From 3aa6c7148fa8077e9369d5b680875734d426644b Mon Sep 17 00:00:00 2001
From: Nabil Ould Hamou <nabil_oh@noreply.localhost>
Date: Tue, 11 Feb 2025 18:21:03 +0100
Subject: [PATCH] Add LogisticRegression.py
---
LogisticRegression.py | 41 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 41 insertions(+)
create mode 100644 LogisticRegression.py
diff --git a/LogisticRegression.py b/LogisticRegression.py
new file mode 100644
index 0000000..84f6616
--- /dev/null
+++ b/LogisticRegression.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+class LogisticRegression:
+ def __init__(self, weights, bias, learning_rate=0.001, epochs=1000):
+ self.learning_rate = learning_rate
+ self.epochs = epochs
+ self.weights = weights
+ self.bias = bias
+
+ def sigmoid(self, z):
+ return 1 / (1 + np.exp(-z))
+
+ def cost_function(self, X, y):
+ N = y.shape[0]
+ pred = self.sigmoid(np.dot(X.T, self.weights) + self.bias)
+ cost = -(1/N) * np.sum(y * np.log(pred) + (1 - y) * np.log(1 - pred))
+ return cost
+
+ def fit(self, X, y):
+ size, features = X.shape
+ self.weights = np.zeros(features)
+ self.bias = 0
+
+ for epoch in range(self.epochs):
+ linear = np.dot(X.T, self.weights) + self.bias
+ pred = self.sigmoid(linear)
+
+ E = 1/size * np.dot(X.T, (pred - y))
+ dB = 1/size * np.sum(pred - y)
+
+ self.weights -= self.learning_rate * E
+ self.bias -= self.learning_rate * dB
+
+ if epoch % 100 == 0:
+ cost = self.cost_function(X, y)
+ print(f'epoch: {epoch}, cost: {cost}')
+
+ def predict(self, X):
+ linear = np.dot(X.T, self.weights) + self.bias
+ pred = self.sigmoid(linear)
+ return [1 if i > 0.5 else 0 for i in pred] # seuil de 0.5 pour la classification
\ No newline at end of file