Source code for tensorly.base

from . import backend as T

[docs]def tensor_to_vec(tensor): """Vectorises a tensor Parameters ---------- tensor : ndarray tensor of shape ``(i_1, ..., i_n)`` Returns ------- 1D-array vectorised tensor of shape ``(i_1 * i_2 * ... * i_n)`` """ return T.reshape(tensor, (-1, ))
[docs]def vec_to_tensor(vec, shape): """Folds a vectorised tensor back into a tensor of shape `shape` Parameters ---------- vec : 1D-array vectorised tensor of shape ``(i_1 * i_2 * ... * i_n)`` shape : tuple shape of the ful tensor Returns ------- ndarray tensor of shape `shape` = ``(i_1, ..., i_n)`` """ return T.reshape(vec, shape)
[docs]def unfold(tensor, mode): """Returns the mode-`mode` unfolding of `tensor` with modes starting at `0`. Parameters ---------- tensor : ndarray mode : int, default is 0 indexing starts at 0, therefore mode is in ``range(0, tensor.ndim)`` Returns ------- ndarray unfolded_tensor of shape ``(tensor.shape[mode], -1)`` """ return T.reshape(T.moveaxis(tensor, mode, 0), (tensor.shape[mode], -1))
[docs]def fold(unfolded_tensor, mode, shape): """Refolds the mode-`mode` unfolding into a tensor of shape `shape` In other words, refolds the n-mode unfolded tensor into the original tensor of the specified shape. Parameters ---------- unfolded_tensor : ndarray unfolded tensor of shape ``(shape[mode], -1)`` mode : int the mode of the unfolding shape : tuple shape of the original tensor before unfolding Returns ------- ndarray folded_tensor of shape `shape` """ full_shape = list(shape) mode_dim = full_shape.pop(mode) full_shape.insert(0, mode_dim) return T.moveaxis(T.reshape(unfolded_tensor, full_shape), 0, mode)
[docs]def partial_unfold(tensor, mode=0, skip_begin=1, skip_end=0, ravel_tensors=False): """Partially unfolds a tensor while ignoring the specified number of dimensions at the beginning and the end. For instance, if the first dimension of the tensor is the number of samples, to unfold each sample, you would set skip_begin=1. This would, for each i in ``range(tensor.shape[0])``, unfold ``tensor[i, ...]``. Parameters ---------- tensor : ndarray tensor of shape n_samples x n_1 x n_2 x ... x n_i mode : int indexing starts at 0, therefore mode is in range(0, tensor.ndim) skip_begin : int, optional number of dimensions to leave untouched at the beginning skip_end : int, optional number of dimensions to leave untouched at the end ravel_tensors : bool, optional if True, the unfolded tensors are also flattened Returns ------- ndarray partially unfolded tensor """ if ravel_tensors: new_shape = [-1] else: new_shape = [tensor.shape[mode + skip_begin], -1] if skip_begin: new_shape = [tensor.shape[i] for i in range(skip_begin)] + new_shape if skip_end: new_shape += [tensor.shape[-i] for i in range(skip_end)] return T.reshape(T.moveaxis(tensor, mode+skip_begin, skip_begin), new_shape)
[docs]def partial_fold(unfolded, mode, shape, skip_begin=1, skip_end=0): """Re-folds a partially unfolded tensor Parameters ---------- unfolded : ndarray a partially unfolded tensor mode : int indexing starts at 0, therefore mode is in range(0, tensor.ndim) shape : tuple the shape of the original full tensor (including skipped dimensions) skip_begin : int, optional, default is 1 number of dimensions to leave untouched at the beginning skip_end : int, optional number of dimensions to leave untouched at the end Returns ------- ndarray partially re-folded tensor """ transposed_shape = list(shape) mode_dim = transposed_shape.pop(skip_begin + mode) transposed_shape.insert(skip_begin, mode_dim) return T.moveaxis(T.reshape(unfolded, transposed_shape), skip_begin, skip_begin + mode)
[docs]def partial_tensor_to_vec(tensor, skip_begin=1, skip_end=0): """Partially vectorises a tensor Partially vectorises a tensor while ignoring the specified dimension at the beginning and the end Parameters ---------- tensor : ndarray tensor to partially vectorise skip_begin : int, optional, default is 1 number of dimensions to leave untouched at the beginning skip_end : int, optional number of dimensions to leave untouched at the end Returns ------- ndarray partially vectorised tensor with the `skip_begin` first and `skip_end` last dimensions untouched """ return partial_unfold(tensor, mode=0, skip_begin=skip_begin, skip_end=skip_end, ravel_tensors=True)
[docs]def partial_vec_to_tensor(matrix, shape, skip_begin=1, skip_end=0): """Refolds a partially vectorised tensor into a full one Parameters ---------- matrix : ndarray a partially vectorised tensor shape : tuple the shape of the original full tensor (including skipped dimensions) skip_begin : int, optional, default is 1 number of dimensions to leave untouched at the beginning skip_end : int, optional number of dimensions to leave untouched at the end Returns ------- ndarray full tensor """ return partial_fold(matrix, mode=0, shape=shape, skip_begin=skip_begin, skip_end=skip_end)