Removed class prior pre-calculation

Since the number of instances in each class are stored, there is no need
to keep the pre-calculated priors.

naive/bernoulli_nb.go

@@ -38,8 +38,6 @@ import (
 // http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
 type BernoulliNBClassifier struct {
     base.BaseEstimator
-    // Logarithm of each class prior
-    logClassPrior map[string]float64
     // Conditional probability for each term. This vector should be
     // accessed in the following way: p(f|c) = condProb[c][f].
     // Logarithm is used in order to avoid underflow.
@@ -47,6 +45,8 @@ type BernoulliNBClassifier struct {
     // Number of instances in each class. This is necessary in order to
     // calculate the laplace smooth value during the Predict step.
     classInstances map[string]int
+    // Number of instances used in training.
+    trainingInstances int
     // Number of features in the training set
     features int
 }
@@ -56,8 +56,8 @@ type BernoulliNBClassifier struct {
 func NewBernoulliNBClassifier() *BernoulliNBClassifier {
     nb := BernoulliNBClassifier{}
     nb.condProb = make(map[string][]float64)
-    nb.logClassPrior = make(map[string]float64)
     nb.features = 0
+    nb.trainingInstances = 0
     return &nb
 }
 
@@ -65,7 +65,8 @@ func NewBernoulliNBClassifier() *BernoulliNBClassifier {
 // necessary for calculating prior probability and p(f_i)
 func (nb *BernoulliNBClassifier) Fit(X *base.Instances) {
 
-    // Number of features in this training set
+    // Number of features and instances in this training set
+    nb.trainingInstances = X.Rows
     nb.features = 0
     if X.Rows > 0 {
         nb.features = len(X.GetRowVectorWithoutClass(0))
@@ -110,7 +111,6 @@ func (nb *BernoulliNBClassifier) Fit(X *base.Instances) {
 
     // Pre-calculate conditional probabilities for each class
     for c, _ := range nb.classInstances {
-        nb.logClassPrior[c] = math.Log((float64(nb.classInstances[c]))/float64(X.Rows))
         nb.condProb[c] = make([]float64, nb.features)
         for feat := 0; feat < nb.features; feat++ {
             classTerms, _ := docsContainingTerm[c]
@@ -146,8 +146,9 @@ func (nb *BernoulliNBClassifier) PredictOne(vector []float64) string {
     bestScore := -math.MaxFloat64
     bestClass := ""
 
-    for class, prior := range nb.logClassPrior {
+    for class, classCount := range nb.classInstances {
-        classScore := prior
+        // Init classScore with log(prior)
+        classScore := math.Log((float64(classCount))/float64(nb.trainingInstances))
         for f := 0; f < nb.features; f++ {
             if vector[f] > 0 {
                 // Test document has feature c

naive/bernoulli_nb_test.go

@@ -1,7 +1,6 @@
 package naive
 
 import (
-    "math"
     "github.com/sjwhitworth/golearn/base"
     "testing"
     . "github.com/smartystreets/goconvey/convey"
@@ -29,12 +28,10 @@ func TestSimple(t *testing.T) {
         nb.Fit(trainingData)
 
         Convey("Check if Fit is working as expected", func() {
-            Convey("All log(prior) should be correctly calculated", func() {
+            Convey("All data needed for prior should be correctly calculated", func() {
-                logPriorBlue := nb.logClassPrior["blue"]
+                So(nb.classInstances["blue"], ShouldEqual, 2)
-                logPriorRed := nb.logClassPrior["red"]
+                So(nb.classInstances["red"], ShouldEqual, 2)
-
+                So(nb.trainingInstances, ShouldEqual, 4)
-                So(logPriorBlue, ShouldAlmostEqual, math.Log(0.5))
-                So(logPriorRed, ShouldAlmostEqual, math.Log(0.5))
             })
 
             Convey("'red' conditional probabilities should be correct", func() {