src/test/java/bae/NaiveBayesClassifierTest.java in bae-0.1.0 vs src/test/java/bae/NaiveBayesClassifierTest.java in bae-0.2.0

@@ -16,10 +16,11 @@
 
         n.train("positive", new Document("bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd ddd"));
         n.train("positive", new Document("aaa bbb bbb"));
         n.train("negative", new Document("ccc ccc ccc ddd ddd ddd ddd"));
+        n.calculateInitialLikelihoods();
 
         assertEquals(1, n.getFrequencyTable().get("positive", "aaa"));
         assertEquals(3, n.getFrequencyTable().get("positive", "bbb"));
         assertEquals(5, n.getFrequencyTable().get("negative", "ccc"));
         assertEquals(7, n.getFrequencyTable().get("negative", "ddd"));
@@ -31,10 +32,11 @@
 
         n.train("positive", new Document("bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd ddd"));
         n.train("positive", new Document("aaa bbb bbb"));
         n.train("negative", new Document("ccc ccc ccc ddd ddd ddd ddd"));
+        n.calculateInitialLikelihoods();
 
         assertEquals(1, (long)n.getWordTable().get("aaa"));
         assertEquals(1, (long)n.getWordTable().get("bbb"));
         assertEquals(1, (long)n.getWordTable().get("ccc"));
         assertEquals(1, (long)n.getWordTable().get("ddd"));
@@ -47,10 +49,11 @@
         n.train("positive", new Document("bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd ddd"));
         n.train("positive", new Document("aaa bbb bbb"));
         n.train("negative", new Document("ccc ccc ccc ddd ddd ddd ddd"));
         n.train("negative", new Document("ccc ccc ccc ddd ddd"));
+        n.calculateInitialLikelihoods();
 
         assertEquals(2, (long)n.getInstanceCount().get("positive"));
         assertEquals(3, (long)n.getInstanceCount().get("negative"));
         assertEquals(5, (long)n.getTotalCount());
     }
@@ -62,10 +65,11 @@
         Document d = new Document("bbb");
         d.addZeroCount("aaa");
 
         n.train("positive", d);
         n.train("negative", new Document("ccc ccc ddd ddd ddd"));
+        n.calculateInitialLikelihoods();
 
         Map<String, Double> results = n.classify(new Document("aaa bbb"));
 
         assertEquals(0.9411764705882353, results.get("positive"), 0.00001);
         assertEquals(0.05882352941176469, results.get("negative"), 0.00001);
@@ -78,24 +82,25 @@
         Document d = new Document("bbb");
         d.addZeroCount("aaa");
 
         n.train("positive", d);
         n.train("negative", new Document("ccc ccc ddd ddd ddd"));
+        n.calculateInitialLikelihoods();
 
         Map<String, Double> results = n.classify(new Document("ccc ccc ccc ddd ddd ddd"));
 
         assertEquals(0.05882352941176469, results.get("positive"), 0.00001);
         assertEquals(0.9411764705882353, results.get("negative"), 0.00001);
     }
 
     @Test
     public void canCorrectlyClassifyPositiveWithThreeLabels() {
         NaiveBayesClassifier n = new NaiveBayesClassifier();
-
         n.train("positive", new Document("aaa aaa bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd"));
         n.train("neutral", new Document("eee eee eee fff fff fff"));
+        n.calculateInitialLikelihoods();
 
         Map<String, Double> results = n.classify(new Document("aaa bbb"));
 
         assertEquals(0.896265560165975, results.get("positive"), 0.00001);
         assertEquals(0.06639004149377592, results.get("negative"), 0.00001);
@@ -107,10 +112,11 @@
         NaiveBayesClassifier n = new NaiveBayesClassifier();
 
         n.train("positive", new Document("aaa aaa bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd"));
         n.train("neutral", new Document("eee eee eee fff fff fff"));
+        n.calculateInitialLikelihoods();
 
         Map<String, Double> results = n.classify(new Document("ccc ccc ccc ddd ddd"));
 
         assertEquals(0.05665722379603399, results.get("positive"), 0.00001);
         assertEquals(0.9178470254957508, results.get("negative"), 0.00001);
@@ -122,13 +128,14 @@
         NaiveBayesClassifier n = new NaiveBayesClassifier();
 
         n.train("positive", new Document("aaa aaa bbb"));
         n.train("negative", new Document("ccc ccc ddd ddd"));
         n.train("neutral", new Document("eee eee eee fff fff fff"));
+        n.calculateInitialLikelihoods();
 
         Map<String, Double> results = n.classify(new Document("aaa ddd ddd eee eee eee fff"));
 
         assertEquals(0.12195121951219513, results.get("positive"), 0.00001);
         assertEquals(0.09756097560975606, results.get("negative"), 0.00001);
         assertEquals(0.7804878048780488, results.get("neutral"), 0.00001);
     }
-}
\ No newline at end of file
+}