# neurolab-examples

**Repository Path**: loveknut/neurolab-examples

## Basic Information

- **Project Name**: neurolab-examples
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2015-01-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

        >>> 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]])
    
    
    
    
    
    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: 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: 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: newlvq
        default train function: neurolab.train.train_lvq() 

    Elman Recurrent network
        create function: neurolab.net.newelm()
        example of use: newelm
        default train function: neurolab.train.train_gdx()
        support train functions: train_gd, train_gda, train_gdm, train_rprop, train_bfgs, train_cg 
    Hopfield Recurrent network
        create function: neurolab.net.newhop()
        example of use: newhop 
    Hemming Recurrent network
        create function: neurolab.net.newhem()
        example of use: newhem