124 lines
2.8 KiB
Plaintext
124 lines
2.8 KiB
Plaintext
@q $Id$ @>
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@q Copyright 2007, Ondra Kamenik @>
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@ Start of {\tt normal\_conjugate.cpp} file.
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@c
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#include "normal_conjugate.h"
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#include "kord_exception.h"
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@<|NormalConj| diffuse prior constructor@>;
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@<|NormalConj| data update constructor@>;
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@<|NormalConj| copy constructor@>;
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@<|NormalConj::update| one observation code@>;
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@<|NormalConj::update| multiple observations code@>;
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@<|NormalConj::update| with |NormalConj| code@>;
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@<|NormalConj::getVariance| code@>;
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@
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@<|NormalConj| diffuse prior constructor@>=
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NormalConj::NormalConj(int d)
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: mu(d), kappa(0), nu(-1), lambda(d,d)
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{
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mu.zeros();
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lambda.zeros();
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}
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@
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@<|NormalConj| data update constructor@>=
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NormalConj::NormalConj(const ConstTwoDMatrix& ydata)
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: mu(ydata.numRows()), kappa(ydata.numCols()), nu(ydata.numCols()-1),
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lambda(ydata.numRows(), ydata.numRows())
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{
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mu.zeros();
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for (int i = 0; i < ydata.numCols(); i++)
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mu.add(1.0/ydata.numCols(), ConstVector(ydata, i));
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lambda.zeros();
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for (int i = 0; i < ydata.numCols(); i++) {
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Vector diff(ConstVector(ydata, i));
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diff.add(-1, mu);
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lambda.addOuter(diff);
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}
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}
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@
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@<|NormalConj| copy constructor@>=
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NormalConj::NormalConj(const NormalConj& nc)
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: mu(nc.mu), kappa(nc.kappa), nu(nc.nu), lambda(nc.lambda)
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{
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}
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@ The method performs the following:
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$$\eqalign{
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\mu_1 = &\; {\kappa_0\over \kappa_0+1}\mu_0 + {1\over \kappa_0+1}y\cr
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\kappa_1 = &\; \kappa_0 + 1\cr
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\nu_1 = &\; \nu_0 + 1\cr
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\Lambda_1 = &\; \Lambda_0 + {\kappa_0\over\kappa_0+1}(y-\mu_0)(y-\mu_0)^T,
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}$$
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@<|NormalConj::update| one observation code@>=
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void NormalConj::update(const ConstVector& y)
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{
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KORD_RAISE_IF(y.length() != mu.length(),
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"Wrong length of a vector in NormalConj::update");
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mu.mult(kappa/(1.0+kappa));
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mu.add(1.0/(1.0+kappa), y);
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Vector diff(y);
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diff.add(-1, mu);
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lambda.addOuter(diff, kappa/(1.0+kappa));
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kappa++;
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nu++;
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}
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@ The method evaluates the formula in the header file.
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@<|NormalConj::update| multiple observations code@>=
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void NormalConj::update(const ConstTwoDMatrix& ydata)
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{
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NormalConj nc(ydata);
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update(nc);
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}
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@
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@<|NormalConj::update| with |NormalConj| code@>=
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void NormalConj::update(const NormalConj& nc)
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{
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double wold = ((double)kappa)/(kappa+nc.kappa);
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double wnew = 1-wold;
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mu.mult(wold);
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mu.add(wnew, nc.mu);
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Vector diff(nc.mu);
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diff.add(-1, mu);
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lambda.add(1.0, nc.lambda);
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lambda.addOuter(diff);
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kappa = kappa + nc.kappa;
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nu = nu + nc.kappa;
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}
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@ This returns ${1\over \nu-d-1}\Lambda$, which is the mean of the
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variance in the posterior distribution. If the number of degrees of
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freedom is less than $d$, then NaNs are returned.
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@<|NormalConj::getVariance| code@>=
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void NormalConj::getVariance(TwoDMatrix& v) const
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{
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if (nu > getDim()+1) {
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v = (const TwoDMatrix&)lambda;
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v.mult(1.0/(nu-getDim()-1));
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} else
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v.nans();
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}
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@ End of {\tt normal\_conjugate.cpp} file.
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