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proof-1d-2neurons.py
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#!/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
if __name__ == '__main__':
import numpy as np
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from network import NG,SOM,DSOM, fromdistance, Identity
from distribution import uniform, normal, ring
# Z = Identity((20,20), (0,0))
# plt.imshow(Z)
# plt.colorbar()
# plt.show()
x1, x2 = 0.1, 0.9
elasticity = 2.5
lrate = 0.01
for i in range(10000):
# Sample 0.0
d = np.sqrt((x1-0)**2)*elasticity
d1 = lrate*np.exp(-(0.0/np.sqrt(2))**2/d**2)*np.sqrt((x1-0)**2)*(x1-0)
d2 = lrate*np.exp(-(1.0/np.sqrt(2))**2/d**2)*np.sqrt((x2-0)**2)*(x2-0)
x1 -= d1
x2 -= d2
#print x1,x2, d1, d2
# Sample 1.0
d = np.sqrt((x2-1)**2)*elasticity
d1_ = lrate*np.exp(-(1.0/np.sqrt(2))**2/d**2)*np.sqrt((x1-1)**2)*(x1-1)
d2_ = lrate*np.exp(-(0.0/np.sqrt(2))**2/d**2)*np.sqrt((x2-1)**2)*(x2-1)
x1 -= d1_
x2 -= d2_
#print x1,x2, d1_, d2_
if (d1+d1_) < 1e-15 and (d2+d2_) < 1e-15:
break
print i,":",x1, x2
# print
# n = 2
# samples1 = uniform(n=1)
# samples1[...] = [0.0,0.5]
# samples2 = uniform(n=1)
# samples2[...] = [1.0,0.5]
# dsom = DSOM((n,1,2), elasticity=elasticity, lrate_i=0.1)
# dsom.codebook[...] = [0.5,0.5]
# for i in range(20000):
# dsom.learn(samples1,1,show_progress=False)
# x1,x2 = dsom.codebook[0,0][0], dsom.codebook[-1,0][0]
# # print x1, x2
# dsom.learn(samples2,1,show_progress=False)
# x1,x2 = dsom.codebook[0,0][0], dsom.codebook[-1,0][0]
# print x1, x2
# print x1, x2