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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 26 11:11:36 2016

@author: Kamalkhan
"""

import numpy as np
import csv
import pandas as pd
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
X = []
Y = []

dataset = load_iris()
iris = pd.DataFrame(dataset.data)
iris.columns = ["s.L", "s.W", "p.L", "p.W"]
iris_normalized = preprocessing.normalize(iris)

X = iris
Y = dataset.target

X_norm = iris_normalized


def scatter_plot():
    ind = 0
    x = np.arange(4)
    ys = [i+x+(i*x)**2 for i in range(4)]
    colors = cm.rainbow(np.linspace(0, 1, len(ys)))
    for i in range(4):
        for j in range(i+1,3):
            print 'Correlation between'+str(X.columns[i]) +' and '+str(X.columns[j])
            print 'Correlation: '+ str((pearsonr(np.array(X)[:,j], np.array(X)[:,i])[0]))
            for feature,target in zip(np.array(X)[:,j], np.array(X)[:,i]):
                plt.scatter(feature,target,color=colors[ind])
            ind+=1
            plt.legend()
            plt.show()



def norm_scatter_plot():
    ind = 0
    x = np.arange(4)
    ys = [i+x+(i*x)**2 for i in range(4)]
    colors = cm.rainbow(np.linspace(0, 1, len(ys)))
    for i in range(4):
        for j in range(i+1,3):
            print 'Correlation between'+str(X.columns[i]) +' and '+str(X.columns[j])
            print 'Correlation: '+ str((pearsonr(np.array(X)[:,j], np.array(X)[:,i])[0]))
            for feature,target in zip(np.array(X_norm)[:,j], np.array(X_norm)[:,i]):
                plt.scatter(feature,target,color=colors[ind])
            ind+=1
            plt.legend()
            plt.show()


print 'After normalization-->'
scatter_plot()

print 'Before normalization-->'
norm_scatter_plot()