secure_learning.utils.util_matrix_vec module

Contains utils for matrices and vectors such as transposing and secure signing

secure_learning.utils.util_matrix_vec.mat_to_vec(matrix, transpose=False)[source]

Transforms a vector in matrix format to vector format.

A = [[1], [2], [3]]
mat_to_vec(A) == [1, 2, 3]

Parameters:

matrix (List[List[TypeVar(AnyTV)]]) – Vector in matrix format
transpose (bool) – Interpret vector as column vector

Return type:

List[TypeVar(AnyTV)]

Returns:

Vector

secure_learning.utils.util_matrix_vec.mat_vec_mult(matrix, vector, transpose=False)[source]

Compute matrix-vector multiplication.

Parameters:

matrix (List[List[TypeVar(SecNumTypesTV, SecureFiniteField, SecureFixedPoint, SecureInteger)]]) – Matrix input with dimensions \(m * r\). Dimensions may be \(r * m\) when combined with tr=True
vector (Union[List[TypeVar(SecNumTypesTV, SecureFiniteField, SecureFixedPoint, SecureInteger)], List[float]]) – Vector input of length \(r\), treated as a column vector
transpose (bool) – If True, first transpose mat

Return type:

List[TypeVar(SecNumTypesTV, SecureFiniteField, SecureFixedPoint, SecureInteger)]

Returns:

Row vector with matrix-vector products

secure_learning.utils.util_matrix_vec.matrix_sum(matrix, cols=False)[source]

Securely add all rows in X.

Parameters:

matrix (List[List[SecureFixedPoint]]) – Matrix to be summed
cols (bool) – If True, sum the columns of X instead.

Return type:

List[SecureFixedPoint]

Returns:

Vector of sums

secure_learning.utils.util_matrix_vec.matrix_transpose(matrix)[source]

Transpose a list of lists.

A = [[31, 64], [32, 68], [33, 72], [34, 76]]
matrix_transpose(A) == [[31, 32, 33, 34], [64, 68, 72, 76]]

Parameters:: matrix (List[List[TypeVar(AnyTV)]]) – Matrix stored as list of lists
Return type:: List[List[TypeVar(AnyTV)]]
Returns:: Transpose of \(A\)

secure_learning.utils.util_matrix_vec.mult_scalar_mul(scalars, matrix, transpose=False)[source]

Vectorized version of mpc.scalar_mul.

scalars = [2, -1]
mat = [[1, 2], [3, 4], [5, 6]]
mult_scalar_mul(scalars, mat) == [[2, -2], [6, -4], [10, -6]]

Parameters:

scalars (Union[float, List[float], SecureFixedPoint, List[SecureFixedPoint]]) – Vector of scalars
matrix (List[List[SecureFixedPoint]]) – Matrix of which the columns need to be scaled.
transpose (bool) – If True, scale the rows of matrix instead.

Return type:

List[List[SecureFixedPoint]]

Returns:

Matrix with scaled columns

secure_learning.utils.util_matrix_vec.permute_matrix(matrix)[source]

Permute matrix randomly.

Parameters:: matrix (Sequence[Sequence[TypeVar(SecNumTypesTV, SecureFiniteField, SecureFixedPoint, SecureInteger)]]) – Matrix to be permuted
Raises:: TypeError – Input is not a matrix
Return type:: List[List[TypeVar(SecNumTypesTV, SecureFiniteField, SecureFixedPoint, SecureInteger)]]
Returns:: Permuted matrix

secure_learning.utils.util_matrix_vec.scale_vector_or_matrix(factor, x)[source]

Corrects a vector or matrix by a given factor.

Parameters:

factor (float) – Factor to scale matrix or vector
x (List[TypeVar(AnyTV)]) – Vector or matrix to be scaled

Return type:

List[TypeVar(AnyTV)]

Returns:

Scaled vector or matrix

secure_learning.utils.util_matrix_vec.vector_to_matrix(vector, transpose=False)[source]

Convert vector to matrix.

vec_to_mat([1, 2, 3]) == [[1, 2, 3]]
vec_to_mat([1, 2, 3], tr=True) == [[1], [2], [3]]

Parameters:

vector (List[TypeVar(AnyTV)]) – Row vector to be converted
transpose (bool) – Interpret vector as column vector

Return type:

List[List[TypeVar(AnyTV)]]

Returns:

Matrix that encapsulates vector