SVM/test.py at main · MASHOD0/SVM · GitHub

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from sklearn import datasets
import matplotlib.pyplot as plt
from main import SVM

X, y = datasets.make_blobs(
    n_samples=50, n_features=2, centers=2, cluster_std=1.05, random_state=40
)
y = np.where(y == 0, -1, 1)

clf = SVM()
clf.fit(X, y)

print(clf.w, clf.b)


def visualize_svm():
    def get_hyperplane_value(x, w, b, offset):
        return (-w[0] * x + b + offset) / w[1]

    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    plt.scatter(X[:, 0], X[:, 1], marker="o", c=y)

    x0_1 = np.amin(X[:, 0])
    x0_2 = np.amax(X[:, 0])

    x1_1 = get_hyperplane_value(x0_1, clf.w, clf.b, 0)
    x1_2 = get_hyperplane_value(x0_2, clf.w, clf.b, 0)

    x1_1_m = get_hyperplane_value(x0_1, clf.w, clf.b, -1)
    x1_2_m = get_hyperplane_value(x0_2, clf.w, clf.b, -1)

    x1_1_p = get_hyperplane_value(x0_1, clf.w, clf.b, 1)
    x1_2_p = get_hyperplane_value(x0_2, clf.w, clf.b, 1)

    ax.plot([x0_1, x0_2], [x1_1, x1_2], "y--")
    ax.plot([x0_1, x0_2], [x1_1_m, x1_2_m], "k")
    ax.plot([x0_1, x0_2], [x1_1_p, x1_2_p], "k")

    x1_min = np.amin(X[:, 1])
    x1_max = np.amax(X[:, 1])
    ax.set_ylim([x1_min - 3, x1_max + 3])

    plt.show()

if __name__ == "__main__":

    visualize_svm()