Adjust naming to best practices

bag.go

@@ -54,13 +54,6 @@ func (b *Bag) GetResults(in string) (r Results) {
 
 	return
 }
-func (b *Bag) toNGrams(in string) (ns []string) {
-	if b.c.NGramType == "word" {
-		return toNGrams(in, b.c.NGramSize)
-	}
-
-	return tocharacterNGrams(in, b.c.NGramSize)
-}
 
 func (b *Bag) Train(in, label string) {
 	// Convert inbound data to a slice of NGrams
@@ -73,16 +66,23 @@ func (b *Bag) Train(in, label string) {
 		v[n]++
 	}
 
-	// Increment count of trained documents for the provided label
-	b.countByLabel[label]++
-	// Increment total count of trained documents
-	b.totalCount++
+	// Increment model counters
+	b.incrementCounts(label)
+}
+
+// toNGrams converts the inbound string into n-grams based on the configuration settings
+func (b *Bag) toNGrams(in string) (ns []string) {
+	if b.c.NGramType == "word" {
+		return toNGrams(in, b.c.NGramSize)
+	}
+
+	return tocharacterNGrams(in, b.c.NGramSize)
 }
 
 // getProbability uses a Naive Bayes classifier to determine probability for a given label
 func (b *Bag) getProbability(ns []string, label string, vocab Vocabulary) (probability float64) {
 	// Set initial probability value as the prior probability value
-	probability = b.getPriorProbability(label)
+	probability = b.getLogPriorProbability(label)
 	// Get the current counts by label (to be used by Laplace smoothing during for-loop)
 	countsByLabel := float64(b.countByLabel[label]) + b.c.SmoothingParameter*float64(len(vocab))
 
@@ -98,7 +98,7 @@ func (b *Bag) getProbability(ns []string, label string, vocab Vocabulary) (proba
 	return
 }
 
-func (b *Bag) getPriorProbability(label string) (probability float64) {
+func (b *Bag) getLogPriorProbability(label string) (probability float64) {
 	count := float64(b.countByLabel[label])
 	total := float64(b.totalCount)
 	// Get the logarithmic value of count divided by total count
@@ -118,3 +118,10 @@ func (b *Bag) getOrCreateVocabulary(label string) (v Vocabulary) {
 
 	return
 }
+
+func (b *Bag) incrementCounts(label string) {
+	// Increment count of trained documents for the provided label
+	b.countByLabel[label]++
+	// Increment total count of trained documents
+	b.totalCount++
+}

ngram.go

@@ -2,57 +2,57 @@ package bag
 
 import "bytes"
 
-// toNGrams will convert inbound data to an NGram of provided size
+// toNGrams will convert inbound data to an nGram of provided size
 func toNGrams(in string, size int) (ns []string) {
-	// Initialize NGram with a provided size
-	n := make(NGram, size)
+	// Initialize nGram with a provided size
+	n := make(nGram, size)
 	// Iterate inbound data as words
 	toWords(in, func(word string) {
-		// Append word to NGram
+		// Append word to nGram
 		n = n.Append(word)
 		if !n.IsFull() {
 			// NGram is not full - we do not want to append yet, return
 			return
 		}
 
-		// Append current NGram to NGrams slice
+		// Append current nGram to nGrams slice
 		ns = append(ns, n.String())
 	})
 
 	if !n.IsFull() && !n.IsZero() {
-		// The NGram is not full, so we haven't appended yet
-		// The NGram is not empty, so we have something to append
-		// Append current NGram to NGrams slice
+		// The nGram is not full, so we haven't appended yet
+		// The nGram is not empty, so we have something to append
+		// Append current nGram to nGrams slice
 		ns = append(ns, n.String())
 	}
 
 	return
 }
 
-// NGram represents an NGram (variable sized)
-type NGram []string
+// nGram represents an N-Gram (variable sized)
+type nGram []string
 
-// Append will append a given string to an NGram and output the new value
-// Note: The original NGram is NOT modified
-func (n NGram) Append(str string) (out NGram) {
-	// Initialize new NGram with the same size as the original NGram
-	out = make(NGram, len(n))
-	// Iterate through original NGram, starting at index 1
+// Append will append a given string to an nGram and output the new value
+// Note: The original nGram is NOT modified
+func (n nGram) Append(str string) (out nGram) {
+	// Initialize new nGram with the same size as the original nGram
+	out = make(nGram, len(n))
+	// Iterate through original nGram, starting at index 1
 	for i := 1; i < len(n); i++ {
-		// Set the value of the current original NGram index as the value for the previous index for the output NGram
+		// Set the value of the current original nGram index as the value for the previous index for the output nGram
 		out[i-1] = n[i]
 	}
 
-	// Set the last value of the output NGram as the input string
+	// Set the last value of the output nGram as the input string
 	out[len(n)-1] = str
 	return
 }
 
-// String will convert the NGram contents to a string
-func (n NGram) String() (out string) {
+// String will convert the nGram contents to a string
+func (n nGram) String() (out string) {
 	// Initialize buffer
 	buf := bytes.NewBuffer(nil)
-	// Iterate through NGram values
+	// Iterate through nGram values
 	n.iterate(func(value string) {
 		if buf.Len() > 0 {
 			// Buffer is not empty, prefix the iterating value with a space
@@ -67,21 +67,21 @@ func (n NGram) String() (out string) {
 	return buf.String()
 }
 
-// IsZero returns whether or not the NGram is empty
-func (n NGram) IsZero() bool {
+// IsZero returns whether or not the nGram is empty
+func (n nGram) IsZero() bool {
 	// Return result of if the value in the last position is empty
 	return len(n[len(n)-1]) == 0
 }
 
-// IsFull returns whether or not the NGram is full
-func (n NGram) IsFull() bool {
+// IsFull returns whether or not the nGram is full
+func (n nGram) IsFull() bool {
 	// Return result of if the value in the first position is populated
 	return len(n[0]) > 0
 }
 
-// iterate will iterate through the NGram values
-func (n NGram) iterate(fn func(word string)) {
-	// Iterate through NGram values
+// iterate will iterate through the nGram values
+func (n nGram) iterate(fn func(word string)) {
+	// Iterate through nGram values
 	for _, str := range n {
 		// Check if value is empty
 		if len(str) == 0 {

samples.go

@@ -0,0 +1,3 @@
+package bag
+
+type Samples []string

samplesbylabel.go

@@ -0,0 +1,3 @@
+package bag
+
+type SamplesByLabel map[string]Samples

trainingset.go

@@ -5,7 +5,3 @@ type TrainingSet struct {
 
 	Samples SamplesByLabel `yaml:"samples"`
 }
-
-type SamplesByLabel map[string]Samples
-
-type Samples []string