Add Logistic Regression Algorithm for Machine Learning

Algorithms.Tests/MachineLearning/LogisticRegressionTests.cs

@@ -0,0 +1,65 @@
+using NUnit.Framework;
+using Algorithms.MachineLearning;
+using System;
+
+namespace Algorithms.Tests.MachineLearning;
+
+[TestFixture]
+public class LogisticRegressionTests
+{
+    [Test]
+    public void Fit_ThrowsOnEmptyInput()
+    {
+        var model = new LogisticRegression();
+        Assert.Throws<ArgumentException>(() => model.Fit(Array.Empty<double[]>(), Array.Empty<int>()));
+    }
+
+    [Test]
+    public void Fit_ThrowsOnMismatchedLabels()
+    {
+        var model = new LogisticRegression();
+        double[][] X = { new double[] { 1, 2 } };
+        int[] y = { 1, 0 };
+        Assert.Throws<ArgumentException>(() => model.Fit(X, y));
+    }
+
+    [Test]
+    public void FitAndPredict_WorksOnSimpleData()
+    {
+        // Simple AND logic
+        double[][] X =
+        {
+            new[] { 0.0, 0.0 },
+            new[] { 0.0, 1.0 },
+            new[] { 1.0, 0.0 },
+            new[] { 1.0, 1.0 }
+        };
+        int[] y = { 0, 0, 0, 1 };
+        var model = new LogisticRegression();
+        model.Fit(X, y, epochs: 2000, learningRate: 0.1);
+        Assert.That(model.Predict(new double[] { 0, 0 }), Is.EqualTo(0));
+        Assert.That(model.Predict(new double[] { 0, 1 }), Is.EqualTo(0));
+        Assert.That(model.Predict(new double[] { 1, 0 }), Is.EqualTo(0));
+        Assert.That(model.Predict(new double[] { 1, 1 }), Is.EqualTo(1));
+    }
+
+    [Test]
+    public void PredictProbability_ThrowsOnFeatureMismatch()
+    {
+        var model = new LogisticRegression();
+        double[][] X = { new double[] { 1, 2 } };
+        int[] y = { 1 };
+        model.Fit(X, y);
+        Assert.Throws<ArgumentException>(() => model.PredictProbability(new double[] { 1 }));
+    }
+
+    [Test]
+    public void FeatureCount_ReturnsCorrectValue()
+    {
+        var model = new LogisticRegression();
+        double[][] X = { new double[] { 1, 2, 3 } };
+        int[] y = { 1 };
+        model.Fit(X, y);
+        Assert.That(model.FeatureCount, Is.EqualTo(3));
+    }
+}

Algorithms/MachineLearning/LogisticRegression.cs

@@ -0,0 +1,87 @@
+using System;
+using System.Linq;
+
+namespace Algorithms.MachineLearning;
+
+/// <summary>
+/// Logistic Regression for binary classification.
+/// </summary>
+public class LogisticRegression
+{
+    private double[] weights = [];
+    private double bias;
+
+    public int FeatureCount => weights.Length;
+
+    /// <summary>
+    /// Fit the model using gradient descent.
+    /// </summary>
+    /// <param name="x">2D array of features (samples x features).</param>
+    /// <param name="y">Array of labels (0 or 1).</param>
+    /// <param name="epochs">Number of iterations.</param>
+    /// <param name="learningRate">Step size.</param>
+    public void Fit(double[][] x, int[] y, int epochs = 1000, double learningRate = 0.01)
+    {
+        if (x.Length == 0 || x[0].Length == 0)
+        {
+            throw new ArgumentException("Input features cannot be empty.");
+        }
+
+        if (x.Length != y.Length)
+        {
+            throw new ArgumentException("Number of samples and labels must match.");
+        }
+
+        int nSamples = x.Length;
+        int nFeatures = x[0].Length;
+        weights = new double[nFeatures];
+        bias = 0;
+
+        for (int epoch = 0; epoch < epochs; epoch++)
+        {
+            double[] dw = new double[nFeatures];
+            double db = 0;
+            for (int i = 0; i < nSamples; i++)
+            {
+                double linear = Dot(x[i], weights) + bias;
+                double pred = Sigmoid(linear);
+                double error = pred - y[i];
+                for (int j = 0; j < nFeatures; j++)
+                {
+                    dw[j] += error * x[i][j];
+                }
+
+                db += error;
+            }
+
+            for (int j = 0; j < nFeatures; j++)
+            {
+                weights[j] -= learningRate * dw[j] / nSamples;
+            }
+
+            bias -= learningRate * db / nSamples;
+        }
+    }
+
+    /// <summary>
+    /// Predict probability for a single sample.
+    /// </summary>
+    public double PredictProbability(double[] x)
+    {
+        if (x.Length != weights.Length)
+        {
+            throw new ArgumentException("Feature count mismatch.");
+        }
+
+        return Sigmoid(Dot(x, weights) + bias);
+    }
+
+    /// <summary>
+    /// Predict class label (0 or 1) for a single sample.
+    /// </summary>
+    public int Predict(double[] x) => PredictProbability(x) >= 0.5 ? 1 : 0;
+
+    private static double Sigmoid(double z) => 1.0 / (1.0 + Math.Exp(-z));
+
+    private static double Dot(double[] a, double[] b) => a.Zip(b).Sum(pair => pair.First * pair.Second);
+}

README.md

@@ -108,6 +108,7 @@ find more than one implementation for the same objective but using different alg
     * [CollaborativeFiltering](./Algorithms/RecommenderSystem/CollaborativeFiltering)
   * [Machine Learning](./Algorithms/MachineLearning)
     * [Linear Regression](./Algorithms/MachineLearning/LinearRegression.cs)
+    * [Logistic Regression](./Algorithms/MachineLearning/LogisticRegression.cs)
   * [Searches](./Algorithms/Search)
     * [A-Star](./Algorithms/Search/AStar/)
     * [Binary Search](./Algorithms/Search/BinarySearcher.cs)