def test_format_matrix_dim_dim_blocksize(blocksize):
# Testing if input matrix is in perfect format (dim,dim,blocksize)
dim = 3
input_matrix = np.random.randn(dim, dim, blocksize)
test_matrix = format_matrix_shape(input_matrix)
# No need to reshape the input matrix
assert_allclose(test_matrix, input_matrix)
def test_format_matrix_blocksize_dim_dim(blocksize):
# Testing if elements of input matrix is (blocksize,dim,dim)
# and taking transpose of the matrix. note that blocksize>dim=3
dim = 3
input_matrix = np.random.randn(blocksize, dim, dim)
test_matrix = format_matrix_shape(input_matrix)
# Reshape the input matrix
correct_matrix = input_matrix.T
assert_allclose(test_matrix, correct_matrix)
def test_format_matrix_dim_power_2_blocksize(blocksize):
# Testing if elements of input matrix is column vector of dim**2
# Change block sizes using parametric testing ##
dim = 3
input_matrix = np.random.randn(dim ** 2, blocksize)
test_matrix = format_matrix_shape(input_matrix)
# Reshape the input matrix
correct_matrix = input_matrix.reshape(dim, dim, blocksize)
assert_allclose(test_matrix, correct_matrix)
def test_format_matrix_dim_dim():
# Testing if elements of input matrix is (dim,dim) and converting it to
# (dim,dim,1) and expanding dimension.
# Change block sizes using parametric testing ##
dim = 3
blocksize = 1
input_matrix = np.random.randn(dim, dim)
test_matrix = format_matrix_shape(input_matrix)
# Reshape the input matrix
correct_matrix = input_matrix.reshape(dim, dim, blocksize)
assert_allclose(test_matrix, correct_matrix)
def test_format_matrix_shape_1D():
# Testing if input array is 1D, and its shape is dim**2
# This is the only possibility for 1D array otherwise, it
# cannot be square matrix.
dim = 3
blocksize = 1
input_matrix = np.random.randn(dim ** 2)
test_matrix = format_matrix_shape(input_matrix)
# Reshape the input matrix
correct_matrix = input_matrix.reshape(dim, dim, blocksize)
assert_allclose(test_matrix, correct_matrix)