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import numpy as np

class SVM:
    def __init__(self, learning_rate = 0.001, lambda_param = 0.01, n_iters = 1000):
        self.lr = learning_rate
        self.lambda_param = lambda_param
        self.n_iters = n_iters
        self.w = None
        self.b = None
    def fit(self, X, y):
        y_ = np.where(y <= 0, -1, 1)
        n_samples, n_features = X.shape
        
        self.w = np.zeros(n_features)
        self.b = 0
        
        for _ in range(self.n_iters):
            for idx, x_i in enumerate(X):
                # chi so, value
                condition = y_[idx] * (np.dot(x_i, self.w) - self.b) >= 1
                if condition:
                    self.w -= self.lr * (2 * self.lambda_param * self.w)
                else:
                    self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_[idx]))
                    self.b -= self.lr * y_[idx]
        
    def predict(self, X):
        linear_output = np.dot(X, self.w) - self.b
        return np.sign(linear_output) # tra ve dau cua ket qua

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import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

X, y = datasets.make_blobs(n_samples = 500, 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()
    
visualize_svm()