Utilities in cr.sparse module¶

Array data type utilities¶

`promote_arg_dtypes`(*args)	Promotes args to a common inexact type.
`check_shapes_are_equal`(array1, array2)	Raise an error if the shapes of the two arrays do not match.

These functions are available under cr.sparse.metrics.

`mean_squared`(array)	Returns the mean squared value of an array
`mean_squared_error`(array1, array2)	Returns the mean square error between two arrays
`root_mean_squared`(array)	Returns the root mean squared value of an array
`root_mse`(array1, array2)	Returns the root mean square error between two arrays
`normalized_root_mse`(reference_arr, test_arr)	Returns the root mean square error between two arrays
`peak_signal_noise_ratio`(reference_arr, test_arr)	Returns the Peak Signal to Noie Ratio between two arrays

`transpose`(A)	Returns the transpose of an array
`hermitian`(a)	Returns the conjugate transpose of an array
`is_matrix`(A)	Checks if an array is a matrix
`is_square`(A)	Checks if an array is a square matrix
`is_symmetric`(A)	Checks if an array is a symmetric matrix
`is_hermitian`(A)	Checks if an array is a Hermitian matrix
`is_positive_definite`(A)	Checks if an array is a symmetric positive definite matrix
`has_orthogonal_columns`(A[, atol])	Checks if a matrix has orthogonal columns
`has_orthogonal_rows`(A[, atol])	Checks if a matrix has orthogonal rows
`has_unitary_columns`(A)	Checks if a matrix has unitary columns
`has_unitary_rows`(A)	Checks if a matrix has unitary rows

`norms_l1_cw`(X)	Computes the l_1 norm of each column of a matrix
`norms_l1_rw`(X)	Computes the l_1 norm of each row of a matrix
`norms_l2_cw`(X)	Computes the l_2 norm of each column of a matrix
`norms_l2_rw`(X)	Computes the l_2 norm of each row of a matrix
`norms_linf_cw`(X)	Computes the l_inf norm of each column of a matrix
`norms_linf_rw`(X)	Computes the l_inf norm of each row of a matrix
`sqr_norms_l2_cw`(X)	Computes the squared l_2 norm of each column of a matrix
`sqr_norms_l2_rw`(X)	Computes the l_2 norm of each row of a matrix
`normalize_l1_cw`(X)	Normalize each column of X per l_1-norm
`normalize_l1_rw`(X)	Normalize each row of X per l_1-norm
`normalize_l2_cw`(X)	Normalize each column of X per l_2-norm
`normalize_l2_rw`(X)	Normalize each row of X per l_2-norm

Following functions analyze or construct representation vectors which are known to be sparse.

`nonzero_values`(x)	Returns the values of non-zero entries in x
`nonzero_indices`(x)	Returns the indices of non-zero entries in x
`randomize_rows`(key, X)	Randomizes the rows in X
`randomize_cols`(key, X)	Randomizes the columns in X
`largest_indices`(x, K)	Returns the indices of K largest entries in x by magnitude
`hard_threshold`(x, K)	Returns the indices and corresponding values of largest K non-zero entries in a vector x
`hard_threshold_sorted`(x, K)	Returns the sorted indices and corresponding values of largest K non-zero entries in a vector x
`sparse_approximation`(x, K)	Keeps only largest K non-zero entries by magnitude in a vector x
`build_signal_from_indices_and_values`(length, …)	Builds a sparse signal from its non-zero entries (specified by their indices and values)
`dynamic_range`(x)	Returns the ratio of largest and smallest values (by magnitude) in x (dB)
`nonzero_dynamic_range`(x)	Returns the ratio of largest and smallest non-zero values (by magnitude) in x (dB)

Sparse representation matrices (row-wise)

`largest_indices_rw`(X, K)	Returns the indices of K largest entries by magnitude in each row of X
`take_along_rows`(X, indices)	Picks K entries from each row of X specified by indices matrix
`sparse_approximation_rw`(X, K)	Keeps only largest K non-zero entries by magnitude in each row of X

Sparse representation matrices (column-wise)

`largest_indices_cw`(X, K)	Returns the indices of K largest entries by magnitude in each column of X
`take_along_cols`(X, indices)	Picks K entries from each column of X specified by indices matrix
`sparse_approximation_cw`(X, K)	Keeps only largest K non-zero entries by magnitude in each column of X