from sklearn import datasets 
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np 

def plot_decision_regions(X, y, classifier,                     
                test_idx=None, resolution=0.02):    
    
    # setup marker generator and color map

    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='', edgecolor='black', alpha=1.0, 
            linewidth=1, marker='o',                 
            s=100, label='test set')

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

# split into training and test data
X_train, X_test, y_train, y_test = train_test_split(
                                X, y, test_size=0.3, random_state=0)

# define scaler
sc = StandardScaler()
sc.fit(X_train)

#scale data
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

# Define and train perceptron
ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)

# Define and train logistic regression
lr = LogisticRegression(C=1000.0, random_state=0)
lr.fit(X_train_std, y_train)

# make predictions 
y_pred_ppn = ppn.predict(X_test_std)
y_pred_lr = lr.predict(X_test_std)

print('Misclassified samples for perceptron: {0}'.format((y_test != y_pred_ppn).sum()))
print('Perceptron Accuracy: {0:.2f}'.format(accuracy_score(y_test, y_pred_ppn)))

print('Misclassified samples for Logistic Regression: {0}'.format((y_test != y_pred_lr).sum()))
print('Logistic Regression Accuracy: {0:.2f}'.format(accuracy_score(y_test, y_pred_lr)))

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

# plot_decision_regions(X_combined_std,
#                       y_combined,
#                       classifier=ppn,
#                       test_idx=range(105,150))

plot_decision_regions(X_combined_std,
                      y_combined,
                      classifier=lr,
                      test_idx=range(105, 150))

plt.xlabel('petal length [standardized]') 
plt.ylabel('petal width [standardized]') 
plt.legend(loc='upper left')
plt.show()