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jbsch 2024-10-23 20:55:20 +05:30
commit 38764378d4
1 changed files with 21 additions and 23 deletions
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machine_learning/ridge_regression/model.py
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@ -6,16 +6,18 @@ class RidgeRegression:
def __init__(self,
alpha: float = 0.001,
regularization_param: float = 0.1,
num_iterations:int=1000) -> None:
num_iterations: int = 1000,
) -> None:
self.alpha: float = alpha
self.regularization_param: float = regularization_param
self.num_iterations: int = num_iterations
self.theta: np.ndarray = None
def feature_scaling(self, x:np.ndarray)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
mean = np.mean(x, axis=0)
std = np.std(x, axis=0)
def feature_scaling(
self, X: np.ndarray
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
mean = np.mean(X, axis=0)
std = np.std(X, axis=0)
# avoid division by zero for constant features (std = 0)
std[std == 0] = 1 # set std=1 for constant features to avoid NaN
@ -23,7 +25,6 @@ class RidgeRegression:
x_scaled = (x - mean) / std
return x_scaled, mean, std
def fit(self, x: np.ndarray, y: np.ndarray) -> None:
x_scaled, mean, std = self.feature_scaling(x)
m, n = x_scaled.shape
@ -39,11 +40,9 @@ class RidgeRegression:
) / m
self.theta -= self.alpha * gradient # updating weights
def predict(self, x:np.ndarray) -> np.ndarray:
x_scaled, _, _ = self.feature_scaling(x)
return x_scaled.dot(self.theta)
def predict(self, X: np.ndarray) -> np.ndarray:
X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta)
def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
x_scaled, _, _ = self.feature_scaling(x)
@ -56,7 +55,6 @@ class RidgeRegression:
) * np.sum(self.theta**2)
return cost
def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
return np.mean(np.abs(y_true - y_pred))