'''

This file runs the Random forest classifier on the Standard dataset. The default number of trees is 5 and the accuracy

is about 90%.

'''

import numpy as np

import pandas as pd

df = pd.read_csv('ex2data1train.csv', delimiter=',')

dataset = np.array(df).tolist()

x = []

y = []

for i in range(len(dataset)):

y.append(float(dataset[i][len(dataset[0])-1]))

t = []

for j in range(len(dataset[0])-1):

t.append(float(dataset[i][j]))

x.append(t)

df1 = pd.read_csv('ex2data1test.csv', delimiter=',')

dataset1 = np.array(df1).tolist()

xt = []

yt = []

for i in range(len(dataset1)):

yt.append(float(dataset1[i][len(dataset1[0])-1]))

t = []

for j in range(len(dataset1[0])-1):

t.append(float(dataset1[i][j]))

xt.append(t)

from RF import RF

rf = RF(B=5, Bagging = True)

rf.train(x, y)

yPredict = rf.predict(xt)

count = 0

for i in range(len(yPredict)):

if yPredict[i] == yt[i]:

count +=1

print(count/float(len(yt)))