pegasos(SVM).py

import numpy as np
import math
import matplotlib.pyplot as plt
#----------Pegasos Code definition------------------------#
def pegasos(feature_matrix,labels,T):
  theta=np.array([0]*feature_matrix.shape[1])
  theta_0=0
  count=0
  L=2 #value for lambda
  for i in range(T):
    for j in range(len(feature_matrix)):
      count+=1
      eta=1/math.sqrt(count) #decreasing eta value as iteration increase
      z=labels[j]*(np.dot(feature_matrix[j],theta)+theta_0)
      if(z<=1):
        theta=theta+eta*labels[j]*feature_matrix[j]-eta*L*theta
        theta_0=theta_0+eta*labels[j]
      else:
        theta=theta-eta*L*theta
  return theta,theta_0
#---------------Ends Pegsos----------------------------------#

if __name__=="__main__":
    x=np.array([[1,2],[2,3],[3,4],[4,5]])
    y=np.array([1,1,-1,-1])
    theta,theta_0=pegasos(x,y,100)
    print("theta ={} theta_0= {}".format(theta,theta_0))
    #----------visualization-----------------------------#
    pos=[[],[]]
    neg=[[],[]]
    for i in range(len(x)):
        if(y[i]==1):
            pos[0].append(x[i][0])
            pos[1].append(x[i][1])
        else:
            neg[0].append(x[i][0])
            neg[1].append(x[i][1])
    plt.scatter(neg[0],neg[1])
    plt.scatter(pos[0],pos[1])
    print(x,y)
    x=[i for i in range(0,5)]
    y=[-(theta[0]*i+theta_0)/theta[1] for i in x]
    plt.plot(x,y)
    plt.show()