linear-SVM.py

import numpy as np

class LinearSVM:
    def __init__(self, learning_rate=0.0001, 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):
        n_samples, n_features = X.shape
        y_ = np.where(y <= 0, -1, 1)
        
        self.w = np.zeros(n_features)
        self.b = 0

        for _ in range(self.n_iters):
            for idx, x_i in enumerate(X):
                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):
        approx = np.dot(X, self.w) - self.b
        return np.sign(approx)

# Example usage:
# X, y would be predefined datasets. Note that y should contain only -1 or 1.
# For example:
# X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
# y = np.array([-1, -1, 1, 1, 1])

# model = LinearSVM()
# model.fit(X, y)
# predictions = model.predict(X)