis : int, optional
Axis along which the cumulative sum is computed. The default
(None) is to compute the cumsum over the flattened array.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults
to the dtype of `a`.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape, type and buffer length as the expected output.
Returns
-------
cumsum_along_axis : ndarray.
A new array holding the result is returned unless `out` is
specified, in which case a reference to `out` is returned. The
result has the same size as `a`, and the same shape as `a` if
`axis` is not None or `a` is a 1-d array.
Examples
--------
>>> a = np.array([[1,2,3], [4,5,6]])
>>> a
array([[1., 2., 3.],
[4., 5., 6.]])
>>> np.cumsum(a)
array([ 1., 3., 6., 10., 15., 21.])
>>> np.cumsum(a, dtype=float)
array([ 1., 3., 6., 10., 15., 21.], dtype=float64)
>>> np.cumsum(a,axis=0)
array([[1., 2., 3.],
[5., 7., 9.]])
>>> np.cumsum(a,axis=1)
array([[ 1., 3., 6.],
[ 4., 9., 15.]])
"""
pass


def _np_dot(a, b, out=None):
"""dot(a, b, out=None)
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63 changes: 1 addition & 62 deletions python/mxnet/ndarray/numpy/_op.py
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Expand Up @@ -28,7 +28,7 @@

__all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange', 'argmax',
'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace',
'clip', 'split', 'swapaxes', 'expand_dims', 'tile',
'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt', 'abs', 'exp', 'arctan']


Expand Down Expand Up @@ -649,67 +649,6 @@ def tile(A, reps):
return _npi.tile(A, reps)


@set_module('mxnet.ndarray.numpy')
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs): # pylint: disable=too-many-arguments
"""Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless endpoint is set to False. In
that case, the sequence consists of all but the last of num + 1
evenly spaced samples, so that stop is excluded. Note that the step
size changes when endpoint is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, stop is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (samples, step), where step is the spacing between samples.
dtype : dtype, optional
The type of the output array. If dtype is not given, infer the data
type from the other input arguments.
axis : int, optional
The axis in the result to store the samples. Relevant only if start or
stop are array-like. By default (0), the samples will be along a new
axis inserted at the beginning. Use -1 to get an axis at the end.
Returns
-------
samples : ndarray
There are num equally spaced samples in the closed interval
`[start, stop]` or the half-open interval `[start, stop)`
(depending on whether endpoint is True or False).
step : float, optional
Only returned if retstep is True
Size of spacing between samples.
Notes
-----
This function currently does not support ``start`` and ``stop`` as ndarrays and
axis could only be 0 now.
"""
if isinstance(start, (list, _np.ndarray, NDArray)) or \
isinstance(stop, (list, _np.ndarray, NDArray)):
raise NotImplementedError('start and stop only support int')
if axis != 0:
raise NotImplementedError("the function only support axis 0")
ctx = kwargs.pop('ctx', current_context())
if ctx is None:
ctx = current_context()
if retstep:
step = (stop - start) / (num - 1)
return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step
else:
return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)


def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
"""Helper function for unary operators.
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54 changes: 1 addition & 53 deletions python/mxnet/ndarray/numpy/random.py
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Expand Up @@ -23,7 +23,7 @@
from ..ndarray import NDArray
from . import _internal as _npi

__all__ = ['uniform', 'normal', 'multinomial']
__all__ = ['uniform', 'normal']


def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
Expand Down Expand Up @@ -137,55 +137,3 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
out = kwargs.pop('out', None)
return _random_helper(_npi.random_normal, None,
[loc, scale], size, dtype, ctx, out, kwargs)


def multinomial(n, pvals, size=None):
"""multinomial(n, pvals, size=None)
Draw samples from a multinomial distribution.
The multinomial distribution is a multivariate generalisation of the binomial distribution.
Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
Parameters
----------
n : int
Number of experiments.
pvals : sequence of floats, length p
Probabilities of each of the p different outcomes. These should sum to 1.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples
are drawn. Default is None, in which case a single value is returned.
Returns
-------
out : ndarray
The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
Examples
--------
Throw a dice 1000 times, and 1000 times again:
>>> np.random.multinomial(1000, [1/6.]*6, size=2)
array([[164, 161, 179, 158, 150, 188],
[178, 162, 177, 143, 163, 177]])
A loaded die is more likely to land on number 6:
>>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
array([19, 14, 12, 11, 21, 23])
>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
array([32, 68])
"""
if isinstance(pvals, NDArray):
return _npi.multinomial(pvals, pvals=None, n=n, size=size)
else:
if isinstance(pvals, np.ndarray):
pvals = pvals.tolist()
if any(isinstance(i, list) for i in pvals):
raise ValueError('object too deep for desired array')
return _npi.multinomial(n=n, pvals=pvals, size=size)
47 changes: 2 additions & 45 deletions python/mxnet/numpy/multiarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@

__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange',
'argmax', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace', 'sin', 'cos',
'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'sin', 'cos',
'sinh', 'cosh', 'log10', 'sqrt', 'abs', 'exp', 'arctan']


Expand Down Expand Up @@ -936,7 +936,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False): # pylin

def cumsum(self, axis=None, dtype=None, out=None):
"""Return the cumulative sum of the elements along the given axis."""
return _mx_np_op.cumsum(self, axis=axis, dtype=dtype, out=out)
raise NotImplementedError

def tolist(self):
return self.asnumpy().tolist()
Expand Down Expand Up @@ -1871,49 +1871,6 @@ def tile(A, reps):
return _npi.tile(A, reps)


@set_module('mxnet.numpy')
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs):
"""Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless endpoint is set to False. In
that case, the sequence consists of all but the last of num + 1
evenly spaced samples, so that stop is excluded. Note that the step
size changes when endpoint is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, stop is the last sample. Otherwise, it is not included.
Default is True.
retstep: bool, optional
If True, return (samples, step), where step is the spacing between samples.
dtype: dtype, optional
The type of the output array. If dtype is not given, infer the data
type from the other input arguments.
axis : int, optional
The axis in the result to store the samples. Relevant only if start or
stop are array-like. By default (0), the samples will be along a new
axis inserted at the beginning. Use -1 to get an axis at the end.
Returns
-------
samples : ndarray
There are num equally spaced samples in the closed interval
`[start, stop]` or the half-open interval `[start, stop)`
(depending on whether endpoint is True or False).
step : float, optional
Only returned if retstep is True
Size of spacing between samples.
"""
return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, **kwargs)


@set_module('mxnet.numpy')
def sin(x, out=None, **kwargs):
r"""Trigonometric sine, element-wise.
Expand Down
45 changes: 0 additions & 45 deletions python/mxnet/numpy/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,48 +98,3 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
This function currently does not support ``loc`` and ``scale`` as ndarrays.
"""
return _mx_nd_np.random.normal(loc, scale, size, **kwargs)


def multinomial(n, pvals, size=None, **kwargs):
"""multinomial(n, pvals, size=None)
Draw samples from a multinomial distribution.
The multinomial distribution is a multivariate generalisation of the binomial distribution.
Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
Parameters
----------
n : int
Number of experiments.
pvals : sequence of floats, length p
Probabilities of each of the p different outcomes. These should sum to 1.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples
are drawn. Default is None, in which case a single value is returned.
Returns
-------
out : ndarray
The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
Examples
--------
Throw a dice 1000 times, and 1000 times again:
>>> np.random.multinomial(1000, [1/6.]*6, size=2)
array([[164, 161, 179, 158, 150, 188],
[178, 162, 177, 143, 163, 177]])
A loaded die is more likely to land on number 6:
>>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
array([19, 14, 12, 11, 21, 23])
>>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
array([32, 68])
"""
return _mx_nd_np.random.multinomial(n, pvals, size, **kwargs)
64 changes: 2 additions & 62 deletions python/mxnet/symbol/numpy/_symbol.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@

__all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'concatenate', 'arange', 'argmax',
'clip', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'split', 'swapaxes',
'expand_dims', 'tile', 'linspace', 'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt',
'expand_dims', 'tile', 'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt',
'abs', 'exp', 'arctan']


Expand Down Expand Up @@ -538,7 +538,7 @@ def mean(self, axis=None, dtype=None, out=None, keepdims=False): # pylint: disa

def cumsum(self, axis=None, dtype=None, out=None):
"""Return the cumulative sum of the elements along the given axis."""
return _mx_np_op.cumsum(self, axis=axis, dtype=dtype, out=out)
raise NotImplementedError

def max(self, axis=None, out=None, keepdims=False): # pylint: disable=arguments-differ
"""Return the maximum along a given axis."""
Expand Down Expand Up @@ -1327,66 +1327,6 @@ def tile(A, reps):
return _npi.tile(A, reps)


@set_module('mxnet.symbol.numpy')
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, **kwargs): # pylint: disable=too-many-arguments
"""Return evenly spaced numbers over a specified interval.
Returns num evenly spaced samples, calculated over the interval [start, stop].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless endpoint is set to False. In
that case, the sequence consists of all but the last of num + 1
evenly spaced samples, so that stop is excluded. Note that the step
size changes when endpoint is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, stop is the last sample. Otherwise, it is not included.
Default is True.
retstep: bool, optional
If True, return (samples, step), where step is the spacing between samples.
dtype: dtype, optional
The type of the output array. If dtype is not given, infer the data
type from the other input arguments.
axis : int, optional
The axis in the result to store the samples. Relevant only if start or
stop are array-like. By default (0), the samples will be along a new
axis inserted at the beginning. Use -1 to get an axis at the end.
Returns
-------
samples : ndarray
There are num equally spaced samples in the closed interval
`[start, stop]` or the half-open interval `[start, stop)`
(depending on whether endpoint is True or False).
step : float, optional
Only returned if retstep is True
Size of spacing between samples.
Notes
-----
This function currently does not support ``start`` and ``stop`` as ndarrays and
axis could only be 0 now.
"""
if isinstance(start, (list, _np.ndarray)) or \
isinstance(stop, (list, _np.ndarray)):
raise NotImplementedError('start and stop only support int')
if axis != 0:
raise NotImplementedError("the function only support axis 0")
ctx = kwargs.pop('ctx', current_context())
if ctx is None:
ctx = current_context()
if retstep:
step = (stop - start) / (num - 1)
return (_npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype), step)
else:
return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)


def _unary_func_helper(x, fn_array, fn_scalar, out=None, **kwargs):
"""Helper function for unary operators.
Expand Down
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