adelie.adelie_core.matrix.MatrixNaiveOneHotDense64F#

class adelie.adelie_core.matrix.MatrixNaiveOneHotDense64F#

Core matrix class for naive (dense) one-hot encoded matrix.

Methods

`__init__`(self, mat, levels, 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

`group_sizes`	List of group sizes corresponding to each element in `groups`.
`groups`	List of starting indices to each group where G is the number of groups.
`ndim`	Number of dimensions.
`shape`	Shape of the matrix.

__init__(self: adelie.adelie_core.matrix.MatrixNaiveOneHotDense64F, mat: numpy.ndarray[numpy.float64[m, n], flags.f_contiguous], levels: 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.MatrixNaiveOneHotDense64F, arg0: numpy.ndarray[numpy.float64[1, n]], arg1: numpy.ndarray[numpy.float64[1, n], flags.writeable]) → None#

Computes the implied column means.

The default method is used for continuous features and the implied mean is zero for categorical features.

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.MatrixNaiveOneHotDense64F, 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 default method is used for continuous features and the implied variance is one for categorical features.

Parameters:

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

group_sizes#: List of group sizes corresponding to each element in groups. group_sizes[i] is the group size of the i th group.

groups#: List of starting indices to each group where G is the number of groups. groups[i] is the starting index of the i th group. The groups are naturally defined by the columns of mat. In the order of the columns of mat, we group all columns of the current matrix corresponding to each column of mat. This way, the continuous features each form a group of size one and the discrete features form a group across their one-hot encodings.

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

shape#: Shape of the matrix.