R&P.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 18 23:42:40 2018

@author: yeaseen
"""
import numpy as np


def split(s, delim=[" ", '\n']):
    words = []
    word = []
    for c in s:
        if c not in delim:
            word.append(c)
        else:
            if word:
                words.append(''.join(word))
                word = []
    if word:
        words.append(''.join(word))
    return words

def loadfile(filename,checkTrain):
    file = open(filename, "r")
    first = checkTrain
    rows = list()
    for line in file:
        if(first) == True:
            dims = split(line)
            first = False
        else:
            vals = split(line, [' ' ,'\t', '\n'])
            #print(vals)
            rows.append(vals)

    if(checkTrain):
        return dims, rows
    else:
        return rows



dims, rows = loadfile('Train1.txt',True)

test = loadfile('Test1.txt',False)



dims=np.array(dims)
dims = dims.astype(np.float)
rows=np.array(rows)
mat = rows.astype(np.float)

test=np.array(test)
test= test.astype(np.float)




att=int(dims[0])
#print(att)
clss=int(dims[1])
#print(clss)


#wactive =np.random.random_sample(((att+1),))

wactive=[0,0,1]
#print(w)


matrix=np.array(mat)

Y=matrix[:,-1].copy()
Y=np.array(Y)
Y=Y.astype(np.int)
Y=np.array(Y).tolist()
#print(Y)


matrix[:,-1]=np.ones((matrix.shape[0]))

targetOutput=test[:,-1].copy()
targetOutput=np.array(targetOutput)
targetOutput=targetOutput.astype(np.int)
targetOutput=np.array(targetOutput).tolist()




test[:,-1] = np.ones((test.shape[0]))


counter=0
maxCounter=matrix.shape[0]
print(maxCounter)
discriminant= True
while(discriminant):
    for i in matrix:
        product=np.dot(i,wactive)
        print(product)
        counter+=1
        if(product < 0):
            classed=1
        else:
            classed=2
        
        if(classed != Y[counter]):
            if(classed == 1):
                #print('sub')
                wactive=np.add(wactive,i)
                counter=0
            else:
                #print('add')
                wactive=np.subtract(wactive,i)
                counter=0
    if(counter == maxCounter):
        discriminant = False

print(wactive)