Metadata-Version: 1.0
Name: neurolab
Version: 0.0.5
Summary: Simple and powerfull neural network library for python
Home-page: http://code.google.com/p/neurolab
Author: Zuev Evgenij
Author-email: zueves@gmail.com
License: LGPL-3
Description: ﻿*******************
        Introduction
        *******************
        
        NeuroLab - a library of basic nueral networks algorithms with flexible network configurations and learning algorithms.
        To simplify migration, the syntax of the library is as close to a package of Neural Network Toolbox (NNT) of MATLAB (c). 
        The library is based on the package numpy (http://numpy.scipy.org), some learning algorithms are used scipy.optymyze (http://scipy.org).
        
        :Create network:
        	>>> import neurolab as nl
        	>>> # create feed forward multilayer perceptron
        	>>> net = nl.net.newff([[0, 0.5], [0, 0.5]], [3,1])
        
        Created two-layer network(3-1) with 2-inputs and one output.
        Input layer contains 3 neurons, the output 1 neuron.
        Input range: 0.0, 0.5
        
        :Train:
        	>>> # Create learning samples
        	>>> input = [[0.1, 0.1], 
        	...          [0.1, 0.2], 
        	...          [0.1, 0.3], 
        	...          [0.1, 0.4], 
        	...          [0.2, 0.2], 
        	...          [0.2, 0.3], 
        	...          [0.2, 0.4], 
        	...          [0.3, 0.3], 
        	...          [0.3, 0.4], 
        	...          [0.4, 0.4]]
        	>>> 
        	>>> target = [[i[0] + i[1]] for i in input]
        	>>> # Train
        	>>> error = net.train(input, target, epochs=500, goal=0.1)
        
        :Train error:
        	>>> print "Finish error:", error[-1]
        	Finish error: 0.125232586274
        
        :Simulate:
        	>>> net.sim([[0.1, 0.5], [0.3, 0. 1]])
        	array([[ 0.59650825],
                   [ 0.41686071]])
        
        :Network Info:
        	>>> # Number of network inputs:
        	>>> net.ci
        	2
        	>>> # Number of network outputs:
        	>>> net.co
        	1
        	>>> # Number of network layers:
        	>>> len(net.layers)
        	2
        	>>> # Weight of first neuron of input layer (net.layers[0])
        	>>> net.layers[0].np['w'][1]
        	array([-0.67211163, -0.87277918])
        	>>> 
        	>>> # Bias output layer:
        	>>> net.layers[-1].np['b']
        	array([-0.69717423])
        	>>> # Train params
        	>>> net.train.defaults
        	{'goal': 0.01, 
        	 'show': 100, 
        	 'epochs': 1000, 
        	 'lr': 0.01, 
        	 'adapt': False, 
        	 'errorf': <neurolab.error.SSE instance at 0x03757EB8>}
        	
        
        :Save/Load:
        	>>> net.save('sum.net')
        	>>> newnet = nl.load('sum.net')
        
        :Change train function:
        	>>> net.trainf = nl.train.TrainCG()
        	>>> # Change error function:
        	>>> net.trainf.defaunts['trainf'] = nl.error.SAE()
        
        :Change transfer function on output layer:
        	>>> net.layers[-1].transf = nl.trans.HardLim()
Keywords: neural network,neural networks,neuron,backpropagation,ann,python,matlab
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
