// StdDevBatch predicts the standard deviation at a set of locations of x.
func (g *GP) StdDevBatch(std []float64, x mat64.Matrix) []float64 {
r, c := x.Dims()
if c != g.inputDim {
panic(badInputLength)
}
if std == nil {
std = make([]float64, r)
}
if len(std) != r {
panic(badStorage)
}
// For a single point, the stddev is
// sigma = k(x,x) - k_*^T * K^-1 * k_*
// where k is the vector of kernels between the input points and the output points
// For many points, the formula is:
// nu_* = k(x_*, k_*) - k_*^T * K^-1 * k_*
// This creates the full covariance matrix which is an rxr matrix. However,
// the standard deviations are just the diagonal of this matrix. Instead, be
// smart about it and compute the diagonal terms one at a time.
kStar := g.formKStar(x)
var tmp mat64.Dense
tmp.SolveCholesky(g.cholK, kStar)
// set k(x_*, x_*) into std then subtract k_*^T K^-1 k_* , computed one row at a time
var tmp2 mat64.Vector
row := make([]float64, c)
for i := range std {
for k := 0; k < c; k++ {
row[k] = x.At(i, k)
}
std[i] = g.kernel.Distance(row, row)
tmp2.MulVec(kStar.ColView(i).T(), tmp.ColView(i))
rt, ct := tmp2.Dims()
if rt != 1 && ct != 1 {
panic("bad size")
}
std[i] -= tmp2.At(0, 0)
std[i] = math.Sqrt(std[i])
}
// Need to scale the standard deviation to be in the same units as y.
floats.Scale(g.std, std)
return std
}
// StdDev predicts the standard deviation of the function at x.
func (g *GP) StdDev(x []float64) float64 {
if len(x) != g.inputDim {
panic(badInputLength)
}
// nu_* = k(x_*, k_*) - k_*^T * K^-1 * k_*
n := len(g.outputs)
kstar := mat64.NewVector(n, nil)
for i := 0; i < n; i++ {
v := g.kernel.Distance(g.inputs.RawRowView(i), x)
kstar.SetVec(i, v)
}
self := g.kernel.Distance(x, x)
var tmp mat64.Vector
tmp.SolveCholeskyVec(g.cholK, kstar)
var tmp2 mat64.Vector
tmp2.MulVec(kstar.T(), &tmp)
rt, ct := tmp2.Dims()
if rt != 1 || ct != 1 {
panic("bad size")
}
return math.Sqrt(self-tmp2.At(0, 0)) * g.std
}