import math import sys A1 = [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1] B1 = [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0] C1 = [0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0] D1 = [1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0] E1 = [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1] J1 = [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0] K1 = [1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1] A2 = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0] B2 = [1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0] C2 = [0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0] D2 = [1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0] E2 = [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] J2 = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0] K2 = [1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0] A3 = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1] B3 = [1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0] C3 = [0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0] D3 = [1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0] E3 = [1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1] J3 = [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0] K3 = [1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1] NAMES = ["A1", "B1", "C1", "D1", "E1", "J1", "K1", "A2", "B2", "C2", "D2", "E2", "J2", "K2", "A3", "B3", "C3", "D3", "E3", "J3", "K3"] TARGET = [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6] NUMBER_OF_CLUSTERS = 7 VEC_LEN = 63 TRAINING_PATTERNS = 21 DECAY_RATE = 0.96 # About 100 iterations. MIN_ALPHA = 0.01 class LVQ_Example1: def __init__(self, numClusters, vectorLength, trainingPatterns, decayRate, minAlpha, patternArray, namesArray, target): self.mNumberOfClusters = numClusters self.mVectorLength = vectorLength self.mTrainingPatterns = trainingPatterns self.mDecayRate = decayRate self.mMinimumAlpha = minAlpha self.alpha = 0.6 self.d = [] # Network nodes. The "clusters" self.w = [] # Weight matrix. self.mPatterns = patternArray self.mNames = namesArray self.mTarget = target return def initialize_arrays(self): self.d = [0.0] * self.mNumberOfClusters for i in range(self.mNumberOfClusters): self.w.append([0.0] * self.mVectorLength) for j in range(self.mVectorLength): self.w[i][j] = self.mPatterns[i][j] sys.stdout.write("Weights for cluster " + str(i) + " initialized to pattern " + self.mNames[i] + "\n") return def compute_input(self, vectorArray): self.d = [0.0] * self.mNumberOfClusters for i in range(self.mNumberOfClusters): for j in range(self.mVectorLength): self.d[i] += math.pow((self.w[i][j] - vectorArray[j]), 2) return def compute_input_2D(self, vectorArray, vectorNumber): self.d = [0.0] * self.mNumberOfClusters for i in range(self.mNumberOfClusters): for j in range(self.mVectorLength): self.d[i] += math.pow((self.w[i][j] - vectorArray[vectorNumber][j]), 2) return def get_cluster(self, inputPattern): # Compute input for all nodes. self.compute_input(inputPattern) return self.get_minimum(self.d) def get_minimum(self, nodeArray): minimum = 0; foundNewMinimum = False done = False while not done: foundNewMinimum = False for i in range(self.mNumberOfClusters): if i != minimum: if nodeArray[i] < nodeArray[minimum]: minimum = i foundNewMinimum = True if foundNewMinimum == False: done = True return minimum def update_weights(self, vectorNumber, dMin): for i in range(self.mVectorLength): # Update the winner. if dMin == self.mTarget[vectorNumber]: self.w[dMin][i] += (self.alpha * (self.mPatterns[vectorNumber][i] - self.w[dMin][i])) else: self.w[dMin][i] -= (self.alpha * (self.mPatterns[vectorNumber][i] - self.w[dMin][i])) return def training(self): dMin = 0 while self.alpha > self.mMinimumAlpha: for i in range(self.mTrainingPatterns): # Compute input for all nodes. self.compute_input_2D(self.mPatterns, i) # See which is smaller? dMin = self.get_minimum(self.d) # Update the weights on the winning unit. self.update_weights(i, dMin) # Reduce the learning rate. self.alpha = self.mDecayRate * self.alpha return if __name__ == '__main__': pattern = [] pattern.append(A1) pattern.append(B1) pattern.append(C1) pattern.append(D1) pattern.append(E1) pattern.append(J1) pattern.append(K1) pattern.append(A2) pattern.append(B2) pattern.append(C2) pattern.append(D2) pattern.append(E2) pattern.append(J2) pattern.append(K2) pattern.append(A3) pattern.append(B3) pattern.append(C3) pattern.append(D3) pattern.append(E3) pattern.append(J3) pattern.append(K3) learningVQ1 = LVQ_Example1(NUMBER_OF_CLUSTERS, VEC_LEN, TRAINING_PATTERNS, DECAY_RATE, MIN_ALPHA, pattern, NAMES, TARGET) learningVQ1.initialize_arrays() learningVQ1.training() # Display results sys.stdout.write("\n") for i in range(TRAINING_PATTERNS): sys.stdout.write("Pattern " + NAMES[i] + " belongs to cluster " + str(learningVQ1.get_cluster(pattern[i])) + "\n")