from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LogisticRegression

def plot_decision_regions(X, y, classifier, resolution=0.1):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))+1])

    # 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 class 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=colors[idx],
                    marker=markers[idx], label=cl)


# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
y = np.concatenate((y, np.ones(50)+2))
y = y.astype(int)
X = np.concatenate((X, X[:50]*2))

lr = LogisticRegression(solver='newton-cg', multi_class='multinomial')
lr.fit(X, y)
plot_decision_regions(X, y, classifier=lr)

iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
y = np.concatenate((y, np.ones(50)+2, np.ones(50)+3))
y = y.astype(int)
X = np.concatenate((X, X[:50]*2, X[:50]*3))

lr = LogisticRegression(solver='newton-cg', multi_class='multinomial')
lr.fit(X, y)
plot_decision_regions(X, y, classifier=lr)