pyFTS.partitioners package

Module contents

Module for pyFTS Universe of Discourse partitioners.

Submodules

pyFTS.partitioners.partitioner module

class pyFTS.partitioners.partitioner.Partitioner(**kwargs)

Bases: object

Universe of Discourse partitioner. Split data on several fuzzy sets

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

build_index()

check_bounds(data)

Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest fuzzy set.

Parameters:data – input data to be verified Returns:the index of the closest fuzzy set when data is outside de universe of discourse or None if

the data is inside the UoD.

defuzzyfy(values, mode='both')

extractor(x)

Extract a single primitive type from an structured instance

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

get_name(counter)

Find the name of the fuzzy set given its counter id.

Parameters:counter – The number of the fuzzy set Returns:String

kdtree = None

A spatial index to help in fuzzyfication

lower_margin = None

Specific lower exceeding margins for the known UoD. The default value is the self.margin parameter

lower_set()

Return the fuzzy set on lower bound of the universe of discourse.

Returns:Fuzzy Set

margin = None

The upper and lower exceeding margins for the known UoD. The default value is .1

membership_function = None

Fuzzy membership function (pyFTS.common.Membership)

name = None

partitioner name

ordered_sets = None

A ordered list of the fuzzy sets names, sorted by their middle point

partitions = None

The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created

plot(ax, rounding=0)

Plot the partitioning using the Matplotlib axis ax

Parameters:ax – Matplotlib axis

plot_set(ax, s)

Plot an isolate fuzzy set on Matplotlib axis

Parameters:

• ax – Matplotlib axis
• s – Fuzzy Set

prefix = None

prefix of auto generated partition names

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.
• results – the number of nearest fuzzy sets to return

Returns:

a list with the nearest fuzzy sets

setnames = None

list of partitions names. If None is given the partitions will be auto named with prefix

sets = None

The fuzzy sets dictionary

transformation = None

data transformation to be applied on data

type = None

The type of fuzzy sets that are generated by this partitioner

upper_margin = None

Specific upper exceeding margins for the known UoD. The default value is the self.margin parameter

upper_set()

Return the fuzzy set on upper bound of the universe of discourse.

Returns:Fuzzy Set

variable = None

In a multivariate context, the variable that contains this partitioner

pyFTS.partitioners.Class module

Class Partitioner with Singleton Fuzzy Sets

class pyFTS.partitioners.Class.ClassPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Class Partitioner: Given a dictionary with class/values pairs, create singleton fuzzy sets for each class

build(data: list)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.CMeans module

class pyFTS.partitioners.CMeans.CMeansPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.CMeans.c_means(k, dados, tam)

pyFTS.partitioners.CMeans.distance(x, y)

pyFTS.partitioners.Entropy module

C. H. Cheng, R. J. Chang, and C. A. Yeh, “Entropy-based and trapezoidal fuzzification-based fuzzy time series approach for forecasting IT project cost,” Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524–542, Jun. 2006.

class pyFTS.partitioners.Entropy.EntropyPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Huarng Entropy Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.Entropy.PMF(data, threshold)

pyFTS.partitioners.Entropy.bestSplit(data, npart)

pyFTS.partitioners.Entropy.entropy(data, threshold)

pyFTS.partitioners.Entropy.informationGain(data, thres1, thres2)

pyFTS.partitioners.Entropy.splitAbove(data, threshold)

pyFTS.partitioners.Entropy.splitBelow(data, threshold)

pyFTS.partitioners.FCM module

S. T. Li, Y. C. Cheng, and S. Y. Lin, “A FCM-based deterministic forecasting model for fuzzy time series,” Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j.camwa.2008.07.033.

class pyFTS.partitioners.FCM.FCMPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.FCM.fuzzy_cmeans(k, data, size, m, deltadist=0.001)

pyFTS.partitioners.FCM.fuzzy_distance(x, y)

pyFTS.partitioners.FCM.membership(val, vals)

pyFTS.partitioners.Grid module

Even Length Grid Partitioner

class pyFTS.partitioners.Grid.GridPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Even Length Grid Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

class pyFTS.partitioners.Grid.PreFixedGridPartitioner(**kwargs)

Bases: pyFTS.partitioners.Grid.GridPartitioner

Prefixed UoD with Even Length Grid Partitioner

pyFTS.partitioners.Huarng module

pyFTS.partitioners.Singleton module

Even Length Grid Partitioner

class pyFTS.partitioners.Singleton.SingletonPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Singleton Partitioner: Create singleton fuzzy sets for each distinct value in UoD

build(data: list)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.Simple module

Simple Partitioner for manually informed fuzzy sets

class pyFTS.partitioners.Simple.SimplePartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Simple Partitioner for manually informed fuzzy sets

append(name, mf, parameters, **kwargs)

Append a new partition (fuzzy set) to the partitioner

Parameters:

• name – Fuzzy set name
• mf – One of the pyFTS.common.Membership functions
• parameters – A list with the parameters for the membership function
• kwargs – Optional arguments for the fuzzy set

append_complex(fs)

pyFTS.partitioners.SubClust module

Chiu, Stephen L. “Fuzzy model identification based on cluster estimation.” Journal of Intelligent & fuzzy systems 2.3 (1994): 267-278.

class pyFTS.partitioners.SubClust.SubClustPartitioner(**kwargs)

Bases: pyFTS.partitioners.partitioner.Partitioner

Subtractive Clustering Partitioner

build(data)

Perform the partitioning of the Universe of Discourse

Parameters:data – training data Returns:

pyFTS.partitioners.SubClust.imax(vec)

pyFTS.partitioners.SubClust.subclust(data, ra, rb, eps_sup, eps_inf)

pyFTS.partitioners.Util module

pyFTS.partitioners.parallel_util module