adelie.adelie_core.matrix.MatrixConstraintDense32F

class adelie.adelie_core.matrix.MatrixConstraintDense32F

Core matrix class for dense constraint matrix.

Methods

__init__(self, mat, n_threads)
cols(self)	Returns the number of columns.
cov(self, arg0, arg1)	Computes the covariance matrix.
mul(self, arg0, arg1)	Computes a matrix-vector multiplication.
rmmul(self, arg0, arg1, arg2)	Computes a row vector-matrix multiplication.
rmmul_safe(self, arg0, arg1, arg2)	Computes a row vector-matrix multiplication.
rows(self)	Returns the number of rows.
rvmul(self, arg0, arg1)	Computes a row vector-vector multiplication.
rvmul_safe(self, arg0, arg1)	Computes a row vector-vector multiplication.
rvtmul(self, arg0, arg1, arg2)	Computes a row vector-scalar multiplication increment.
sp_mul(self, arg0, arg1, arg2)	Computes a matrix-sparse vector multiplication.
tmul(self, arg0, arg1)	Computes a matrix transpose-vector multiplication.

Attributes

ndim	Number of dimensions.
shape	Shape of the matrix.

__init__(self: adelie.adelie_core.matrix.MatrixConstraintDense32F, mat: numpy.ndarray[numpy.float32[m, n], flags.f_contiguous], n_threads: int) → None

cols(self: adelie.adelie_core.matrix.MatrixConstraintBase32) → int

Returns the number of columns.

Returns:

colsint

Number of columns.

cov(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: numpy.ndarray[numpy.float32[m, n], flags.f_contiguous], arg1: numpy.ndarray[numpy.float32[m, n], flags.writeable, flags.f_contiguous]) → None

Computes the covariance matrix.

Computes the covariance matrix A @ Q @ A.T.

Parameters:

Q(d, d) ndarray

Matrix of weights.

out(m, m) ndarray

Matrix to store in-place the result.

mul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: numpy.ndarray[numpy.float32[1, n]], arg1: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a matrix-vector multiplication.

Computes the matrix-vector multiplication v.T @ A.

Parameters:

v(m,) ndarray

Vector to multiply with the matrix.

out(d,) ndarray

Vector to store in-place the result.

rmmul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: int, arg1: numpy.ndarray[numpy.float32[m, n], flags.f_contiguous], arg2: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a row vector-matrix multiplication.

Computes the matrix-vector multiplication A[j].T @ Q.

Warning

This function is not thread-safe!

Parameters:

jint

Row index.

Q(d, d) ndarray

Matrix to dot product with the j th row.

out(d,) ndarray

Vector to store in-place the result.

rmmul_safe(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: int, arg1: numpy.ndarray[numpy.float32[m, n], flags.f_contiguous], arg2: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a row vector-matrix multiplication.

Thread-safe version of rmmul().

Parameters:

jint

Row index.

Q(d, d) ndarray

Matrix to dot product with the j th row.

out(d,) ndarray

Vector to store in-place the result.

rows(self: adelie.adelie_core.matrix.MatrixConstraintBase32) → int

Returns the number of rows.

Returns:

rowsint

Number of rows.

rvmul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: int, arg1: numpy.ndarray[numpy.float32[1, n]]) → float

Computes a row vector-vector multiplication.

Computes the dot-product A[j].T @ v.

Warning

This function is not thread-safe!

Parameters:

jint

Row index.

v(d,) ndarray

Vector to dot product with the j th row.

Returns:

dotfloat

Row vector-vector multiplication.

rvmul_safe(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: int, arg1: numpy.ndarray[numpy.float32[1, n]]) → float

Computes a row vector-vector multiplication.

Thread-safe version of rvmul().

Parameters:

jint

Row index.

v(d,) ndarray

Vector to dot product with the j th row.

Returns:

dotfloat

Row vector-vector multiplication.

rvtmul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: int, arg1: float, arg2: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a row vector-scalar multiplication increment.

Computes the vector-scalar multiplication A[j] * v. The result is incremented into the output vector.

Warning

This function is not thread-safe!

Parameters:

jint

Row index.

vfloat

Scalar to multiply with the j th row.

out(d,) ndarray

Vector to increment in-place the result.

sp_mul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: numpy.ndarray[numpy.int64[1, n]], arg1: numpy.ndarray[numpy.float32[1, n]], arg2: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a matrix-sparse vector multiplication.

Computes the matrix-sparse vector multiplication v.T @ A where v is represented by the sparse-format indices and values.

Parameters:

indices(nnz,) ndarray

Vector of indices with non-zero values of v. It does not have to be sorted in increasing order.

values(nnz,) ndarray

Vector of values corresponding to indices.

out(d,) ndarray

Vector to store in-place the result.

tmul(self: adelie.adelie_core.matrix.MatrixConstraintBase32, arg0: numpy.ndarray[numpy.float32[1, n]], arg1: numpy.ndarray[numpy.float32[1, n], flags.writeable]) → None

Computes a matrix transpose-vector multiplication.

Computes the matrix transpose-vector multiplication v.T @ A.T.

Parameters:

v(d,) ndarray

Vector to multiply with the matrix.

out(m,) ndarray

Vector to store in-place the result.

ndim

Number of dimensions. It is always 2.

shape

Shape of the matrix.