func NewScatter(p int) (this *Scatter) {
this = new(Scatter)
this.S = matrix.Zeros(p, p)
this.Mean = matrix.Zeros(p, 1)
this.Count = 0
return
}
func (this *Scatter) RemoveScatter(other *Scatter) {
/*
int newCount = count-s.getCount();
Matrix newMean = mean.times(count).minus(s.getMean().times(s.getCount())).times(1.0/newCount);
Matrix newScatter = scatter.minus(s.getScatter());
newScatter = newScatter.minus(s.getMean().times(s.getMean().transpose()).times(s.getCount()));
newScatter = newScatter.minus(newMean.times(newMean.transpose()).times(newCount));
newScatter = newScatter.plus(mean.times(mean.transpose()).times(count));
count = newCount;
mean = newMean;
scatter = newScatter;
*/
if other.Count == 0 {
return
}
newCount := this.Count - other.Count
if newCount == 0 {
p := this.S.Rows()
this.S = matrix.Zeros(p, p)
this.Mean = matrix.Zeros(p, 1)
this.Count = newCount
return
}
newMean := this.Mean.Copy()
newMean.Scale(float64(this.Count))
otherMean := other.Mean.Copy()
otherMean.Scale(float64(other.Count))
newMean.Subtract(otherMean)
newMean.Scale(1 / float64(newCount))
this.S.Subtract(other.S)
thisCross, _ := this.Mean.Times(this.Mean.Transpose())
thisCross.Scale(float64(this.Count))
otherCross, _ := other.Mean.Times(other.Mean.Transpose())
otherCross.Scale(float64(other.Count))
newCross, _ := newMean.Times(newMean.Transpose())
newCross.Scale(float64(newCount))
this.S.Add(thisCross)
this.S.Subtract(otherCross)
this.S.Subtract(newCross)
this.Count = newCount
this.Mean = newMean
}
func (this *Scatter) Remove(x *matrix.DenseMatrix) {
/*
int new_count = count-1;
Matrix new_mu = mean;
if (useSampleMean)
new_mu = mean.plus(mean.minus(x).times(1.0/new_count));
scatter = scatter.minus((x.minus(new_mu)).times(x.minus(mean).transpose()));
count = new_count;
mean = new_mu;
if (count == 0) {
if (useSampleMean)
mean = new Matrix(p, 1, 0);
scatter = new Matrix(p, p, 0);
}
*/
newCount := this.Count - 1
if newCount == 0 {
p := this.S.Rows()
this.S = matrix.Zeros(p, p)
this.Mean = matrix.Zeros(p, 1)
this.Count = newCount
return
}
newMean, _ := this.Mean.MinusDense(x)
newMean.Scale(1 / float64(newCount))
newMean.Add(this.Mean)
xMinusNewMean, _ := x.MinusDense(newMean)
xMinusMean, _ := x.MinusDense(this.Mean)
xMinusMeanT := xMinusMean.Transpose()
xCross, _ := xMinusNewMean.TimesDense(xMinusMeanT)
this.S.Subtract(xCross)
this.Count = newCount
this.Mean = newMean
}
/*
M is r x c, o x i
Sigma is r x r, o x o
Phi is c x c, i x i
Sigma matches Y o x 1 output dimension
Phi matches X i x 1 input dimension
*/
func NewKnownVarianceLRPosterior(M, Sigma, Phi *mx.DenseMatrix) (this *KnownVarianceLRPosterior) {
if M.Rows() != Sigma.Rows() {
panic("M.Rows != Sigma.Rows")
}
if M.Cols() != Phi.Cols() {
panic("M.Cols != Phi.Cols")
}
if Sigma.Rows() != Sigma.Cols() {
panic("Sigma is not square")
}
if Phi.Rows() != Phi.Cols() {
panic("Phi is not square")
}
this = &KnownVarianceLRPosterior{
M: M,
Sigma: Sigma,
Phi: Phi,
XXt: mx.Zeros(Phi.Cols(), Phi.Cols()),
YXt: mx.Zeros(Sigma.Cols(), Phi.Cols()),
}
return
}
