figure-drop.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2009 Nicolas Rougier - INRIA - CORTEX Project
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either  version 3 of the  License, or (at your  option)
# any later version.
# 
# This program is  distributed in the hope that it will  be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR  A  PARTICULAR PURPOSE.  See  the GNU  General  Public 
# License for  more details.
# 
# You should have received a copy  of the GNU General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
# 
# Contact:  CORTEX Project - INRIA
#           INRIA Lorraine, 
#           Campus Scientifique, BP 239
#           54506 VANDOEUVRE-LES-NANCY CEDEX 
#           FRANCE

def Gaussian(shape,center,sigma=0.5):
    ''' Return a two-dimensional gaussian with given shape.

    :Parameters:
       `shape` : (int,int)
           Shape of the output array
       `center`: (int,int)
           Center of Gaussian
       `sigma` : float
           Width of Gaussian
    '''
    def g(x):
        return np.exp(-x**2/sigma**2)
    return fromdistance(g,shape,center)


if __name__ == '__main__':
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from network import NG,SOM,DSOM
    from distribution import uniform, normal, ring

    n = 4
    epochs = 20000
    N = 4

    #np.random.seed(123)
    samples = uniform(n=N) 
    samples[:,0] = [0,1,0,1]
    samples[:,1] = [0,0,1,1]

    print 'Neural Gas' 
    np.random.seed(123)
    ng = NG((n,n,2))
    ng.learn(samples,epochs)
    print 'Self-Organizing Map'
    np.random.seed(123)
    som = SOM((n,n,2))
    som.learn(samples,epochs)
    print 'Dynamic Self-Organizing Map'
    np.random.seed(123)
    dsom = DSOM((n,n,2), elasticity=1.5)
    dsom.learn(samples,epochs)

    fig = plt.figure(figsize=(21,8))
    axes = plt.subplot(1,3,1)
    ng.plot(axes)
    axes = fig.add_subplot(1,3,2)
    som.plot(axes)
    axes = fig.add_subplot(1,3,3)
    dsom.plot(axes)
    fig.savefig('test.png',dpi=150)