Quantized classifier Machine Learning in GO

ML Quantized classifier in GO

A general purpose, high performance machine learning classifier. Source Code

Examples /  Tests cover:

  • ASL Sign language Gesture recognition
  • Classify breast cancer from 9 features. 96% accuracy at 100% recall first pass with no optimization. Using 623 training rows.
  • Predict death or survival of Titanic passengers.
  • Predict Diabetes
  • Please send me data sets you would like to add to the test.

We Offer Consulting services see: contact

The library includes TensorFlow Deep Learning implementation of classifiers using the same data to compare the run-time performance combined with classification recall and accuracy.

Quantized Classifier

The design for Quantized classifiers was inspired by design elements in KNN, Bayesian and SVM engines. A key design goal was a faster mechanism to identify similarity for a given feature while providing very fast classification using moderate hardware resources.

In KNN we find similar records for a given feature by finding those with the most similar value. This works but consumes a lot of space and run-time. In Quantized approach we look at the range of data and attempt to group data based on similar values. EG: if a given feature has a value from 0.0 to 1.0 then a 10 bucket system could consider all records that have a value from 0 to 0.1 as similar. Those from 0.1 to 0.2 are similar, etc. Rather than keeping all the training records we only need to keep the statistics for have many of the records in a given bucket belong to each class which we can then use to compute a base probability by feature by bucket by class. Applying this across active features gives us a set of probabilities that can be combined using ensemble techniques into the probability a given row would belong to any of the classes.

Quantizing the data allows a small memory foot print with fast training without the need to keep all the training records in memory. Retaining only the statistics allows very large training sets with moderate memory use. The trade off is loosing some of KNN ability to adjust the number of closest neighbors considered quick at runtime. The offset is that training is so fast that the quanta size can be adjusted quickly. The memory use is so much smaller that we can afford to keep multiple models with different quanta sizes loaded and updated simultaneously.

Source code is available on BigBucket  Most of the Source is written in GO with some utilities and Examples in Python 3.5.2.  Some utilities and that also act as examples for invocation are written as windows Bat files but they could be easily adapted for use in Linux.

We Offer Consulting services see: contact

Easy Invocation using CSV Files

This utility uses the executable classifyFiles built from GO source code to read one CSV file for training data then test it’s ability to classify that data using data in a second CSV file.  The two files were produced from the source data using the splitCSVFile executable also included in the system.

Main Quantized Classifier in GO File

package qprob

/* Methods to build and train the qprob classifier
data structure.  Also includes retraining functionality
to allow single column to be retrained as needed by the
optimizers */

import (
	s "strings"

const (
	QTBucket = 1 // Quantize using bucket logic
	QTInt    = 2 // Quantize with simple int index
	QTText   = 3 // Quantize as tokenized text field
const MaxFloat32 = math.MaxFloat32

type QuantType uint8
type TQIndex int16  // type index into Quant Buckets
type TQCnt int32    // type Counts made for Quant Buckets
type TQProb float32 // type for Probability compute
type TClassId int16
type TQuantId int32
type TQuantCnt int32

// contains the count for each class
// identfied as having at least one row.
// eg for bucket 10 we have 4 classes.
// each id=1, count=10, id=5, count=4
// totalCnt is the sum of the Counts
// for all classes
type QuantList struct {
	Counts map[TClassId]TQuantCnt
	totCnt int64

// includes the data and descriptive meta
// data for 1 feature of data or in CSV this
// would be 1 column of data.
type Feature struct {
	Buckets []map[TQuantId]*QuantList // The list is for each of number
	// of buckets then the quant list for each inside of it.
	BuckSize   []float32 // Need to know bucket size for each numBucket
	ColNum     int
	Spec       *CSVCol
	Enabled    bool
	ProcType   QuantType
	EffMaxVal  float32
	EffMinVal  float32
	EffRange   float32
	FeatWeight float32
	MaxNumBuck int16
	MinNumBuck int16
	// Need the following as part of feature to support sparse
	// feature matrix where not all rows have all featurs.
	// an example would be text processing where  # feaures
	// is # of unique phrases + # unique tokens but each
	// row only contains a small words.
	ClassCounts map[TClassId]int64   // total Count of all Records of Class
	ClassProb   map[TClassId]float32 // Prob of any record being in class
	NumRow      float32              // used to allow sparse feature matrix
	IsCategory  bool                 // when false then numeric otherwise category.
	CatMap      map[string]TQuantId  // Maps unique strings to category ID
	CatCurMaxId TQuantId
	CatNdxById  map[TQuantId]string

// Includes the metadaa and classifer
// data for a given classifer
type Classifier struct {
	ColDef       []*Feature
	ColNumByName map[string]int
	ColByName    map[string]*Feature
	Info         *CSVInfo
	TrainFiName  string
	TestFiName   string
	Label        string
	NumCol       int
	NumRow       int64
	ClassCol     int
	MaxNumBuck   int16
	ClassCounts  map[TClassId]int64   // total Count of all Records of Class
	ClassProb    map[TClassId]float32 // Prob of any record being in class
	NumTrainRow  int32
	Req          *ClassifyRequest

// A set of classifiers indexed by
// name to allow a single process
// to serve data from multiple training
// data sets
type Classifiers struct {
	classifiers map[string]*Classifier

func (fier *Classifier) PrintTrainClassProb() {
	fmt.Printf("Train Probability of being in any class\n")
	for classId, prob := range fier.ClassProb {
		fmt.Printf("class=%v,  cnt=%v  prob=%v\n", classId, fier.ClassCounts[classId], prob)
	fmt.Printf("Num Train Row=%v NumCol=%v\n", fier.NumRow, fier.NumCol)

func (feat *Feature) printDistElem() {
	csv := feat.Spec
	//fmt.Printf("L108: CSV Dist Elem absMin=%v absMax=%v absRange=%v csvStep=%v\n", csv.MinFlt, csv.MaxFlt, csv.AbsRange, csv.distStepSize)
	for ndx, drow := range csv.distCounts {
		if drow > 0 {
			fmt.Printf("   L111: ndx=%v  cnt=%v\n", ndx, drow)

/* Determines reasonable effective Min/Max value range for
the statistically significant values that represent the bulk
of the data.  Without this feature the quantize process can
compute poor bucket sizes that are negatively effected by a very
small amoun of data.  We do this by dividing all inbound data
counts between 1,000 buckets across the absolute min/max range
then to find the effective range we scan from the top and bottom
until the next bucket would exteed the numRemove specification
then return the value equivilant to the last bucket on each end
that did not exceed the numRemove requirement.  This allows us
to remove the influence for a given fraction of the data extreeme
value data on each end when computing effective range which
provides a better computation of bucket size based on the dominant
data.  This is not always desirable but in stock data it is very
common to have 1% to 2% with extreeme values which ends up causing
buckets that are too large forcing values in the main distribution
into a smaller number of buckets than intended. If either value
for bottom or top meet each other before reachign the number of
records then returns a error. */
func (fe *Feature) setFeatEffMinMax(fier *Classifier, numRemMin int, numRemMax int) (float32, float32) {
	minCnt := 0
	maxCnt := 0
	if fe.Enabled == false || fe.IsCategory == false {
		return 1.0, 1000.0
	csv := fe.Spec
	dist := csv.distCounts
	stepSize := csv.distStepSize
	//fmt.Printf("L131: setFeatEffMinMax numRemMin=%v  numRemMax=%v absMax=%v absMin=%v stepSize=%v\n", numRemMin, numRemMax, csv.MaxFlt, csv.MinFlt, stepSize)
	// Scan from Bottom removing large numbers
	// We look ahead 1 so if it would place us
	// over our targetCnt then we abort.
	effMinVal := csv.MinFlt
	for bndx := 0; bndx < 1000; bndx++ {
		distNum := dist[bndx]
		if (minCnt + distNum) >= numRemMin {
			//fmt.Printf("L140: Set Min Next dist Num would overflow  minCnt=%v, distnum=%v numRemMin=%v\n", minCnt, distNum, numRemMin)
		minCnt += distNum
		effMinVal += stepSize
		//fmt.Printf("L144: bndx=%v effMinVal=%v minCnt=%v distNum=%v numRemMin=%v\n",
		//	bndx, effMinVal, minCnt, distNum, numRemMin)

	// Scan From Top removing large numbers
	// We look back by 1 so if that value would put us
	// over target removed then abort based on curr Val.
	effMaxVal := csv.MaxFlt

	for tndx := 999; tndx >= 0; tndx-- {
		distNum := dist[tndx]
		if (maxCnt + distNum) > numRemMax {
			//fmt.Printf("L157 setMax Next dist Num would overflow  maxCnt=%v, distnum=%v numRemMax=%v\n", maxCnt, distNum, numRemMax)

		maxCnt += distNum
		effMaxVal -= stepSize

		//fmt.Printf("L155: tndx=%v effMaxVal=%v maxCnt=%v distNum=%v numRemMax=%v\n",
		//	tndx, effMaxVal, maxCnt, distNum, numRemMax)


	if effMaxVal > effMinVal {
		fe.EffMinVal = effMinVal
		fe.EffMaxVal = effMaxVal
	} else {
		fmt.Printf("\n\n\nERR effMax < effMin in setFeatEffMinMax\n\n\n")
	//fmt.Printf("L185: setFeatEffMinMax numRemMin=%v  numRemMax=%v absMax=%v absMin=%v stepSize=%v\n", numRemMin, numRemMax, csv.MaxFlt, csv.MinFlt, stepSize)

	return fe.EffMinVal, fe.EffMaxVal

// set Min Max Effective Range for every feature.
func (fier *Classifier) SetEffMinMax(numRemMin int, numRemMax int) {
	for cn, fe := range fier.ColDef {
		if fe.Enabled && fe.IsCategory == false {
			_, _ = fe.setFeatEffMinMax(fier, numRemMin, numRemMax)

			csv := fe.Spec
			fmt.Printf("SetEffMinMax colNum=%v absMin=%v effMin=%v absMax=%v effMax=%v remMin=%v remMax=%v\n",
				cn, csv.MinFlt, fe.EffMinVal, csv.MaxFlt, fe.EffMaxVal, numRemMin, numRemMax)

// Remove the top portion of the outlier records
// where portion of set is a ratio expressed between
// 0.0 and 1.0.  Applied to all features.
func (fier *Classifier) SetEffMinMaxPortSet(portSet float32) {
	numRemoveRec := int(float32(fier.NumRow) * portSet)
	fmt.Printf("SetEffMinMaxPortSet portSet=%v  numToRem=%v\n", portSet, numRemoveRec)
	fier.SetEffMinMax(numRemoveRec, numRemoveRec)

func (fier *Classifier) totFeatWeight() float32 {
	tout := float32(0.0)
	for ndx, feat := range fier.ColDef {
		if feat.Enabled == true && ndx != fier.ClassCol {
			tout += feat.FeatWeight
	return tout

/* printClassifierModel */
/* LoadClassifierTrainingModel */

/* For an existing pre-loaded training data sett
add additional traiing data. This will not adjust
the bucket size or effective min, max values used
to compute step size for bucket indexing.   */
/*func AddClassifierTrainingFile(fiName string ) *Classifier {


func (cl *Classifier) updateStepValues(afeat *Feature) {
	maxBuck := int(afeat.MaxNumBuck)
	for nb := 0; nb <= maxBuck; nb++ {
		afeat.EffRange = afeat.EffMaxVal - afeat.EffMinVal
		afeat.BuckSize[nb] = afeat.EffRange / float32(nb)

// Compue
func (fe *Feature) BuckValRange(fier *Classifier, buckId TQuantId, numBuck int16) (float32, float32) {
	stepSize := float32(fe.BuckSize[numBuck])
	amtOverMin := float32(buckId) * stepSize
	lower := fe.Spec.MinFlt + amtOverMin
	return lower, (lower + stepSize)

// Compute BucketId for current data value for this
// feature.
func (fe *Feature) BucketId(fier *Classifier, dval float32, numBuck int16) TQuantId {
	amtOverMin := dval - fe.Spec.MinFlt
	//fmt.Printf("L265 bucketId() dval=%v numBuck=%v amtOverMin=%v lenBuckSize=%v\n ", dval, numBuck, amtOverMin, len(fe.BuckSize))
	buckSize := float32(fe.BuckSize[numBuck])
	bucket := TQuantId(amtOverMin / buckSize)
	//fmt.Printf("L268:  bucketId=%v\n", bucket)
	//fmt.Printf("L267: dval=%v bucket=%v amtOverMin=%v effMinVal=%v  effMaxVal=%v effRange=%v absMaxVal=%v absMinVal=%v  absRange=%v numBuck=%v fe.BuckSize=%v\n",
	//dval, bucket, amtOverMin, fe.EffMinVal, fe.EffMaxVal, fe.EffRange, fe.Spec.MaxFlt, fe.Spec.MinFlt, fe.Spec.AbsRange, fe.NumBuck, fe.BuckSize)
	return bucket

/* separated out the makeEmptyClassifier so
accessible by other construction techniques
like from a string */
func makEmptyClassifier(req *ClassifyRequest, fiName string, label string) *Classifier {
	fier := new(Classifier)
	fier.Req = req
	fier.TrainFiName = fiName
	fier.Label = label
	fier.ClassCol = 0
	fier.NumTrainRow = 0
	fier.MaxNumBuck = req.MaxNumBuck
	// can not set ColDef until we know how many
	// colums to allocate space for.
	//fier.ColDef = make([]*Feature, 0fier.Info.NumCol)
	fier.ColByName = make(map[string]*Feature)
	fier.ColNumByName = make(map[string]int)
	fier.ClassCounts = make(map[TClassId]int64)
	fier.ClassProb = make(map[TClassId]float32)
	return fier

func (fier *Classifier) initFromCSV(csv *CSVInfo) {
	fier.Info = csv
	fier.NumCol = fier.Info.NumCol
	fier.NumRow = int64(fier.Info.NumRow)
	fier.ColDef = make([]*Feature, fier.NumCol)
	fmt.Println("loadCSVMetadata complete")

	// build up the feature description
	// for each column.
	for i := 0; i < fier.NumCol; i++ {
		col := fier.Info.Col[i]
		// TODO: Lookup Cateogry From feature Request
		fier.ColDef[i] = fier.makeFeature(col, i, false)

func (cl *Classifier) isFeatureCategory(featName string) bool {
	_, fnd := cl.Req.CatColumns[featName]
	return fnd

func (cl *Classifier) isFeatureIgnored(featName string) bool {
	//fmt.Printf("L310 isFeatureIgnored featName=%v  IgnoreColumns=%v\n", featName, cl.Req.IgnoreColumns)
	_, fnd := cl.Req.IgnoreColumns[featName]
	return fnd

func (feat *Feature) initializeBuckets() {
	feat.Buckets = make([]map[TQuantId]*QuantList, feat.MaxNumBuck+1)
	feat.BuckSize = make([]float32, feat.MaxNumBuck+1)
	for nb := int16(0); nb <= feat.MaxNumBuck; nb++ {
		feat.Buckets[nb] = make(map[TQuantId]*QuantList)

func (cl *Classifier) makeFeature(col *CSVCol, colNum int, isCat bool) *Feature {
	afeat := new(Feature)
	afeat.Spec = col
	afeat.ColNum = colNum
	if cl.isFeatureIgnored(col.ColName) {
		afeat.Enabled = false
	} else {
		afeat.Enabled = true
	afeat.ProcType = QTBucket
	afeat.MaxNumBuck = cl.Req.MaxNumBuck
	afeat.MinNumBuck = cl.Req.MinNumBuck

	//afeat.WeightByLevel = make([]float32, cl.MaxNumBuck)
	afeat.FeatWeight = 1.0
	afeat.NumRow = 0
	afeat.ClassCounts = make(map[TClassId]int64)
	afeat.ClassProb = make(map[TClassId]float32)
	if cl.isFeatureCategory(col.ColName) == true {
		// When defined as a category column then
		// category labels start at 0 counting up
		afeat.IsCategory = true
		afeat.CatCurMaxId = TQuantId(0)
		afeat.MaxNumBuck = 1
		afeat.MinNumBuck = 1
		afeat.EffMaxVal = 1
		afeat.EffMinVal = 1000

	} else {
		// When not defined as a category then
		// category labels start at MinInt16 counting
		// up. We only use category label on non category
		// values when they fail to parse.
		afeat.IsCategory = false
		afeat.CatCurMaxId = TQuantId(math.MinInt16 + 1)
		afeat.EffMaxVal = col.MaxFlt
		afeat.EffMinVal = col.MinFlt


	afeat.CatMap = make(map[string]TQuantId) // Maps unique strings to category ID
	afeat.CatNdxById = make(map[TQuantId]string)
	return afeat

/* Retrive the class id for the row based on
the column that has been chosen to be used for
class Id */
func (fier *Classifier) classId(vals []string) int16 {
	ctx := vals[fier.ClassCol]
	id, err := strconv.ParseInt(ctx, 10, 16)
	if err != nil {
		//fmt.Printf("classId() Encountered int parsing error val=%v err=%v", ctx, err)
		return -9999
	return int16(id)

// Update Class Probability for a given
// row being in any class This is used in
// later probability computations to adjust
// observed probability
func (fier *Classifier) updateClassProb() {

	for classId, classCnt := range fier.ClassCounts {
		fier.ClassProb[classId] = float32(classCnt) / float32(fier.NumTrainRow)

	// TODO: Add  Feature Class Prob

func (feat *Feature) GetCategoryBucketId(sval string) TQuantId {
	// Compute Bucket ID based on unique strings
	buckId, fnd := feat.CatMap[sval]
	if fnd == false {
		// allocate the next bucket id
		feat.CatCurMaxId += 1
		buckId = feat.CatCurMaxId
		feat.CatMap[sval] = buckId
		feat.CatNdxById[buckId] = sval
	return buckId

// use this function when it is unknown if the feature
// is a column or is parsable.
func (feat *Feature) GetBucketIdFromStr(fier *Classifier, numBuck int16, sval string) TQuantId {
	if feat.IsCategory {
		// Compute Bucket ID based on unique strings
		return feat.GetCategoryBucketId(sval)
	} else {
		// Compute Bucket ID based numeric values.
		f64, err := strconv.ParseFloat(sval, 32)
		f32 := float32(f64)
		if err != nil {
			f32 = math.MaxFloat32
		return feat.BucketId(fier, f32, numBuck)

/* Train a single feature with a single value for current class
by computing which bucket the value would lie in.  */
func (fier *Classifier) TrainRowFeat(feat *Feature, classId TClassId, fldValStr string) {
	feat.NumRow += 1
	// Update The Feaure Class Counts
	_, found := feat.ClassCounts[classId]
	if found == false {
		feat.ClassCounts[classId] = 1
	} else {
		feat.ClassCounts[classId] += 1

	if feat.IsCategory {
		// For category features we only create
		// the single level of buckets rather than
		// max number of buckets.
		buckets := feat.Buckets[1]
		buckId := feat.GetCategoryBucketId(fldValStr)
		// TODO: Move this to separate funciton since it duplicates
		// UpdateBucketCount in the for loop below
		abuck, bexist := buckets[buckId]
		if bexist == false {
			abuck = new(QuantList)
			abuck.Counts = make(map[TClassId]TQuantCnt)
			abuck.totCnt = 0
			buckets[buckId] = abuck

		_, cntExist := abuck.Counts[classId]
		if cntExist == false {
			abuck.Counts[classId] = 1
		} else {
			abuck.Counts[classId] += 1
		abuck.totCnt += 1

	} else {
		maxNumBuck := feat.MaxNumBuck
		fldVal := ParseStrAsFloat32(fldValStr)
		if fldVal == MaxFloat32 {
			// Can not convert an Non parsable values
			// to numeric bin.
		// Ieterate every number from 1 to maxNumBuck
		// creating buckets for every level up to maxNumBuck
		// this is used to allow fallback to less precise buckets
		// when we find a value that doesn't match.
		for nb := feat.MinNumBuck; nb <= maxNumBuck; nb++ {
			// Update the feature Bucket Counts
			buckets := feat.Buckets[nb]
			buckId := feat.BucketId(fier, fldVal, nb)
			abuck, bexist := buckets[buckId]
			if bexist == false {
				abuck = new(QuantList)
				abuck.Counts = make(map[TClassId]TQuantCnt)
				abuck.totCnt = 0
				buckets[buckId] = abuck

			_, cntExist := abuck.Counts[classId]
			if cntExist == false {
				abuck.Counts[classId] = 1
			} else {
				abuck.Counts[classId] += 1
			abuck.totCnt += 1
	//fmt.Printf("i=%v ctxt=%s f32=%v maxStr=%s minStr=%s\n", i, ctxt, f32, col.MaxStr, col.MinStr)

// Moved getClassId to a separate method because
// want to support category style classes in the
// future and wanted to isolate logic for conversion
// of category classes into integer class ID.
func (fier *Classifier) getClassId(vals []string) TClassId {
	// Update Counts of all rows of
	return TClassId(ParseStrAsInt16(vals[fier.ClassCol]))

func (fier *Classifier) UpdateClassCounts(classId TClassId, vals []string) {
	// class so we can compute probability
	// of any row being of a given class
	_, classFnd := fier.ClassCounts[classId]
	if classFnd == false {
		fier.ClassCounts[classId] = 1
	} else {
		fier.ClassCounts[classId] += 1

/* Train each feature with data for the current
row.   Figure out which bucket the current
value qualifies for and then update the count for
that class within that bucket. Creats the bucket and
the class if it is the first time we have seen them */
func (fier *Classifier) TrainRow(vals []string) {
	classCol := fier.ClassCol
	fier.NumTrainRow += 1
	classId := fier.getClassId(vals)
	if classId == math.MaxInt16 {
		return // this row has invalid class
	fier.UpdateClassCounts(classId, vals)

	for i := 0; i < fier.NumCol; i++ {
		if i == classCol {
			continue // skip the class
		feat := fier.ColDef[i]
		if feat.Enabled == false {
			continue // no need testing disabled features

		fldval := vals[i]

		fier.TrainRowFeat(feat, classId, fldval)

	} // for columns
} // func

// Retrains a single feature using the input training
// data supplied in trainArr.
// Training by feature may be useful for
// multi-threading since we could theroretically
// use one core per feature since the feature
// level status never affect each other.
func (fier *Classifier) TrainFeature(featNum int16, trainArr [][]string) {
	feat := fier.ColDef[featNum]
	for _, row := range trainArr {
		classId := fier.getClassId(row)
		if classId == TClassId(math.MaxInt16) {
			continue // this row has invalid class
		fier.TrainRowFeat(feat, classId, row[featNum])
	} // for
} // func

// Reset the training stats for a given feature
// in preparation for retraining.  This is needed
// for individual features to allow the optimizer
// to only retrain a single feature when it
// changes things.
func (fier *Classifier) clearFeatureForRetrain(feat *Feature) {
	feat.Buckets = make([]map[TQuantId]*QuantList, fier.MaxNumBuck+1)
	for nb := int16(0); nb <= fier.MaxNumBuck; nb++ {
		feat.Buckets[nb] = make(map[TQuantId]*QuantList)
	feat.NumRow = 0

// Reset the training data to accept new training.
// we will keep the effective range and other meta
// data from the larger data set because this only
// really used by the optimizer
func (fier *Classifier) clearForRetrain() {
	feats := fier.ColDef
	for _, feat := range feats {

// Throw away current training model dataand
// retrain it with the set of data passed in
// the array rows must contain as many columns
// as there are features.
func (fier *Classifier) Retrain(rows [][]string) {
	//classCol := fier.ClassCol
	for _, darr := range rows {
		//classId := int16(darr[classCol])

// Retrains a given feature.  This is used to support
// the optimizer which is able to change the number of buckets
// for a given feature.
func (fier *Classifier) RetrainFeature(featNdx int16, dataArr [][]string) {
	feat := fier.ColDef[featNdx]
	fier.TrainFeature(featNdx, dataArr)

// Load the entire training data file as a simple
// [][]float and return in.   May actually want
// to change the main parser / loader to use this
// for performance since the CSV is currently read
// 3 times and we could load it just once.  But
// if we make that change we still need to be able
// to revert revert to approach that will allow
// training sets larger than physical ram.
func (fier *Classifier) GetTrainRowsAsArr(maxBytes int64) [][]string {
	_, rows := LoadCSVStrRows(fier.TrainFiName, maxBytes)
	return rows

// Return an integer array containing all
// currently used class ID
func (fier *Classifier) ClassIds() []TClassId {
	cc := fier.ClassCounts
	tout := make([]TClassId, 0, len(cc))
	for key, _ := range cc {
		tout = append(tout, key)
	return tout

func LoadClassifierTrainStream(fier *Classifier, scanner *bufio.Scanner) {
	rowCnt := 0
	scanner.Scan() // skip headers
	for scanner.Scan() {
		txt := s.TrimSpace(scanner.Text())
		if err := scanner.Err(); err != nil {
		a := ParseStrAsArrStr(txt)
		if len(a) < fier.NumCol {
		rowCnt += 1
	} // for row

func LoadClassifierTrainFile(req *ClassifyRequest, fiName string, label string) *Classifier {
	fmt.Printf("fiName=%s", fiName)

	// Instantiate the basic File Information
	// NOTE: This early construction will have to
	// be duplciates for construction from
	// string when using in POST.
	fier := makEmptyClassifier(req, fiName, label)
	csv := LoadCSVMetaDataFile(fiName)

	// Now process the actual CSV file
	// to do the training work
	file, err := os.Open(fiName)
	if err != nil {
	defer file.Close()
	// Return the Header and use it's
	// contents to create the columns
	scanner := bufio.NewScanner(file)
	LoadClassifierTrainStream(fier, scanner)
	fier.SetEffMinMaxPortSet(0.000015) // remove 1.5% of outlier records from top and bottom
	// when computing range.
	return fier

// Function Describe Training Result Finding by Class

// Saves the model wide parameters in INI file key=value
// parameters in file fiName.model.txt  Saves the feature
// defenitions in CSV format.
// featureNum, NumBuck, FeatWeight, TotCnt, Bucket1Id, Bucket1Cnt, Buck1EffMinVal, Buck1MaxVal, Bucket1AbsMinVAl, Bucket1AbsMaxVal
// Where each of the Bucket1* features are repeated for 1..N buckets.
// This should give us everything we need to restore a model with
// all of it's optimized settings intack.  It also gives us a nice
// representation to support the discovery aspects of the system.
// TODO:  Make a brower display utility.
// function SaveModel

// Loads the previously saved model so it can process classification
// results.
// function LoadModel

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