func (lr *LogisticRegression) Predict(X base.FixedDataGrid) base.FixedDataGrid {
// Only support 1 class Attribute
classAttrs := X.AllClassAttributes()
if len(classAttrs) != 1 {
panic(fmt.Sprintf("%d Wrong number of classes", len(classAttrs)))
}
// Generate return structure
ret := base.GeneratePredictionVector(X)
classAttrSpecs := base.ResolveAttributes(ret, classAttrs)
// Retrieve numeric non-class Attributes
numericAttrs := base.NonClassFloatAttributes(X)
numericAttrSpecs := base.ResolveAttributes(X, numericAttrs)
// Allocate row storage
row := make([]float64, len(numericAttrSpecs))
X.MapOverRows(numericAttrSpecs, func(rowBytes [][]byte, rowNo int) (bool, error) {
for i, r := range rowBytes {
row[i] = base.UnpackBytesToFloat(r)
}
val := Predict(lr.model, row)
vals := base.PackFloatToBytes(val)
ret.Set(classAttrSpecs[0], rowNo, vals)
return true, nil
})
return ret
}
func ChiMBuildFrequencyTable(attr base.Attribute, inst base.FixedDataGrid) []*FrequencyTableEntry {
ret := make([]*FrequencyTableEntry, 0)
attribute := attr.(*base.FloatAttribute)
attrSpec, err := inst.GetAttribute(attr)
if err != nil {
panic(err)
}
attrSpecs := []base.AttributeSpec{attrSpec}
err = inst.MapOverRows(attrSpecs, func(row [][]byte, rowNo int) (bool, error) {
value := row[0]
valueConv := attribute.GetFloatFromSysVal(value)
class := base.GetClass(inst, rowNo)
// Search the frequency table for the value
found := false
for _, entry := range ret {
if entry.Value == valueConv {
found = true
entry.Frequency[class] += 1
}
}
if !found {
newEntry := &FrequencyTableEntry{
valueConv,
make(map[string]int),
}
newEntry.Frequency[class] = 1
ret = append(ret, newEntry)
}
return true, nil
})
return ret
}
// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error) {
predictions := base.GeneratePredictionVector(what)
classAttr := getClassAttr(predictions)
classAttrSpec, err := predictions.GetAttribute(classAttr)
if err != nil {
panic(err)
}
predAttrs := base.AttributeDifferenceReferences(what.AllAttributes(), predictions.AllClassAttributes())
predAttrSpecs := base.ResolveAttributes(what, predAttrs)
what.MapOverRows(predAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
cur := d
for {
if cur.Children == nil {
predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
break
} else {
splitVal := cur.SplitRule.SplitVal
at := cur.SplitRule.SplitAttr
ats, err := what.GetAttribute(at)
if err != nil {
//predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
//break
panic(err)
}
var classVar string
if _, ok := ats.GetAttribute().(*base.FloatAttribute); ok {
// If it's a numeric Attribute (e.g. FloatAttribute) check that
// the value of the current node is greater than the old one
classVal := base.UnpackBytesToFloat(what.Get(ats, rowNo))
if classVal > splitVal {
classVar = "1"
} else {
classVar = "0"
}
} else {
classVar = ats.GetAttribute().GetStringFromSysVal(what.Get(ats, rowNo))
}
if next, ok := cur.Children[classVar]; ok {
cur = next
} else {
// Suspicious of this
var bestChild string
for c := range cur.Children {
bestChild = c
if c > classVar {
break
}
}
cur = cur.Children[bestChild]
}
}
}
return true, nil
})
return predictions, nil
}
func getNumericAttributeEntropy(f base.FixedDataGrid, attr *base.FloatAttribute) (float64, float64) {
// Resolve Attribute
attrSpec, err := f.GetAttribute(attr)
if err != nil {
panic(err)
}
// Build sortable vector
_, rows := f.Size()
refs := make([]numericSplitRef, rows)
f.MapOverRows([]base.AttributeSpec{attrSpec}, func(val [][]byte, row int) (bool, error) {
cls := base.GetClass(f, row)
v := base.UnpackBytesToFloat(val[0])
refs[row] = numericSplitRef{v, cls}
return true, nil
})
// Sort
sort.Sort(splitVec(refs))
generateCandidateSplitDistribution := func(val float64) map[string]map[string]int {
presplit := make(map[string]int)
postplit := make(map[string]int)
for _, i := range refs {
if i.val < val {
presplit[i.class]++
} else {
postplit[i.class]++
}
}
ret := make(map[string]map[string]int)
ret["0"] = presplit
ret["1"] = postplit
return ret
}
minSplitEntropy := math.Inf(1)
minSplitVal := math.Inf(1)
// Consider each possible function
for i := 0; i < len(refs)-1; i++ {
val := refs[i].val + refs[i+1].val
val /= 2
splitDist := generateCandidateSplitDistribution(val)
splitEntropy := getSplitEntropy(splitDist)
if splitEntropy < minSplitEntropy {
minSplitEntropy = splitEntropy
minSplitVal = val
}
}
return minSplitEntropy, minSplitVal
}
// Predict is just a wrapper for the PredictOne function.
//
// IMPORTANT: Predict panics if Fit was not called or if the
// document vector and train matrix have a different number of columns.
func (nb *BernoulliNBClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Generate return vector
ret := base.GeneratePredictionVector(what)
// Get the features
featAttrSpecs := base.ResolveAttributes(what, nb.attrs)
what.MapOverRows(featAttrSpecs, func(row [][]byte, i int) (bool, error) {
base.SetClass(ret, i, nb.PredictOne(row))
return true, nil
})
return ret
}
func processData(x base.FixedDataGrid) instances {
_, rows := x.Size()
result := make(instances, rows)
// Retrieve numeric non-class Attributes
numericAttrs := base.NonClassFloatAttributes(x)
numericAttrSpecs := base.ResolveAttributes(x, numericAttrs)
// Retrieve class Attributes
classAttrs := x.AllClassAttributes()
if len(classAttrs) != 1 {
panic("Only one classAttribute supported!")
}
// Check that the class Attribute is categorical
// (with two values) or binary
classAttr := classAttrs[0]
if attr, ok := classAttr.(*base.CategoricalAttribute); ok {
if len(attr.GetValues()) != 2 {
panic("To many values for Attribute!")
}
} else if _, ok := classAttr.(*base.BinaryAttribute); ok {
} else {
panic("Wrong class Attribute type!")
}
// Convert each row
x.MapOverRows(numericAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
// Allocate a new row
probRow := make([]float64, len(numericAttrSpecs))
// Read out the row
for i, _ := range numericAttrSpecs {
probRow[i] = base.UnpackBytesToFloat(row[i])
}
// Get the class for the values
class := base.GetClass(x, rowNo)
instance := instance{class, probRow}
result[rowNo] = instance
return true, nil
})
return result
}
// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what base.FixedDataGrid) base.FixedDataGrid {
predictions := base.GeneratePredictionVector(what)
classAttr := getClassAttr(predictions)
classAttrSpec, err := predictions.GetAttribute(classAttr)
if err != nil {
panic(err)
}
predAttrs := base.AttributeDifferenceReferences(what.AllAttributes(), predictions.AllClassAttributes())
predAttrSpecs := base.ResolveAttributes(what, predAttrs)
what.MapOverRows(predAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
cur := d
for {
if cur.Children == nil {
predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
break
} else {
at := cur.SplitAttr
ats, err := what.GetAttribute(at)
if err != nil {
predictions.Set(classAttrSpec, rowNo, classAttr.GetSysValFromString(cur.Class))
break
}
classVar := ats.GetAttribute().GetStringFromSysVal(what.Get(ats, rowNo))
if next, ok := cur.Children[classVar]; ok {
cur = next
} else {
var bestChild string
for c := range cur.Children {
bestChild = c
if c > classVar {
break
}
}
cur = cur.Children[bestChild]
}
}
}
return true, nil
})
return predictions
}
func convertInstancesToLabelVec(X base.FixedDataGrid) []float64 {
// Get the class Attributes
classAttrs := X.AllClassAttributes()
// Only support 1 class Attribute
if len(classAttrs) != 1 {
panic(fmt.Sprintf("%d ClassAttributes (1 expected)", len(classAttrs)))
}
// ClassAttribute must be numeric
if _, ok := classAttrs[0].(*base.FloatAttribute); !ok {
panic(fmt.Sprintf("%s: ClassAttribute must be a FloatAttribute", classAttrs[0]))
}
// Allocate return structure
_, rows := X.Size()
labelVec := make([]float64, rows)
// Resolve class Attribute specification
classAttrSpecs := base.ResolveAttributes(X, classAttrs)
X.MapOverRows(classAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
labelVec[rowNo] = base.UnpackBytesToFloat(row[0])
return true, nil
})
return labelVec
}
func convertInstancesToProblemVec(X base.FixedDataGrid) [][]float64 {
// Allocate problem array
_, rows := X.Size()
problemVec := make([][]float64, rows)
// Retrieve numeric non-class Attributes
numericAttrs := base.NonClassFloatAttributes(X)
numericAttrSpecs := base.ResolveAttributes(X, numericAttrs)
// Convert each row
X.MapOverRows(numericAttrSpecs, func(row [][]byte, rowNo int) (bool, error) {
// Allocate a new row
probRow := make([]float64, len(numericAttrSpecs))
// Read out the row
for i, _ := range numericAttrSpecs {
probRow[i] = base.UnpackBytesToFloat(row[i])
}
// Add the row
problemVec[rowNo] = probRow
return true, nil
})
return problemVec
}
func (lr *LinearRegression) Predict(X base.FixedDataGrid) (base.FixedDataGrid, error) {
if !lr.fitted {
return nil, NoTrainingDataError
}
ret := base.GeneratePredictionVector(X)
attrSpecs := base.ResolveAttributes(X, lr.attrs)
clsSpec, err := ret.GetAttribute(lr.cls)
if err != nil {
return nil, err
}
X.MapOverRows(attrSpecs, func(row [][]byte, i int) (bool, error) {
var prediction float64 = lr.disturbance
for j, r := range row {
prediction += base.UnpackBytesToFloat(r) * lr.regressionCoefficients[j]
}
ret.Set(clsSpec, i, base.PackFloatToBytes(prediction))
return true, nil
})
return ret, nil
}
// Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Check what distance function we are using
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
// Check Compatibility
allAttrs := base.CheckCompatible(what, KNN.TrainingData)
if allAttrs == nil {
// Don't have the same Attributes
return nil
}
// Remove the Attributes which aren't numeric
allNumericAttrs := make([]base.Attribute, 0)
for _, a := range allAttrs {
if fAttr, ok := a.(*base.FloatAttribute); ok {
allNumericAttrs = append(allNumericAttrs, fAttr)
}
}
// Generate return vector
ret := base.GeneratePredictionVector(what)
// Resolve Attribute specifications for both
whatAttrSpecs := base.ResolveAttributes(what, allNumericAttrs)
trainAttrSpecs := base.ResolveAttributes(KNN.TrainingData, allNumericAttrs)
// Reserve storage for most the most similar items
distances := make(map[int]float64)
// Reserve storage for voting map
maxmap := make(map[string]int)
// Reserve storage for row computations
trainRowBuf := make([]float64, len(allNumericAttrs))
predRowBuf := make([]float64, len(allNumericAttrs))
// Iterate over all outer rows
what.MapOverRows(whatAttrSpecs, func(predRow [][]byte, predRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
}
predMat := utilities.FloatsToMatrix(predRowBuf)
// Find the closest match in the training data
KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
trainRowBuf[i] = base.UnpackBytesToFloat(trainRow[i])
}
// Compute the distance
trainMat := utilities.FloatsToMatrix(trainRowBuf)
distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
return true, nil
})
sorted := utilities.SortIntMap(distances)
values := sorted[:KNN.NearestNeighbours]
// Reset maxMap
for a := range maxmap {
maxmap[a] = 0
}
// Refresh maxMap
for _, elem := range values {
label := base.GetClass(KNN.TrainingData, elem)
if _, ok := maxmap[label]; ok {
maxmap[label]++
} else {
maxmap[label] = 1
}
}
// Sort the maxMap
var maxClass string
maxVal := -1
for a := range maxmap {
if maxmap[a] > maxVal {
maxVal = maxmap[a]
maxClass = a
}
}
base.SetClass(ret, predRowNo, maxClass)
return true, nil
})
return ret
}
// Fill data matrix with Bernoulli Naive Bayes model. All values
// necessary for calculating prior probability and p(f_i)
func (nb *BernoulliNBClassifier) Fit(X base.FixedDataGrid) {
// Check that all Attributes are binary
classAttrs := X.AllClassAttributes()
allAttrs := X.AllAttributes()
featAttrs := base.AttributeDifference(allAttrs, classAttrs)
for i := range featAttrs {
if _, ok := featAttrs[i].(*base.BinaryAttribute); !ok {
panic(fmt.Sprintf("%v: Should be BinaryAttribute", featAttrs[i]))
}
}
featAttrSpecs := base.ResolveAttributes(X, featAttrs)
// Check that only one classAttribute is defined
if len(classAttrs) != 1 {
panic("Only one class Attribute can be used")
}
// Number of features and instances in this training set
_, nb.trainingInstances = X.Size()
nb.attrs = featAttrs
nb.features = len(featAttrs)
// Number of instances in class
nb.classInstances = make(map[string]int)
// Number of documents with given term (by class)
docsContainingTerm := make(map[string][]int)
// This algorithm could be vectorized after binarizing the data
// matrix. Since mat64 doesn't have this function, a iterative
// version is used.
X.MapOverRows(featAttrSpecs, func(docVector [][]byte, r int) (bool, error) {
class := base.GetClass(X, r)
// increment number of instances in class
t, ok := nb.classInstances[class]
if !ok {
t = 0
}
nb.classInstances[class] = t + 1
for feat := 0; feat < len(docVector); feat++ {
v := docVector[feat]
// In Bernoulli Naive Bayes the presence and absence of
// features are considered. All non-zero values are
// treated as presence.
if v[0] > 0 {
// Update number of times this feature appeared within
// given label.
t, ok := docsContainingTerm[class]
if !ok {
t = make([]int, nb.features)
docsContainingTerm[class] = t
}
t[feat] += 1
}
}
return true, nil
})
// Pre-calculate conditional probabilities for each class
for c, _ := range nb.classInstances {
nb.condProb[c] = make([]float64, nb.features)
for feat := 0; feat < nb.features; feat++ {
classTerms, _ := docsContainingTerm[c]
numDocs := classTerms[feat]
docsInClass, _ := nb.classInstances[c]
classCondProb, _ := nb.condProb[c]
// Calculate conditional probability with laplace smoothing
classCondProb[feat] = float64(numDocs+1) / float64(docsInClass+1)
}
}
}
// Predict returns a classification for the vector, based on a vector input, using the KNN algorithm.
func (KNN *KNNClassifier) Predict(what base.FixedDataGrid) base.FixedDataGrid {
// Check what distance function we are using
var distanceFunc pairwise.PairwiseDistanceFunc
switch KNN.DistanceFunc {
case "euclidean":
distanceFunc = pairwise.NewEuclidean()
case "manhattan":
distanceFunc = pairwise.NewManhattan()
default:
panic("unsupported distance function")
}
// Check Compatibility
allAttrs := base.CheckCompatible(what, KNN.TrainingData)
if allAttrs == nil {
// Don't have the same Attributes
return nil
}
// Use optimised version if permitted
if KNN.AllowOptimisations {
if KNN.DistanceFunc == "euclidean" {
if KNN.canUseOptimisations(what) {
return KNN.optimisedEuclideanPredict(what.(*base.DenseInstances))
}
}
}
fmt.Println("Optimisations are switched off")
// Remove the Attributes which aren't numeric
allNumericAttrs := make([]base.Attribute, 0)
for _, a := range allAttrs {
if fAttr, ok := a.(*base.FloatAttribute); ok {
allNumericAttrs = append(allNumericAttrs, fAttr)
}
}
// Generate return vector
ret := base.GeneratePredictionVector(what)
// Resolve Attribute specifications for both
whatAttrSpecs := base.ResolveAttributes(what, allNumericAttrs)
trainAttrSpecs := base.ResolveAttributes(KNN.TrainingData, allNumericAttrs)
// Reserve storage for most the most similar items
distances := make(map[int]float64)
// Reserve storage for voting map
maxmap := make(map[string]int)
// Reserve storage for row computations
trainRowBuf := make([]float64, len(allNumericAttrs))
predRowBuf := make([]float64, len(allNumericAttrs))
_, maxRow := what.Size()
curRow := 0
// Iterate over all outer rows
what.MapOverRows(whatAttrSpecs, func(predRow [][]byte, predRowNo int) (bool, error) {
if (curRow%1) == 0 && curRow > 0 {
fmt.Printf("KNN: %.2f %% done\n", float64(curRow)*100.0/float64(maxRow))
}
curRow++
// Read the float values out
for i, _ := range allNumericAttrs {
predRowBuf[i] = base.UnpackBytesToFloat(predRow[i])
}
predMat := utilities.FloatsToMatrix(predRowBuf)
// Find the closest match in the training data
KNN.TrainingData.MapOverRows(trainAttrSpecs, func(trainRow [][]byte, srcRowNo int) (bool, error) {
// Read the float values out
for i, _ := range allNumericAttrs {
trainRowBuf[i] = base.UnpackBytesToFloat(trainRow[i])
}
// Compute the distance
trainMat := utilities.FloatsToMatrix(trainRowBuf)
distances[srcRowNo] = distanceFunc.Distance(predMat, trainMat)
return true, nil
})
sorted := utilities.SortIntMap(distances)
values := sorted[:KNN.NearestNeighbours]
maxClass := KNN.vote(maxmap, values)
base.SetClass(ret, predRowNo, maxClass)
return true, nil
})
return ret
}
func (lr *LinearRegression) Fit(inst base.FixedDataGrid) error {
// Retrieve row size
_, rows := inst.Size()
// Validate class Attribute count
classAttrs := inst.AllClassAttributes()
if len(classAttrs) != 1 {
return fmt.Errorf("Only 1 class variable is permitted")
}
classAttrSpecs := base.ResolveAttributes(inst, classAttrs)
// Retrieve relevant Attributes
allAttrs := base.NonClassAttributes(inst)
attrs := make([]base.Attribute, 0)
for _, a := range allAttrs {
if _, ok := a.(*base.FloatAttribute); ok {
attrs = append(attrs, a)
}
}
cols := len(attrs) + 1
if rows < cols {
return NotEnoughDataError
}
// Retrieve relevant Attribute specifications
attrSpecs := base.ResolveAttributes(inst, attrs)
// Split into two matrices, observed results (dependent variable y)
// and the explanatory variables (X) - see http://en.wikipedia.org/wiki/Linear_regression
observed := mat64.NewDense(rows, 1, nil)
explVariables := mat64.NewDense(rows, cols, nil)
// Build the observed matrix
inst.MapOverRows(classAttrSpecs, func(row [][]byte, i int) (bool, error) {
val := base.UnpackBytesToFloat(row[0])
observed.Set(i, 0, val)
return true, nil
})
// Build the explainatory variables
inst.MapOverRows(attrSpecs, func(row [][]byte, i int) (bool, error) {
// Set intercepts to 1.0
explVariables.Set(i, 0, 1.0)
for j, r := range row {
explVariables.Set(i, j+1, base.UnpackBytesToFloat(r))
}
return true, nil
})
n := cols
qr := new(mat64.QR)
qr.Factorize(explVariables)
var q, reg mat64.Dense
q.QFromQR(qr)
reg.RFromQR(qr)
var transposed, qty mat64.Dense
transposed.Clone(q.T())
qty.Mul(&transposed, observed)
regressionCoefficients := make([]float64, n)
for i := n - 1; i >= 0; i-- {
regressionCoefficients[i] = qty.At(i, 0)
for j := i + 1; j < n; j++ {
regressionCoefficients[i] -= regressionCoefficients[j] * reg.At(i, j)
}
regressionCoefficients[i] /= reg.At(i, i)
}
lr.disturbance = regressionCoefficients[0]
lr.regressionCoefficients = regressionCoefficients[1:]
lr.fitted = true
lr.attrs = attrs
lr.cls = classAttrs[0]
return nil
}
