b = np.concatenate((v2,-1*l),axis = 1)
# concatenate the data to form a single training set
train_data = np.concatenate((a,b),axis =0)
return train_data
# Instantiate the class Perceptron
c,d = data_generate(60) # Generate test data
show_data(c,d)
# Add labels +1 to 'a' and -1 to 'b'
Training = labels(60,c,d)
w = Perceptron() # Instantiate the Perceptron class
# Now pass the training data for the perceptron to learn the weight vectors
# using the Rosenblatt learning algorithm
weight_vector = w.learning(Training)
t1,t2 = data_generate(10)
Test = labels(10,t1,t2)
correct = 0
incorrect = 0
for x in Test:
d = x[3]
x = np.delete(x,3)
y = w.response(np.vdot(weight_vector,x))
if y == d:
correct+=1
else:
incorrect+=1
print 'Number of correctly classified points is' +' '+str(correct)
print 'Number of incorrectly classified points is' +' '+str(incorrect)
