pyFTS.probabilistic package

Module contents

Probability Distribution objects

Submodules

pyFTS.probabilistic.ProbabilityDistribution module

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

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)[source]
append_interval(intervals)[source]
averageloglikelihood(data)[source]
bins = None

Number of bins on a discrete PDF

build_cdf_qtl()[source]
crossentropy(q)[source]
cummulative(values)[source]
density(values)[source]
differential_offset(value)[source]
empiricalloglikelihood()[source]
entropy()[source]
expected_value()[source]
kullbackleiblerdivergence(q)[source]
labels = None

Bins labels on a discrete PDF

plot(axis=None, color='black', tam=[10, 6], title=None)[source]
pseudologlikelihood(data)[source]
quantile(values)[source]
set(value, density)[source]
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')[source]

Bases: object

Kernel Density Estimation

h = None

Width parameter

kernel = None

Kernel function

kernel_function(u)[source]
probability(x, data)[source]

Probability of the point x on data

Parameters:
  • x
  • data
Returns: