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提交 c172b4c4 authored 作者: Iban Harlouchet's avatar Iban Harlouchet
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numpydoc for theano/tensor/extra_ops.py

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theano/tensor/extra_ops.py
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......@@ -14,8 +14,9 @@ tensor = basic
class CpuContiguous(theano.Op):
"""
Check to see if the input is c-contiguous,
if it is, do nothing, else return a contiguous array
if it is, do nothing, else return a contiguous array.
"""
__props__ = ()
view_map = {0: [0]}
......@@ -171,12 +172,16 @@ def cumsum(x, axis=None):
Wraping of numpy.cumsum.
:param x: Input tensor variable.
:param axis: The axis along which the cumulative sum is computed.
Parameters
----------
x
Input tensor variable.
axis
The axis along which the cumulative sum is computed.
The default (None) is to compute the cumsum over the flattened array.
.. versionadded:: 0.7
"""
return CumsumOp(axis=axis)(x)
......@@ -291,18 +296,24 @@ def cumprod(x, axis=None):
Wraping of numpy.cumprod.
:param x: Input tensor variable.
Parameters
----------
x
Input tensor variable.
:param axis: The axis along which the cumulative product is computed.
axis
The axis along which the cumulative product is computed.
The default (None) is to compute the cumprod over the flattened array.
.. versionadded:: 0.7
"""
return CumprodOp(axis=axis)(x)
class DiffOp(theano.Op):
# See function diff for docstring
__props__ = ("n", "axis")
def __init__(self, n=1, axis=-1):
......@@ -354,23 +365,29 @@ def diff(x, n=1, axis=-1):
along the given axis, higher order differences are calculated by
using diff recursively. Wraping of numpy.diff.
:param x: Input tensor variable.
Parameters
----------
x
Input tensor variable.
:param n: The number of times values are differenced, default is 1.
n
The number of times values are differenced, default is 1.
:param axis: The axis along which the difference is taken,
default is the last axis.
axis
The axis along which the difference is taken, default is the last axis.
.. versionadded:: 0.6
"""
return DiffOp(n=n, axis=axis)(x)
class BinCountOp(theano.Op):
"""
DEPRECATED: use bincount() instead.
.. note:: Deprecated
Use bincount() instead.
See function bincount for docstring.
See function bincount for docstring
"""
compatible_type = ('int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32', 'uint64')
......@@ -473,17 +490,19 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
specified the input array is weighted by it, i.e. if a value n
is found at position i, out[n] += weight[i] instead of out[n] += 1.
:param x: 1 dimension, nonnegative ints
:param weights: array of the same shape as x with corresponding weights.
Parameters
----------
x : 1 dimension, nonnegative ints
weights : array of the same shape as x with corresponding weights.
Optional.
:param minlength: A minimum number of bins for the output array.
minlength : A minimum number of bins for the output array.
Optional.
:param assert_nonneg: A flag that inserts an assert_op to check if
assert_nonneg : A flag that inserts an assert_op to check if
every input x is nonnegative.
Optional.
.. versionadded:: 0.6
"""
compatible_type = ('int8', 'int16', 'int32', 'int64',
'uint8', 'uint16', 'uint32')
......@@ -527,11 +546,17 @@ def squeeze(x):
broadcastable dimensions removed. This is
always `x` itself or a view into `x`.
:param x: Input data, tensor variable.
Parameters
----------
x
Input data, tensor variable.
:return: `x` without its broadcastable dimensions.
Returns
-------
`x` without its broadcastable dimensions.
.. versionadded:: 0.6
"""
view = x.dimshuffle([i for i in range(x.ndim)
if not x.broadcastable[i]])
......@@ -542,17 +567,24 @@ def compress(condition, x, axis=None):
"""Return selected slices of an array along given axis.
It returns the input tensor, but with selected slices along a given axis
retained. If no axis is provided, the tensor is flattened
retained. If no axis is provided, the tensor is flattened.
Corresponds to numpy.compress
:param x: Input data, tensor variable
Parameters
----------
x
Input data, tensor variable.
:param condition: 1 dimensional array of non-zero and zero values
corresponding to indices of slices along a selected axis
condition
1 dimensional array of non-zero and zero values
corresponding to indices of slices along a selected axis.
:return: `x` with selected slices
Returns
-------
`x` with selected slices
.. versionadded:: 0.7
"""
indices = theano.tensor.basic.flatnonzero(condition)
return x.take(indices, axis=axis)
......@@ -560,6 +592,7 @@ def compress(condition, x, axis=None):
class RepeatOp(theano.Op):
# See the repeat function for docstring
__props__ = ("axis",)
def __init__(self, axis=None):
......@@ -678,14 +711,19 @@ def repeat(x, repeats, axis=None):
The number of repetitions for each element is `repeat`.
`repeats` is broadcasted to fit the length of the given `axis`.
:param x: Input data, tensor variable.
:param repeats: int, scalar or tensor variable.
Parameters
----------
x
Input data, tensor variable.
repeats : int, scalar or tensor variable
axis : int, optional
:param axis: int, optional.
:see: :func:`tensor.tile <tensor.tile>`
See Also
--------
`tensor.tile <tensor.tile>`
.. versionadded:: 0.6
"""
repeats = tensor.as_tensor_variable(repeats)
......@@ -769,13 +807,18 @@ def bartlett(M):
processing for tapering a signal, without generating too much ripple in
the frequency domain.
:param M: (integer scalar) Number of points in the output
window. If zero or less, an empty vector is returned.
Parameters
----------
M : integer scalar
Number of points in the output window. If zero or less,
an empty vector is returned.
:return: (vector of doubles) The triangular window, with the
maximum value normalized to one (the value one appears only if
the number of samples is odd), with the first and last samples
equal to zero.
Returns
-------
vector of doubles
The triangular window, with the maximum value normalized to one
(the value one appears only if the number of samples is odd), with
the first and last samples equal to zero.
.. versionadded:: 0.6
......@@ -823,8 +866,10 @@ class FillDiagonal(gof.Op):
def grad(self, inp, cost_grad):
"""
Note: The gradient is currently implemented for matrices
only.
Notes
-----
The gradient is currently implemented for matrices only.
"""
a, val = inp
grad = cost_grad[0]
......@@ -846,12 +891,18 @@ def fill_diagonal(a, val):
""" Returns a copy of an array with all
elements of the main diagonal set to a specified scalar value.
:param a: Rectangular array of at least two dimensions.
:param val: Scalar value to fill the diagonal whose type must be
Parameters
----------
a :
Rectangular array of at least two dimensions.
val :
Scalar value to fill the diagonal whose type must be
compatible with that of array 'a' (i.e. 'val' cannot be viewed
as an upcast of 'a').
:return: An array identical to 'a' except that its main diagonal
Returns
-------
An array identical to 'a' except that its main diagonal
is filled with scalar 'val'. (For an array 'a' with a.ndim >=
2, the main diagonal is the list of locations a[i, i, ..., i]
(i.e. with indices all identical).)
......@@ -860,6 +911,7 @@ def fill_diagonal(a, val):
if the later have all dimensions are equals.
.. versionadded:: 0.6
"""
return fill_diagonal_(a, val)
......@@ -902,13 +954,16 @@ class FillDiagonalOffset(gof.Op):
height, width = a.shape
"""
Note: The fill_diagonal only support rectangular matrix. The output
Notes
-----
The fill_diagonal only support rectangular matrix. The output
of tall matrix is "wrapped", which is an option in numpy 1.9.0
but was regarded as a bug in numpy 1.6.2. Here I implement the
fill_diagonal_offset with unwrapped output, so fill_diagonal_offset
supports tall matrix.(This make a little difference between the output
of fill_diagonal and fill_diagonal_offset only in the case of tall
matrix)
"""
if offset >= 0:
start = offset
......@@ -925,8 +980,9 @@ class FillDiagonalOffset(gof.Op):
def grad(self, inp, cost_grad):
"""
Note: The gradient is currently implemented for matrices
only.
Notes
-----
The gradient is currently implemented for matrices only.
"""
a, val, offset = inp
grad = cost_grad[0]
......@@ -972,14 +1028,23 @@ def fill_diagonal_offset(a, val, offset):
Returns a copy of an array with all
elements of the main diagonal set to a specified scalar value.
:param a: Rectangular array of two dimensions.
:param val: Scalar value to fill the diagonal whose type must be
compatible with that of array 'a' (i.e. 'val' cannot be viewed
as an upcast of 'a').
:param offset: Scalar value Offset of the diagonal from the main
diagonal. Can be positive or negative integer.
:return: An array identical to 'a' except that its offset diagonal
is filled with scalar 'val'. The output is unwrapped.
Parameters
----------
a
Rectangular array of two dimensions.
val
Scalar value to fill the diagonal whose type must be
compatible with that of array 'a' (i.e. 'val' cannot be viewed
as an upcast of 'a').
offset
Scalar value Offset of the diagonal from the main
diagonal. Can be positive or negative integer.
Returns
-------
An array identical to 'a' except that its offset diagonal
is filled with scalar 'val'. The output is unwrapped.
"""
return fill_diagonal_offset_(a, val, offset)
......@@ -988,13 +1053,19 @@ def to_one_hot(y, nb_class, dtype=None):
"""Return a matrix where each row correspond to the one hot
encoding of each element in y.
:param y: A vector of integer value between 0 and nb_class - 1.
:param nb_class: The number of class in y.
:param dtype: The dtype of the returned matrix. Default floatX.
Parameters
----------
y
A vector of integer value between 0 and nb_class - 1.
nb_class : int
The number of class in y.
dtype : data-type
The dtype of the returned matrix. Default floatX.
:return: A matrix of shape (y.shape[0], nb_class), where each
row ``i`` is the one hot encoding of the corresponding ``y[i]``
value.
Returns
-------
A matrix of shape (y.shape[0], nb_class), where each row ``i`` is
the one hot encoding of the corresponding ``y[i]`` value.
"""
ret = theano.tensor.zeros((y.shape[0], nb_class),
......@@ -1006,11 +1077,10 @@ def to_one_hot(y, nb_class, dtype=None):
class Unique(theano.Op):
"""
Wraps numpy.unique.
This op is not implemented on the GPU.
Wraps numpy.unique. This op is not implemented on the GPU.
Examples
========
--------
>>> import numpy as np
>>> x = theano.tensor.vector()
......@@ -1022,7 +1092,9 @@ class Unique(theano.Op):
>>> g = theano.function([y], Unique(True, True, False)(y))
>>> g([[1, 1, 1.0], (2, 3, 3.0)])
[array([ 1., 2., 3.]), array([0, 3, 4]), array([0, 0, 0, 1, 2, 2])]
"""
__props__ = ("return_index", "return_inverse", "return_counts")
def __init__(self, return_index=False, return_inverse=False,
......
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