Source code for pyCP_APR.numpy_backend.tenmat_sptensor

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
tenmat.py creates a matricized tensor.

References
========================================
[1] General software, latest release: Brett W. Bader, Tamara G. Kolda and others, Tensor Toolbox for MATLAB, Version 3.2.1, www.tensortoolbox.org, April 5, 2021.\n
[2] Dense tensors: B. W. Bader and T. G. Kolda, Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping, ACM Trans. Mathematical Software, 32(4):635-653, 2006, http://dx.doi.org/10.1145/1186785.1186794.\n
[3] Sparse, Kruskal, and Tucker tensors: B. W. Bader and T. G. Kolda, Efficient MATLAB Computations with Sparse and Factored Tensors, SIAM J. Scientific Computing, 30(1):205-231, 2007, http://dx.doi.org/10.1137/060676489.\n
[4] Chi, E.C. and Kolda, T.G., 2012. On tensors, sparsity, and nonnegative factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4), pp.1272-1299.

@author: Maksim Ekin Eren
"""
import copy
import numpy as np
import sparse


[docs]def tenmat(X, mode): """ Create a matricized tenso, i.e. the unfolding of the tensor. Parameters ---------- X : class Sparse tensor. sptensor.SP_TENSOR. mode : int Dimension number to unfold on. Returns ------- X : np.ndarray Matriced version of the sparse tensor in as dense matrix. """ X = copy.deepcopy(X) rdims = [mode] tmp = [True] * len(X.Size) tmp[rdims[0]] = False cdims = np.where(tmp)[0] order = rdims + list(cdims) x = np.prod([X.Size[i] for i in rdims]) y = np.prod([X.Size[i] for i in cdims]) X.Coords = X.Coords[:, order] X.Size = tuple(np.array(X.Size)[order]) X = sparse.COO(np.array(X.Coords, dtype='int').T, X.data, shape=X.Size) X = X.reshape([x,y]) X = X.todense() return X