## [Psyco-checkins] CVS: psyco/test bpnn.py,NONE,1.1

 [Psyco-checkins] CVS: psyco/test bpnn.py,NONE,1.1 From: Petru Paler - 2002-01-08 19:37:00 ```Update of /cvsroot/psyco/psyco/test In directory usw-pr-cvs1:/tmp/cvs-serv7268/test Added Files: bpnn.py Log Message: Adding Tim Hochberg's float stuff. --- NEW FILE: bpnn.py --- #!/usr/bin/python # Back-Propagation Neural Networks # # Written in Python. See http://www.python.org/ # # Neil Schemenauer import math import random import operator import string random.seed(0) def time(fn, *args): import time, traceback begin = time.clock() try: result = fn(*args) except: end = time.clock() traceback.print_exc() result = '' else: end = time.clock() return result, end-begin # calculate a random number where: a <= rand < b def rand(a, b): return (b-a)*random.random() + a # Make a matrix (we could use NumPy to speed this up) def makeMatrix(I, J, fill=0.0): m = [] for i in range(I): m.append([fill]*J) return m class NN: def __init__(self, ni, nh, no): # number of input, hidden, and output nodes self.ni = ni + 1 # +1 for bias node self.nh = nh self.no = no # activations for nodes self.ai = [1.0]*self.ni self.ah = [1.0]*self.nh self.ao = [1.0]*self.no # create weights self.wi = makeMatrix(self.ni, self.nh) self.wo = makeMatrix(self.nh, self.no) # set them to random vaules for i in range(self.ni): for j in range(self.nh): self.wi[i][j] = rand(-2.0, 2.0) for j in range(self.nh): for k in range(self.no): self.wo[j][k] = rand(-2.0, 2.0) # last change in weights for momentum self.ci = makeMatrix(self.ni, self.nh) self.co = makeMatrix(self.nh, self.no) def update(self, inputs): if len(inputs) != self.ni-1: raise ValueError, 'wrong number of inputs' # input activations for i in range(self.ni-1): #self.ai[i] = 1.0/(1.0+math.exp(-inputs[i])) self.ai[i] = inputs[i] # hidden activations for j in range(self.nh): sum = 0.0 for i in range(self.ni): sum = sum + self.ai[i] * self.wi[i][j] self.ah[j] = 1.0/(1.0+math.exp(-sum)) # output activations for k in range(self.no): sum = 0.0 for j in range(self.nh): sum = sum + self.ah[j] * self.wo[j][k] self.ao[k] = 1.0/(1.0+math.exp(-sum)) return self.ao[:] def backPropagate(self, targets, N, M): if len(targets) != self.no: raise ValueError, 'wrong number of target values' # calculate error terms for output output_deltas = [0.0] * self.no for k in range(self.no): ao = self.ao[k] output_deltas[k] = ao*(1-ao)*(targets[k]-ao) # calculate error terms for hidden hidden_deltas = [0.0] * self.nh for j in range(self.nh): sum = 0.0 for k in range(self.no): sum = sum + output_deltas[k]*self.wo[j][k] hidden_deltas[j] = self.ah[j]*(1-self.ah[j])*sum # update output weights for j in range(self.nh): for k in range(self.no): change = output_deltas[k]*self.ah[j] self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k] self.co[j][k] = change #print N*change, M*self.co[j][k] # update input weights for i in range(self.ni): for j in range(self.nh): change = hidden_deltas[j]*self.ai[i] self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j] self.ci[i][j] = change # calculate error error = 0.0 for k in range(len(targets)): error = error + 0.5*(targets[k]-self.ao[k])**2 return error def test(self, patterns): for p in patterns: print p[0], '->', self.update(p[0]) def weights(self): print 'Input weights:' for i in range(self.ni): print self.wi[i] print print 'Output weights:' for j in range(self.nh): print self.wo[j] def train(self, patterns, iterations=2000, N=0.5, M=0.1): # N: learning rate # M: momentum factor for i in xrange(iterations): error = 0.0 for p in patterns: inputs = p[0] targets = p[1] self.update(inputs) error = error + self.backPropagate(targets, N, M) if i % 100 == 0: print 'error %-14f' % error def demo(): # Teach network XOR function pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ] # create a network with two input, two hidden, and two output nodes n = NN(2, 3, 1) # train it with some patterns n.train(pat, 2000) # test it n.test(pat) if __name__ == '__main__': import _psyco v, t1 = time(demo) v, t2 = time(demo) demo = _psyco.proxy(demo, 99) v, t3 = time(demo) v, t4 = time(demo) v, t5 = time(demo) print t1, t2, t3, t4, t5 ```

 [Psyco-checkins] CVS: psyco/test bpnn.py,NONE,1.1 From: Petru Paler - 2002-01-08 19:37:00 ```Update of /cvsroot/psyco/psyco/test In directory usw-pr-cvs1:/tmp/cvs-serv7268/test Added Files: bpnn.py Log Message: Adding Tim Hochberg's float stuff. --- NEW FILE: bpnn.py --- #!/usr/bin/python # Back-Propagation Neural Networks # # Written in Python. See http://www.python.org/ # # Neil Schemenauer import math import random import operator import string random.seed(0) def time(fn, *args): import time, traceback begin = time.clock() try: result = fn(*args) except: end = time.clock() traceback.print_exc() result = '' else: end = time.clock() return result, end-begin # calculate a random number where: a <= rand < b def rand(a, b): return (b-a)*random.random() + a # Make a matrix (we could use NumPy to speed this up) def makeMatrix(I, J, fill=0.0): m = [] for i in range(I): m.append([fill]*J) return m class NN: def __init__(self, ni, nh, no): # number of input, hidden, and output nodes self.ni = ni + 1 # +1 for bias node self.nh = nh self.no = no # activations for nodes self.ai = [1.0]*self.ni self.ah = [1.0]*self.nh self.ao = [1.0]*self.no # create weights self.wi = makeMatrix(self.ni, self.nh) self.wo = makeMatrix(self.nh, self.no) # set them to random vaules for i in range(self.ni): for j in range(self.nh): self.wi[i][j] = rand(-2.0, 2.0) for j in range(self.nh): for k in range(self.no): self.wo[j][k] = rand(-2.0, 2.0) # last change in weights for momentum self.ci = makeMatrix(self.ni, self.nh) self.co = makeMatrix(self.nh, self.no) def update(self, inputs): if len(inputs) != self.ni-1: raise ValueError, 'wrong number of inputs' # input activations for i in range(self.ni-1): #self.ai[i] = 1.0/(1.0+math.exp(-inputs[i])) self.ai[i] = inputs[i] # hidden activations for j in range(self.nh): sum = 0.0 for i in range(self.ni): sum = sum + self.ai[i] * self.wi[i][j] self.ah[j] = 1.0/(1.0+math.exp(-sum)) # output activations for k in range(self.no): sum = 0.0 for j in range(self.nh): sum = sum + self.ah[j] * self.wo[j][k] self.ao[k] = 1.0/(1.0+math.exp(-sum)) return self.ao[:] def backPropagate(self, targets, N, M): if len(targets) != self.no: raise ValueError, 'wrong number of target values' # calculate error terms for output output_deltas = [0.0] * self.no for k in range(self.no): ao = self.ao[k] output_deltas[k] = ao*(1-ao)*(targets[k]-ao) # calculate error terms for hidden hidden_deltas = [0.0] * self.nh for j in range(self.nh): sum = 0.0 for k in range(self.no): sum = sum + output_deltas[k]*self.wo[j][k] hidden_deltas[j] = self.ah[j]*(1-self.ah[j])*sum # update output weights for j in range(self.nh): for k in range(self.no): change = output_deltas[k]*self.ah[j] self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k] self.co[j][k] = change #print N*change, M*self.co[j][k] # update input weights for i in range(self.ni): for j in range(self.nh): change = hidden_deltas[j]*self.ai[i] self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j] self.ci[i][j] = change # calculate error error = 0.0 for k in range(len(targets)): error = error + 0.5*(targets[k]-self.ao[k])**2 return error def test(self, patterns): for p in patterns: print p[0], '->', self.update(p[0]) def weights(self): print 'Input weights:' for i in range(self.ni): print self.wi[i] print print 'Output weights:' for j in range(self.nh): print self.wo[j] def train(self, patterns, iterations=2000, N=0.5, M=0.1): # N: learning rate # M: momentum factor for i in xrange(iterations): error = 0.0 for p in patterns: inputs = p[0] targets = p[1] self.update(inputs) error = error + self.backPropagate(targets, N, M) if i % 100 == 0: print 'error %-14f' % error def demo(): # Teach network XOR function pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ] # create a network with two input, two hidden, and two output nodes n = NN(2, 3, 1) # train it with some patterns n.train(pat, 2000) # test it n.test(pat) if __name__ == '__main__': import _psyco v, t1 = time(demo) v, t2 = time(demo) demo = _psyco.proxy(demo, 99) v, t3 = time(demo) v, t4 = time(demo) v, t5 = time(demo) print t1, t2, t3, t4, t5 ```