mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-02-17 14:58:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot] 2024-10-23 15:15:26 +00:00
parent b1353dddd4
commit 2eeb450e2d
1 changed files with 18 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
machine_learning/ridge_regression/model.py
View File

@ -3,14 +3,20 @@ import pandas as pd
class RidgeRegression: class RidgeRegression:
def __init__(self, alpha:float=0.001, regularization_param:float=0.1, num_iterations:int=1000) -> None: def __init__(
self.alpha:float = alpha self,
self.regularization_param:float = regularization_param alpha: float = 0.001,
self.num_iterations:int = num_iterations regularization_param: float = 0.1,
self.theta:np.ndarray = 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(
def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: self, X: np.ndarray
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
mean = np.mean(X, axis=0) mean = np.mean(X, axis=0)
std = np.std(X, axis=0) std = np.std(X, axis=0)
@ -20,13 +26,11 @@ class RidgeRegression:
X_scaled = (X - mean) / std X_scaled = (X - mean) / std
return X_scaled, mean, std return X_scaled, mean, std
def fit(self, X: np.ndarray, y: np.ndarray) -> None:
def fit(self, X:np.ndarray, y:np.ndarray) -> None:
X_scaled, mean, std = self.feature_scaling(X) X_scaled, mean, std = self.feature_scaling(X)
m, n = X_scaled.shape m, n = X_scaled.shape
self.theta = np.zeros(n) # initializing weights to zeros self.theta = np.zeros(n) # initializing weights to zeros
for i in range(self.num_iterations): for i in range(self.num_iterations):
predictions = X_scaled.dot(self.theta) predictions = X_scaled.dot(self.theta)
error = predictions - y error = predictions - y
@ -37,13 +41,11 @@ class RidgeRegression:
) / m ) / m
self.theta -= self.alpha * gradient # updating weights self.theta -= self.alpha * gradient # updating weights
def predict(self, X: np.ndarray) -> np.ndarray:
def predict(self, X:np.ndarray) -> np.ndarray:
X_scaled, _, _ = self.feature_scaling(X) X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta) return X_scaled.dot(self.theta)
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)
m = len(y) m = len(y)
@ -53,8 +55,7 @@ class RidgeRegression:
) * np.sum(self.theta**2) ) * np.sum(self.theta**2)
return cost return cost
def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:
def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
return np.mean(np.abs(y_true - y_pred)) return np.mean(np.abs(y_true - y_pred))
@ -66,7 +67,7 @@ if __name__ == "__main__":
y = (y - np.mean(y)) / np.std(y) y = (y - np.mean(y)) / np.std(y)
# 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]
# 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)