adelie.adelie_core.matrix.MatrixNaiveRSubset64#

class adelie.adelie_core.matrix.MatrixNaiveRSubset64#

Core matrix class for naive row-subsetted matrix.

Methods

`__init__`(self, mat, subset, n_threads)
`bmul`(self, arg0, arg1, arg2, arg3, arg4)	Computes a column block matrix-vector multiplication.
`btmul`(self, arg0, arg1, arg2, arg3)	Computes a column block matrix transpose-vector multiplication increment.
`cmul`(self, arg0, arg1, arg2)	Computes a column vector-vector multiplication.
`cols`(self)	Returns the number of columns.
`cov`(self, arg0, arg1, arg2, arg3, arg4)	Computes a weighted covariance matrix.
`ctmul`(self, arg0, arg1, arg2)	Computes a column vector-scalar multiplication increment.
`mean`(self, arg0, arg1)	Computes the implied column means.
`mul`(self, arg0, arg1, arg2)	Computes a matrix-vector multiplication.
`rows`(self)	Returns the number of rows.
`sp_tmul`(self, arg0, arg1)	Computes a matrix transpose-sparse matrix multiplication.
`sq_mul`(self, arg0, arg1)	Computes a squared matrix-vector multiplication.
`var`(self, arg0, arg1, arg2)	Computes the implied column variances.

Attributes

`ndim`	Number of dimensions.
`shape`	Shape of the matrix.

__init__(self: adelie.adelie_core.matrix.MatrixNaiveRSubset64, mat: adelie.adelie_core.matrix.MatrixNaiveBase64, subset: numpy.ndarray[numpy.int64[1, n]], n_threads: int) → None#

bmul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: int, arg1: int, arg2: numpy.ndarray[numpy.float64[1, n]], arg3: numpy.ndarray[numpy.float64[1, n]], arg4: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a column block matrix-vector multiplication.

Computes the matrix-vector multiplication (v * w).T @ X[:, j:j+q].

Parameters:

jint: Column index.
qint: Number of columns.
v(n,) ndarray: Vector to multiply with the block matrix.
w(n,) ndarray: Vector of weights.
out(q,) ndarray: Vector to store in-place the result.

btmul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: int, arg1: int, arg2: numpy.ndarray[numpy.float64[1, n]], arg3: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a column block matrix transpose-vector multiplication increment.

Computes the matrix-vector multiplication v.T @ X[:, j:j+q].T. The result is incremented into the output vector.

Parameters:

jint: Column index.
qint: Number of columns.
v(q,) ndarray: Vector to multiply with the block matrix.
out(n,) ndarray: Vector to increment in-place the result.

cmul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: int, arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n]]) → float#

Computes a column vector-vector multiplication.

Computes the dot-product (v * w).T @ X[:,j].

Parameters:

jint: Column index.
v(n,) ndarray: Vector to dot product with the j th column.
w(n,) ndarray: Vector of weights.

Returns:

dotfloat: Column vector-vector multiplication.

cols(self: adelie.adelie_core.matrix.MatrixNaiveBase64) → int#

Returns the number of columns.

Returns:

colsint: Number of columns.

cov(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: int, arg1: int, arg2: numpy.ndarray[numpy.float64[1, n]], arg3: numpy.ndarray[numpy.float64[m, n], flags.writeable, flags.f_contiguous], arg4: numpy.ndarray[numpy.float64[m, n], flags.writeable, flags.f_contiguous]) → None#

Computes a weighted covariance matrix.

Computes the weighted covariance matrix X[:, j:j+q].T @ W @ X[:, j:j+q].

Note

Although the name is “covariance”, we do not center the columns of X!

Parameters:

jint: Column index.
qint: Number of columns.
sqrt_weights(n,) ndarray: Square-root of the weights.
out(q, q) ndarray: Matrix to store in-place the result.
buffer(n, q) ndarray: Extra buffer space if needed.

ctmul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: int, arg1: float, arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a column vector-scalar multiplication increment.

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

Parameters:

jint: Column index.
vfloat: Scalar to multiply with the j th column.
out(n,) ndarray: Vector to increment in-place the result.

mean(self: adelie.adelie_core.matrix.MatrixNaiveRSubset64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes the implied column means.

The implied column means are the implied column means of the underlying matrix where the weights are zero outside of the subset.

Parameters:

weights(n,) ndarray: Vector of weights.
out(p,) ndarray: Vector to store in-place the result.

mul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a matrix-vector multiplication.

Computes the matrix-vector multiplication (v * w).T @ X.

Parameters:

v(n,) ndarray: Vector to multiply with the matrix.
w(n,) ndarray: Vector of weights.
out(p,) ndarray: Vector to store in-place the result.

rows(self: adelie.adelie_core.matrix.MatrixNaiveBase64) → int#

Returns the number of rows.

Returns:

rowsint: Number of rows.

sp_tmul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: scipy.sparse.csr_matrix[numpy.float64], arg1: numpy.ndarray[numpy.float64[m, n], flags.writeable, flags.c_contiguous]) → None#

Computes a matrix transpose-sparse matrix multiplication.

Computes the matrix transpose-sparse matrix multiplication v.T @ X.T.

Parameters:

v(L, p) csr_matrix: Sparse matrix to multiply with the matrix.
out(L, n) ndarray: Matrix to store in-place the result.

sq_mul(self: adelie.adelie_core.matrix.MatrixNaiveBase64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes a squared matrix-vector multiplication.

Computes the squared matrix-vector multiplication w.T @ X ** 2.

Parameters:

weights(n,) ndarray: Vector of weights.
out(p,) ndarray: Vector to store in-place the result.

var(self: adelie.adelie_core.matrix.MatrixNaiveRSubset64, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n]], arg2: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes the implied column variances.

The implied column variances are the implied column variances of the underlying matrix where the weights are zero outside of the subset.

Parameters:

centers(p,) ndarray: Vector of centers.
weights(n,) ndarray: Vector of weights.
out(p,) ndarray: Vector to store in-place the result.

ndim#: Number of dimensions. It is always 2.

shape#: Shape of the matrix.