Example #1
0
func (fm *FeatureMatrix) Mat64(header, transpose bool) *mat64.Dense {
	var (
		idx   int
		iter  fmIt
		dense *mat64.Dense
	)

	ncol := len(fm.Data)
	nrow := len(fm.CaseLabels)

	if !transpose {
		iter = rowIter(fm, header)
		dense = mat64.NewDense(nrow, ncol, nil)
	} else {
		iter = colIter(fm, header)
		dense = mat64.NewDense(ncol, nrow+1, nil)
	}

	for row, ok := iter(); ok; idx++ {
		for j, val := range row {
			flt, _ := strconv.ParseFloat(val, 64)
			dense.Set(idx, j, flt)
		}
		row, ok = iter()
	}

	return dense
}
Example #2
0
// LatinHypercube generates rows(batch) samples using Latin hypercube sampling
// from the given distribution. If src is not nil, it will be used to generate
// random numbers, otherwise rand.Float64 will be used.
//
// Latin hypercube sampling divides the cumulative distribution function into equally
// spaced bins and guarantees that one sample is generated per bin. Within each bin,
// the location is randomly sampled. The distmv.UnitNormal variable can be used
// for easy generation from the unit interval.
func LatinHypercube(batch *mat64.Dense, q distmv.Quantiler, src *rand.Rand) {
	r, c := batch.Dims()
	var f64 func() float64
	var perm func(int) []int
	if src != nil {
		f64 = src.Float64
		perm = src.Perm
	} else {
		f64 = rand.Float64
		perm = rand.Perm
	}
	r64 := float64(r)
	for i := 0; i < c; i++ {
		p := perm(r)
		for j := 0; j < r; j++ {
			var v float64
			v = f64()
			v = v/r64 + float64(j)/r64
			batch.Set(p[j], i, v)
		}
	}
	p := make([]float64, c)
	for i := 0; i < r; i++ {
		copy(p, batch.RawRowView(i))
		q.Quantile(batch.RawRowView(i), p)
	}
}
Example #3
0
// Generate generates a list of n random features given an input dimension d
func (iso IsoSqExp) Generate(n int, dim int, features *mat64.Dense) {
	scale := math.Exp(iso.LogScale)

	for i := 0; i < n; i++ {
		for j := 0; j < dim; j++ {
			features.Set(i, j, rand.NormFloat64()*scale)
		}
	}
}
Example #4
0
File: gp.go Project: reggo/reggo
// Add adds a new point to the gaussian process
func (gp *Trainer) Add(newInput []float64, newOutput []float64) {

	gp.nData++
	// See if we need to allocate new memory
	var inputAtCap bool
	if len(gp.inputData) == cap(gp.inputData) {
		inputAtCap = true
	}
	/*
		var outputAtCap bool
		if len(gp.outputData) == cap(gp.outputData) {
			outputAtCap = true
		}
	*/

	gp.inputData = append(gp.inputData, newInput)
	gp.outputData = append(gp.outputData, newOutput)

	// If we had to allocate memory, allocate new memory for the kernel matrix
	if gp.Implicit {
		// If it's implicit, just need to update matrix size, because the kernel
		// is computed on the fly
		//gp.kernelMat =
		panic("not coded")
	}
	var newKernelMatrix *mat64.Dense
	if inputAtCap {
		oldKernelMatrix := gp.kernelMatrix
		// If we had to allocate new memory for the inputs, then need to expand
		// the size of the matrix as well
		newKernelData := make([]float64, cap(gp.inputData)*cap(gp.inputData))

		panic("Need to use raw matrix")
		//newKernelMatrix = mat64.NewDense(gp.nData, gp.nData, newKernelData)

		// Copy the old kernel data into the new one. View and newKernelMatrix share
		// the same underlying array
		view := &mat64.Dense{}
		view.View(newKernelMatrix, 0, 0, gp.nData-1, gp.nData-1)
		view.Copy(oldKernelMatrix)

		gp.kernelData = newKernelData
	} else {
		// We aren't at capacity, so just need to increase the size
		newKernelMatrix = mat64.NewDense(nData, nData, gp.kernelData)
	}
	// Set the new values of the kernel matrix
	for i := 0; i < nData; i++ {
		oldInput := gp.inputData[i*gp.inputDim : (i+1)*gp.inputDim]
		ker := gp.Kernel(oldData, newInput)
		newKernelMatrix.Set(i, gp.nData, ker)
		newKernelMatrix.Set(gp.nData, i, ker)
	}
	gp.kernelMatrix = newKernelMatrix
}
Example #5
0
File: loss.go Project: reggo/reggo
func (SquaredDistance) LossDerivHess(prediction, truth, derivative []float64, hessian *mat64.Dense) (loss float64) {
	if len(prediction) != len(truth) || len(prediction) != len(derivative) {
		panic(lenMismatch)
	}
	n, m := hessian.Dims()
	if len(prediction) != n {
		panic(lenMismatch)
	}
	if len(prediction) != m {
		panic(lenMismatch)
	}
	for i := range prediction {
		diff := prediction[i] - truth[i]
		derivative[i] = diff
		loss += diff * diff
	}

	nFloat := float64(n)
	loss /= nFloat

	corr := 2 / nFloat

	for i := range derivative {
		derivative[i] *= corr
	}

	for i := 0; i < n; i++ {
		for j := 0; j < n; j++ {
			if i == j {
				hessian.Set(i, j, corr)
			} else {
				hessian.Set(i, j, 0)
			}
		}
	}

	return loss
}
Example #6
0
// Rejection generates rows(batch) samples using the rejection sampling algorithm and
// stores them in place into samples.
// Sampling continues until batch is filled. Rejection returns the total number of proposed
// locations and a boolean indicating if the rejection sampling assumption is
// violated (see details below). If the returned boolean is false, all elements
// of samples are set to NaN. If src != nil, it will be used to generate random
// numbers, otherwise rand.Float64 will be used.
//
// Rejection sampling generates points from the target distribution by using
// the proposal distribution. At each step of the algorithm, the proposaed point
// is accepted with probability
//  p = target(x) / (proposal(x) * c)
// where target(x) is the probability of the point according to the target distribution
// and proposal(x) is the probability according to the proposal distribution.
// The constant c must be chosen such that target(x) < proposal(x) * c for all x.
// The expected number of proposed samples is len(samples) * c.
//
// Target may return the true (log of) the probablity of the location, or it may return
// a value that is proportional to the probability (logprob + constant). This is
// useful for cases where the probability distribution is only known up to a normalization
// constant.
func Rejection(batch *mat64.Dense, target distmv.LogProber, proposal distmv.RandLogProber, c float64, src *rand.Rand) (nProposed int, ok bool) {
	if c < 1 {
		panic("rejection: acceptance constant must be greater than 1")
	}
	f64 := rand.Float64
	if src != nil {
		f64 = src.Float64
	}
	r, dim := batch.Dims()
	v := make([]float64, dim)
	var idx int
	for {
		nProposed++
		proposal.Rand(v)
		qx := proposal.LogProb(v)
		px := target.LogProb(v)
		accept := math.Exp(px-qx) / c
		if accept > 1 {
			// Invalidate the whole result and return a failure.
			for i := 0; i < r; i++ {
				for j := 0; j < dim; j++ {
					batch.Set(i, j, math.NaN())
				}
			}
			return nProposed, false
		}
		if accept > f64() {
			batch.SetRow(idx, v)
			idx++
			if idx == r {
				break
			}
		}
	}
	return nProposed, true
}