Metadata-Version: 1.0
Name: neurolab
Version: 0.0.6
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 the using of the library, interface is similar to the 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,neural nets,backpropagation,python,matlab,numpy,machine learning
Platform: Any
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 :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
