Metadata-Version: 2.1
Name: fylearn
Version: 0.1.9
Summary: Fuzzy Machine Learning Algorithms
Home-page: https://github.com/sorend/fylearn
Author: Søren Atmakuri Davidsen
Author-email: sorend@cs.svu-ac.in
License: MIT
Download-URL: https://github.com/sorend/fylearn/tarball/0.1.9
Keywords: machine learning,fuzzy logic,scikit-learn
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Dist: numpy (>=1.17)
Requires-Dist: scipy (>=1.3)
Requires-Dist: scikit-learn (>=0.22)

|Build Status| |PyPi version|

fylearn is a fuzzy machine learning library, built on top of
`SciKit-Learn <http://scikit-learn.org/>`__.

SciKit-Learn contains many common machine learning algorithms, and is a
good place to start if you want to play or program anything related to
machine learning in Python. fylearn is not intended to be a replacement
for SciKit-Learn (in fact fylearn depends on SciKit-Learn), but to
provide an extra set of machine learning algorithms from the fuzzy logic
community.

Machine learning algorithms
---------------------------

Fuzzy pattern classifiers
~~~~~~~~~~~~~~~~~~~~~~~~~

Fuzzy pattern classifiers are classifiers that describe data using fuzzy
sets and fuzzy aggregation functions.

Several fuzzy pattern classifiers are implemented in the library: -
fylearn.frr.FuzzyReductionRuleClassifier – based on learning membership
functions from min/max. - fylearn.fpcga.FuzzyPatternClassifierGA –
optimizes membership functions globally. -
fylearn.fpcga.FuzzyPatternClassifierLocalGA – optimizes membership
functions locally. - fylearn.fpt.FuzzyPatternTreeClassifier – builds
fuzzy pattern trees using bottom-up method. -
fylearn.fpt.FuzzyPatternTreeTopDownClassifier – builds fuzzy pattern
trees using top-down method. - fylearn.nfpc.FuzzyPatternClassifier –
base class for fuzzy pattern classifiers (see parameters).

Genetic Algorithm rule based classifiers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A type of classifier that uses GA to optimize rules

-  fylearn.garules.MultimodalEvolutionaryClassifer – learns rules using
   genetic algorithm.

Installation
------------

You can add fylearn to your project by using pip:

::

   pip install fylearn

Usage
~~~~~

You can use the classifiers as any other SciKit-Learn classifier:

::

   from fylearn.nfpc import FuzzyPatternClassifier
   from fylearn.garules import MultimodalEvolutionaryClassifier
   from fylearn.fpt import FuzzyPatternTreeTopDownClassifier

   C = (FuzzyPatternClassifier(),
        MultimodalEvolutionaryClassifier(),
        FuzzyPatternTreeTopDownClassifier())

   for c in C:
       print c.fit(X, y).predict([1, 2, 3, 4])

Heuristic search methods
------------------------

Several heuristic search methods are implemented. These are used in the
learning algorithms for parameter assignment, but, are also usable
directly.

-  fylearn.local_search.PatternSearchOptimizer
-  fylearn.local_search.LocalUnimodalSamplingOptimizer
-  fylearn.ga.GeneticAlgorithm: Search parameters using modification and
   a scaling
-  fylearn.ga.UnitIntervalGeneticAlgorithm: Search parameters in unit
   interval universe.
-  fylearn.ga.DiscreteGeneticAlgorithm: Search parameters from discrete
   universe.
-  fylearn.tlbo.TeachingLearningBasedOptimizer: Search using
   teaching-learning based optimization.
-  fylearn.jaya.JayaOptimizer: Search based on moving towards best
   solution while avoiding worst.

Example use:

::

   import numpy as np
   from fylearn.ga import UnitIntervalGeneticAlgorithm, helper_fitness, helper_n_generations
   from fylearn.local_search import LocalUnimodalSamplingOptimizer, helper_num_runs
   from fylearn.tlbo import TeachingLearningBasedOptimizer
   from fylearn.jaya import JayaOptimizer

   def fitness(x):  # defined for a single chromosome, so we need helper_fitness for GA
       return np.sum(x**2)

   ga = UnitIntervalGeneticAlgorithm(fitness_function=helper_fitness(fitness), n_chromosomes=100, n_genes=10)
   ga = helper_n_generations(ga, 100)
   best_chromosomes, best_fitness = ga.best(1)
   print "GA solution", best_chromosomes[0], "fitness", best_fitness[0]

   lower_bounds, upper_bounds = np.ones(10) * -10., np.ones(10) * 10.
   lus = LocalUnimodalSamplingOptimizer(fitness, lower_bounds, upper_bounds)
   best_solution, best_fitness = helper_num_runs(lus, 100)
   print "LUS solution", best_solution, "fitness", best_fitness

   tlbo = TeachingLearningBasedOptimizer(fitness, lower_bounds, upper_bounds)
   tlbo = helper_n_generations(tlbo, 100)
   best_solution, best_fitness = tlbo.best()
   print "TLBO solution", best_solution, "fitness", best_fitness

   jaya = JayaOptimizer(fitness, lower_bounds, upper_bounds)
   jaya = helper_n_generations(jaya, 100)
   best_solution, best_fitness = jaya.best()
   print "Jaya solution", best_solution, "fitness", best_fitness

A tiny fuzzy logic library
--------------------------

Tiny, but hopefully useful. The focus of the library is on providing
membership functions and aggregations that work with NumPy, for using in
the implemented learning algorithms.

Membership functions
~~~~~~~~~~~~~~~~~~~~

-  fylearn.fuzzylogic.TriangularSet
-  fylearn.fuzzylogic.TrapezoidalSet
-  fylearn.fuzzylogic.PiSet

Example use:

::

   import numpy as np
   from fylearn.fuzzylogic import TriangularSet
   t = TriangularSet(1.0, 4.0, 5.0)
   print t(3)   # use with singletons
   print t(np.array([[1, 2, 3], [4, 5, 6]]))  # use with arrays

Aggregation functions
~~~~~~~~~~~~~~~~~~~~~

Here focus has been on providing aggregation functions that support
aggregation along a specified axis for 2-dimensional matrices.

Example use:

::

   import numpy as np
   from fylearn.fuzzylogic import meowa, OWA
   a = OWA([1.0, 0.0, 0.0])  # pure AND in OWA
   X = np.random.rand(5, 3)
   print a(X)  # AND row-wise
   a = meowa(5, 0.2)  # OR, andness = 0.2
   print a(X.T)  # works column-wise, so apply to transposed X

To Do
-----

We are working on adding the following algorithms:

-  ANFIS.
-  FRBCS.

About
-----

fylearn is supposed to mean “FuzzY learning”, but in Danish the word
“fy” means loosely translated “for shame”. It has been created by the
Department of Computer Science at Sri Venkateswara University, Tirupati,
INDIA by a `PhD student <http://www.cs.svu-ac.in/~sorend/>`__ as part of
his research.

Contributions:
--------------

-  fylearn.local_search Python code by `M. E. H.
   Pedersen <http://hvass-labs.org/>`__ (M. E. H. Pedersen, *Tuning and
   Simplifying Heuristical Optimization*, PhD Thesis, University of
   Southampton, U.K., 2010)

.. |Build Status| image:: https://travis-ci.com/sorend/fylearn.svg?branch=master
   :target: https://travis-ci.com/sorend/fylearn
.. |PyPi version| image:: https://pypip.in/v/fylearn/badge.png
   :target: https://crate.io/packages/fylearn/


