askcarl package

Submodules

askcarl.gaussian module

Multivariate Gaussians with support for upper limits and missing data.

askcarl.gaussian.pdfcdf(x, mask, mean, cov)

Compute the mixed PDF and CDF for a multivariate Gaussian distribution.

Parameters:

• x (array) – The point (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

• mean (array) – mean vector of the multivariate normal distribution.

• cov (array) – covariance matrix of the multivariate normal distribution.

Returns:

pdf – Probability density

Return type:

float

class askcarl.gaussian.Gaussian(mean, cov)

Bases: object

Multivariate Gaussians with support for upper limits and missing data.

Initialize.

Parameters:

• mean (array) – mean vector of the multivariate normal distribution.

• cov (array) – covariance matrix of the multivariate normal distribution.

get_conditional_rv(mask)

Build conditional distribution.

Parameters:

mask (array) – A boolean mask, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

• cov_cross (array) – Covariance matrix part of upper bound and value dimensions.

• cov_exact (array) – Covariance matrix part of value dimensions.

• inv_cov_exact (array) – Inverse covariance matrix part of value dimensions.

• rv (scipy.stats.multivariate_normal) – Multivariate Normal Distribution of the upper bound dimensions, conditioned with mask.

conditional_pdf(x, mask=Ellipsis)

Compute conditional PDF.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x.shape[1], indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – Probability density. One value for each x.

Return type:

array

conditional_logpdf(x, mask=Ellipsis)

Compute conditional log-PDF.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x.shape[1], indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

pdf(x, mask)

Compute conditional PDF.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – probability density. One value for each x.

Return type:

array

logpdf(x, mask)

Compute conditional log-PDF.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

askcarl.mixture module

Mixture of Gaussians.

class askcarl.mixture.GaussianMixture(weights, means, covs)

Bases: object

Mixture of Gaussians.

weights

weight for each Gaussian component

Type:

list

components

list of Gaussian components.

Type:

list

Initialize.

Parameters:

• weights (list) – weight for each Gaussian component

• means (list) – mean vector for each Gaussian component.

• covs (list) – covariance matrix for each Gaussian component.

static from_pypmc(mix)

Initialize from a pypmc Gaussian mixture model (GMM).

Parameters:

mix (pypmc.density.mixture.GaussianMixture) – Gaussian mixture.

Returns:

mix – Generalized Gaussian mixture.

Return type:

GaussianMixture

static from_sklearn(skgmm)

Initialize from a scikit-learn Gaussian mixture model (GMM).

Parameters:

mix (sklearn.mixture.GaussianMixture) – Gaussian mixture.

Returns:

mix – Generalized Gaussian mixture.

Return type:

GaussianMixture

pdf(x, mask)

Compute probability density at x.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

pdf – probability density. One value for each x.

Return type:

array

logpdf(x, mask)

Compute logarithm of probability density.

Parameters:

• x (array) – The points (vector) at which to evaluate the probability.

• mask (array) – A boolean mask of the same shape as x, indicating whether the entry is a value (True) or a upper bound (False).

Returns:

logpdf – logarithm of the probability density. One value for each x.

Return type:

array

Module contents

Multivariate Gaussians with support for upper limits and missing data.