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added ridge regression

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jbsch 2024-10-23 19:47:34 +05:30
parent b72320b402
commit a84d209c08
1 changed files with 12 additions and 12 deletions
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24
machine_learning/ridge_regression/model.py
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@ -2,14 +2,14 @@ import numpy as np
import pandas as pd import pandas as pd
class RidgeRegression: class RidgeRegression:
def __init__(self, alpha=0.001, regularization_param=0.1, num_iterations=1000): def __init__(self, alpha:float=0.001, regularization_param:float=0.1, num_iterations:int=1000) -> None:
self.alpha = alpha self.alpha:float = alpha
self.regularization_param = regularization_param self.regularization_param:float = regularization_param
self.num_iterations = num_iterations self.num_iterations:int = num_iterations
self.theta = None self.theta:np.ndarray = None
def feature_scaling(self, X): def feature_scaling(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)
@ -20,7 +20,7 @@ class RidgeRegression:
return X_scaled, mean, std return X_scaled, mean, std
def fit(self, X, y): 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
@ -34,12 +34,12 @@ class RidgeRegression:
self.theta -= self.alpha * gradient # updating weights self.theta -= self.alpha * gradient # updating weights
def predict(self, X): 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, y): 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)
@ -48,7 +48,7 @@ class RidgeRegression:
return cost return cost
def mean_absolute_error(self, y_true, y_pred): 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))
@ -59,10 +59,10 @@ if __name__ == "__main__":
y = df["ADR"].values y = df["ADR"].values
y = (y - np.mean(y)) / np.std(y) y = (y - np.mean(y)) / np.std(y)
# Add bias term (intercept) 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)
model.fit(X, y) model.fit(X, y)