q7_1.py

import numpy as np
import random
import pdb
import os
import sys

def update(W, X_vec, n):
	W_new = W + n*X_vec
	return W_new

def get_weights(output_dimension):
    W = np.random.randn(1, output_dimension)
    return W

def augment_data(X, y):
    f = lambda x,y: x*(-1) if y == -1 else x
    for i in range(X.shape[0]):
        X[i,:] = f(X[i,:],y[i])
    return X, y

def train(X, y, wt):
  errors = np.ones(y.shape)
  while sum(errors) > 0:
    for i in range(X.shape[0]):
      if np.dot(X[i,:], np.transpose(wt)) > 0 :
        errors[i] = 0
      else:
          wt = update(wt, X[i], 0.1)
      print(wt)

if __name__ == '__main__':
	X = np.array([[1, 1], [-1, -1], [2, 2], [-2, -2], [-1, 1],\
	 [1, -1]], dtype=np.float64)
	y = np.array([1, -1, 1, -1, 1, 1], dtype=np.float64)
	X, y = augment_data(X, y)
	wt = get_weights(X.shape[1])
	# pdb.set_trace()
	train(X, y, wt)