# -*- coding: utf-8 -*-
"""
Created on Wed Aug 17 10:02:34 2016

@author: Apu
"""
from matplotlib.colors import ListedColormap
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris
X = iris.data[:, [2, 3]]
y = iris.target

def plot_decision_regions(X, y, classifier, test_idx = None, resolution = 0.02):
    
    # set up marker gnerator and the colour map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    
    # plotting trhe decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())
    # plot all samples
    X_test, y_test = X[test_idx, :], y[test_idx]
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl)
    # highlight test samples
    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0], X_test[:, 1], c='', alpha=1.0, linewidth=1, marker='o', s=55, label='test set')



X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
lr = LogisticRegression(C  = 1000.00, random_state = 0)
lr.fit(X_train_std, y_train)
X_comblned_std = np.vstack((X_train_std,X_test_std))
y_combined = np.vstack((y_train,y_test))
plot_decision_regions(X_comblned_std,y_combined,classifier=lr,test_idx=range(105,150))
plt.legend(loc= 'upper left')
plt.show()
