pyFTS.partitioners package

Module contents

Module for pyFTS Universe of Discourse partitioners.

Submodules

pyFTS.partitioners.partitioner module

class pyFTS.partitioners.partitioner.Partitioner(**kwargs)[source]

Bases: object

Universe of Discourse partitioner. Split data on several fuzzy sets

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:
get_name(counter)[source]
lower_set()[source]
membership_function = None

Fuzzy membership function (pyFTS.common.Membership)

name = None

partitioner name

partitions = None

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

plot(ax)[source]

Plot the :param ax: :return:

plot_set(ax, s)[source]
prefix = None

prefix of auto generated partition names

setnames = None

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

transformation = None

data transformation to be applied on data

upper_set()[source]

pyFTS.partitioners.Grid module

Even Length Grid Partitioner

class pyFTS.partitioners.Grid.GridPartitioner(**kwargs)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

Even Length Grid Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:

pyFTS.partitioners.Huarng module

K. H. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,” Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.

class pyFTS.partitioners.Huarng.HuarngPartitioner(**kwargs)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

Huarng Empirical Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:

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)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

Huarng Entropy Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:
pyFTS.partitioners.Entropy.PMF(data, threshold)[source]
pyFTS.partitioners.Entropy.bestSplit(data, npart)[source]
pyFTS.partitioners.Entropy.entropy(data, threshold)[source]
pyFTS.partitioners.Entropy.informationGain(data, thres1, thres2)[source]
pyFTS.partitioners.Entropy.splitAbove(data, threshold)[source]
pyFTS.partitioners.Entropy.splitBelow(data, threshold)[source]

pyFTS.partitioners.CMeans module

class pyFTS.partitioners.CMeans.CMeansPartitioner(**kwargs)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:
pyFTS.partitioners.CMeans.c_means(k, dados, tam)[source]
pyFTS.partitioners.CMeans.distance(x, y)[source]

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)[source]

Bases: pyFTS.partitioners.partitioner.Partitioner

build(data)[source]

Perform the partitioning of the Universe of Discourse

Parameters:data – training data
Returns:
pyFTS.partitioners.FCM.fuzzy_cmeans(k, dados, tam, m, deltadist=0.001)[source]
pyFTS.partitioners.FCM.fuzzy_distance(x, y)[source]
pyFTS.partitioners.FCM.membership(val, vals)[source]

pyFTS.partitioners.Util module

Facility methods for pyFTS partitioners module

pyFTS.partitioners.Util.explore_partitioners(data, npart, methods=None, mf=None, transformation=None, size=[12, 10], save=False, file=None)[source]

Create partitioners for the mf membership functions and npart partitions and show the partitioning images. :data: Time series data :npart: Maximum number of partitions of the universe of discourse :methods: A list with the partitioning methods to be used :mf: A list with the membership functions to be used :transformation: a transformation to be used in partitioner :size: list, the size of the output image [width, height] :save: boolean, if the image will be saved on disk :file: string, the file path to save the image :return: the list of the built partitioners

pyFTS.partitioners.Util.plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None)[source]
pyFTS.partitioners.Util.plot_sets(data, sets, titles, size=[12, 10], save=False, file=None, axis=None)[source]

pyFTS.partitioners.parallel_util module

pyFTS.partitioners.parallel_util.explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None)[source]