# -*- coding: utf-8 -*-
"""
Created on Sat Feb 15 19:17:12 2020

@author: DELL
"""
#importing all the required modules
from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
""" loading the dataset iris from scikit_learn this dataset is already
    available in scikit_learn"""
iris = datasets.load_iris()
X = iris.data[:,[2, 3]]
y = iris.target
X_train ,X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
ppn = Perceptron(n_iter_no_change=40, eta0 = 0.1, random_state = 0)
ppn.fit(X_train_std, y_train)
y_pred = ppn.predict(X_test_std)
print("Misclassified samples:{}".format((y_test!=y_pred).sum()))
print("Accuracy:{}".format(accuracy_score(y_test,y_pred)))
def plot_decision_region(X, y, classifier, test_idx= None, resolution = 1):
    #setup markers
    markers = ("s","x","o","^","v")
    colors= ("red","blue","lightgreen", "gray","cyan")
    cmap = ListedColormap(colors[:len(np.unique(y))])
    #plot the 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
    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_combined_std= np.vstack((X_train_std,X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_region(X= X_combined_std, y= y_combined,classifier= ppn,test_idx = range(105,150))
plt.xlabel("petal length[standardised]")
plt.ylabel("petal width[standardised]")
plt.legend(loc = "upper left")
plt.show    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    