pyFTS.probabilistic package

Submodules

pyFTS.probabilistic.ProbabilityDistribution module

class pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution(type='KDE', **kwargs)

Bases: object

Represents a discrete or continous probability distribution If type is histogram, the PDF is discrete If type is KDE the PDF is continuous

append(values)

append_interval(intervals)

averageloglikelihood(data)

bins = None

Number of bins on a discrete PDF

build_cdf_qtl()

crossentropy(q)

cummulative(values)

density(values)

differential_offset(value)

empiricalloglikelihood()

entropy()

expected_value()

kullbackleiblerdivergence(q)

labels = None

Bins labels on a discrete PDF

plot(axis=None, color='black', tam=[10, 6], title=None)

pseudologlikelihood(data)

quantile(values)

set(value, density)

type = None

If type is histogram, the PDF is discrete If type is KDE the PDF is continuous

uod = None

Universe of discourse

pyFTS.probabilistic.kde module

Kernel Density Estimation

class pyFTS.probabilistic.kde.KernelSmoothing(h, kernel='epanechnikov')

Bases: object

Kernel Density Estimation

h = None

Width parameter

kernel = None

Kernel function

kernel_function(u)

probability(x, data)

Probability of the point x on data

Parameters:

• x –
• data –

Returns:

Module contents

Probability Distribution objects