tensor.extra_ops – Tensor Extra Ops

aesara.tensor.extra_ops.bartlett(M)[source]

An instance of this class returns the Bartlett spectral window in the time-domain. The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain.

New in version 0.6.

Parameters:M (integer scalar) – Number of points in the output window. If zero or less, an empty vector is returned.
Returns: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.
Return type:vector of doubles
aesara.tensor.extra_ops.bincount(x, weights=None, minlength=None, assert_nonneg=False)[source]

Count number of occurrences of each value in an array of integers.

The number of bins (of size 1) is one larger than the largest value in x. If minlength is specified, there will be at least this number of bins in the output array (though it will be longer if necessary, depending on the contents of x). Each bin gives the number of occurrences of its index value in x. If weights is 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.

Parameters:
  • x – A one dimensional array of non-negative integers
  • weights – An array of the same shape as x with corresponding weights. Optional.
  • minlength – A minimum number of bins for the output array. Optional.
  • assert_nonneg – A flag that inserts an assert_op to check if every input x is non-negative. Optional.
  • versionadded: (..) – 0.6:
aesara.tensor.extra_ops.broadcast_arrays(*args: TensorVariable) Tuple[TensorVariable, ...][source]

Broadcast any number of arrays against each other.

Parameters:*args – The arrays to broadcast.
aesara.tensor.extra_ops.broadcast_shape(*arrays, **kwargs) Tuple[ScalarVariable, ...][source]

Compute the shape resulting from broadcasting arrays.

Parameters:
  • *arrays (TensorVariable) – The tensor variables, or their shapes (as tuples), for which the broadcast shape is computed.
  • arrays_are_shapes (bool (Optional)) – Indicates whether or not the arrays contains shape tuples. If you use this approach, make sure that the broadcastable dimensions are (scalar) constants with the value 1–or simply the integer 1.
aesara.tensor.extra_ops.broadcast_to(x: TensorVariable, shape: Union[TensorVariable, Tuple[Variable]]) TensorVariable[source]

Broadcast an array to a new shape.

Parameters:
  • array – The array to broadcast.
  • shape – The shape of the desired array.
Returns:

A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location.

Return type:

broadcast

aesara.tensor.extra_ops.compress(condition, x, axis=None)[source]

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. Corresponds to numpy.compress

New in version 0.7.

Parameters:
  • condition – One dimensional array of non-zero and zero values corresponding to indices of slices along a selected axis.
  • x – Input data, tensor variable.
  • axis – The axis along which to slice.
Return type:

x with selected slices.

aesara.tensor.extra_ops.cumprod(x, axis=None)[source]

Return the cumulative product of the elements along a given axis.

This wraps numpy.cumprod.

Parameters:
  • x – Input tensor variable.
  • axis – The axis along which the cumulative product is computed. The default (None) is to compute the cumprod over the flattened array.

New in version 0.7.

aesara.tensor.extra_ops.cumsum(x, axis=None)[source]

Return the cumulative sum of the elements along a given axis.

This wraps numpy.cumsum.

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.

New in version 0.7.

aesara.tensor.extra_ops.diff(x, n=1, axis=-1)[source]

Calculate the n-th order discrete difference along the given axis.

The first order difference is given by out[i] = a[i + 1] - a[i] along the given axis, higher order differences are calculated by using diff recursively. This is heavily inspired by numpy.diff.

Parameters:
  • x – Input tensor variable.
  • n – The number of times values are differenced, default is 1.
  • axis – The axis along which the difference is taken, default is the last axis.

New in version 0.6.

aesara.tensor.extra_ops.fill_diagonal(a, val)[source]

Returns a copy of an array with all elements of the main diagonal set to a specified scalar value.

New in version 0.6.

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).
Returns:

  • array – 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).)
  • Support rectangular matrix and tensor with more than two dimensions
  • if the later have all dimensions are equals.

aesara.tensor.extra_ops.fill_diagonal_offset(a, val, offset)[source]

Returns a copy of an array with all elements of the main diagonal set to a specified scalar value.

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 type:

array

aesara.tensor.extra_ops.ravel_multi_index(multi_index, dims, mode='raise', order='C')[source]

Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.

Parameters:
  • multi_index (tuple of Aesara or NumPy arrays) – A tuple of integer arrays, one array for each dimension.
  • dims (tuple of ints) – The shape of array into which the indices from multi_index apply.
  • mode ({'raise', 'wrap', 'clip'}, optional) – Specifies how out-of-bounds indices are handled. Can specify either one mode or a tuple of modes, one mode per index. * ‘raise’ – raise an error (default) * ‘wrap’ – wrap around * ‘clip’ – clip to the range In ‘clip’ mode, a negative index which would normally wrap will clip to 0 instead.
  • order ({'C', 'F'}, optional) – Determines whether the multi-index should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order.
Returns:

raveled_indices – An array of indices into the flattened version of an array of dimensions dims.

Return type:

TensorVariable

See also

unravel_index

aesara.tensor.extra_ops.repeat(x, repeats, axis=None)[source]

Repeat elements of an array.

It returns an array which has the same shape as x, except along the given axis. The axis parameter is used to specify the axis along which values are repeated. By default, a flattened version of x is used.

The number of repetitions for each element is repeats. repeats is broadcasted to fit the length of the given axis.

Parameters:
  • x – Input data, tensor variable.
  • repeats – int, scalar or tensor variable
  • axis (int, optional) –

See also

tensor.tile,

aesara.tensor.extra_ops.searchsorted(x, v, side='left', sorter=None)[source]

Find indices where elements should be inserted to maintain order.

This wraps numpy.searchsorted. Find the indices into a sorted array x such that, if the corresponding elements in v were inserted before the indices, the order of x would be preserved.

Parameters:
  • x (1-D tensor (array-like)) – Input array. If sorter is None, then it must be sorted in ascending order, otherwise sorter must be an array of indices which sorts it.
  • v (tensor (array-like)) – Contains the values to be inserted into x.
  • side ({'left', 'right'}, optional.) – If 'left' (default), the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of x).
  • sorter (1-D tensor of integers (array-like), optional) – Contains indices that sort array x into ascending order. They are typically the result of argsort.
Returns:

indices – Array of insertion points with the same shape as v.

Return type:

tensor of integers (int64)

Notes

  • Binary search is used to find the required insertion points.
  • This Op is working only on CPU currently.

Examples

>>> from aesara import tensor as at
>>> from aesara.tensor import extra_ops
>>> x = at.dvector()
>>> idx = x.searchsorted(3)
>>> idx.eval({x: [1,2,3,4,5]})
array(2)
>>> extra_ops.searchsorted([1,2,3,4,5], 3).eval()
array(2)
>>> extra_ops.searchsorted([1,2,3,4,5], 3, side='right').eval()
array(3)
>>> extra_ops.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]).eval()
array([0, 5, 1, 2])

New in version 0.9.

aesara.tensor.extra_ops.squeeze(x, axis=None)[source]

Remove broadcastable (length 1) dimensions from the shape of an array.

It returns the input array, but with the broadcastable dimensions removed. This is always x itself or a view into x.

New in version 0.6.

Parameters:
  • x – Input data, tensor variable.
  • axis (None or int or tuple of ints, optional) – Selects a subset of broadcastable dimensions to be removed. If a non broadcastable dimension is selected, an error is raised. If axis is None, all broadcastable dimensions will be removed.

Notes

The behavior can differ from that of NumPy in two ways:
1. If an axis is chosen for a dimension that is not known to be broadcastable an error is raised, even if this dimension would be broadcastable when the variable is evaluated. 2. Similarly, if axis is None, only dimensions known to be broadcastable will be removed, even if there are more dimensions that happen to be broadcastable when the variable is evaluated.
Return type:x without axis dimensions.
aesara.tensor.extra_ops.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)[source]

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements:

  • the indices of the input array that give the unique values
  • the indices of the unique array that reconstruct the input array
  • the number of times each unique value comes up in the input array
aesara.tensor.extra_ops.unravel_index(indices, dims, order='C')[source]

Converts a flat index or array of flat indices into a tuple of coordinate arrays.

Parameters:
  • indices (Aesara or NumPy array) – An integer array whose elements are indices into the flattened version of an array of dimensions dims.
  • dims (tuple of ints) – The shape of the array to use for unraveling indices.
  • order ({'C', 'F'}, optional) – Determines whether the indices should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order.
Returns:

unraveled_coords – Each array in the tuple has the same shape as the indices array.

Return type:

tuple of ndarray