def perceptron(numAttributes, Examples, Targets):
print "Computing weights..."
weight = []
eta = 0.1 # Learning rate
epsilon = 0.5 # Threshold value
passNum = 1
for i in range(numAttributes): # Initialize weights to 0
weight.append(0.0)
error = 1
error_count = -1
iteration = 1
errors = []
while error_count < 30: # Until our weights are perfect...
print "On pass number {0}...".format(passNum)
passNum += 1
error_count = 0
for i in range(len(Examples)): # Go through each example...
dotProd = dotProduct.dotProduct(Examples[i],weight) # Compute a dot product of the example in question and the current weight vector
result = dotProd > epsilon # Is the product closer to 1 or 0 and class accordingly
error = Targets[i] - result # Is the classification wrong?
if error != 0:
error_count += 1
for j in range(len(weight)):
weight[j] += eta * error * Examples[i][j] # Update weights if it was wrong
errors.append([iteration,error_count])
iteration += 1
if error_count < 25:
break
f = open("../DataFiles/errors.txt",'w+') # Record the errors
for value in errors:
print>>f,value
f.close()
return weight
def testError(Examples,Targets,weights):
error = 0.0
for example, target in zip(Examples, Targets):
result = dotProduct.dotProduct(example, weights)
if result > .5:
result = 1
else:
result = 0
if result != target:
error += 1
return error / len(Examples)
