`_matutils` – Compiled extension for math utils¶

gensim._matutils.digamma¶

Digamma function for positive floats, using _digamma().

gensim._matutils.dirichlet_expectation(alpha)¶

Expected value of log(theta) where theta is drawn from a Dirichlet distribution. Using dirichlet_expectation_1d() or dirichlet_expectation_2d().

Parameters: alpha (numpy.ndarray) – Dirichlet parameter 2d matrix or 1d vector, if 2d - each row is treated as a separate parameter vector, supports float16, float32 and float64.
Returns: Log of expected values, dimension same as alpha.ndim.
Return type: numpy.ndarray

gensim._matutils.dirichlet_expectation_1d(alpha)¶

Expected value of log(theta) where theta is drawn from a Dirichlet distribution. Using _dirichlet_expectation_1d().

Parameters: alpha (numpy.ndarray) – Dirichlet parameter 1d vector, supports float16, float32 and float64.
Returns: Log of expected values, 1d vector.
Return type: numpy.ndarray

gensim._matutils.dirichlet_expectation_2d(alpha)¶

Expected value of log(theta) where theta is drawn from a Dirichlet distribution. Using _dirichlet_expectation_2d().

Parameters: alpha (numpy.ndarray) – Dirichlet parameter 2d matrix, each row is treated as a separate parameter vector, supports float16, float32 and float64.
Returns: Log of expected values, 2d matrix.
Return type: numpy.ndarray

gensim._matutils.logsumexp(x)¶

Log of sum of exponentials, using _logsumexp_2d().

Parameters: x (numpy.ndarray) – Input 2d matrix, supports float16, float32 and float64.
Returns: log of sum of exponentials of elements in x.
Return type: float

Warning

By performance reasons, doesn’t support NaNs or 1d, 3d, etc arrays like scipy.special.logsumexp().

gensim._matutils.mean_absolute_difference(a, b)¶

Mean absolute difference between two arrays, using _mean_absolute_difference().

Parameters

Returns

mean(abs(a - b)).

Return type

float

_matutils – Compiled extension for math utils¶