spateo.alignment.methods.backend¶
Attributes¶
Classes¶
Backend abstract class. |
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NumPy implementation of the backend. |
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PyTorch implementation of the backend |
Functions¶
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Returns instances of all available backends. |
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Returns the list of available backend implementations. |
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Returns the proper backend for a list of input arrays |
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Returns numpy arrays from any compatible backend |
Module Contents¶
- spateo.alignment.methods.backend.str_type_error = 'All array should be from the same type/backend. Current types are : {}'[source]¶
- spateo.alignment.methods.backend.get_backend_list()[source]¶
Returns instances of all available backends.
Note that the function forces all detected implementations to be instantiated even if specific backend was not use before. Be careful as instantiation of the backend might lead to side effects, like GPU memory pre-allocation. See the documentation for more details. If you only need to know which implementations are available, use :py:func:`ot.backend.get_available_backend_implementations, which does not force instance of the backend object to be created.
- spateo.alignment.methods.backend.get_available_backend_implementations()[source]¶
Returns the list of available backend implementations.
- spateo.alignment.methods.backend.get_backend(*args)[source]¶
Returns the proper backend for a list of input arrays
Accepts None entries in the arguments, and ignores them
Also raises TypeError if all arrays are not from the same backend
- spateo.alignment.methods.backend.to_numpy(*args)[source]¶
Returns numpy arrays from any compatible backend
- class spateo.alignment.methods.backend.Backend[source]¶
Backend abstract class. Implementations:
JaxBackend
,NumpyBackend
,TorchBackend
,CupyBackend
,TensorflowBackend
The __name__ class attribute refers to the name of the backend.
The __type__ class attribute refers to the data structure used by the backend.
- from_numpy(*arrays, type_as=None)[source]¶
Creates tensors cloning a numpy array, with the given precision (defaulting to input’s precision) and the given device (in case of GPUs)
- abstract _from_numpy(a, type_as=None)[source]¶
Creates a tensor cloning a numpy array, with the given precision (defaulting to input’s precision) and the given device (in case of GPUs)
- abstract zeros(shape, type_as=None)[source]¶
Creates a tensor full of zeros.
This function follows the api from
numpy.zeros
See: https://numpy.org/doc/stable/reference/generated/numpy.zeros.html
- abstract ones(shape, type_as=None)[source]¶
Creates a tensor full of ones.
This function follows the api from
numpy.ones
See: https://numpy.org/doc/stable/reference/generated/numpy.ones.html
- abstract full(shape, fill_value, type_as=None)[source]¶
Creates a tensor with given shape, filled with given value.
This function follows the api from
numpy.full
See: https://numpy.org/doc/stable/reference/generated/numpy.full.html
- abstract eye(N, M=None, type_as=None)[source]¶
Creates the identity matrix of given size.
This function follows the api from
numpy.eye
See: https://numpy.org/doc/stable/reference/generated/numpy.eye.html
- abstract sum(a, axis=None, keepdims=False)[source]¶
Sums tensor elements over given dimensions.
This function follows the api from
numpy.sum
See: https://numpy.org/doc/stable/reference/generated/numpy.sum.html
- abstract arange(stop, start=0, step=1, type_as=None)[source]¶
Returns evenly spaced values within a given interval.
This function follows the api from
numpy.arange
See: https://numpy.org/doc/stable/reference/generated/numpy.arange.html
- abstract max(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amax
See: https://numpy.org/doc/stable/reference/generated/numpy.amax.html
- abstract min(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amin
See: https://numpy.org/doc/stable/reference/generated/numpy.amin.html
- abstract maximum(a, b)[source]¶
Returns element-wise maximum of array elements.
This function follows the api from
numpy.maximum
See: https://numpy.org/doc/stable/reference/generated/numpy.maximum.html
- abstract minimum(a, b)[source]¶
Returns element-wise minimum of array elements.
This function follows the api from
numpy.minimum
See: https://numpy.org/doc/stable/reference/generated/numpy.minimum.html
- abstract dot(a, b)[source]¶
Returns the dot product of two tensors.
This function follows the api from
numpy.dot
See: https://numpy.org/doc/stable/reference/generated/numpy.dot.html
- abstract log(a)[source]¶
Computes the natural logarithm, element-wise.
This function follows the api from
numpy.log
See: https://numpy.org/doc/stable/reference/generated/numpy.log.html
- abstract sqrt(a)[source]¶
Returns the non-ngeative square root of a tensor, element-wise.
This function follows the api from
numpy.sqrt
See: https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html
- abstract power(a, exponents)[source]¶
First tensor elements raised to powers from second tensor, element-wise.
This function follows the api from
numpy.power
See: https://numpy.org/doc/stable/reference/generated/numpy.power.html
- abstract norm(a, axis=None, keepdims=False)[source]¶
Computes the matrix frobenius norm.
This function follows the api from
numpy.linalg.norm
See: https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html
- abstract any(a)[source]¶
Tests whether any tensor element along given dimensions evaluates to True.
This function follows the api from
numpy.any
See: https://numpy.org/doc/stable/reference/generated/numpy.any.html
- abstract isnan(a)[source]¶
Tests element-wise for NaN and returns result as a boolean tensor.
This function follows the api from
numpy.isnan
See: https://numpy.org/doc/stable/reference/generated/numpy.isnan.html
- abstract isinf(a)[source]¶
Tests element-wise for positive or negative infinity and returns result as a boolean tensor.
This function follows the api from
numpy.isinf
See: https://numpy.org/doc/stable/reference/generated/numpy.isinf.html
- abstract einsum(subscripts, *operands)[source]¶
Evaluates the Einstein summation convention on the operands.
This function follows the api from
numpy.einsum
See: https://numpy.org/doc/stable/reference/generated/numpy.einsum.html
- abstract sort(a, axis=-1)[source]¶
Returns a sorted copy of a tensor.
This function follows the api from
numpy.sort
See: https://numpy.org/doc/stable/reference/generated/numpy.sort.html
- abstract argsort(a, axis=None)[source]¶
Returns the indices that would sort a tensor.
This function follows the api from
numpy.argsort
See: https://numpy.org/doc/stable/reference/generated/numpy.argsort.html
- abstract searchsorted(a, v, side='left')[source]¶
Finds indices where elements should be inserted to maintain order in given tensor.
This function follows the api from
numpy.searchsorted
See: https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html
- abstract flip(a, axis=None)[source]¶
Reverses the order of elements in a tensor along given dimensions.
This function follows the api from
numpy.flip
See: https://numpy.org/doc/stable/reference/generated/numpy.flip.html
- abstract clip(a, a_min, a_max)[source]¶
Limits the values in a tensor.
This function follows the api from
numpy.clip
See: https://numpy.org/doc/stable/reference/generated/numpy.clip.html
- abstract repeat(a, repeats, axis=None)[source]¶
Repeats elements of a tensor.
This function follows the api from
numpy.repeat
See: https://numpy.org/doc/stable/reference/generated/numpy.repeat.html
- abstract take_along_axis(arr, indices, axis)[source]¶
Gathers elements of a tensor along given dimensions.
This function follows the api from
numpy.take_along_axis
See: https://numpy.org/doc/stable/reference/generated/numpy.take_along_axis.html
- abstract concatenate(arrays, axis=0)[source]¶
Joins a sequence of tensors along an existing dimension.
This function follows the api from
numpy.concatenate
See: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
- abstract zero_pad(a, pad_width, value=0)[source]¶
Pads a tensor with a given value (0 by default).
This function follows the api from
numpy.pad
See: https://numpy.org/doc/stable/reference/generated/numpy.pad.html
- abstract argmax(a, axis=None)[source]¶
Returns the indices of the maximum values of a tensor along given dimensions.
This function follows the api from
numpy.argmax
See: https://numpy.org/doc/stable/reference/generated/numpy.argmax.html
- abstract argmin(a, axis=None)[source]¶
Returns the indices of the minimum values of a tensor along given dimensions.
This function follows the api from
numpy.argmin
See: https://numpy.org/doc/stable/reference/generated/numpy.argmin.html
- abstract mean(a, axis=None)[source]¶
Computes the arithmetic mean of a tensor along given dimensions.
This function follows the api from
numpy.mean
See: https://numpy.org/doc/stable/reference/generated/numpy.mean.html
- abstract median(a, axis=None)[source]¶
Computes the median of a tensor along given dimensions.
This function follows the api from
numpy.median
See: https://numpy.org/doc/stable/reference/generated/numpy.median.html
- abstract std(a, axis=None)[source]¶
Computes the standard deviation of a tensor along given dimensions.
This function follows the api from
numpy.std
See: https://numpy.org/doc/stable/reference/generated/numpy.std.html
- abstract linspace(start, stop, num, type_as=None)[source]¶
Returns a specified number of evenly spaced values over a given interval.
This function follows the api from
numpy.linspace
See: https://numpy.org/doc/stable/reference/generated/numpy.linspace.html
- abstract meshgrid(a, b)[source]¶
Returns coordinate matrices from coordinate vectors (Numpy convention).
This function follows the api from
numpy.meshgrid
See: https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html
- abstract diag(a, k=0)[source]¶
Extracts or constructs a diagonal tensor.
This function follows the api from
numpy.diag
See: https://numpy.org/doc/stable/reference/generated/numpy.diag.html
- abstract unique(a, return_inverse=False)[source]¶
Finds unique elements of given tensor.
This function follows the api from
numpy.unique
See: https://numpy.org/doc/stable/reference/generated/numpy.unique.html
- abstract logsumexp(a, axis=None)[source]¶
Computes the log of the sum of exponentials of input elements.
This function follows the api from
scipy.special.logsumexp
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.logsumexp.html
- abstract stack(arrays, axis=0)[source]¶
Joins a sequence of tensors along a new dimension.
This function follows the api from
numpy.stack
See: https://numpy.org/doc/stable/reference/generated/numpy.stack.html
- abstract outer(a, b)[source]¶
Computes the outer product between two vectors.
This function follows the api from
numpy.outer
See: https://numpy.org/doc/stable/reference/generated/numpy.outer.html
- abstract reshape(a, shape)[source]¶
Gives a new shape to a tensor without changing its data.
This function follows the api from
numpy.reshape
See: https://numpy.org/doc/stable/reference/generated/numpy.reshape.html
- abstract seed(seed=None)[source]¶
Sets the seed for the random generator.
This function follows the api from
numpy.random.seed
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html
- abstract rand(*size, type_as=None)[source]¶
Generate uniform random numbers.
This function follows the api from
numpy.random.rand
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.rand.html
- abstract randn(*size, type_as=None)[source]¶
Generate normal Gaussian random numbers.
This function follows the api from
numpy.random.rand
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.rand.html
- abstract coo_matrix(data, rows, cols, shape=None, type_as=None)[source]¶
Creates a sparse tensor in COOrdinate format.
This function follows the api from
scipy.sparse.coo_matrix
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html
- abstract issparse(a)[source]¶
Checks whether or not the input tensor is a sparse tensor.
This function follows the api from
scipy.sparse.issparse
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.issparse.html
- abstract tocsr(a)[source]¶
Converts this matrix to Compressed Sparse Row format.
This function follows the api from
scipy.sparse.coo_matrix.tocsr
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.tocsr.html
- abstract eliminate_zeros(a, threshold=0.0)[source]¶
Removes entries smaller than the given threshold from the sparse tensor.
This function follows the api from
scipy.sparse.csr_matrix.eliminate_zeros
- abstract todense(a)[source]¶
Converts a sparse tensor to a dense tensor.
This function follows the api from
scipy.sparse.csr_matrix.toarray
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.toarray.html
- abstract where(condition, x, y)[source]¶
Returns elements chosen from x or y depending on condition.
This function follows the api from
numpy.where
See: https://numpy.org/doc/stable/reference/generated/numpy.where.html
- abstract copy(a)[source]¶
Returns a copy of the given tensor.
This function follows the api from
numpy.copy
See: https://numpy.org/doc/stable/reference/generated/numpy.copy.html
- abstract allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]¶
Returns True if two arrays are element-wise equal within a tolerance.
This function follows the api from
numpy.allclose
See: https://numpy.org/doc/stable/reference/generated/numpy.allclose.html
- abstract assert_same_dtype_device(a, b)[source]¶
Checks whether or not the two given inputs have the same dtype as well as the same device
- abstract squeeze(a, axis=None)[source]¶
Remove axes of length one from a.
This function follows the api from
numpy.squeeze
.See: https://numpy.org/doc/stable/reference/generated/numpy.squeeze.html
- abstract bitsize(type_as)[source]¶
Gives the number of bits used by the data type of the given tensor.
- abstract device_type(type_as)[source]¶
Returns CPU or GPU depending on the device where the given tensor is located.
- abstract _bench(callable, *args, n_runs=1, warmup_runs=1)[source]¶
Executes a benchmark of the given callable with the given arguments.
- abstract solve(a, b)[source]¶
Solves a linear matrix equation, or system of linear scalar equations.
This function follows the api from
numpy.linalg.solve
.See: https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html
- abstract trace(a)[source]¶
Returns the sum along diagonals of the array.
This function follows the api from
numpy.trace
.See: https://numpy.org/doc/stable/reference/generated/numpy.trace.html
- abstract inv(a)[source]¶
Computes the inverse of a matrix.
This function follows the api from
scipy.linalg.inv
.See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.inv.html
- abstract sqrtm(a)[source]¶
Computes the matrix square root. Requires input to be definite positive.
This function follows the api from
scipy.linalg.sqrtm
.See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.sqrtm.html
- abstract eigh(a)[source]¶
Computes the eigenvalues and eigenvectors of a symmetric tensor.
This function follows the api from
scipy.linalg.eigh
.See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eigh.html
- abstract kl_div(p, q, mass=False, eps=1e-16)[source]¶
Computes the (Generalized) Kullback-Leibler divergence.
This function follows the api from
scipy.stats.entropy
.Parameter eps is used to avoid numerical errors and is added in the log.
\[KL(p,q) = \langle \mathbf{p}, log(\mathbf{p} / \mathbf{q} + eps \rangle + \mathbb{1}_{mass=True} \langle \mathbf{q} - \mathbf{p}, \mathbf{1} \rangle\]See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
- abstract isfinite(a)[source]¶
Tests element-wise for finiteness (not infinity and not Not a Number).
This function follows the api from
numpy.isfinite
.See: https://numpy.org/doc/stable/reference/generated/numpy.isfinite.html
- abstract array_equal(a, b)[source]¶
True if two arrays have the same shape and elements, False otherwise.
This function follows the api from
numpy.array_equal
.See: https://numpy.org/doc/stable/reference/generated/numpy.array_equal.html
- abstract tile(a, reps)[source]¶
Construct an array by repeating a the number of times given by reps
See: https://numpy.org/doc/stable/reference/generated/numpy.tile.html
- abstract floor(a)[source]¶
Return the floor of the input element-wise
See: https://numpy.org/doc/stable/reference/generated/numpy.floor.html
- abstract prod(a, axis=None)[source]¶
Return the product of all elements.
See: https://numpy.org/doc/stable/reference/generated/numpy.prod.html
- abstract sort2(a, axis=None)[source]¶
Return the sorted array and the indices to sort the array
See: https://pytorch.org/docs/stable/generated/torch.sort.html
- abstract qr(a)[source]¶
Return the QR factorization
See: https://numpy.org/doc/stable/reference/generated/numpy.linalg.qr.html
- abstract atan2(a, b)[source]¶
Element wise arctangent
See: https://numpy.org/doc/stable/reference/generated/numpy.arctan2.html
- abstract transpose(a, axes=None)[source]¶
Returns a tensor that is a transposed version of a. The given dimensions dim0 and dim1 are swapped.
See: https://numpy.org/doc/stable/reference/generated/numpy.transpose.html
- abstract matmul(a, b)[source]¶
Matrix product of two arrays.
See: https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy.matmul
- abstract nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)[source]¶
Replace NaN with zero and infinity with large finite numbers or with the numbers defined by the user.
See: https://numpy.org/doc/stable/reference/generated/numpy.nan_to_num.html#numpy.nan_to_num
- abstract randperm(length)[source]¶
Returns a random permutation of integers from 0 to length - 1.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.permutation.html
- abstract choice(a, size, replace=False)[source]¶
Generates a random sample from a given 1-D array.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html
- abstract topk(a, topk, axis=-1)[source]¶
Returns the indices of the topk elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.argpartition.html
- abstract dstack(a)[source]¶
Stack arrays in sequence along the third axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.dstack.html
- abstract vstack(a)[source]¶
Stack arrays in sequence vertically (row wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.vstack.html
- abstract hstack(a)[source]¶
Stack arrays in sequence horizontally (column wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.hstack.html
- abstract chunk(a, chunk_num, axis=0)[source]¶
Split the tensor into a list of sub-tensors.
See: https://numpy.org/doc/stable/reference/generated/numpy.array_split.html
- abstract roll(a, shift, axis=None)[source]¶
Roll array elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.roll.html
- class spateo.alignment.methods.backend.NumpyBackend[source]¶
Bases:
Backend
NumPy implementation of the backend.
__name__ is “numpy”
__type__ is np.ndarray
- _from_numpy(a, type_as=None)[source]¶
Creates a tensor cloning a numpy array, with the given precision (defaulting to input’s precision) and the given device (in case of GPUs)
- zeros(shape, type_as=None)[source]¶
Creates a tensor full of zeros.
This function follows the api from
numpy.zeros
See: https://numpy.org/doc/stable/reference/generated/numpy.zeros.html
- einsum(subscripts, *operands)[source]¶
Evaluates the Einstein summation convention on the operands.
This function follows the api from
numpy.einsum
See: https://numpy.org/doc/stable/reference/generated/numpy.einsum.html
- mean(a, axis=None)[source]¶
Computes the arithmetic mean of a tensor along given dimensions.
This function follows the api from
numpy.mean
See: https://numpy.org/doc/stable/reference/generated/numpy.mean.html
- full(shape, fill_value, type_as=None)[source]¶
Creates a tensor with given shape, filled with given value.
This function follows the api from
numpy.full
See: https://numpy.org/doc/stable/reference/generated/numpy.full.html
- sqrt(a)[source]¶
Returns the non-ngeative square root of a tensor, element-wise.
This function follows the api from
numpy.sqrt
See: https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html
- ones(shape, type_as=None)[source]¶
Creates a tensor full of ones.
This function follows the api from
numpy.ones
See: https://numpy.org/doc/stable/reference/generated/numpy.ones.html
- maximum(a, b)[source]¶
Returns element-wise maximum of array elements.
This function follows the api from
numpy.maximum
See: https://numpy.org/doc/stable/reference/generated/numpy.maximum.html
- minimum(a, b)[source]¶
Returns element-wise minimum of array elements.
This function follows the api from
numpy.minimum
See: https://numpy.org/doc/stable/reference/generated/numpy.minimum.html
- max(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amax
See: https://numpy.org/doc/stable/reference/generated/numpy.amax.html
- min(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amin
See: https://numpy.org/doc/stable/reference/generated/numpy.amin.html
- eye(N, M=None, type_as=None)[source]¶
Creates the identity matrix of given size.
This function follows the api from
numpy.eye
See: https://numpy.org/doc/stable/reference/generated/numpy.eye.html
- argsort(a, axis=-1)[source]¶
Returns the indices that would sort a tensor.
This function follows the api from
numpy.argsort
See: https://numpy.org/doc/stable/reference/generated/numpy.argsort.html
- log(a)[source]¶
Computes the natural logarithm, element-wise.
This function follows the api from
numpy.log
See: https://numpy.org/doc/stable/reference/generated/numpy.log.html
- concatenate(arrays, axis=0)[source]¶
Joins a sequence of tensors along an existing dimension.
This function follows the api from
numpy.concatenate
See: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
- sum(a, axis=None, keepdims=False)[source]¶
Sums tensor elements over given dimensions.
This function follows the api from
numpy.sum
See: https://numpy.org/doc/stable/reference/generated/numpy.sum.html
- arange(stop, start=0, step=1, type_as=None)[source]¶
Returns evenly spaced values within a given interval.
This function follows the api from
numpy.arange
See: https://numpy.org/doc/stable/reference/generated/numpy.arange.html
- unique(a, return_inverse=False, axis=None)[source]¶
Finds unique elements of given tensor.
This function follows the api from
numpy.unique
See: https://numpy.org/doc/stable/reference/generated/numpy.unique.html
- power(a, exponents)[source]¶
First tensor elements raised to powers from second tensor, element-wise.
This function follows the api from
numpy.power
See: https://numpy.org/doc/stable/reference/generated/numpy.power.html
- dot(a, b)[source]¶
Returns the dot product of two tensors.
This function follows the api from
numpy.dot
See: https://numpy.org/doc/stable/reference/generated/numpy.dot.html
- prod(a, axis=0)[source]¶
Return the product of all elements.
See: https://numpy.org/doc/stable/reference/generated/numpy.prod.html
- chunk(a, chunk_num, axis=0)[source]¶
Split the tensor into a list of sub-tensors.
See: https://numpy.org/doc/stable/reference/generated/numpy.array_split.html
- randperm(length)[source]¶
Returns a random permutation of integers from 0 to length - 1.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.permutation.html
- roll(a, shift, axis=None)[source]¶
Roll array elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.roll.html
- choice(a, size, replace=False)[source]¶
Generates a random sample from a given 1-D array.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html
- topk(a, topk, axis=-1)[source]¶
Returns the indices of the topk elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.argpartition.html
- dstack(a)[source]¶
Stack arrays in sequence along the third axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.dstack.html
- vstack(a)[source]¶
Stack arrays in sequence vertically (row wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.vstack.html
- hstack(a)[source]¶
Stack arrays in sequence horizontally (column wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.hstack.html
- stack(arrays, axis=0)[source]¶
Joins a sequence of tensors along a new dimension.
This function follows the api from
numpy.stack
See: https://numpy.org/doc/stable/reference/generated/numpy.stack.html
- where(condition, x=None, y=None)[source]¶
Returns elements chosen from x or y depending on condition.
This function follows the api from
numpy.where
See: https://numpy.org/doc/stable/reference/generated/numpy.where.html
- copy(a)[source]¶
Returns a copy of the given tensor.
This function follows the api from
numpy.copy
See: https://numpy.org/doc/stable/reference/generated/numpy.copy.html
- repeat(a, repeats, axis=None)[source]¶
Repeats elements of a tensor.
This function follows the api from
numpy.repeat
See: https://numpy.org/doc/stable/reference/generated/numpy.repeat.html
- sort2(a, axis=-1, descending=False)[source]¶
Return the sorted array and the indices to sort the array
See: https://pytorch.org/docs/stable/generated/torch.sort.html
- coo_matrix(data, rows, cols, shape=None, type_as=None)[source]¶
Creates a sparse tensor in COOrdinate format.
This function follows the api from
scipy.sparse.coo_matrix
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html
- issparse(a)[source]¶
Checks whether or not the input tensor is a sparse tensor.
This function follows the api from
scipy.sparse.issparse
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.issparse.html
- eliminate_zeros(a, threshold=0.0)[source]¶
Removes entries smaller than the given threshold from the sparse tensor.
This function follows the api from
scipy.sparse.csr_matrix.eliminate_zeros
- todense(a)[source]¶
Converts a sparse tensor to a dense tensor.
This function follows the api from
scipy.sparse.csr_matrix.toarray
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.toarray.html
- class spateo.alignment.methods.backend.TorchBackend[source]¶
Bases:
Backend
PyTorch implementation of the backend
__name__ is “torch”
__type__ is torch.Tensor
- _from_numpy(a, type_as=None)[source]¶
Creates a tensor cloning a numpy array, with the given precision (defaulting to input’s precision) and the given device (in case of GPUs)
- zeros(shape, type_as=None)[source]¶
Creates a tensor full of zeros.
This function follows the api from
numpy.zeros
See: https://numpy.org/doc/stable/reference/generated/numpy.zeros.html
- einsum(subscripts, *operands)[source]¶
Evaluates the Einstein summation convention on the operands.
This function follows the api from
numpy.einsum
See: https://numpy.org/doc/stable/reference/generated/numpy.einsum.html
- mean(a, axis=None)[source]¶
Computes the arithmetic mean of a tensor along given dimensions.
This function follows the api from
numpy.mean
See: https://numpy.org/doc/stable/reference/generated/numpy.mean.html
- full(shape, fill_value, type_as=None)[source]¶
Creates a tensor with given shape, filled with given value.
This function follows the api from
numpy.full
See: https://numpy.org/doc/stable/reference/generated/numpy.full.html
- sqrt(a)[source]¶
Returns the non-ngeative square root of a tensor, element-wise.
This function follows the api from
numpy.sqrt
See: https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html
- ones(shape, type_as=None)[source]¶
Creates a tensor full of ones.
This function follows the api from
numpy.ones
See: https://numpy.org/doc/stable/reference/generated/numpy.ones.html
- arange(stop, start=0, step=1, type_as=None)[source]¶
Returns evenly spaced values within a given interval.
This function follows the api from
numpy.arange
See: https://numpy.org/doc/stable/reference/generated/numpy.arange.html
- maximum(a, b)[source]¶
Returns element-wise maximum of array elements.
This function follows the api from
numpy.maximum
See: https://numpy.org/doc/stable/reference/generated/numpy.maximum.html
- minimum(a, b)[source]¶
Returns element-wise minimum of array elements.
This function follows the api from
numpy.minimum
See: https://numpy.org/doc/stable/reference/generated/numpy.minimum.html
- max(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amax
See: https://numpy.org/doc/stable/reference/generated/numpy.amax.html
- min(a, axis=None, keepdims=False)[source]¶
Returns the maximum of an array or maximum along given dimensions.
This function follows the api from
numpy.amin
See: https://numpy.org/doc/stable/reference/generated/numpy.amin.html
- eye(N, M=None, type_as=None)[source]¶
Creates the identity matrix of given size.
This function follows the api from
numpy.eye
See: https://numpy.org/doc/stable/reference/generated/numpy.eye.html
- argsort(a, axis=-1)[source]¶
Returns the indices that would sort a tensor.
This function follows the api from
numpy.argsort
See: https://numpy.org/doc/stable/reference/generated/numpy.argsort.html
- log(a)[source]¶
Computes the natural logarithm, element-wise.
This function follows the api from
numpy.log
See: https://numpy.org/doc/stable/reference/generated/numpy.log.html
- concatenate(arrays, axis=0)[source]¶
Joins a sequence of tensors along an existing dimension.
This function follows the api from
numpy.concatenate
See: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
- sum(a, axis=None, keepdims=False)[source]¶
Sums tensor elements over given dimensions.
This function follows the api from
numpy.sum
See: https://numpy.org/doc/stable/reference/generated/numpy.sum.html
- unique(a, return_inverse=False, axis=None)[source]¶
Finds unique elements of given tensor.
This function follows the api from
numpy.unique
See: https://numpy.org/doc/stable/reference/generated/numpy.unique.html
- power(a, exponents)[source]¶
First tensor elements raised to powers from second tensor, element-wise.
This function follows the api from
numpy.power
See: https://numpy.org/doc/stable/reference/generated/numpy.power.html
- dot(a, b)[source]¶
Returns the dot product of two tensors.
This function follows the api from
numpy.dot
See: https://numpy.org/doc/stable/reference/generated/numpy.dot.html
- prod(a, axis=0)[source]¶
Return the product of all elements.
See: https://numpy.org/doc/stable/reference/generated/numpy.prod.html
- chunk(a, chunk_num, axis=0)[source]¶
Split the tensor into a list of sub-tensors.
See: https://numpy.org/doc/stable/reference/generated/numpy.array_split.html
- randperm(length)[source]¶
Returns a random permutation of integers from 0 to length - 1.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.permutation.html
- roll(a, shift, axis=None)[source]¶
Roll array elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.roll.html
- choice(a, size, replace=False)[source]¶
Generates a random sample from a given 1-D array.
See: https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html
- topk(a, topk, axis=-1)[source]¶
Returns the indices of the topk elements along a given axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.argpartition.html
- dstack(a)[source]¶
Stack arrays in sequence along the third axis.
See: https://numpy.org/doc/stable/reference/generated/numpy.dstack.html
- vstack(a)[source]¶
Stack arrays in sequence vertically (row wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.vstack.html
- hstack(a)[source]¶
Stack arrays in sequence horizontally (column wise).
See: https://numpy.org/doc/stable/reference/generated/numpy.hstack.html
- stack(arrays, axis=0)[source]¶
Joins a sequence of tensors along a new dimension.
This function follows the api from
numpy.stack
See: https://numpy.org/doc/stable/reference/generated/numpy.stack.html
- where(condition, x=None, y=None)[source]¶
Returns elements chosen from x or y depending on condition.
This function follows the api from
numpy.where
See: https://numpy.org/doc/stable/reference/generated/numpy.where.html
- copy(a)[source]¶
Returns a copy of the given tensor.
This function follows the api from
numpy.copy
See: https://numpy.org/doc/stable/reference/generated/numpy.copy.html
- repeat(a, repeats, axis=None)[source]¶
Repeats elements of a tensor.
This function follows the api from
numpy.repeat
See: https://numpy.org/doc/stable/reference/generated/numpy.repeat.html
- sort2(a, axis=-1, descending=False)[source]¶
Return the sorted array and the indices to sort the array
See: https://pytorch.org/docs/stable/generated/torch.sort.html
- coo_matrix(data, rows, cols, shape=None, type_as=None)[source]¶
Creates a sparse tensor in COOrdinate format.
This function follows the api from
scipy.sparse.coo_matrix
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html
- issparse(a)[source]¶
Checks whether or not the input tensor is a sparse tensor.
This function follows the api from
scipy.sparse.issparse
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.issparse.html
- eliminate_zeros(a, threshold=0.0)[source]¶
Removes entries smaller than the given threshold from the sparse tensor.
This function follows the api from
scipy.sparse.csr_matrix.eliminate_zeros
- todense(a)[source]¶
Converts a sparse tensor to a dense tensor.
This function follows the api from
scipy.sparse.csr_matrix.toarray
See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.toarray.html