pyFTS.models.multivariate package

Module contents

Multivariate Fuzzy Time Series methods

Submodules

pyFTS.models.multivariate.FLR module

class pyFTS.models.multivariate.FLR.FLR

Bases: object

Multivariate Fuzzy Logical Relationship

set_lhs(var, set)

set_rhs(set)

pyFTS.models.multivariate.common module

class pyFTS.models.multivariate.common.MultivariateFuzzySet(**kwargs)

Bases: pyFTS.common.Composite.FuzzySet

Multivariate Composite Fuzzy Set

append_set(variable, set)

Appends a new fuzzy set from a new variable

Parameters:

• variable – an multivariate.variable instance
• set – an common.FuzzySet instance

membership(x)

Calculate the membership value of a given input

Parameters:x – input value Returns:membership value of x at this fuzzy set

set_target_variable(variable)

pyFTS.models.multivariate.common.fuzzyfy_instance(data_point, var, tuples=True)

pyFTS.models.multivariate.common.fuzzyfy_instance_clustered(data_point, cluster, **kwargs)

pyFTS.models.multivariate.variable module

pyFTS.models.multivariate.flrg module

class pyFTS.models.multivariate.flrg.FLRG(**kwargs)

Bases: pyFTS.common.flrg.FLRG

Multivariate Fuzzy Logical Rule Group

append_rhs(fset, **kwargs)

get_lower(sets)

Returns the lower bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:lower bound value

get_membership(data, variables)

Returns the membership value of the FLRG for the input data

Parameters:

• data – input data
• sets – fuzzy sets

Returns:

the membership value

get_upper(sets)

Returns the upper bound value for the RHS fuzzy sets

Parameters:sets – fuzzy sets Returns:upper bound value

set_lhs(var, fset)

pyFTS.models.multivariate.partitioner module

class pyFTS.models.multivariate.partitioner.MultivariatePartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Base class for partitioners which use the MultivariateFuzzySet

append(fset)

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

build_index()

change_target_variable(variable)

format_data(data)

fuzzyfy(data, **kwargs)

Fuzzyfy the input data according to this partitioner fuzzy sets.

Parameters:

• data – input value to be fuzzyfied
• alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
• method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
• mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership

values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )

:returns a list with the fuzzyfied values, depending on the mode

prune()

search(data, **kwargs)

Perform a search for the nearest fuzzy sets of the point ‘data’. This function were designed to work with several overlapped fuzzy sets.

Parameters:

• data – the value to search for the nearest fuzzy sets
• type – the return type: ‘index’ for the fuzzy set indexes or ‘name’ for fuzzy set names.

Returns:

a list with the nearest fuzzy sets

pyFTS.models.multivariate.grid module

class pyFTS.models.multivariate.grid.GridCluster(**kwargs)

Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

A cartesian product of all fuzzy sets of all variables

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

defuzzyfy(values, mode='both')

class pyFTS.models.multivariate.grid.IncrementalGridCluster(**kwargs)

Bases: pyFTS.models.multivariate.partitioner.MultivariatePartitioner

Create combinations of fuzzy sets of the variables on demand, incrementally increasing the multivariate fuzzy set base.

fuzzyfy(data, **kwargs)

Fuzzyfy the input data according to this partitioner fuzzy sets.

Parameters:

• data – input value to be fuzzyfied
• alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
• method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
• mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership

values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )

:returns a list with the fuzzyfied values, depending on the mode

incremental_search(data, **kwargs)

prune()

pyFTS.models.multivariate.mvfts module

pyFTS.models.multivariate.wmvfts module

pyFTS.models.multivariate.cmvfts module

pyFTS.models.multivariate.granular module