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Resolved ruff checks

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This commit is contained in:
Harmanaya Sharma 2024-10-23 00:14:37 +05:30
parent a2d07af8c1
commit 8f1f091aa4
1 changed files with 9 additions and 9 deletions
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18
machine_learning/ridge_regression.py
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@ -68,7 +68,7 @@ class RidgeRegression:
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
for i in range(self.iterations): for _ in range(self.iterations):
predictions = features_scaled.dot(self.theta) predictions = features_scaled.dot(self.theta)
error = predictions - target error = predictions - target
@ -149,21 +149,21 @@ if __name__ == "__main__":
data = pd.read_csv( data = pd.read_csv(
"https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv" "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/master/Week1/ADRvsRating.csv"
) )
x = data[["Rating"]].to_numpy() # Feature: Rating data_x = data[["Rating"]].to_numpy() # Feature: Rating
y = data["ADR"].to_numpy() # Target: ADR data_y = data["ADR"].to_numpy() # Target: ADR
y = (y - np.mean(y)) / np.std(y) data_y = (data_y - np.mean(data_y)) / np.std(data_y)
# Add bias term (intercept) to the feature matrix # 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 # Initialize and train the Ridge Regression model
model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000) model = RidgeRegression(alpha=0.01, lambda_=0.1, iterations=1000)
model.fit(x, y) model.fit(data_x, data_y)
# Predictions # Predictions
predictions = model.predict(x) predictions = model.predict(data_x)
# Results # Results
print("Optimized Weights:", model.theta) print("Optimized Weights:", model.theta)
print("Cost:", model.compute_cost(x, y)) print("Cost:", model.compute_cost(data_x, data_y))
print("Mean Absolute Error:", model.mean_absolute_error(y, predictions)) print("Mean Absolute Error:", model.mean_absolute_error(data_y, predictions))