Perceptron Learning - Implement online perceptron algorithm in python

The perceptron is a linear classifier, therefore it will never get to the state with all the input vectors classified correctly if the training set D is not linearly separable, i.e. if the positive examples can not be separated from the negative examples by a hyperplane. (Source: Wikipedia)

Sample Data:  C1, C2 and C3 represents three different class of data. It is guaranteed that these data set are linearly separable.

Linearly separable data sample (Three classes C1, C2 and C3)

Problem statement:- Write a program to generate
1.linear classifier for class C1 and C2
2. linear classifier for class C2 and C3

Sample code: - 

import sys
import matplotlib.pyplot as plt
import numpy as np

# Make a prediction with weights
def compute(row, weights):
 bias = weights[2]
 output = bias
 #output = (w1 * X1) + (w2 * X2) + bias
 for i in range(len(row)-1):
  output += weights[i] * row[i]
  ##print "output is",output
 return 1 if output > 0 else 0

#extrapolate classifer line with same slope as computed by final weights 
def getMultiplePoints(x,y,weight,boundX1,boundX2):
 x1 =[x,0]
 x2 =[0,y]
 pointsX = []
 pointsY = []
 pointsX.insert(1,y)
 pointsX.insert(2,0)
 pointsY.insert(1,0)
 pointsY.insert(2,x)
 #for boundX1
 pointsX.insert(0,boundX1)
 temp = -(weight[0]*boundX1 + weight[2])/weight[1]
 pointsY.insert(0,temp)
 #for boundX2
 pointsX.insert(3,boundX2)
 temp = -((weight[0]*boundX2) + weight[2])/weight[1]
 pointsY.insert(3,temp) 
 return (pointsX,pointsY)

#plot points 
def plotCoordinates(dataset,weightPlot):
 XList1 =[]
 YList1 =[]
 XList2 =[]
 YList2 =[] 
 count = 0
 boundX = -8
 boundY = 10
 x1 = - (weightPlot[2]/weightPlot[1])
 y1 = 0
 x2 = 0
 y2 = - (weightPlot[2]/weightPlot[0])
 #print x1 , y2
 # compute some random point with slope as W and bias b 
 plotTup = getMultiplePoints(x1,y2,weightPlot,boundX,boundY)
 for row in dataset:
  if(count<=9):
   XList1.append(row[0])
   YList1.append(row[1])
  else:
   XList2.append(row[0])
   YList2.append(row[1])
  count = count+1
 #Draw points with red and Blue color 
 plt.plot(XList1, YList1, 'ro',XList2, YList2, 'bo')
 plt.axis([boundX, boundY, boundX, boundY])
 plt.plot(plotTup[0],plotTup[1])
 plt.show()


#Update weight and bias 
def updateWeight(weights,x,l_rate,error):
 #update bias
 weights[2] = weights[2] + x[2] + l_rate * error
 #update weight part w1, w2
 for i in range(len(x)-1):
  weights[i] = weights[i] + l_rate * error * x[i]
 return weights

#Find linear classifier, predit outcome for each point and if error compute weight  
def findPerceptronClassifier(dataset,weights):
 flag = True
 epoch = 0
 retList = []
 l_rate = 0.2
 count = 0
 #lastWeight = []
 while(flag):
  #flag = False
  epoch = epoch + 1  
  #print("\nepoch = epoch + 1 is %d\n",epoch)
  count = 0
  for row in dataset:
   
   predicted_val = compute(row, weights)
   error = row[-1] - predicted_val
     
   #update weights
   if error != 0:
    weights = updateWeight(weights,row,l_rate,error)
    count = count + 1
   lastWeight = weights
  if error == 0 and count == 0:   
    flag = False
  else:
   flag = True
 retList.append(epoch)
 #print "Weight is ",weights
 #print "last Weight is ",lastWeight
 
 retList.append(weights) 
 return retList
 
# Input dataset for classifier 
datasetC1C2 =[[0.1,1.1,0], [6.8 ,7.1,0], [-3.5 ,-4.1,0], [2.0 ,2.7,0] , [4.1 ,2.8,0] ,
   [3.1 ,5.0,0], [-0.8 ,-1.3,0],[0.9 ,1.2,0], [5.0 ,6.4,0], [3.9, 4.0,0],
   [7.1 ,4.2,1], [-1.4, -4.3,1],[4.5 ,0.0,1 ], [6.3 ,1.6,1 ],[4.2 ,1.9,1 ], 
   [1.4 ,-3.2,1], [2.4 ,-4.0,1 ],[2.5 ,-6.1,1 ],[8.4 ,3.7,1], [4.1 ,-2.2,1]]


datasetC2C3 = [[-3.0 , -2.9,0], [0.5,  8.7,0], [2.9 , 2.1,0], [-0.1,  5.2,0], 
 [-4.0 , 2.2,0], [-1.3,  3.7,0], 
 [-3.4,  6.2,0], [-4.1,  3.4,0], [-5.1,  1.6,0], [1.9 , 5.1,0],[7.1 ,4.2,1], 
 [-1.4, -4.3,1],[4.5 ,0.0,1 ], [6.3 ,1.6,1 ],[4.2 ,1.9,1 ],    
 [1.4 ,-3.2,1], [2.4 ,-4.0,1 ],[2.5 ,-6.1,1 ],[8.4 ,3.7,1], [4.1 ,-2.2,1]]

#initialize inital weight and bias
initial_weights = [0,0,0]
#Iteration count 
epoch = 0
outList = []
def C1C2Classifier():
 #Iteration count for convergence - Dataset C1 and C2  
 outList = findPerceptronClassifier(datasetC1C2,initial_weights)
 epoch = outList[0]
 weightPlot = outList[1]
 ##print "Weight plot is ",weightPlot
 plotCoordinates(datasetC1C2,weightPlot)
 
def C2C3Classifier():
     #Iteration count for convergence - Dataset C2 and C3  
 outList = findPerceptronClassifier(datasetC2C3,initial_weights)
 epoch = outList[0]
 weightPlot = outList[1]
 ##print "Weight plot is ",weightPlot
 plotCoordinates(datasetC2C3,weightPlot)

# map the inputs to the function blocks
options = {
  1 : C1C2Classifier,
         2 : C2C3Classifier,
 }

#start
if __name__ == '__main__':
 print "1. Run C1C2 classifier \n2. Run C2C3 classifier\n"
 print "Enter your choice:\t"
 num = int(raw_input())
 options[num]()

Sample output:-
[zytham@s158519-vm perceptron]$ python Perceptron.py
1. Run C1C2 classifier
2. Run C2C3 classifier

Enter your choice: 1

[zytham@s158519-vm perceptron]$ python Perceptron.py
1. Run C1C2 classifier
2. Run C2C3 classifier

Enter your choice:
2