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

[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 14:10:46 +00:00
parent b72320b402
commit d4fc2bf852
1 changed files with 17 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
machine_learning/ridge_regression/model.py
View File

@ -1,6 +1,7 @@
import numpy as np
import pandas as pd
class RidgeRegression:
def __init__(self, alpha=0.001, regularization_param=0.1, num_iterations=1000):
self.alpha = alpha
@ -8,7 +9,6 @@ class RidgeRegression:
self.num_iterations = num_iterations
self.theta = None
def feature_scaling(self, X):
mean = np.mean(X, axis=0)
std = np.std(X, axis=0)
@ -19,7 +19,6 @@ class RidgeRegression:
X_scaled = (X - mean) / std
return X_scaled, mean, std
def fit(self, X, y):
X_scaled, mean, std = self.feature_scaling(X)
m, n = X_scaled.shape
@ -30,24 +29,25 @@ class RidgeRegression:
error = predictions - y
# computing gradient with L2 regularization
gradient = (X_scaled.T.dot(error) + self.regularization_param * self.theta) / m
gradient = (
X_scaled.T.dot(error) + self.regularization_param * self.theta
) / m
self.theta -= self.alpha * gradient # updating weights
def predict(self, X):
X_scaled, _, _ = self.feature_scaling(X)
return X_scaled.dot(self.theta)
def compute_cost(self, X, y):
X_scaled, _, _ = self.feature_scaling(X)
m = len(y)
predictions = X_scaled.dot(self.theta)
cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (self.regularization_param / (2 * m)) * np.sum(self.theta**2)
cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
self.regularization_param / (2 * m)
) * np.sum(self.theta**2)
return cost
def mean_absolute_error(self, y_true, y_pred):
return np.mean(np.abs(y_true - y_pred))