mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-01-31 06:33:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

resolved conflicts

Browse Source
This commit is contained in:
jbsch 2024-10-23 20:55:20 +05:30
commit 38764378d4
1 changed files with 21 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
machine_learning/ridge_regression/model.py
View File

@ -3,19 +3,21 @@ import pandas as pd
class RidgeRegression: class RidgeRegression:
def __init__(self, def __init__(self,
alpha:float=0.001, alpha: float = 0.001,
regularization_param:float=0.1, regularization_param: float = 0.1,
num_iterations:int=1000) -> None: num_iterations: int = 1000,
self.alpha:float = alpha ) -> None:
self.regularization_param:float = regularization_param self.alpha: float = alpha
self.num_iterations:int = num_iterations self.regularization_param: float = regularization_param
self.theta:np.ndarray = None 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
mean = np.mean(x, axis=0) ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
std = np.std(x, axis=0) mean = np.mean(X, axis=0)
std = np.std(X, axis=0)
# avoid division by zero for constant features (std = 0) # avoid division by zero for constant features (std = 0)
std[std == 0] = 1 # set std=1 for constant features to avoid NaN std[std == 0] = 1 # set std=1 for constant features to avoid NaN
@ -23,8 +25,7 @@ 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
@ -39,13 +40,11 @@ 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:
X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta)
def predict(self, x:np.ndarray) -> np.ndarray: def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:
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) x_scaled, _, _ = self.feature_scaling(x)
m = len(y) m = len(y)
@ -56,8 +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))
@ -69,7 +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)