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class LinearRegression: def fit(self, train_features, train_target, **kwargs): X = np.concatenate((np.ones((train_features.shape[0], 1)), train_features), axis=1) y = train_target w = ((np.linalg.inv(X.T @ X) @ X.T) @ y) if 'key' in kwargs: key = kwargs['key'] self.w = inv(key) @ w[1:] else: self.w = w[1:] self.w0 = w[0] def predict(self, test_features): return test_features.dot(self.w) + self.w0
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