// Creates the features from the inputs. Features must be nSamples x nFeatures or nil
func FeaturizeTrainable(t Trainable, inputs common.RowMatrix, featurizedInputs *mat64.Dense) *mat64.Dense {
nSamples, nDim := inputs.Dims()
if featurizedInputs == nil {
nFeatures := t.NumFeatures()
featurizedInputs = mat64.NewDense(nSamples, nFeatures, nil)
}
rowViewer, isRowViewer := inputs.(mat64.RawRowViewer)
var f func(start, end int)
if isRowViewer {
f = func(start, end int) {
featurizer := t.NewFeaturizer()
for i := start; i < end; i++ {
featurizer.Featurize(rowViewer.RawRowView(i), featurizedInputs.RawRowView(i))
}
}
} else {
f = func(start, end int) {
featurizer := t.NewFeaturizer()
input := make([]float64, nDim)
for i := start; i < end; i++ {
inputs.Row(input, i)
featurizer.Featurize(input, featurizedInputs.RawRowView(i))
}
}
}
common.ParallelFor(nSamples, common.GetGrainSize(nSamples, minGrain, maxGrain), f)
return featurizedInputs
}
// ObjDeriv computes the objective value and stores the derivative in place
func (g *BatchGradBased) ObjGrad(parameters []float64, derivative []float64) (loss float64) {
c := make(chan lossDerivStruct, 10)
// Set the channel for parallel for
f := func(start, end int) {
g.lossDerivFunc(start, end, c, parameters)
}
go func() {
wg := &sync.WaitGroup{}
// Compute the losses and the derivatives all in parallel
wg.Add(2)
go func() {
common.ParallelFor(g.nTrain, g.grainSize, f)
wg.Done()
}()
// Compute the regularization
go func() {
deriv := make([]float64, g.nParameters)
loss := g.regularizer.LossDeriv(parameters, deriv)
//fmt.Println("regularizer loss = ", loss)
//fmt.Println("regularizer deriv = ", deriv)
c <- lossDerivStruct{
loss: loss,
deriv: deriv,
}
wg.Done()
}()
// Wait for all of the results to be sent on the channel
wg.Wait()
// Close the channel
close(c)
}()
// zero the derivative
for i := range derivative {
derivative[i] = 0
}
// Range over the channel, incrementing the loss and derivative
// as they come in
for l := range c {
loss += l.loss
floats.Add(derivative, l.deriv)
}
//fmt.Println("nTrain", g.nTrain)
//fmt.Println("final deriv", derivative)
// Normalize by the number of training samples
loss /= float64(g.nTrain)
floats.Scale(1/float64(g.nTrain), derivative)
return loss
}
// UnscaleData is a wrapper for unscaling data in parallel.
// TODO: Make this work better so that if there is an error somewhere data isn't changed
func UnscaleData(scaler Scaler, data *mat64.Dense) error {
m := &sync.Mutex{}
var e ErrorList
f := func(start, end int) {
for r := start; r < end; r++ {
errTmp := scaler.Unscale(data.RawRowView(r))
if errTmp != nil {
m.Lock()
e = append(e, &SliceError{Header: "scale", Idx: r, Err: errTmp})
m.Unlock()
}
}
}
nSamples, _ := data.Dims()
grain := common.GetGrainSize(nSamples, 1, 500)
common.ParallelFor(nSamples, grain, f)
if len(e) != 0 {
return e
}
return nil
}
func BatchPredict(batch BatchPredictor, inputs common.RowMatrix, outputs common.MutableRowMatrix, inputDim, outputDim int, grainSize int) (common.MutableRowMatrix, error) {
// TODO: Add in something about error
// Check that the inputs and outputs are the right sizes
nSamples, dimInputs := inputs.Dims()
if inputDim != dimInputs {
return outputs, errors.New("predict batch: input dimension mismatch")
}
if outputs == nil {
outputs = mat64.NewDense(nSamples, outputDim, nil)
} else {
nOutputSamples, dimOutputs := outputs.Dims()
if dimOutputs != outputDim {
return outputs, errors.New("predict batch: output dimension mismatch")
}
if nSamples != nOutputSamples {
return outputs, errors.New("predict batch: rows mismatch")
}
}
// Perform predictions in parallel. For each parallel call, form a new predictor so that
// memory allocations are saved and no race condition happens.
// If the input and/or output is a RowViewer, save time by avoiding a copy
inputRVer, inputIsRowViewer := inputs.(mat64.RawRowViewer)
outputRVer, outputIsRowViewer := outputs.(mat64.RawRowViewer)
var f func(start, end int)
// wrapper function to allow parallel prediction. Uses RowView if the type has it
switch {
default:
panic("Shouldn't be here")
case inputIsRowViewer, outputIsRowViewer:
f = func(start, end int) {
p := batch.NewPredictor()
for i := start; i < end; i++ {
p.Predict(inputRVer.RawRowView(i), outputRVer.RawRowView(i))
}
}
case inputIsRowViewer && !outputIsRowViewer:
f = func(start, end int) {
p := batch.NewPredictor()
output := make([]float64, outputDim)
for i := start; i < end; i++ {
outputs.Row(output, i)
p.Predict(inputRVer.RawRowView(i), output)
outputs.SetRow(i, output)
}
}
case !inputIsRowViewer && outputIsRowViewer:
f = func(start, end int) {
p := batch.NewPredictor()
input := make([]float64, inputDim)
for i := start; i < end; i++ {
inputs.Row(input, i)
p.Predict(input, outputRVer.RawRowView(i))
}
}
case !inputIsRowViewer && !outputIsRowViewer:
f = func(start, end int) {
p := batch.NewPredictor()
input := make([]float64, inputDim)
output := make([]float64, outputDim)
for i := start; i < end; i++ {
inputs.Row(input, i)
outputs.Row(output, i)
p.Predict(input, output)
outputs.SetRow(i, output)
}
}
}
common.ParallelFor(nSamples, grainSize, f)
return outputs, nil
}
