def test_log_det(self):
for sym in [True, False]:
np.random.seed(0)
A = [np.random.rand(5, 5) + np.eye(5) for i in range(2)]
A = [Ai.dot(Ai.T) + 1e-6 * np.eye(5) for Ai in A] # make it SPD
A = KronMatrix(A, sym=sym)
eig = A.eig_vals()
assert_array_almost_equal(eig.log_det(),\
np.linalg.slogdet(A.expand())[1])
def test_row_col_khatri_rao_matrix(self):
np.random.seed(0)
N, p, d = 5, 6, 3
grid_shape = np.random.randint(low=2, high=15, size=d)
R = np.empty(d, dtype=object)
R[:] = [np.random.rand(p, m) - 0.5 for m in grid_shape]
K = np.empty(d, dtype=object)
K[:] = [np.random.rand(m, m) - 0.5 for m in grid_shape]
C = np.empty(d, dtype=object)
C[:] = [np.random.rand(m, N) - 0.5 for m in grid_shape]
for i in range(d):
R[i][
0, :] = 0. # set this to zero so there's a zero in the final matrix
vec = np.random.rand(N, 1) - 0.5
vecT = np.random.rand(p, 1) - 0.5
# initialize RowKronColKhatriRaoMatrix
A = RowColKhatriRaoMatrix(R=R, K=K, C=C)
# initialize RowColKhatriRaoMatrixTransposed
AT = RowColKhatriRaoMatrixTransposed(R=R, K=K, C=C)
# initialize KhatriRaoMatrix's and KronMatrix to test
R = KhatriRaoMatrix(R, partition=0)
C = KhatriRaoMatrix(C, partition=1)
K = KronMatrix(K)
# test matvec
assert_array_almost_equal(A * vec, R * (K * (C * vec)))
# test matvec with transpose
assert_array_almost_equal(A.T * vecT, C.T * (K.T * (R.T * vecT)))
# now try with RowColKhatriRaoMatrixTransposed
assert_array_almost_equal(AT * vecT, C.T * (K.T * (R.T * vecT)))
# test the expand method to compute the whole matrix
RKC = R.expand().dot(K.expand().dot(C.expand()))
assert_array_almost_equal(A.expand(), RKC)
# test the log expand
log_A, sign = A.expand(logged=True)
assert_array_almost_equal(sign * np.exp(log_A), RKC)
import pytest
import numpy as np
from numpy.testing import assert_array_almost_equal
from gp_grief.tensors import KronMatrix
"""
SET UP
"""
np.random.seed(1)
d = 10
n = 3
eigs = KronMatrix([np.random.rand(n) for i in range(d)])
all_eigs = eigs.expand() # compute all the eigenvalues for comparison
n_eigs = 5 # this is the number of largest/smallest that I want to find
"""
TESTS
"""
class TestKronEigenvalues:
def test_False_largest(self):
log_expand = False
mode = 'largest'
# get the n_eigs largest/smallest
eig_order, extreme_eigs, global_loc = eigs.find_extremum_eigs(
n_eigs,
mode=mode,
log_expand=log_expand,
sort=True,
compute_global_loc=True)
# check if extreme_eigs is being computed correctly
assert_array_almost_equal(extreme_eigs, [