From f726756a2dfc5c30e8e4f8843ae4e9e6b9fd3d2e Mon Sep 17 00:00:00 2001
From: Endrit Berberi <berberi.endrit@ufl.edu>
Date: Sun, 11 Feb 2024 12:47:41 -0500
Subject: [PATCH] Naive Bayes Algorithm contribution (java)

---
 .../src/naive_bayes/NaiveBayes.java           | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 code/artificial_intelligence/src/naive_bayes/NaiveBayes.java

diff --git a/code/artificial_intelligence/src/naive_bayes/NaiveBayes.java b/code/artificial_intelligence/src/naive_bayes/NaiveBayes.java
new file mode 100644
index 0000000000..ac0f91bb8f
--- /dev/null
+++ b/code/artificial_intelligence/src/naive_bayes/NaiveBayes.java
@@ -0,0 +1,95 @@
+package naive_bayes;
+
+import java.util.HashMap;
+import java.util.Map;
+
+public class NaiveBayes {
+    private Map<String, double[]> featureProbabilities; // Probability of features given a class
+    private Map<String, Double> classProbabilities; // Probability of each class
+
+    public NaiveBayes() {
+        this.featureProbabilities = new HashMap<>();
+        this.classProbabilities = new HashMap<>();
+    }
+
+    public void train(double[][] data, String[] labels) {
+        // Count class occurrences
+        Map<String, Integer> classCounts = new HashMap<>();
+        for (String label : labels) {
+            classCounts.put(label, classCounts.getOrDefault(label, 0) + 1);
+        }
+
+        // Calculate class probabilities
+        for (Map.Entry<String, Integer> entry : classCounts.entrySet()) {
+            classProbabilities.put(entry.getKey(), (double) entry.getValue() / labels.length);
+        }
+
+        // Initialize feature probabilities for each class
+        for (String className : classProbabilities.keySet()) {
+            featureProbabilities.put(className, new double[data[0].length]);
+        }
+
+        // Calculate probabilities of features given a class
+        for (int i = 0; i < data.length; i++) {
+            String label = labels[i];
+            for (int j = 0; j < data[i].length; j++) {
+                if (data[i][j] == 1) {
+                    featureProbabilities.get(label)[j] += 1;
+                }
+            }
+        }
+
+        // Normalize feature probabilities
+        for (Map.Entry<String, double[]> entry : featureProbabilities.entrySet()) {
+            String className = entry.getKey();
+            double[] probs = entry.getValue();
+            for (int i = 0; i < probs.length; i++) {
+                probs[i] /= classCounts.get(className);
+            }
+        }
+    }
+
+    public String predict(double[] features) {
+        String bestClass = null;
+        double bestLogProb = Double.NEGATIVE_INFINITY;
+
+        for (Map.Entry<String, Double> classEntry : classProbabilities.entrySet()) {
+            String className = classEntry.getKey();
+            double classProb = classEntry.getValue();
+            double logProb = Math.log(classProb); // Use log probability to avoid underflow
+
+            for (int i = 0; i < features.length; i++) {
+                double featureProb = featureProbabilities.get(className)[i];
+                logProb += features[i] == 1 ? Math.log(featureProb) : Math.log(1 - featureProb);
+            }
+
+            if (logProb > bestLogProb) {
+                bestLogProb = logProb;
+                bestClass = className;
+            }
+        }
+
+        return bestClass;
+    }
+
+    public static void main(String[] args) {
+        NaiveBayes nb = new NaiveBayes();
+
+        // Example training data and labels
+        double[][] data = {
+                {1, 0},
+                {0, 1},
+                {0, 0},
+                {1, 1}
+        };
+        String[] labels = {"Yes", "No", "No", "Yes"};
+
+        // Train the classifier
+        nb.train(data, labels);
+
+        // Test the classifier
+        double[] testPoint = {1, 0};
+        String predictedClass = nb.predict(testPoint);
+        System.out.println("Predicted class: " + predictedClass);
+    }
+}