Changed method name to LogScores.

README.md

@@ -47,7 +47,7 @@ and train it:
 Then you can ascertain the scores of each class and
 the most likely class your data belongs to:
 
-    scores, likely, _ := classifier.Score(
+    scores, likely, _ := classifier.LogScores(
                             []string{"tall", "girl"}
                          )
 

bayesian.go

@@ -205,7 +205,7 @@ func (c *Classifier) Learn(words []string, which Class) {
 // is classifier.Classes[inx]. If more than one of the
 // returned probabilities has the maximum values, then
 // strict is false.
-func (c *Classifier) Scores(words []string) (scores []float64, inx int, strict bool) {
+func (c *Classifier) LogScores(words []string) (scores []float64, inx int, strict bool) {
     n := len(c.Classes)
     scores = make([]float64, n, n)
     priors := c.getPriors()

bayesian_test.go

@@ -48,19 +48,19 @@ func TestLearn(t *testing.T) {
     c.Learn([]string{"tall", "handsome", "rich"}, Good)
     c.Learn([]string{"bald", "poor", "ugly"}, Bad)
 
-    score, likely, strict := c.Scores([]string{"the", "tall", "man"})
+    score, likely, strict := c.LogScores([]string{"the", "tall", "man"})
     fmt.Printf("%v\n", score)
     Assert(t, score[0]>score[1], "not good, round 1") // this is good
     Assert(t, likely == 0, "not good, round 1")
     Assert(t, strict == true, "not strict, round 1")
 
-    score, likely, strict = c.Scores([]string{"poor", "ugly", "girl"})
+    score, likely, strict = c.LogScores([]string{"poor", "ugly", "girl"})
     fmt.Printf("%v\n", score)
     Assert(t, score[0]<score[1]) // this is bad
     Assert(t, likely == 1)
     Assert(t, strict == true)
 
-    score, likely, strict  = c.Scores([]string{"the", "bad", "man"})
+    score, likely, strict  = c.LogScores([]string{"the", "bad", "man"})
     fmt.Printf("%v\n", score)
     Assert(t, score[0]==score[1], "not the same") // same
     Assert(t, likely == 0, "not good") // first one is picked