visualize_boundary_linear.py

import numpy as np
from matplotlib import pyplot as plt

from plot_data import plot_data

def visualize_boundary_linear(X, y, clf):

    plot_data(X, y)

    coef = clf.coef_.ravel()
    intercept = clf.intercept_.ravel()

    xp = np.linspace(np.min(X[:, 0]), np.max(X[:, 0]), 100)
    yp = -1.0 * (coef[0] * xp + intercept[0]) / coef[1]

    plt.plot(xp, yp, linestyle='-', color='b')

    """
    Plots a linear decision boundary learned by the SVM.

    Parameters
    ----------
    X : ndarray, shape (n_samples, n_features)
        Samples, where n_samples is the number of samples and n_features is the number of features.
    y : ndarray, shape (n_samples,)
        Labels.
    clf :   Support Vector Classification
            The trained SVM.
    """