def test_svm():
from models.svm import SVM
x, y = np.random.randn(2, 400, 2), np.zeros([2, 400], dtype=int)
y[0] = -1
y[1] = 1
for i, theta in enumerate(np.linspace(0, 2 * np.pi, 40)):
x[0, (10 * i):(
10 * i +
10)] += 5 * np.array([np.cos(theta), np.sin(theta)])
x = x.reshape(-1, 2)
y = y.flatten()
plot_scatter([x[y == i] for i in [-1, 1]], 'Real')
# train
svm = SVM(C=10, sigma=1, kernel='rbf', max_iter=100)
svm.fit(x, y)
pred = np.array(svm.predict(x))
plot_scatter([x[pred == i] for i in [-1, 1]], 'Pred')
acc = np.sum(pred == y) / len(pred)
print(f'Acc = {100 * acc:.2f}%')
print(svm.support_vectors)
from sklearn.datasets import load_breast_cancer from sklearn.datasets import make_blobs from models.svm import SVM import matplotlib.pyplot as plt from validation.classification import A_micro_average from preprocessing.features_enginering import normalize_dataset from preprocessing.split import train_test_split #X, y = load_wine(return_X_y=True) X = make_blobs(1000, centers=3) y = X[1] X = X[0] #normalize_dataset(X) x_train, y_train, x_test, y_test = train_test_split(X, y, .8) plt.scatter(x=X[:, 0], y=X[:, 1], c=y) plt.show() # %% svm = SVM(C=1) svm.fit(x_train, y_train) # %% res = svm.predict(x_test) A = A_micro_average(y_test, res) #%% from mlxtend.plotting import plot_decision_regions plot_decision_regions(X=X, y=y, clf=svm) plt.show()
