Merge branch 'master' into feat/concurrent-merge-sort

Author: DenizAltunkapan

.github/workflows/codeql.yml

@@ -30,15 +30,15 @@ jobs:
           distribution: 'temurin'
 
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v4.37.1
+        uses: github/codeql-action/init@v4.37.3
         with:
           languages: 'java-kotlin'
 
       - name: Build
         run: mvn --batch-mode --update-snapshots verify
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v4.37.1
+        uses: github/codeql-action/analyze@v4.37.3
         with:
           category: "/language:java-kotlin"
 
@@ -55,12 +55,12 @@ jobs:
         uses: actions/checkout@v7
 
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v4.37.1
+        uses: github/codeql-action/init@v4.37.3
         with:
           languages: 'actions'
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v4.37.1
+        uses: github/codeql-action/analyze@v4.37.3
         with:
           category: "/language:actions"
 ...

.github/workflows/project_structure.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v7
-      - uses: actions/setup-python@v6.3.0
+      - uses: actions/setup-python@v7.0.0
         with:
           python-version: '3.13'
 

src/main/java/com/thealgorithms/datastructures/graphs/RottingOranges.java

@@ -0,0 +1,114 @@
+package com.thealgorithms.datastructures.graphs;
+
+import java.util.LinkedList;
+import java.util.Queue;
+
+/**
+ * Multi-source Breadth-First Search (BFS) implementation for the Rotting Oranges problem.
+ *
+ * <p>Algorithm explanation:
+ * https://en.wikipedia.org/wiki/Breadth-first_search
+ *
+ * <p>Problem reference:
+ * https://leetcode.com/problems/rotting-oranges/
+ *
+ * <p>Given a grid where:
+ * <ul>
+ *     <li>0 represents an empty cell</li>
+ *     <li>1 represents a fresh orange</li>
+ *     <li>2 represents a rotten orange</li>
+ * </ul>
+ *
+ * <p>Returns the minimum number of minutes required for all fresh oranges
+ * to become rotten. Returns {@code -1} if it is impossible.
+ *
+ * <p>Time Complexity: O(m × n)
+ * <br>Space Complexity: O(m × n)
+ */
+public class RottingOranges {
+
+    private static final int[] DEL_ROW = {-1, 0, 1, 0};
+    private static final int[] DEL_COL = {0, 1, 0, -1};
+
+    private static final class Cell {
+        private final int row;
+        private final int col;
+        private final int minute;
+
+        Cell(int row, int col, int minute) {
+            this.row = row;
+            this.col = col;
+            this.minute = minute;
+        }
+    }
+
+    /**
+     * Executes the Rotting Oranges algorithm.
+     *
+     * @param grid the input grid
+     * @return minimum minutes required to rot all fresh oranges,
+     *         or -1 if impossible
+     */
+    public int run(int[][] grid) {
+
+        if (grid == null || grid.length == 0 || grid[0].length == 0) {
+            return 0;
+        }
+
+        int rows = grid.length;
+        int cols = grid[0].length;
+
+        // Create a copy so original input is not modified
+        int[][] copy = new int[rows][cols];
+
+        for (int i = 0; i < rows; i++) {
+            copy[i] = grid[i].clone();
+        }
+
+        Queue<Cell> queue = new LinkedList<>();
+        int freshOranges = 0;
+
+        // Find all rotten oranges and count fresh oranges
+        for (int row = 0; row < rows; row++) {
+            for (int col = 0; col < cols; col++) {
+
+                if (copy[row][col] == 2) {
+                    queue.offer(new Cell(row, col, 0));
+                } else if (copy[row][col] == 1) {
+                    freshOranges++;
+                }
+            }
+        }
+
+        if (freshOranges == 0) {
+            return 0;
+        }
+
+        int rottedFresh = 0;
+        int minutes = 0;
+
+        // Multi-source BFS
+        while (!queue.isEmpty()) {
+
+            Cell current = queue.poll();
+
+            minutes = Math.max(minutes, current.minute);
+
+            for (int i = 0; i < 4; i++) {
+
+                int newRow = current.row + DEL_ROW[i];
+                int newCol = current.col + DEL_COL[i];
+
+                if (newRow >= 0 && newRow < rows && newCol >= 0 && newCol < cols && copy[newRow][newCol] == 1) {
+
+                    copy[newRow][newCol] = 2;
+                    rottedFresh++;
+
+                    queue.offer(new Cell(newRow, newCol, current.minute + 1));
+                }
+            }
+        }
+
+        return rottedFresh == freshOranges ? minutes : -1;
+    }
+}

src/main/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifier.java

@@ -0,0 +1,143 @@
+package com.thealgorithms.machinelearning;
+
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * Multinomial Naive Bayes classifier.
+ *
+ * <p>Suited to discrete, count-based features (e.g. word frequencies in text
+ * classification). Class priors and feature likelihoods are estimated from
+ * training data with Laplace (add-alpha) smoothing to avoid zero
+ * probabilities for unseen feature/class combinations. Predictions are made
+ * by comparing summed log-probabilities across classes, which avoids the
+ * numerical underflow that repeated multiplication of small probabilities
+ * would cause.
+ *
+ * <p>Reference: <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier">
+ * Naive Bayes classifier</a>
+ *
+ * @author Vraj Prajapati(Rosander0)
+ */
+public final class MultinomialNaiveBayesClassifier {
+
+    private final double alpha;
+    private final Map<Integer, Double> logPriors;
+    private final Map<Integer, double[]> logLikelihoods;
+    private int numFeatures;
+
+    /**
+     * Constructs a classifier with the given Laplace smoothing parameter.
+     *
+     * @param alpha smoothing constant; must be greater than 0. A value of 1.0
+     *              corresponds to standard Laplace smoothing.
+     */
+    public MultinomialNaiveBayesClassifier(double alpha) {
+        if (alpha <= 0) {
+            throw new IllegalArgumentException("alpha must be greater than 0");
+        }
+        this.alpha = alpha;
+        this.logPriors = new HashMap<>();
+        this.logLikelihoods = new HashMap<>();
+    }
+
+    /** Constructs a classifier using the standard Laplace smoothing constant of 1.0. */
+    public MultinomialNaiveBayesClassifier() {
+        this(1.0);
+    }
+
+    /**
+     * Fits the classifier on the given feature matrix and labels.
+     *
+     * @param features training samples, each row a vector of non-negative
+     *                 feature counts
+     * @param labels   class label for each row of {@code features}
+     */
+    public void fit(double[][] features, int[] labels) {
+        if (features.length == 0 || features.length != labels.length) {
+            throw new IllegalArgumentException("features and labels must be non-empty and of equal length");
+        }
+        logPriors.clear();
+        logLikelihoods.clear();
+        numFeatures = features[0].length;
+
+        Map<Integer, Integer> classCounts = new HashMap<>();
+        Map<Integer, double[]> featureSums = new HashMap<>();
+        Map<Integer, Double> totalFeatureCount = new HashMap<>();
+
+        for (int i = 0; i < features.length; i++) {
+            int label = labels[i];
+            classCounts.merge(label, 1, Integer::sum);
+            double[] sums = featureSums.computeIfAbsent(label, k -> new double[numFeatures]);
+            double total = totalFeatureCount.getOrDefault(label, 0.0);
+            for (int j = 0; j < numFeatures; j++) {
+                sums[j] += features[i][j];
+                total += features[i][j];
+            }
+            totalFeatureCount.put(label, total);
+        }
+
+        int totalSamples = features.length;
+        for (Map.Entry<Integer, double[]> entry : featureSums.entrySet()) {
+            int label = entry.getKey();
+            double[] sums = entry.getValue();
+            int count = classCounts.getOrDefault(label, 0);
+            double total = totalFeatureCount.getOrDefault(label, 0.0);
+
+            logPriors.put(label, Math.log((double) count / totalSamples));
+
+            double denom = total + alpha * numFeatures;
+            double[] logLikelihood = new double[numFeatures];
+            for (int j = 0; j < numFeatures; j++) {
+                logLikelihood[j] = Math.log((sums[j] + alpha) / denom);
+            }
+            logLikelihoods.put(label, logLikelihood);
+        }
+    }
+
+    /**
+     * Predicts the most likely class for a single sample.
+     *
+     * @param sample feature vector of non-negative counts
+     * @return the predicted class label
+     */
+    public int predict(double[] sample) {
+        if (logPriors.isEmpty()) {
+            throw new IllegalStateException("classifier has not been fitted");
+        }
+        if (sample.length != numFeatures) {
+            throw new IllegalArgumentException("sample length must match training feature count");
+        }
+
+        int bestLabel = -1;
+        double bestScore = Double.NEGATIVE_INFINITY;
+
+        for (Map.Entry<Integer, double[]> entry : logLikelihoods.entrySet()) {
+            int label = entry.getKey();
+            double[] logLikelihood = entry.getValue();
+            double score = logPriors.getOrDefault(label, Double.NEGATIVE_INFINITY);
+            for (int j = 0; j < numFeatures; j++) {
+                score += sample[j] * logLikelihood[j];
+            }
+            if (score > bestScore) {
+                bestScore = score;
+                bestLabel = label;
+            }
+        }
+        return bestLabel;
+    }
+
+    /**
+     * Predicts class labels for a batch of samples.
+     *
+     * @param samples feature vectors of non-negative counts
+     * @return predicted class label for each row of {@code samples}
+     */
+    public int[] predict(double[][] samples) {
+        int[] predictions = new int[samples.length];
+        for (int i = 0; i < samples.length; i++) {
+            predictions[i] = predict(samples[i]);
+        }
+        return predictions;
+    }
+}

src/main/java/com/thealgorithms/maths/SumOfSquares.java

@@ -5,7 +5,6 @@
  * Find minimum number of perfect squares that sum to given number
  *
  * @see <a href="https://en.wikipedia.org/wiki/Lagrange%27s_four-square_theorem">Lagrange's Four Square Theorem</a>
- * @author BEASTSHRIRAM
  */
 public final class SumOfSquares {
 
@@ -16,10 +15,15 @@ private SumOfSquares() {
     /**
      * Find minimum number of perfect squares that sum to n
      *
-     * @param n the target number
+     * @param n the target number (must be non-negative)
      * @return minimum number of squares needed
+     * @throws IllegalArgumentException if n is negative
      */
     public static int minSquares(int n) {
+        if (n < 0) {
+            throw new IllegalArgumentException("Input must be non-negative");
+        }
+
         if (isPerfectSquare(n)) {
             return 1;
         }