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ridge regression

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jbsch 2024-10-23 20:40:28 +05:30
parent 6fc134d96c
commit 7484cda516
1 changed files with 0 additions and 14 deletions
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machine_learning/ridge_regression/model.py
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@ -9,12 +9,8 @@ class RidgeRegression:
self.num_iterations:int = num_iterations self.num_iterations:int = num_iterations
self.theta:np.ndarray = None self.theta:np.ndarray = None
<<<<<<< HEAD
def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
=======
def feature_scaling(self, X):
>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
mean = np.mean(X, axis=0) mean = np.mean(X, axis=0)
std = np.std(X, axis=0) std = np.std(X, axis=0)
@ -43,13 +39,8 @@ class RidgeRegression:
X_scaled, _, _ = self.feature_scaling(X) X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta) return X_scaled.dot(self.theta)
<<<<<<< HEAD
def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float: def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
X_scaled, _, _ = self.feature_scaling(X) X_scaled, _, _ = self.feature_scaling(X)
=======
def compute_cost(self, X, y):
X_scaled, _, _ = self.feature_scaling(X)
>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
m = len(y) m = len(y)
predictions = X_scaled.dot(self.theta) predictions = X_scaled.dot(self.theta)
@ -69,13 +60,8 @@ if __name__ == "__main__":
y = df["ADR"].values y = df["ADR"].values
y = (y - np.mean(y)) / np.std(y) y = (y - np.mean(y)) / np.std(y)
<<<<<<< HEAD
# added bias term to the feature matrix # added bias term to the feature matrix
X = np.c_[np.ones(X.shape[0]), X] X = np.c_[np.ones(X.shape[0]), X]
=======
# Add bias term (intercept) to the feature matrix
X = np.c_[np.ones(X.shape[0]), X]
>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
# initialize and train the ridge regression model # initialize and train the ridge regression model
model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000) model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)