#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Perceptron
from sklearn.metrics import accuracy_score
sys.path.append('/Users/user/PycharmProjects/DeepLearningPy') # 深い別のフォルダから指定フォルダのlibを読み出す場合
from matplotlib.colors import ListedColormap

def main():

    iris = datasets.load_iris()
    X = iris.data[:, [2,3]]
    T = iris.target
    X_train, X_test, T_train, T_test = train_test_split(X, T, 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)

    #エポック40、学習率0.1
    ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0, shuffle=True)
    ppn.fit(X_train_std, T_train)

    Y_pred = ppn.predict(X_test_std)
    print('Misclassied samples:%d' % (T_test != Y_pred).sum())
    print('Accuracy: %.2f' % accuracy_score(T_test, Y_pred))

    X_combined_std = np.vstack((X_train_std, X_test_std))
    y_combined = np.hstack((T_train, T_test))
    plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105,150))

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

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
    markers = ('s','x','o','^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    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())
    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)

    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, linewidths=1, marker='o', s=55, label='test set')





if __name__ == '__main__':
    main()