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# Introduction

Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks.

Features: | Pure python + numpy API like Neural Network Toolbox (NNT) from MATLAB Interface to use train algorithms form scipy.optimize Flexible network configurations and learning algorithms. You may change: train, error, initializetion and activation functions Variety of supported types of Artificial Neural Network and learning algorithms
Example: |
    >>> import numpy as np
    >>> import neurolab as nl
    >>> # Create train samples
    >>> input = np.random.uniform(-0.5, 0.5, (10, 2))
    >>> target = (input[:, 0] + input[:, 1]).reshape(10, 1)
    >>> # Create network with 2 inputs, 5 neurons in input layer and 1 in output layer
    >>> net = nl.net.newff([[-0.5, 0.5], [-0.5, 0.5]], [5, 1])
    >>> # Train process
    >>> err = net.train(input, target, show=15)
    Epoch: 15; Error: 0.150308402918;
    Epoch: 30; Error: 0.072265865089;
    Epoch: 45; Error: 0.016931355131;
    The goal of learning is reached
    >>> # Test
    >>> net.sim([[0.2, 0.1]]) # 0.2 + 0.1
    array([[ 0.28757596]])
Now support neural networks types:
| Single layer perceptron create function: neurolab.net.newp() example of use: newp default train function: `neurolab.train.train_delta()` support train functions: train_gd, train_gda, train_gdm, train_gdx, train_rprop, train_bfgs, train_cg Multilayer feed forward perceptron create function: `neurolab.net.newff()` example of use: Feed Forward Multilayer Perceptron (newff) default train function: `neurolab.train.train_gdx()` support train functions: train_gd, train_gda, train_gdm, train_rprop, train_bfgs, train_cg Competing layer (Kohonen Layer) create function: `neurolab.net.newc()` example of use: Competing layer (newc) default train function: `neurolab.train.train_cwta()` support train functions: train_wta Learning Vector Quantization (LVQ) create function: `neurolab.net.newlvq()` example of use: Learning Vector Quantization (newlvq) default train function: `neurolab.train.train_lvq()`

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