Cone Distribution

class pints.toy.ConeLogPDF(dimensions=2, beta=1)[source]

Toy distribution based on a d-dimensional distribution of the form,

\[f(x) \propto e^{-|x|^\beta}\]

where x is a d-dimensional real, and |x| is the Euclidean norm. The mean and variance that are returned relate to expectations on |x| not the multidimensional x.

Extends pints.toy.ToyLogPDF.

Parameters:

  • dimensions (int) – The dimensionality of the cone.

  • beta (float) – The power to which |x| is raised in the exponential term, which must be positive.

CDF(x)[source]

Returns the cumulative density function in terms of |x|.

__call__(x)[source]

Evaluates this LogPDF for parameters x.

beta()[source]

Returns the exponent in the pdf

distance(samples)[source]

Calculates a measure of normed distance of samples from exact mean and covariance matrix.

See pints.toy.ToyLogPDF.distance().

evaluateS1(x)[source]

See LogPDF.evaluateS1().

mean_normed()[source]

Returns the mean of the normed distance from the origin

n_parameters()[source]

Returns the dimension of the space this LogPDF is defined over.

sample(n_samples)[source]

See ToyLogPDF.sample().

var_normed()[source]

Returns the variance of the normed distance from the origin.