from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

def get_iris_data():

    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, random_state=1, stratify=y)

    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)

    return X_train_std, X_test_std, y_train, y_test


# def plot(a):
#     pass

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
    markers = ('o', 's', '^', '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))
    lab = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    lab = lab.reshape(xx1.shape)
    plt.contourf(xx1, xx2, lab, alpha=0.3, 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=colors[idx],
                    marker=markers[idx],
                    label=f'Class {cl}',
                    edgecolors='black'
                    )

    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0], X_test[:, 1], c='none', edgecolors='black', alpha=1.0,
            linewidths=1, marker='o',
            s=100, label='Test set')

    plt.xlabel('petal length [standertizsed]')
    plt.ylabel('petal width [standertised]')
    plt.legend()
    plt.tight_layout()
    plt.show()
    



