commit 3aa6c7148fa8077e9369d5b680875734d426644b
Author: Nabil Ould Hamou <nabil_oh@noreply.localhost>
Date:   Tue Feb 11 18:21:03 2025 +0100

    Add 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
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