// Run applies a trained BinningFilter to a set of Instances,
// discretising any numeric attributes added.
//
// IMPORTANT: Run discretises in-place, so make sure to take
// a copy if the original instances are still needed
//
// IMPORTANT: This function panic()s if the filter has not been
// trained. Call Build() before running this function
//
// IMPORTANT: Call Build() after adding any additional attributes.
// Otherwise, the training structure will be out of date from
// the values expected and could cause a panic.
func (b *BinningFilter) Run(on *base.Instances) {
if !b.trained {
panic("Call Build() beforehand")
}
for attr := range b.Attributes {
minVal := b.MinVals[attr]
maxVal := b.MaxVals[attr]
disc := 0
// Casts to float32 to replicate a floating point precision error
delta := float32(maxVal - minVal)
delta /= float32(b.BinCount)
for i := 0; i < on.Rows; i++ {
val := on.Get(i, attr)
if val <= minVal {
disc = 0
} else {
disc = int(math.Floor(float64(float32(val-minVal) / delta)))
if disc >= b.BinCount {
disc = b.BinCount - 1
}
}
on.Set(i, attr, float64(disc))
}
newAttribute := new(base.CategoricalAttribute)
newAttribute.SetName(on.GetAttr(attr).GetName())
for i := 0; i < b.BinCount; i++ {
newAttribute.GetSysValFromString(fmt.Sprintf("%d", i))
}
on.ReplaceAttr(attr, newAttribute)
}
}
// Predict outputs a base.Instances containing predictions from this tree
func (d *DecisionTreeNode) Predict(what *base.Instances) *base.Instances {
outputAttrs := make([]base.Attribute, 1)
outputAttrs[0] = what.GetClassAttr()
predictions := base.NewInstances(outputAttrs, what.Rows)
for i := 0; i < what.Rows; i++ {
cur := d
for {
if cur.Children == nil {
predictions.SetAttrStr(i, 0, cur.Class)
break
} else {
at := cur.SplitAttr
j := what.GetAttrIndex(at)
if j == -1 {
predictions.SetAttrStr(i, 0, cur.Class)
break
}
classVar := at.GetStringFromSysVal(what.Get(i, j))
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 predictions
}
func convertInstancesToLabelVec(X *base.Instances) []float64 {
labelVec := make([]float64, X.Rows)
for i := 0; i < X.Rows; i++ {
labelVec[i] = X.Get(i, X.ClassIndex)
}
return labelVec
}
// Run discretises the set of Instances `on'
//
// IMPORTANT: ChiMergeFilter discretises in place.
func (c *ChiMergeFilter) Run(on *base.Instances) {
if !c._Trained {
panic("Call Build() beforehand")
}
for attr := range c.Tables {
table := c.Tables[attr]
for i := 0; i < on.Rows; i++ {
val := on.Get(i, attr)
dis := 0
for j, k := range table {
if k.Value < val {
dis = j
continue
}
break
}
on.Set(i, attr, float64(dis))
}
newAttribute := new(base.CategoricalAttribute)
newAttribute.SetName(on.GetAttr(attr).GetName())
for _, k := range table {
newAttribute.GetSysValFromString(fmt.Sprintf("%f", k.Value))
}
on.ReplaceAttr(attr, newAttribute)
}
}
func ChiMBuildFrequencyTable(attr int, inst *base.Instances) []*FrequencyTableEntry {
ret := make([]*FrequencyTableEntry, 0)
var attribute *base.FloatAttribute
attribute, ok := inst.GetAttr(attr).(*base.FloatAttribute)
if !ok {
panic("only use Chi-M on numeric stuff")
}
for i := 0; i < inst.Rows; i++ {
value := inst.Get(i, attr)
valueConv := attribute.GetUsrVal(value)
class := inst.GetClass(i)
// 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 ret
}
func (lr *LinearRegression) Fit(inst *base.Instances) error {
if inst.Rows < inst.GetAttributeCount() {
return NotEnoughDataError
}
// 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(inst.Rows, 1, nil)
explVariables := mat64.NewDense(inst.Rows, inst.GetAttributeCount(), nil)
for i := 0; i < inst.Rows; i++ {
observed.Set(i, 0, inst.Get(i, inst.ClassIndex)) // Set observed data
for j := 0; j < inst.GetAttributeCount(); j++ {
if j == 0 {
// Set intercepts to 1.0
// Could / should be done better: http://www.theanalysisfactor.com/interpret-the-intercept/
explVariables.Set(i, 0, 1.0)
} else {
explVariables.Set(i, j, inst.Get(i, j-1))
}
}
}
n := inst.GetAttributeCount()
qr := mat64.QR(explVariables)
q := qr.Q()
reg := qr.R()
var transposed, qty mat64.Dense
transposed.TCopy(q)
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
return nil
}
func (lr *LogisticRegression) Predict(X *base.Instances) *base.Instances {
ret := X.GeneratePredictionVector()
row := make([]float64, X.Cols-1)
for i := 0; i < X.Rows; i++ {
rowCounter := 0
for j := 0; j < X.Cols; j++ {
if j != X.ClassIndex {
row[rowCounter] = X.Get(i, j)
rowCounter++
}
}
fmt.Println(Predict(lr.model, row), row)
ret.Set(i, 0, Predict(lr.model, row))
}
return ret
}
func convertInstancesToProblemVec(X *base.Instances) [][]float64 {
problemVec := make([][]float64, X.Rows)
for i := 0; i < X.Rows; i++ {
problemVecCounter := 0
problemVec[i] = make([]float64, X.Cols-1)
for j := 0; j < X.Cols; j++ {
if j == X.ClassIndex {
continue
}
problemVec[i][problemVecCounter] = X.Get(i, j)
problemVecCounter++
}
}
fmt.Println(problemVec, X)
return problemVec
}
func (lr *LinearRegression) Predict(X *base.Instances) (*base.Instances, error) {
if !lr.fitted {
return nil, NoTrainingDataError
}
ret := X.GeneratePredictionVector()
for i := 0; i < X.Rows; i++ {
var prediction float64 = lr.disturbance
for j := 0; j < X.Cols; j++ {
if j != X.ClassIndex {
prediction += X.Get(i, j) * lr.regressionCoefficients[j]
}
}
ret.Set(i, 0, prediction)
}
return ret, nil
}
