minor: changing class name

Author: Rosander0

src/test/java/com/thealgorithms/machinelearning/MultinomialNaiveBayesClassifierTest.java

@@ -6,22 +6,15 @@
 
 import org.junit.jupiter.api.Test;
 
-class NaiveBayesClassifierTest {
+class MultinomialNaiveBayesClassifierTest {
 
     @Test
     void predictsCorrectClassOnSeparableToyDataset() {
         // Class 0 samples are dominated by feature 0; class 1 samples by feature 1.
-        double[][] features = {
-            {5, 1},
-            {6, 0},
-            {4, 1},
-            {1, 5},
-            {0, 6},
-            {1, 4},
-        };
+        double[][] features = {{5, 1},{6, 0},{4, 1},{1, 5},{0, 6},{1, 4},};
         int[] labels = {0, 0, 0, 1, 1, 1};
 
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         classifier.fit(features, labels);
 
         assertEquals(0, classifier.predict(new double[] {5, 0}));
@@ -38,7 +31,7 @@ void predictBatchMatchesIndividualPredictions() {
         };
         int[] labels = {0, 0, 1, 1};
 
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         classifier.fit(features, labels);
 
         double[][] samples = {{4, 0}, {0, 4}};
@@ -59,7 +52,7 @@ void laplaceSmoothingKeepsZeroCountFeatureLogProbabilityFinite() {
         };
         int[] labels = {0, 0, 1, 1};
 
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         classifier.fit(features, labels);
 
         // A sample that hits class 0's zero-count feature should still produce
@@ -70,14 +63,14 @@ void laplaceSmoothingKeepsZeroCountFeatureLogProbabilityFinite() {
 
     @Test
     void predictBeforeFitThrowsIllegalStateException() {
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         assertThrows(IllegalStateException.class, () -> classifier.predict(new double[] {1, 2}));
     }
 
     @Test
     void nonPositiveAlphaThrowsIllegalArgumentException() {
-        assertThrows(IllegalArgumentException.class, () -> new NaiveBayesClassifier(0.0));
-        assertThrows(IllegalArgumentException.class, () -> new NaiveBayesClassifier(-1.0));
+        assertThrows(IllegalArgumentException.class, () -> new MultinomialNaiveBayesClassifier(0.0));
+        assertThrows(IllegalArgumentException.class, () -> new MultinomialNaiveBayesClassifier(-1.0));
     }
 
     @Test
@@ -88,7 +81,7 @@ void mismatchedSampleLengthThrowsIllegalArgumentException() {
         };
         int[] labels = {0, 1};
 
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         classifier.fit(features, labels);
 
         assertThrows(IllegalArgumentException.class, () -> classifier.predict(new double[] {1, 2, 3}));
@@ -102,7 +95,7 @@ void mismatchedFeatureAndLabelLengthsThrowsIllegalArgumentException() {
         };
         int[] labels = {0};
 
-        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
+        MultinomialNaiveBayesClassifier classifier = new MultinomialNaiveBayesClassifier();
         assertThrows(IllegalArgumentException.class, () -> classifier.fit(features, labels));
     }
 }