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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot] 2024-10-22 18:39:20 +00:00
parent 61945d03c6
commit a2d07af8c1
1 changed files with 10 additions and 4 deletions
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12
machine_learning/ridge_regression.py
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@ -62,7 +62,9 @@ class RidgeRegression:
>>> rr.theta is not None >>> rr.theta is not None
True True
""" """
features_scaled, mean, std = self.feature_scaling(features) # Normalize features features_scaled, mean, std = self.feature_scaling(
features
) # Normalize features
m, n = features_scaled.shape m, n = features_scaled.shape
self.theta = np.zeros(n) # Initialize weights to zeros self.theta = np.zeros(n) # Initialize weights to zeros
@ -90,7 +92,9 @@ class RidgeRegression:
>>> predictions.shape == target.shape >>> predictions.shape == target.shape
True True
""" """
features_scaled, _, _ = self.feature_scaling(features) # Scale features using training data features_scaled, _, _ = self.feature_scaling(
features
) # Scale features using training data
return features_scaled.dot(self.theta) return features_scaled.dot(self.theta)
def compute_cost(self, features: np.ndarray, target: np.ndarray) -> float: def compute_cost(self, features: np.ndarray, target: np.ndarray) -> float:
@ -110,7 +114,9 @@ class RidgeRegression:
>>> isinstance(cost, float) >>> isinstance(cost, float)
True True
""" """
features_scaled, _, _ = self.feature_scaling(features) # Scale features using training data features_scaled, _, _ = self.feature_scaling(
features
) # Scale features using training data
m = len(target) m = len(target)
predictions = features_scaled.dot(self.theta) predictions = features_scaled.dot(self.theta)
cost = (1 / (2 * m)) * np.sum((predictions - target) ** 2) + ( cost = (1 / (2 * m)) * np.sum((predictions - target) ** 2) + (