public void outputMatrix(String file) throws Exception {
int row = lpsolver.getNrows() + 1;
int col = lpsolver.getNcolumns() + 1;
ArrayRealVector f = new ArrayRealVector(col - 1);
for (int j = 1; j < col; j++) {
f.setEntry(j - 1, lpsolver.getMat(0, j));
}
Array2DRowRealMatrix Aeq = new Array2DRowRealMatrix(numOfBus, col - 1);
ArrayRealVector beq = new ArrayRealVector(numOfBus);
for (int i = 1; i < numOfBus + 1; i++) {
for (int j = 1; j < col; j++) {
Aeq.setEntry(i - 1, j - 1, lpsolver.getMat(i, j));
}
beq.setEntry(i - 1, lpsolver.getRh(i));
}
Array2DRowRealMatrix Aiq = new Array2DRowRealMatrix(row - numOfBus - 1, col - 1);
ArrayRealVector biq = new ArrayRealVector(row - numOfBus - 1);
int cnt = 0;
for (int i = numOfBus + 1; i < row; i++) {
for (int j = 1; j < col; j++) {
Aiq.setEntry(cnt, j - 1, lpsolver.getMat(i, j));
}
biq.setEntry(cnt, lpsolver.getRh(i));
cnt++;
}
ArrayRealVector lb = new ArrayRealVector(col - 1);
for (int i = 1; i < col - 1; i++) {
lb.setEntry(i - 1, lpsolver.getLowbo(i));
}
ArrayRealVector ub = new ArrayRealVector(col - 1);
for (int i = 1; i < col; i++) {
ub.setEntry(i - 1, lpsolver.getUpbo(i));
}
writeMatlabInputFile(file, f, Aeq, beq, Aiq, biq, ub, lb);
}
/**
* The main fitting method of Random Walk smoother.
*
* @param additiveFit - object of AdditiveFit class
* @return residuals
*/
public ArrayRealVector solve(AdditiveFit additiveFit) {
ArrayRealVector y = additiveFit.getZ();
ArrayRealVector w = additiveFit.getW();
int whichDistParameter = additiveFit.getWhichDistParameter();
// if (any(is.na(y)))
for (int i = 0; i < y.getDimension(); i++) {
if (Double.isNaN(y.getEntry(i))) {
// weights[is.na(y)] <- 0
w.setEntry(i, 0.0);
// y[is.na(y)] <- 0
y.setEntry(i, 0.0);
}
}
// N <- length(y)
n = y.getDimension();
// ------------------------------------------------------- BECOMES VERY SLOW HERE
// W <- diag.spam(x=weights) # weights
wM = (BlockRealMatrix) MatrixUtils.createRealDiagonalMatrix(w.getDataRef());
// E <- diag.spam(N)
eM = MatrixFunctions.buildIdentityMatrix(n);
// D <- diff(E, diff = order) # order
dM = MatrixFunctions.diff(eM, Controls.RW_ORDER);
// ------------------------------------------------------- FREEZES HERE
// G <- t(D)%*%D
gM = dM.transpose().multiply(dM);
// logsig2e <- log(sig2e) # getting logs
double logsig2e = FastMath.log(sigmasHash.get(whichDistParameter)[0]);
// logsig2b <- log(sig2b)
double logsig2b = FastMath.log(sigmasHash.get(whichDistParameter)[1]);
// fv <- solve(exp(-logsig2e)*W + exp(-logsig2b)*G,
// (exp(-logsig2e)*W)%*% as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-logsig2e))
.add(gM.scalarMultiply(FastMath.exp(-logsig2b))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-logsig2e)).operate(y));
// if (sig2e.fix==FALSE && sig2b.fix==FALSE) # both estimated{
if (Controls.SIG2E_FIX) {
if (Controls.SIG2B_FIX) {
// out <- list()
// par <- c(logsig2e, logsig2b)
// out$par <- par
// value.of.Q <- -Qf(par)
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
// se <- NULL
se = null;
} else {
// par <- log(sum((D%*%fv)^2)/N)
tempV = (ArrayRealVector) dM.operate(fv);
logsig2b = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV)) / n);
// Qf1 <- function(par) Qf(c(logsig2e, par))
// out<-nlminb(start = par,objective = Qf1,
// lower = c(-20), upper = c(20))
nonLinObj1.setResponse(y);
nonLinObj1.setWeights(w);
nonLinObj1.setLogsig2e(logsig2e);
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2b, logsig2b});
bounds =
new SimpleBounds(
new double[] {-Double.MAX_VALUE, -Double.MAX_VALUE},
new double[] {Double.MAX_VALUE, Double.MAX_VALUE});
obj = new ObjectiveFunction(nonLinObj1);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2b = values.getPoint()[0];
if (logsig2b > 20.0) {
logsig2b = 20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else if (logsig2b < -20.0) {
logsig2b = -20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else {
valueOfQ = -values.getValue();
}
// out$hessian <- optimHess(out$par, Qf1)
// value.of.Q <- -out$objective
// shes <- 1/out$hessian
// se1 <- ifelse(shes>0, sqrt(shes), NA)
// par <- c(logsig2e, out$par)
// names(par) <- c("logsig2e", "logsig2b")
/// out$par <- par
// se <- c(NA, se1)
// System.out.println(logsig2e+" "+logsig2b +" "+qF(new double[]{logsig2e, logsig2b}, y,
// w));
}
} else {
if (Controls.SIG2B_FIX) {
// par <- log(sum(weights*(y-fv)^2)/N)
tempV = y.subtract(fv);
logsig2e = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV).ebeMultiply(w)) / n);
// Qf2 <- function(par) Qf(c(par, logsig2b))
// out <- nlminb(start = par, objective = Qf2,
// lower = c(-20), upper = c(20))
nonLinObj2.setResponse(y);
nonLinObj2.setWeights(w);
nonLinObj2.setLogsig2b(logsig2b);
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2e, logsig2e});
bounds =
new SimpleBounds(
new double[] {-Double.MAX_VALUE, -Double.MAX_VALUE},
new double[] {Double.MAX_VALUE, Double.MAX_VALUE});
obj = new ObjectiveFunction(nonLinObj2);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2e = values.getPoint()[0];
// out$hessian <- optimHess(out$par, Qf2)
// value.of.Q <- -out$objective
if (logsig2e > 20.0) {
logsig2e = 20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else if (logsig2e < -20.0) {
logsig2e = -20.0;
valueOfQ = -qF(new double[] {logsig2e, logsig2b}, y, w);
} else {
valueOfQ = -values.getValue();
}
// shes <- 1/out$hessian
// se1 <- ifelse(shes>0, sqrt(shes), NA)
// par <- c( out$par, logsig2b)
// names(par) <- c("logsig2e", "logsig2b")
// out$par <- par
// se <- c(se1, NA)
// System.out.println(logsig2e+" "+logsig2b +" "+qF(new double[]{logsig2e, logsig2b}, y,
// w));
} else {
// par <- c(logsig2e <- log(sum(weights*(y-fv)^2)/N),
// logsig2b <-log(sum((D%*%fv)^2)/N))
tempV = y.subtract(fv);
logsig2e = FastMath.log(MatrixFunctions.sumV(w.ebeMultiply(tempV).ebeMultiply(tempV)) / n);
tempV = (ArrayRealVector) dM.operate(fv);
logsig2b = FastMath.log(MatrixFunctions.sumV(tempV.ebeMultiply(tempV)) / n);
nonLinObj.setResponse(y);
nonLinObj.setWeights(w);
// out <- nlminb(start = par, objective = Qf,
// lower = c(-20, -20), upper = c(20, 20))
maxiter = new MaxIter(Controls.BOBYQA_MAX_ITER);
maxeval = new MaxEval(Controls.BOBYQA_MAX_EVAL);
initguess = new InitialGuess(new double[] {logsig2e, logsig2b});
bounds = new SimpleBounds(new double[] {-20.0, -20.0}, new double[] {20.0, 20.0});
obj = new ObjectiveFunction(nonLinObj);
PointValuePair values =
optimizer.optimize(maxiter, maxeval, obj, initguess, bounds, GoalType.MINIMIZE);
logsig2e = values.getPoint()[0];
logsig2b = values.getPoint()[1];
// System.out.println(logsig2e+" "+logsig2b +" "+values.getValue());
// out$hessian <- optimHess(out$par, Qf) !!! Missing in Java
// value.of.Q <- -out$objective
valueOfQ = -values.getValue();
// shes <- try(solve(out$hessian))
// se <- if (any(class(shes)%in%"try-error"))
// rep(NA_real_,2) else sqrt(diag(shes))
}
}
// fv <- solve(exp(-out$par[1])*W + exp(-out$par[2])*G,
// (exp(-out$par[1])*W)%*% as.vector(y))
solver =
new QRDecomposition(
wM.scalarMultiply(FastMath.exp(-logsig2e))
.add(gM.scalarMultiply(FastMath.exp(-logsig2b))))
.getSolver();
fv = (ArrayRealVector) solver.solve(wM.scalarMultiply(FastMath.exp(-logsig2e)).operate(y));
sigmasHash.put(
whichDistParameter, new Double[] {FastMath.exp(logsig2e), FastMath.exp(logsig2b)});
// b <- diff(fv, differences=order)
b = MatrixFunctions.diffV(fv, Controls.RW_ORDER);
// tr1 <- order + sum(b^2)/(exp(out$par[2])) # this always right
// attributes(tr1) <- NULL
double tr1 =
Controls.RW_ORDER + MatrixFunctions.sumV(b.ebeMultiply(b)) / (FastMath.exp(logsig2b));
tempV = null;
return y.subtract(fv);
}