From 8f1f091aa4db5a1ca8f8e2dfd0a7f6caf5d56b11 Mon Sep 17 00:00:00 2001 From: Harmanaya Sharma Date: Wed, 23 Oct 2024 00:14:37 +0530 Subject: [PATCH] Resolved ruff checks --- machine_learning/ridge_regression.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/machine_learning/ridge_regression.py b/machine_learning/ridge_regression.py index 0cd32caeb..1206d41b5 100644 --- a/machine_learning/ridge_regression.py +++ b/machine_learning/ridge_regression.py @@ -68,7 +68,7 @@ class RidgeRegression: m, n = features_scaled.shape self.theta = np.zeros(n) # Initialize weights to zeros - for i in range(self.iterations): + for _ in range(self.iterations): predictions = features_scaled.dot(self.theta) error = predictions - target @@ -149,21 +149,21 @@ if __name__ == "__main__": data = pd.read_csv( "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv" ) - x = data[["Rating"]].to_numpy() # Feature: Rating - y = data["ADR"].to_numpy() # Target: ADR - y = (y - np.mean(y)) / np.std(y) + data_x = data[["Rating"]].to_numpy() # Feature: Rating + data_y = data["ADR"].to_numpy() # Target: ADR + data_y = (data_y - np.mean(data_y)) / np.std(data_y) # Add bias term (intercept) to the feature matrix - x = np.c_[np.ones(X.shape[0]), x] # Add intercept term + data_x = np.c_[np.ones(data_x.shape[0]), data_x] # Add intercept term # Initialize and train the Ridge Regression model model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000) - model.fit(x, y) + model.fit(data_x, data_y) # Predictions - predictions = model.predict(x) + predictions = model.predict(data_x) # Results print("Optimized Weights:", model.theta) - print("Cost:", model.compute_cost(x, y)) - print("Mean Absolute Error:", model.mean_absolute_error(y, predictions)) + print("Cost:", model.compute_cost(data_x, data_y)) + print("Mean Absolute Error:", model.mean_absolute_error(data_y, predictions))