public void CalculatePrediction(int steps_ahead) {
if (steps_ahead > 0) {
predicted_means_ = new DoubleMatrix[steps_ahead];
predicted_variances_ = new DoubleMatrix[steps_ahead];
DoubleMatrix previous_state_mean = estimated_state_means_[estimated_state_means_.length - 1];
DoubleMatrix previous_state_covariance =
estimated_state_covariances_[estimated_state_covariances_.length - 1];
for (int ii = 0; ii < steps_ahead; ii++) {
DoubleMatrix a = state_transition_matrix_ref_.mmul(previous_state_mean);
DoubleMatrix R =
state_transition_matrix_ref_
.mmul(previous_state_covariance)
.mmul(state_transition_matrix_ref_.transpose())
.addi(transition_covariance_matrix_ref_);
// One-step-ahead observation prediction.
predicted_means_[ii] = observation_matrix_ref_.mmul(a);
predicted_variances_[ii] =
observation_matrix_ref_
.mmul(R)
.mmul(observation_matrix_ref_.transpose())
.addi(observation_covariance_matrix_ref_);
previous_state_mean = a;
previous_state_covariance = R;
}
} else {
// smoothing
predicted_means_ = predicted_observation_means_;
predicted_variances_ = predicted_observation_covariances_;
}
}
public void weight_contribution(
DoubleMatrix h0, DoubleMatrix v0, DoubleMatrix h1, DoubleMatrix v1) {
this.w1
.addi(h0.transpose().mmul(v0).subi(h1.transpose().mmul(v1)))
.muli(learningRate / v_data.rows)
.subi(weights.mul(weightCost));
this.vb1.addi(v0.sub(v1).muli(learningRate / v_data.rows).columnMeans());
this.hb1.addi(h0.sub(h1).muli(learningRate / v_data.rows).columnMeans());
}
/**
* 求广义逆矩阵,使用正交投影方法
*
* @param ht
* @return
*/
public DoubleMatrix getMPMatrixByOP(DoubleMatrix H) {
DoubleMatrix M;
try {
M = H.mmul(H.transpose()); // HHt
addPositiveValue(M);
return H.transpose().mmul(inverse(M));
} catch (LapackException e) {
M = H.transpose().mmul(H); // HtH
addPositiveValue(M);
return inverse(M).mmul(H.transpose());
}
}
private void UpdateFilteredStates() {
DoubleMatrix state_transition_matrix_T = state_transition_matrix_ref_.transpose();
DoubleMatrix observation_matrix_T = observation_matrix_ref_.transpose();
for (int ii = 0; ii < number_of_observations_; ii++) {
DoubleMatrix previous_state_mean =
(ii == 0) ? initial_state_mean_ref_ : estimated_state_means_[ii - 1];
DoubleMatrix previous_state_covariance =
(ii == 0) ? initial_state_covariance_ref_ : estimated_state_covariances_[ii - 1];
// One-step-ahead state prediction. (i)
DoubleMatrix a = state_transition_matrix_ref_.mmul(previous_state_mean);
DoubleMatrix R =
state_transition_matrix_ref_
.mmul(previous_state_covariance)
.mmul(state_transition_matrix_T)
.addi(transition_covariance_matrix_ref_);
// One-step-ahead observation prediction(ii)
predicted_observation_means_[ii] = observation_matrix_ref_.mmul(a);
predicted_observation_covariances_[ii] =
observation_matrix_ref_
.mmul(R)
.mmul(observation_matrix_T)
.addi(observation_covariance_matrix_ref_);
// if current observation is null:
if (observations_ref_[ii] == null) {
estimated_state_means_[ii] = a;
estimated_state_covariances_[ii] = R;
} else {
DoubleMatrix inv_predicted_observation_covariances =
InverseMatrix.inverse(predicted_observation_covariances_[ii]);
DoubleMatrix R_obs_mat_T_inv_pred_obs_cov =
R.mmul(observation_matrix_T).mmuli(inv_predicted_observation_covariances);
// forward filtering states.(iii)
estimated_state_means_[ii] =
R_obs_mat_T_inv_pred_obs_cov.mmul(
observations_ref_[ii].sub(predicted_observation_means_[ii]))
.addi(a);
estimated_state_covariances_[ii] =
R.sub(R_obs_mat_T_inv_pred_obs_cov.mmul(observation_matrix_ref_).mmuli(R));
estimated_means_[ii] = observation_matrix_ref_.mmul(estimated_state_means_[ii]);
estimated_covariances_[ii] =
observation_matrix_ref_
.mmul(estimated_state_covariances_[ii])
.mmul(observation_matrix_T)
.addi(observation_covariance_matrix_ref_);
}
}
}
/**
* 求广义逆矩阵 Theory:最大秩分解 U*S*Vt = A C=U*sqrt(S) D=sqrt(S)*Vt <==> A=C*D MP(A) =
* D'*inv(D*D')*inv(C'*C)*C'
*
* @param ht
* @return
*/
public DoubleMatrix getMPMatrix(DoubleMatrix ht) {
int m = ht.rows;
int n = ht.columns;
DoubleMatrix[] USV = Singular.fullSVD(ht);
DoubleMatrix U = USV[0];
double[] s_raw = USV[1].data.clone(); // 奇异值
// 去除0
int k = 0;
while (k < s_raw.length && s_raw[k] > 0.1e-5) {
k++;
}
double[] s = new double[k];
for (int i = 0; i < s.length; i++) {
s[i] = s_raw[i];
}
DoubleMatrix Vt = USV[2].transpose(); // n*n
int sn = s.length;
for (int i = 0; i < sn; i++) {
s[i] = Math.sqrt(s[i]);
}
// 构造m*sn sn*n矩阵
DoubleMatrix S1 = new DoubleMatrix(m, sn);
DoubleMatrix S2 = new DoubleMatrix(sn, n);
for (int i = 0; i < sn; i++) {
S1.put(i, i, s[i]);
S2.put(i, i, s[i]);
}
DoubleMatrix C = U.mmul(S1);
DoubleMatrix D = S2.mmul(Vt);
DoubleMatrix DT = D.transpose();
DoubleMatrix DD = D.mmul(DT);
DoubleMatrix invDD = inverse(DD);
DoubleMatrix DDD = DT.mmul(invDD);
DoubleMatrix CT = C.transpose();
DoubleMatrix CC = CT.mmul(C);
DoubleMatrix invCC = inverse(CC);
DoubleMatrix CCC = invCC.mmul(CT);
DoubleMatrix Ainv = DDD.mmul(CCC);
return Ainv;
}
public static void main(String argv[]) {
modshogun.init_shogun_with_defaults();
int dim = 7;
DoubleMatrix data = DoubleMatrix.rand(dim, dim);
RealFeatures feats = new RealFeatures(data);
DoubleMatrix data_T = data.transpose();
DoubleMatrix symdata = data.add(data_T);
int cols = (1 + dim) * dim / 2;
DoubleMatrix lowertriangle = DoubleMatrix.zeros(1, cols);
int count = 0;
for (int i = 0; i < dim; i++) {
for (int j = 0; j < dim; j++) {
if (j <= i) {
lowertriangle.put(0, count++, symdata.get(i, j));
}
}
}
CustomKernel kernel = new CustomKernel();
kernel.set_triangle_kernel_matrix_from_triangle(lowertriangle);
DoubleMatrix km_triangletriangle = kernel.get_kernel_matrix();
kernel.set_triangle_kernel_matrix_from_full(symdata);
DoubleMatrix km_fulltriangle = kernel.get_kernel_matrix();
kernel.set_full_kernel_matrix_from_full(data);
DoubleMatrix km_fullfull = kernel.get_kernel_matrix();
modshogun.exit_shogun();
}
@Override
public NeuralNetwork transpose() {
try {
Constructor<?> c = Dl4jReflection.getEmptyConstructor(getClass());
c.setAccessible(true);
NeuralNetwork ret = (NeuralNetwork) c.newInstance();
ret.setMomentumAfter(momentumAfter);
ret.setConcatBiases(concatBiases);
ret.setResetAdaGradIterations(resetAdaGradIterations);
ret.setVBiasAdaGrad(hBiasAdaGrad);
ret.sethBias(vBias.dup());
ret.setvBias(DoubleMatrix.zeros(hBias.rows, hBias.columns));
ret.setnHidden(getnVisible());
ret.setnVisible(getnHidden());
ret.setW(W.transpose());
ret.setL2(l2);
ret.setMomentum(momentum);
ret.setRenderEpochs(getRenderIterations());
ret.setSparsity(sparsity);
ret.setRng(getRng());
ret.setDist(getDist());
ret.setAdaGrad(wAdaGrad);
ret.setLossFunction(lossFunction);
ret.setOptimizationAlgorithm(optimizationAlgo);
ret.setConstrainGradientToUnitNorm(constrainGradientToUnitNorm);
return ret;
} catch (Exception e) {
throw new RuntimeException(e);
}
}
public DoubleMatrix free_energy(DoubleMatrix v_sample) {
DoubleMatrix wv_hb =
weights.mmul(v_sample.transpose()).addi(this.hbias.repmat(v_sample.rows, 1).transpose());
DoubleMatrix vb = v_sample.mmul(this.vbias.transpose());
DoubleMatrix hi = MatrixMath.sum(MatrixMath.log(MatrixMath.exp(wv_hb).addi(1.0)), 1);
return hi.mul(-1.0).subi(vb);
}
/**
* Reconstruction entropy. This compares the similarity of two probability distributions, in this
* case that would be the input and the reconstructed input with gaussian noise. This will account
* for either regularization or none depending on the configuration.
*
* @return reconstruction error
*/
public double getReConstructionCrossEntropy() {
DoubleMatrix preSigH = input.mmul(W).addRowVector(hBias);
DoubleMatrix sigH = sigmoid(preSigH);
DoubleMatrix preSigV = sigH.mmul(W.transpose()).addRowVector(vBias);
DoubleMatrix sigV = sigmoid(preSigV);
DoubleMatrix inner = input.mul(log(sigV)).add(oneMinus(input).mul(log(oneMinus(sigV))));
double ret = inner.rowSums().mean();
if (normalizeByInputRows) ret /= input.rows;
return ret;
}
private void modifyWeights(
DoubleMatrix trainingExample,
DoubleMatrix result,
DoubleMatrix output,
double learningFactor,
double momentum,
List<DoubleMatrix> previousModifications,
EvaluationContext evalCtx) {
List<DoubleMatrix> errors = countErrors(result, output);
List<Layer> layers = getLayers();
evalCtx.resetContext();
Iterator<ActivationFunction> activationFunctionIter = evalCtx.getActivationFunction();
DoubleMatrix temporalResult = trainingExample;
for (int i = 0; i < errors.size(); i++) {
DoubleMatrix error = errors.get(i);
int layerIndex = i + 1;
Layer layer = layers.get(layerIndex);
DoubleMatrix previousModification = previousModifications.get(i);
ActivationFunction activationFunc = activationFunctionIter.next();
ActivationFunctionDerivative derivative =
DerivativeFactory.getInstance().getDerivative(activationFunc);
if (layer.includeBias()) {
temporalResult = addBiasInput(temporalResult);
}
DoubleMatrix oldVal = temporalResult.dup();
temporalResult = layer.getWeights().mmul(temporalResult);
temporalResult = activationFunc.eval(temporalResult);
// dla kazdego neuronu w warstwie
for (int j = 0; j < layer.getWeights().rows; j++) {
double derVal = derivative.evaluate(temporalResult.get(j));
DoubleMatrix oldDelta = previousModification.getRow(j);
DoubleMatrix delta = oldVal.mul(derVal).mul(learningFactor).mul(error.get(j));
delta = delta.transpose();
delta = delta.add(oldDelta.mul(momentum));
previousModification.putRow(j, delta);
DoubleMatrix oldWeights = layer.getWeights().getRow(j);
DoubleMatrix newWeights = oldWeights.add(delta);
layer.getWeights().putRow(j, newWeights);
}
}
}
public static void exportResult(
TrueSolutionCTM trueSolutionCTM,
TrueSolution[] trueSolution,
TrueSolution[] trueSolution1,
TrueSolution[] trueSolution2,
int limite,
String folder) {
try {
int bdry = 1; // *set boundary type
int numSections = trueSolution[0].numSections;
int numSections1 = trueSolution1[0].numSections;
int numSections2 = trueSolution2[0].numSections;
BufferedWriter[] writerSection = new BufferedWriter[numSections];
BufferedWriter[] writerSection1 = new BufferedWriter[numSections1];
BufferedWriter[] writerSection2 = new BufferedWriter[numSections2];
BufferedWriter[] LyapSection = new BufferedWriter[numSections];
BufferedWriter writerTrue;
BufferedWriter writerConsensusTerm;
BufferedWriter writerAvr;
BufferedWriter writerAvr1;
BufferedWriter writerAvr2;
BufferedWriter Error;
BufferedWriter Error1;
BufferedWriter Error2;
BufferedWriter Lyap;
BufferedWriter[] Mode = new BufferedWriter[numSections];
BufferedWriter[] Mode1 = new BufferedWriter[numSections1];
BufferedWriter[] Mode2 = new BufferedWriter[numSections2];
BufferedWriter[] GammaWrite = new BufferedWriter[numSections];
BufferedWriter writerDisagreement;
BufferedWriter writerDisagreement1;
BufferedWriter writerDisagreement2;
System.out.print("Beginning of simulation ");
new File("results/" + folder).mkdir();
RoadModel[] roadModels = new RoadModel[numSections];
RoadModel[] roadModels1 = new RoadModel[numSections1];
RoadModel[] roadModels2 = new RoadModel[numSections2];
for (int i = 0; i < numSections; i++) {
trueSolution[i].section = trueSolution[i].setSections();
}
for (int i = 0; i < numSections1; i++) {
trueSolution1[i].section = trueSolution1[i].setSections();
}
for (int i = 0; i < numSections2; i++) {
trueSolution2[i].section = trueSolution2[i].setSections();
}
for (int i = 0; i < numSections; i++) {
roadModels[i] = trueSolution[i].setRoadModels();
}
for (int i = 0; i < numSections1; i++) {
roadModels1[i] = trueSolution1[i].setRoadModels();
}
for (int i = 0; i < numSections2; i++) {
roadModels2[i] = trueSolution2[i].setRoadModels();
}
writerTrue =
new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "trueState.csv")));
writerConsensusTerm =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "consensusTerm.csv")));
writerAvr =
new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "writerAvr.csv")));
writerAvr1 =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "writerAvr1.csv")));
writerAvr2 =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "writerAvr2.csv")));
Error = new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "Error.csv")));
Error1 =
new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "Error1.csv")));
Error2 =
new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "Error2.csv")));
Lyap =
new BufferedWriter(new FileWriter(new File("results/" + folder + "/" + "Lyapunov.csv")));
writerDisagreement =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "Disagreement.csv")));
writerDisagreement1 =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "Disagreement1.csv")));
writerDisagreement2 =
new BufferedWriter(
new FileWriter(new File("results/" + folder + "/" + "Disagreement2.csv")));
Estimation[] estimations = new Estimation[numSections];
Estimation[] estimations1 = new Estimation[numSections1];
Estimation[] estimations2 = new Estimation[numSections2];
for (int i = 0; i < numSections; i++) {
estimations[i] =
new Estimation(
roadModels[i],
"KCF",
true); // *set the last variable to indicate if consensus is needed
String S = "results/" + folder + "/" + roadModels[i].nameModel;
writerSection[i] = new BufferedWriter(new FileWriter(new File(S + ".csv")));
Mode[i] = new BufferedWriter(new FileWriter(new File(S + "Mode.csv")));
GammaWrite[i] = new BufferedWriter(new FileWriter(new File(S + "Gamma.csv")));
LyapSection[i] = new BufferedWriter(new FileWriter(new File(S + "LyapSection.csv")));
}
for (int i = 0; i < numSections1; i++) {
estimations1[i] =
new Estimation(
roadModels1[i],
"KCF",
false); // *set the last variable to indicate if consensus is needed
String S = "results/" + folder + "/" + roadModels1[i].nameModel;
writerSection1[i] = new BufferedWriter(new FileWriter(new File(S + "_1.csv")));
Mode1[i] = new BufferedWriter(new FileWriter(new File(S + "Mode1.csv")));
}
for (int i = 0; i < numSections2; i++) {
estimations2[i] =
new Estimation(
roadModels2[i],
"KCF",
false); // *set the last variable to indicate if consensus is needed
String S = "results/" + folder + "/" + roadModels2[i].nameModel;
writerSection2[i] = new BufferedWriter(new FileWriter(new File(S + "_2.csv")));
Mode2[i] = new BufferedWriter(new FileWriter(new File(S + "Mode2.csv")));
}
int overlap = roadModels[0].trueSolution.overlapsec;
int cellsec = roadModels[0].section.cellsec;
int overlap1 = roadModels1[0].trueSolution.overlapsec;
int cellsec1 = roadModels1[0].section.cellsec;
int overlap2 = roadModels2[0].trueSolution.overlapsec;
int cellsec2 = roadModels2[0].section.cellsec;
DoubleMatrix I1 = DoubleMatrix.zeros(roadModels[0].section.cellsec, overlap);
for (int i = 0; i < overlap; i++) {
I1.put(i, i, 1);
}
DoubleMatrix I2 = DoubleMatrix.zeros(roadModels[0].section.cellsec, overlap);
for (int i = roadModels[0].section.cellsec - overlap;
i < roadModels[0].section.cellsec;
i++) {
I2.put(i, i - (roadModels[0].section.cellsec - overlap), 1);
}
DoubleMatrix I123 = DoubleMatrix.concatVertically(I1.transpose(), I2.transpose());
DoubleMatrix H_hat = I2.transpose();
for (int i = 0; i < numSections - 2; i++) {
H_hat = Concat.Diagonal(H_hat, I123);
}
H_hat = Concat.Diagonal(H_hat, I1.transpose());
DoubleMatrix L_tilde =
DoubleMatrix.zeros((numSections - 1) * 2 * overlap, numSections * cellsec);
for (int i = 0; i < numSections - 1; i++) {
for (int j = 0; j < overlap; j++) {
L_tilde.put(i * (2 * overlap) + j, (i + 1) * (cellsec) + j, 1);
}
for (int j = 0; j < overlap; j++) {
L_tilde.put(i * (2 * overlap) + overlap + j, (i + 1) * (cellsec) - overlap + j, 1);
}
for (int j = 0; j < overlap; j++) {
L_tilde.put(i * (2 * overlap) + j, (i + 1) * (cellsec) - overlap + j, -1);
}
for (int j = 0; j < overlap; j++) {
L_tilde.put(i * (2 * overlap) + overlap + j, (i + 1) * (cellsec) + j, -1);
}
}
DoubleMatrix[] S = new DoubleMatrix[numSections];
trueSolutionCTM.getMeasurement();
for (int i = 0; i < numSections; i++) {
if (i == 0) {
estimations[i].filter.Ltilde = L_tilde.getRange(i, i + overlap, i, i + 2 * cellsec);
} else if (i == numSections - 1) {
estimations[i].filter.Ltilde =
L_tilde.getRange(
(2 * (numSections - 1) - 1) * overlap,
(2 * (numSections - 1) - 1) * overlap + overlap,
(i - 1) * cellsec,
(i + 1) * cellsec);
} else {
estimations[i].filter.Ltilde =
L_tilde.getRange(
(2 * i - 1) * overlap,
(2 * i + 1) * overlap,
(i - 1) * cellsec,
(i + 2) * cellsec);
}
estimations[i].filter.I1 = I1;
estimations[i].filter.I2 = I2;
}
double[] disagAvg = new double[3];
double[] errorAvg = new double[3];
double[] errAvgEntire = new double[3];
for (int k = 0; k < limite; k++) {
System.out.println("time step=" + k);
for (int i = 0; i < trueSolutionCTM.trueStatesCTM.getRows(); i++) {
writerTrue.write(trueSolutionCTM.trueStatesCTM.get(i, 0) + ",");
}
writerTrue.write("\n");
DoubleMatrix[] mean = new DoubleMatrix[numSections];
DoubleMatrix[] mean_1 = new DoubleMatrix[numSections1];
DoubleMatrix[] mean_2 = new DoubleMatrix[numSections2];
double ErrorAvg = 0;
double ErrorEntire = 0;
double ErrorAvg1 = 0;
double ErrorAvg2 = 0;
double LyapAvg = 0;
double[] errorAvgk = new double[3];
for (int i = 0; i < numSections; i++) {
mean[i] = estimations[i].getDensityMean();
if (numSections == 1) {
for (int j = 0; j < mean[i].length; j++) {
writerAvr.write(mean[i].get(j) + ",");
errorAvgk[0] =
(mean[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[0];
}
} else {
if (i == 0) {
for (int j = 0; j < mean[i].length - overlap; j++) {
writerAvr.write(mean[i].get(j) + ",");
errorAvgk[0] =
(mean[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[0];
}
} else if (i > 0 && i < numSections - 1) {
for (int j = 0; j < overlap; j++) {
writerAvr.write(
(mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2 + ",");
errorAvgk[0] =
((mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + j, 0))
* ((mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + j, 0))
+ errorAvgk[0];
}
for (int j = 0; j < mean[i].length - 2 * overlap; j++) {
writerAvr.write(mean[i].get(overlap + j) + ",");
errorAvgk[0] =
(mean[i].get(overlap + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + overlap + j, 0))
* (mean[i].get(overlap + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + overlap + j, 0))
+ errorAvgk[0];
}
} else if (i == numSections - 1) {
for (int j = 0; j < overlap; j++) {
writerAvr.write(
(mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2 + ",");
errorAvgk[0] =
((mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + j, 0))
* ((mean[i].get(j) + mean[i - 1].get(cellsec - overlap + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + j, 0))
+ errorAvgk[0];
}
for (int j = 0; j < mean[i].length - overlap; j++) {
writerAvr.write(mean[i].get(overlap + j) + ",");
errorAvgk[0] =
(mean[i].get(overlap + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + overlap + j, 0))
* (mean[i].get(overlap + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean[i].length - overlap) + overlap + j, 0))
+ errorAvgk[0];
}
}
}
for (int j = 0; j < mean[i].length; j++) {
writerSection[i].write(mean[i].get(j) + ",");
}
double errorSection =
(((mean[i].sub(trueSolution[i].trueStates))
.transpose()
.mmul((mean[i].sub(trueSolution[i].trueStates))))
.get(0, 0))
/ ((double) cellsec);
Error.write(errorSection + ",");
ErrorAvg = ErrorAvg + errorSection / ((double) numSections);
writerSection[i].write("\n");
double lyapunovSection =
((mean[i].sub(trueSolution[i].trueStates)).transpose())
.mmul(InverseMatrix.invPoSym(estimations[i].filter.var))
.mmul(mean[i].sub(trueSolution[i].trueStates))
.get(0, 0);
double tracevar = 0;
for (int c = 0; c < mean[i].getRows(); c++) {
tracevar = tracevar + estimations[i].filter.var.get(c, c);
}
LyapSection[i].write(lyapunovSection + ",");
LyapSection[i].write(tracevar + "\n");
if (i != 0) {
LyapAvg = LyapAvg + lyapunovSection / ((double) (numSections - 1));
}
}
errAvgEntire[0] =
errorAvgk[0] / ((double) (limite * trueSolutionCTM.cells)) + errAvgEntire[0];
Error.write(ErrorAvg + ",");
errorAvg[0] = errorAvg[0] + ErrorAvg / ((double) (limite));
Lyap.write(LyapAvg + "\n");
for (int i = 0; i < numSections1; i++) {
mean_1[i] = estimations1[i].getDensityMean();
if (numSections1 == 1) {
for (int j = 0; j < mean_1[i].length; j++) {
writerAvr1.write(mean_1[i].get(j) + ",");
errorAvgk[1] =
(mean_1[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean_1[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[1];
}
} else {
if (i == 0) {
for (int j = 0; j < mean_1[i].length - overlap1; j++) {
writerAvr1.write(mean_1[i].get(j) + ",");
errorAvgk[1] =
(mean_1[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean_1[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[1];
}
} else if (i > 0 && i < numSections1 - 1) {
for (int j = 0; j < overlap1; j++) {
writerAvr1.write(
(mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2 + ",");
errorAvgk[1] =
((mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + j, 0))
* ((mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + j, 0))
+ errorAvgk[1];
}
for (int j = 0; j < mean_1[i].length - 2 * overlap1; j++) {
writerAvr1.write(mean_1[i].get(overlap1 + j) + ",");
errorAvgk[1] =
(mean_1[i].get(overlap1 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + overlap1 + j, 0))
* (mean_1[i].get(overlap1 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + overlap1 + j, 0))
+ errorAvgk[1];
}
} else if (i == numSections1 - 1) {
for (int j = 0; j < overlap1; j++) {
writerAvr1.write(
(mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2 + ",");
errorAvgk[1] =
((mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + j, 0))
* ((mean_1[i].get(j) + mean_1[i - 1].get(cellsec1 - overlap1 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + j, 0))
+ errorAvgk[1];
}
for (int j = 0; j < mean_1[i].length - overlap1; j++) {
writerAvr1.write(mean_1[i].get(overlap1 + j) + ",");
errorAvgk[1] =
(mean_1[i].get(overlap1 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + overlap1 + j, 0))
* (mean_1[i].get(overlap1 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_1[i].length - overlap1) + overlap1 + j, 0))
+ errorAvgk[1];
}
}
}
for (int j = 0; j < mean_1[i].length; j++) {
writerSection1[i].write(mean_1[i].get(j) + ",");
}
double errorSection1 =
(((mean_1[i].sub(trueSolution1[i].trueStates).transpose())
.mmul((mean_1[i].sub(trueSolution1[i].trueStates))))
.get(0, 0))
/ ((double) cellsec1);
Error1.write(errorSection1 + ",");
ErrorAvg1 = ErrorAvg1 + errorSection1 / ((double) numSections1);
writerSection1[i].write("\n");
}
Error1.write(ErrorAvg1 + ",");
errorAvg[1] = errorAvg[1] + ErrorAvg1 / ((double) (limite));
errAvgEntire[1] =
errorAvgk[1] / ((double) (limite * trueSolutionCTM.cells)) + errAvgEntire[1];
for (int i = 0; i < numSections2; i++) {
mean_2[i] = estimations2[i].getDensityMean();
if (numSections2 == 1) {
for (int j = 0; j < mean_2[i].length; j++) {
writerAvr2.write(mean_2[i].get(j) + ",");
errorAvgk[2] =
(mean_2[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean_2[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[2];
}
} else {
if (i == 0) {
for (int j = 0; j < mean_2[i].length - overlap2; j++) {
writerAvr2.write(mean_2[i].get(j) + ",");
errorAvgk[2] =
(mean_2[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
* (mean_2[i].get(j) - trueSolutionCTM.trueStatesCTM.get(j, 0))
+ errorAvgk[2];
}
} else if (i > 0 && i < numSections2 - 1) {
for (int j = 0; j < overlap2; j++) {
writerAvr2.write(
(mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2 + ",");
errorAvgk[2] =
((mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + j, 0))
* ((mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + j, 0))
+ errorAvgk[2];
}
for (int j = 0; j < mean_2[i].length - 2 * overlap2; j++) {
writerAvr2.write(mean_2[i].get(overlap2 + j) + ",");
errorAvgk[2] =
(mean_2[i].get(overlap2 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + overlap2 + j, 0))
* (mean_2[i].get(overlap2 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + overlap2 + j, 0))
+ errorAvgk[2];
}
} else if (i == numSections2 - 1) {
for (int j = 0; j < overlap2; j++) {
writerAvr2.write(
(mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2 + ",");
errorAvgk[2] =
((mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + j, 0))
* ((mean_2[i].get(j) + mean_2[i - 1].get(cellsec2 - overlap2 + j)) / 2
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + j, 0))
+ errorAvgk[2];
}
for (int j = 0; j < mean_2[i].length - overlap2; j++) {
writerAvr2.write(mean_2[i].get(overlap2 + j) + ",");
errorAvgk[2] =
(mean_2[i].get(overlap2 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + overlap2 + j, 0))
* (mean_2[i].get(overlap2 + j)
- trueSolutionCTM.trueStatesCTM.get(
i * (mean_2[i].length - overlap2) + overlap2 + j, 0))
+ errorAvgk[2];
}
}
}
for (int j = 0; j < mean_2[i].length; j++) {
writerSection2[i].write(mean_2[i].get(j) + ",");
}
double errorSection2 =
(((mean_2[i].sub(trueSolution2[i].trueStates))
.transpose()
.mmul((mean_2[i].sub(trueSolution2[i].trueStates))))
.get(0, 0))
/ ((double) (cellsec2));
Error2.write(errorSection2 + ",");
ErrorAvg2 = ErrorAvg2 + errorSection2 / ((double) numSections2);
writerSection2[i].write("\n");
}
Error2.write(ErrorAvg2 + ",");
errorAvg[2] = errorAvg[2] + ErrorAvg2 / ((double) (limite));
errAvgEntire[2] =
errorAvgk[2] / ((double) (limite * trueSolutionCTM.cells)) + errAvgEntire[2];
writerAvr.write("\n");
writerAvr1.write("\n");
writerAvr2.write("\n");
Error.write("\n");
Error1.write("\n");
Error2.write("\n");
Section[] secs = new Section[numSections];
Section[] secs1 = new Section[numSections1];
Section[] secs2 = new Section[numSections2];
for (int i = 0; i < numSections; i++) {
secs[i] = trueSolution[i].setSections(); // every step, update mode
}
for (int i = 0; i < numSections1; i++) {
secs1[i] = trueSolution1[i].setSections();
}
for (int i = 0; i < numSections2; i++) {
secs2[i] = trueSolution2[i].setSections();
}
for (int i = 0; i < numSections; i++) {
secs[i].Estimates = mean[i];
}
for (int i = 0; i < numSections1; i++) {
secs1[i].Estimates = mean_1[i];
}
for (int i = 0; i < numSections2; i++) {
secs2[i].Estimates = mean_2[i];
}
for (int i = 0; i < numSections; i++) {
if (secs[i].getClass().getSimpleName().equals("FC")) {
secs[i].getwavefrontfromEstimation();
if (secs[i].wavefront < 0) {
secs[i].wavefront = 0;
}
if (secs[i].wavefront <= cellsec - 2) {
if (trueSolution[i].flux(secs[i].Estimates.get(secs[i].wavefront, 0))
- trueSolution[i].flux(secs[i].Estimates.get(secs[i].wavefront + 1, 0))
> 0) {
secs[i].wavedirection = 0;
} else {
secs[i].wavedirection = 1;
}
} else if (secs[i].wavefront == cellsec - 1) {
if (secs[i].index != numSections - 1) {
if (trueSolution[i].flux(secs[i].Estimates.get(secs[i].wavefront, 0))
- trueSolution[i + 1].flux(secs[i].Estimates.get(overlap, 0))
> 0) {
secs[i].wavedirection = 0;
} else {
secs[i].wavedirection = 1;
}
} else {
secs[i].wavedirection = 0;
}
}
secs[i].getModelA();
secs[i].getModelB1();
secs[i].getModelB2();
} else if (secs[i].getClass().getSimpleName().equals("CF")) {
secs[i].getwavefrontfromEstimation();
secs[i].getModelA();
secs[i].getModelB1();
secs[i].getModelB2();
}
}
for (int i = 0; i < numSections1; i++) {
if (secs1[i].getClass().getSimpleName().equals("FC")) {
secs1[i].getwavefrontfromEstimation();
if (secs1[i].wavefront < 0) {
secs1[i].wavefront = 0;
}
if (secs1[i].wavefront <= cellsec1 - 2) {
if (trueSolution1[i].flux(secs1[i].Estimates.get(secs1[i].wavefront, 0))
- trueSolution1[i].flux(secs1[i].Estimates.get(secs1[i].wavefront + 1, 0))
> 0) {
secs1[i].wavedirection = 0;
} else {
secs1[i].wavedirection = 1;
}
} else if (secs1[i].wavefront == cellsec1 - 1) {
if (secs1[i].index != numSections1 - 1) {
if (trueSolution1[i].flux(secs1[i].Estimates.get(secs1[i].wavefront, 0))
- trueSolution1[i + 1].flux(secs1[i].Estimates.get(overlap1, 0))
> 0) {
secs1[i].wavedirection = 0;
} else {
secs1[i].wavedirection = 1;
}
} else {
secs1[i].wavedirection = 0;
}
}
secs1[i].getModelA();
secs1[i].getModelB1();
secs1[i].getModelB2();
} else if (secs1[i].getClass().getSimpleName().equals("CF")) {
secs1[i].getwavefrontfromEstimation();
secs1[i].getModelA();
secs1[i].getModelB1();
secs1[i].getModelB2();
}
}
for (int i = 0; i < numSections2; i++) {
if (secs2[i].getClass().getSimpleName().equals("FC")) {
secs2[i].getwavefrontfromEstimation();
if (secs2[i].wavefront < 0) {
secs2[i].wavefront = 0;
}
if (secs2[i].wavefront <= cellsec2 - 2) {
if (trueSolution2[i].flux(secs2[i].Estimates.get(secs2[i].wavefront, 0))
- trueSolution2[i].flux(secs2[i].Estimates.get(secs2[i].wavefront + 1, 0))
> 0) {
secs2[i].wavedirection = 0;
} else {
secs2[i].wavedirection = 1;
}
} else if (secs2[i].wavefront == cellsec2 - 1) {
if (secs2[i].index != numSections2 - 1) {
if (trueSolution2[i].flux(secs2[i].Estimates.get(secs2[i].wavefront, 0))
- trueSolution2[i + 1].flux(secs2[i].Estimates.get(overlap2, 0))
> 0) {
secs2[i].wavedirection = 0;
} else {
secs2[i].wavedirection = 1;
}
} else {
secs2[i].wavedirection = 0;
}
}
secs2[i].getModelA();
secs2[i].getModelB1();
secs2[i].getModelB2();
} else if (secs2[i].getClass().getSimpleName().equals("CF")) {
secs2[i].getwavefrontfromEstimation();
secs2[i].getModelA();
secs2[i].getModelB1();
secs2[i].getModelB2();
}
}
for (int i = 0; i < numSections; i++) {
estimations[i].setSection(secs[i]);
}
for (int i = 0; i < numSections1; i++) {
estimations1[i].setSection(secs1[i]);
}
for (int i = 0; i < numSections2; i++) {
estimations2[i].setSection(secs2[i]);
}
for (int i = 0; i < numSections; i++) {
trueSolution[i].section = secs[i];
}
for (int i = 0; i < numSections1; i++) {
trueSolution1[i].section = secs1[i];
}
for (int i = 0; i < numSections2; i++) {
trueSolution2[i].section = secs2[i];
}
for (int i = 0; i < numSections; i++) {
if (i == 0) {
GammaWrite[i].write(estimations[i].filter.gammaStar + ",");
} else if (i == numSections - 1) {
GammaWrite[i].write(estimations[i].filter.gammaStar + ",");
} else {
GammaWrite[i].write(estimations[i].filter.gammaStar + ",");
}
GammaWrite[i].write("\n");
}
for (int i = 0; i < numSections; i++) {
Mode[i].write(estimations[i].roadModel.section.getClass().getSimpleName() + ",");
Mode[i].write(
estimations[i].roadModel.section.wavefront + ","); // estimated location of shock
Mode[i].write(
estimations[i].roadModel.section._wavefront + ","); // true location of shock
Mode[i].write(estimations[i].roadModel.section.wavedirection + ",");
Mode[i].write(estimations[i].roadModel.section._wavedirection + ",");
Mode[i].write("\n");
}
for (int i = 0; i < numSections1; i++) {
Mode1[i].write(estimations1[i].roadModel.section.getClass().getSimpleName() + ",");
Mode1[i].write(estimations1[i].roadModel.section.wavefront + ",");
Mode1[i].write(estimations1[i].roadModel.section._wavefront + ",");
Mode1[i].write(estimations1[i].roadModel.section.wavedirection + ",");
Mode1[i].write(estimations1[i].roadModel.section._wavedirection + ",");
Mode1[i].write("\n");
}
for (int i = 0; i < numSections2; i++) {
Mode2[i].write(estimations2[i].roadModel.section.getClass().getSimpleName() + ",");
Mode2[i].write(estimations2[i].roadModel.section.wavefront + ",");
Mode2[i].write(estimations2[i].roadModel.section._wavefront + ",");
Mode2[i].write(estimations2[i].roadModel.section.wavedirection + ",");
Mode2[i].write(estimations2[i].roadModel.section._wavedirection + ",");
Mode2[i].write("\n");
}
for (int i = 0; i < numSections; i++) {
// trueSolution[i].updateMeasurement();
// roadModels[i].updateMeasurement();
estimations[i].filter.getNewParametersFromModel();
}
for (int i = 0; i < numSections1; i++) {
// trueSolution[i].updateMeasurement();
// roadModels[i].updateMeasurement();
estimations1[i].filter.getNewParametersFromModel();
}
for (int i = 0; i < numSections2; i++) {
// trueSolution[i].updateMeasurement();
// roadModels[i].updateMeasurement();
estimations2[i].filter.getNewParametersFromModel();
}
for (int i = 0; i < numSections; i++) {
S[i] =
(estimations[i].filter.measure.transpose())
.mmul(InverseMatrix.invPoSym(estimations[i].filter.measureVar))
.mmul(estimations[i].filter.measure);
}
double disag = 0;
for (int i = 0; i < numSections - 1; i++) {
disag =
disag
+ (((mean[i]
.getRange(cellsec - overlap, cellsec, 0, 1)
.sub(mean[i + 1].getRange(0, overlap, 0, 1)))
.transpose()
.mmul(
mean[i]
.getRange(cellsec - overlap, cellsec, 0, 1)
.sub(mean[i + 1].getRange(0, overlap, 0, 1))))
.get(0, 0))
/ ((double) overlap);
}
disag = disag / ((double) (numSections - 1));
writerDisagreement.write(disag + ",");
disagAvg[0] = disagAvg[0] + disag / ((double) limite);
for (int i = 0; i < numSections - 1; i++) {
writerDisagreement.write(
((mean[i]
.getRange(cellsec - overlap, cellsec, 0, 1)
.sub(mean[i + 1].getRange(0, overlap, 0, 1)))
.transpose()
.mmul(
mean[i]
.getRange(cellsec - overlap, cellsec, 0, 1)
.sub(mean[i + 1].getRange(0, overlap, 0, 1))))
.get(0, 0)
+ ",");
}
writerDisagreement.write("\n");
double disag1 = 0;
for (int i = 0; i < numSections1 - 1; i++) {
disag1 =
disag1
+ (((mean_1[i]
.getRange(cellsec1 - overlap1, cellsec1, 0, 1)
.sub(mean_1[i + 1].getRange(0, overlap1, 0, 1)))
.transpose()
.mmul(
mean_1[i]
.getRange(cellsec1 - overlap1, cellsec1, 0, 1)
.sub(mean_1[i + 1].getRange(0, overlap1, 0, 1))))
.get(0, 0))
/ ((double) overlap1);
}
disag1 = disag1 / ((double) (numSections1 - 1));
writerDisagreement1.write(disag1 + ",");
disagAvg[1] = disagAvg[1] + disag1 / ((double) limite);
for (int i = 0; i < numSections1 - 1; i++) {
writerDisagreement1.write(
((mean_1[i]
.getRange(cellsec1 - overlap1, cellsec1, 0, 1)
.sub(mean_1[i + 1].getRange(0, overlap1, 0, 1)))
.transpose()
.mmul(
mean_1[i]
.getRange(cellsec1 - overlap1, cellsec1, 0, 1)
.sub(mean_1[i + 1].getRange(0, overlap1, 0, 1))))
.get(0, 0)
+ ",");
}
writerDisagreement1.write("\n");
double disag2 = 0;
for (int i = 0; i < numSections2 - 1; i++) {
disag2 =
disag2
+ (((mean_2[i]
.getRange(cellsec2 - overlap2, cellsec2, 0, 1)
.sub(mean_2[i + 1].getRange(0, overlap2, 0, 1)))
.transpose()
.mmul(
mean_2[i]
.getRange(cellsec2 - overlap2, cellsec2, 0, 1)
.sub(mean_2[i + 1].getRange(0, overlap2, 0, 1))))
.get(0, 0))
/ ((double) overlap2);
}
disag2 = disag2 / ((double) (numSections2 - 1));
writerDisagreement2.write(disag2 + ",");
disagAvg[2] = disagAvg[2] + disag2 / ((double) limite);
for (int i = 0; i < numSections2 - 1; i++) {
writerDisagreement2.write(
((mean_2[i]
.getRange(cellsec2 - overlap2, cellsec2, 0, 1)
.sub(mean_2[i + 1].getRange(0, overlap2, 0, 1)))
.transpose()
.mmul(
mean_2[i]
.getRange(cellsec2 - overlap2, cellsec2, 0, 1)
.sub(mean_2[i + 1].getRange(0, overlap2, 0, 1))))
.get(0, 0)
+ ",");
}
writerDisagreement2.write("\n");
trueSolutionCTM.update();
// **start defining boundary condition
int cells = trueSolutionCTM.trueStatesCTMPrior.getRows();
if (bdry == 0) {
double inflow = trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(0, 0));
double outflow =
trueSolutionCTM.sending(trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
double upOut =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(0, 0),
trueSolutionCTM.trueStatesCTMPrior.get(1, 0));
trueSolutionCTM.trueStatesCTM.put(
0,
0,
trueSolutionCTM.trueStatesCTMPrior.get(0, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (inflow - upOut));
double downIn =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(cells - 2, 0),
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
trueSolutionCTM.trueStatesCTM.put(
cells - 1,
0,
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (downIn - outflow));
} else if (bdry == 1) {
double inflow = 0.1125 + 0.1125 * Math.sin(k * (Math.PI / 4000) + (Math.PI));
if (inflow > trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(0, 0))) {
inflow = trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(0, 0));
}
double outflow =
trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
// double outflow=0.1125+0.1125*Math.sin(k*(Math.PI/1000));
// if
// (outflow>trueSolution[numSections-1].sending(trueSolution[numSections-1].trueStatesPrior.get(cellsec-1,0))){
//
// outflow=trueSolution[numSections-1].sending(trueSolution[numSections-1].trueStatesPrior.get(cellsec-1,0));
// }
double upOut =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(0, 0),
trueSolutionCTM.trueStatesCTMPrior.get(1, 0));
trueSolutionCTM.trueStatesCTM.put(
0,
0,
trueSolutionCTM.trueStatesCTMPrior.get(0, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (inflow - upOut));
double downIn =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(cells - 2, 0),
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
trueSolutionCTM.trueStatesCTM.put(
cells - 1,
0,
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (downIn - outflow));
} else {
double inflow = 0.1125 + 0.1125 * Math.sin(k * (Math.PI / 8000) + (Math.PI));
if (inflow > trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(0, 0))) {
inflow = trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(0, 0));
}
double outflow =
trueSolutionCTM.receiving(trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
// double outflow=0.1125+0.1125*Math.sin(k*(Math.PI/1000));
// if
// (outflow>trueSolution[numSections-1].sending(trueSolution[numSections-1].trueStatesPrior.get(cellsec-1,0))){
//
// outflow=trueSolution[numSections-1].sending(trueSolution[numSections-1].trueStatesPrior.get(cellsec-1,0));
// }
double upOut =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(0, 0),
trueSolutionCTM.trueStatesCTMPrior.get(1, 0));
trueSolutionCTM.trueStatesCTM.put(
0,
0,
trueSolutionCTM.trueStatesCTMPrior.get(0, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (inflow - upOut));
double downIn =
trueSolutionCTM.computeFlux(
trueSolutionCTM.trueStatesCTMPrior.get(cells - 2, 0),
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0));
trueSolutionCTM.trueStatesCTM.put(
cells - 1,
0,
trueSolutionCTM.trueStatesCTMPrior.get(cells - 1, 0)
+ (trueSolutionCTM.dt / trueSolutionCTM.dx) * (downIn - outflow));
// *define boundary conditions here
}
// **end defining boundary condition
trueSolutionCTM.getMeasurement();
for (int i = 0; i < numSections; i++) {
trueSolution[i].update();
}
for (int i = 0; i < numSections; i++) {
trueSolution[i].updatemeasurement();
}
for (int i = 0; i < numSections1; i++) {
trueSolution1[i].update();
}
for (int i = 0; i < numSections1; i++) {
trueSolution1[i].updatemeasurement();
}
for (int i = 0; i < numSections2; i++) {
trueSolution2[i].update();
}
for (int i = 0; i < numSections2; i++) {
trueSolution2[i].updatemeasurement();
}
for (int i = 0; i < numSections; i++) {
estimations[i].nextStepWithoutUpdateTrue();
}
for (int i = 0; i < numSections1; i++) {
estimations1[i].nextStepWithoutUpdateTrue();
}
for (int i = 0; i < numSections2; i++) {
estimations2[i].nextStepWithoutUpdateTrue();
}
if (estimations[0].consensus) {
for (int i = 0; i < numSections; i++) {
estimations[i].filter.ComputeEig();
}
for (int i = 0; i < numSections; i++) {
if (i == 0) {
estimations[i].filter.ComputeGammaStar2(estimations[1].filter);
} else if (i == numSections - 1) {
estimations[i].filter.ComputeGammaStar1(estimations[numSections - 2].filter);
} else {
estimations[i].filter.ComputeGammaStar(
estimations[i - 1].filter, estimations[i + 1].filter);
}
}
DoubleMatrix[] mean1 = new DoubleMatrix[numSections];
mean1[0] = estimations[0].filter.Consensus2(estimations[1].filter);
writerConsensusTerm.write(estimations[0].filter.scaling.get(0) + ",");
writerConsensusTerm.write(estimations[0].filter.scaling.get(1) + ",");
for (int i = 1; i < numSections - 1; i++) {
mean1[i] =
estimations[i].filter.Consensus(
estimations[i - 1].filter, estimations[i + 1].filter);
writerConsensusTerm.write(estimations[i].filter.scaling.get(0) + ",");
writerConsensusTerm.write(estimations[i].filter.scaling.get(1) + ",");
}
mean1[numSections - 1] =
estimations[numSections - 1].filter.Consensus1(estimations[numSections - 2].filter);
writerConsensusTerm.write(estimations[numSections - 1].filter.scaling.get(0) + ",");
writerConsensusTerm.write(estimations[numSections - 1].filter.scaling.get(1) + ",");
writerConsensusTerm.write("\n");
for (int i = 0; i < numSections; i++) {
estimations[i].filter.mean = mean1[i];
}
}
}
for (int i = 0; i < numSections; i++) {
writerSection[i].flush();
writerSection[i].close();
Mode[i].flush();
Mode[i].close();
LyapSection[i].flush();
LyapSection[i].close();
GammaWrite[i].flush();
GammaWrite[i].close();
}
for (int i = 0; i < numSections1; i++) {
writerSection1[i].flush();
writerSection1[i].close();
Mode1[i].flush();
Mode1[i].close();
}
for (int i = 0; i < numSections2; i++) {
writerSection2[i].flush();
writerSection2[i].close();
Mode2[i].flush();
Mode2[i].close();
}
writerAvr.flush();
writerAvr.close();
writerConsensusTerm.flush();
writerConsensusTerm.close();
writerAvr1.flush();
writerAvr1.close();
writerAvr2.flush();
writerAvr2.close();
Error.flush();
Error.close();
Error1.flush();
Error1.close();
Error2.flush();
Error2.close();
Lyap.flush();
Lyap.close();
writerTrue.flush();
writerTrue.close();
writerDisagreement.flush();
writerDisagreement.close();
writerDisagreement1.flush();
writerDisagreement1.close();
writerDisagreement2.flush();
writerDisagreement2.close();
System.out.print("Disag_avg_DLKCF=" + disagAvg[0] + "\n");
System.out.print("Disag_avg_DLKF=" + disagAvg[1] + "\n");
System.out.print("Disag_avg_LKF=" + disagAvg[2] + "\n");
System.out.print("Err_avg_DLKCF=" + errorAvg[0] + "\n");
System.out.print("Err_avg_DLKF=" + errorAvg[1] + "\n");
System.out.print("Err_avg_LKF=" + errorAvg[2] + "\n");
System.out.println(" End");
} catch (Exception e) {
e.printStackTrace();
}
}
public static double[] polyFit2D(
final DoubleMatrix x, final DoubleMatrix y, final DoubleMatrix z, final int degree)
throws IllegalArgumentException {
logger.setLevel(Level.INFO);
if (x.length != y.length || !x.isVector() || !y.isVector()) {
logger.error("polyfit: require same size vectors.");
throw new IllegalArgumentException("polyfit: require same size vectors.");
}
final int numOfObs = x.length;
final int numOfUnkn = numberOfCoefficients(degree) + 1;
DoubleMatrix A = new DoubleMatrix(numOfObs, numOfUnkn); // designmatrix
DoubleMatrix mul;
/** Set up design-matrix */
for (int p = 0; p <= degree; p++) {
for (int q = 0; q <= p; q++) {
mul = pow(y, (p - q)).mul(pow(x, q));
if (q == 0 && p == 0) {
A = mul;
} else {
A = DoubleMatrix.concatHorizontally(A, mul);
}
}
}
// Fit polynomial
logger.debug("Solving lin. system of equations with Cholesky.");
DoubleMatrix N = A.transpose().mmul(A);
DoubleMatrix rhs = A.transpose().mmul(z);
// solution seems to be OK up to 10^-09!
rhs = Solve.solveSymmetric(N, rhs);
DoubleMatrix Qx_hat = Solve.solveSymmetric(N, DoubleMatrix.eye(N.getRows()));
double maxDeviation = (N.mmul(Qx_hat).sub(DoubleMatrix.eye(Qx_hat.rows))).normmax();
logger.debug("polyfit orbit: max(abs(N*inv(N)-I)) = " + maxDeviation);
// ___ report max error... (seems sometimes this can be extremely large) ___
if (maxDeviation > 1e-6) {
logger.warn("polyfit orbit: max(abs(N*inv(N)-I)) = {}", maxDeviation);
logger.warn("polyfit orbit interpolation unstable!");
}
// work out residuals
DoubleMatrix y_hat = A.mmul(rhs);
DoubleMatrix e_hat = y.sub(y_hat);
if (e_hat.normmax() > 0.02) {
logger.warn(
"WARNING: Max. polyFit2D approximation error at datapoints (x,y,or z?): {}",
e_hat.normmax());
} else {
logger.info(
"Max. polyFit2D approximation error at datapoints (x,y,or z?): {}", e_hat.normmax());
}
if (logger.isDebugEnabled()) {
logger.debug("REPORTING POLYFIT LEAST SQUARES ERRORS");
logger.debug(" time \t\t\t y \t\t\t yhat \t\t\t ehat");
for (int i = 0; i < numOfObs; i++) {
logger.debug(
" ("
+ x.get(i)
+ ","
+ y.get(i)
+ ") :"
+ "\t"
+ y.get(i)
+ "\t"
+ y_hat.get(i)
+ "\t"
+ e_hat.get(i));
}
}
return rhs.toArray();
}
public static double[] polyFit(DoubleMatrix t, DoubleMatrix y, final int degree)
throws IllegalArgumentException {
logger.setLevel(Level.INFO);
if (t.length != y.length || !t.isVector() || !y.isVector()) {
logger.error("polyfit: require same size vectors.");
throw new IllegalArgumentException("polyfit: require same size vectors.");
}
// Normalize _posting_ for numerical reasons
final int numOfPoints = t.length;
// Check redundancy
final int numOfUnknowns = degree + 1;
logger.debug("Degree of interpolating polynomial: {}", degree);
logger.debug("Number of unknowns: {}", numOfUnknowns);
logger.debug("Number of data points: {}", numOfPoints);
if (numOfPoints < numOfUnknowns) {
logger.error("Number of points is smaller than parameters solved for.");
throw new IllegalArgumentException("Number of points is smaller than parameters solved for.");
}
// Set up system of equations to solve coeff :: Design matrix
logger.debug("Setting up linear system of equations");
DoubleMatrix A = new DoubleMatrix(numOfPoints, numOfUnknowns);
// work with columns
for (int j = 0; j <= degree; j++) {
A.putColumn(j, pow(t, j));
}
// Fit polynomial through computed vector of phases
logger.debug("Solving lin. system of equations with Cholesky.");
DoubleMatrix N = A.transpose().mmul(A);
DoubleMatrix rhs = A.transpose().mmul(y);
// solution seems to be OK up to 10^-09!
DoubleMatrix x = Solve.solveSymmetric(N, rhs);
DoubleMatrix Qx_hat = Solve.solveSymmetric(N, DoubleMatrix.eye(N.getRows()));
double maxDeviation = (N.mmul(Qx_hat).sub(DoubleMatrix.eye(Qx_hat.rows))).normmax();
logger.debug("polyfit orbit: max(abs(N*inv(N)-I)) = " + maxDeviation);
// ___ report max error... (seems sometimes this can be extremely large) ___
if (maxDeviation > 1e-6) {
logger.warn("polyfit orbit: max(abs(N*inv(N)-I)) = {}", maxDeviation);
logger.warn("polyfit orbit interpolation unstable!");
}
// work out residuals
DoubleMatrix y_hat = A.mmul(x);
DoubleMatrix e_hat = y.sub(y_hat);
// 0.05 is already 1 wavelength! (?)
if (e_hat.normmax() > 0.02) {
logger.warn(
"WARNING: Max. approximation error at datapoints (x,y,or z?): {}", e_hat.normmax());
} else {
logger.debug("Max. approximation error at datapoints (x,y,or z?): {}", e_hat.normmax());
}
if (logger.isDebugEnabled()) {
logger.debug("REPORTING POLYFIT LEAST SQUARES ERRORS");
logger.debug(" time \t\t\t y \t\t\t yhat \t\t\t ehat");
for (int i = 0; i < numOfPoints; i++) {
logger.debug(" " + t.get(i) + "\t" + y.get(i) + "\t" + y_hat.get(i) + "\t" + e_hat.get(i));
}
for (int i = 0; i < numOfPoints - 1; i++) {
// ___ check if dt is constant, not necessary for me, but may ___
// ___ signal error in header data of SLC image ___
double dt = t.get(i + 1) - t.get(i);
logger.debug("Time step between point " + i + 1 + " and " + i + "= " + dt);
if (Math.abs(dt - (t.get(1) - t.get(0))) > 0.001) // 1ms of difference we allow...
logger.warn("WARNING: Orbit: data does not have equidistant time interval?");
}
}
return x.toArray();
}
public DoubleMatrix propup(DoubleMatrix v) {
return MatrixMath.sigmoid(
this.weights.mmul(v.transpose()).transpose().addi(hbias.repmat(v_data.rows, 1)));
}
/**
* 测试模型
*
* @param filename
*/
public void test_model(String filename, int testCount) {
try {
this.test_data_Count = testCount;
test_set = loadMatrix(filename, test_data_Count, totalColCount);
} catch (Exception e) {
e.printStackTrace();
}
printInfo("测试数据载入完毕…………");
// 测试数据输入矩阵初始化 横向即每列为一组输入
testP = new DoubleMatrix(NumberofInputNeurons, test_data_Count);
// 测试机输出矩阵 横向
testT = new DoubleMatrix(NumberofOutputNeurons, test_data_Count);
// 初始化
for (int i = 0; i < test_data_Count; i++) {
for (int j = 0; j < totalColCount; j++) {
if (j < NumberofInputNeurons) {
// 输入部分
testP.put(j, i, test_set.get(i, j));
} else {
testT.put(j - NumberofInputNeurons, i, test_set.get(i, j));
}
}
}
// end 初始化
printInfo("测试数据初始化完毕…………");
long start_time_test = System.currentTimeMillis();
// 计算输入权重与阈值部分
DoubleMatrix tmpH = new DoubleMatrix(NumberofHiddenNeurons, test_data_Count);
tmpH = InputWeight.mmul(testP);
printInfo("权重计算完毕…………");
// 加阈值
// 注意横向问题
for (int i = 0; i < NumberofHiddenNeurons; i++) {
for (int j = 0; j < test_data_Count; j++) {
tmpH.put(i, j, tmpH.get(i, j) + BiasofHiddenNeurons.get(i, 0));
}
}
printInfo("阈值计算完毕…………");
// 输出矩阵
DoubleMatrix H = new DoubleMatrix(NumberofHiddenNeurons, test_data_Count);
if (func.startsWith("sig")) {
for (int i = 0; i < NumberofHiddenNeurons; i++) {
for (int j = 0; j < test_data_Count; j++) {
double tmp = tmpH.get(i, j);
tmp = 1.0f / (1 + Math.exp(-tmp));
H.put(i, j, tmp);
}
}
} else {
throw new IllegalArgumentException("不支持的激活函数类型");
}
printInfo("激活函数计算完毕…………");
// 将H转置
DoubleMatrix Ht = H.transpose();
// 测试误差
DoubleMatrix Yt = new DoubleMatrix(test_data_Count, NumberofOutputNeurons);
Yt = Ht.mmul(OutputWeight);
printInfo("测试完毕…………");
long end_time_test = System.currentTimeMillis();
TestingTime = (end_time_test - start_time_test) * 1.0f / 1000;
double MSE = 0;
printInfo("共有" + NumberofOutputNeurons + "个输出值");
for (int out = 0; out < NumberofOutputNeurons; out++) {
printInfo("计算第" + (out + 1) + "个输出值");
double mseTem = 0.0;
for (int i = 0; i < test_data_Count; i++) {
mseTem += (Yt.get(i, out) - testT.get(out, i)) * (Yt.get(i, out) - testT.get(out, i));
}
printInfo(
"第" + NumberofOutputNeurons + "个输出值计算完毕,MSE=" + Math.sqrt(mseTem / train_data_Count));
MSE += mseTem;
}
TestingAccuracy = Math.sqrt(MSE / test_data_Count);
printInfo("计算MSE完毕…………");
}
/** 训练模型 */
private void train() {
printInfo("训练数据载入完毕，开始训练模型………………训练数据规模" + train_set.rows + "x" + train_set.columns);
// 输入矩阵初始化 横向即每列为一组输入
/*
* 4 8 …… N
*
* 1 5 …… N
* 2 6 …… N
* 3 7 …… N
*/
P = new DoubleMatrix(NumberofInputNeurons, train_data_Count);
// 测试机输出矩阵 横向
T = new DoubleMatrix(NumberofOutputNeurons, train_data_Count);
// 初始化
for (int i = 0; i < train_data_Count; i++) {
for (int j = 0; j < totalColCount; j++) {
if (j < NumberofInputNeurons) {
// 输入部分
P.put(j, i, train_set.get(i, j));
} else {
T.put(j - NumberofInputNeurons, i, train_set.get(i, j));
}
}
}
printInfo("训练矩阵初始化完毕………………开始隐含层神经元权重与阈值");
// end 初始化
long start_time_train = System.currentTimeMillis();
// 随机初始化输入权值矩阵
// 行数为隐含层神经元个数，列数为输入层神经元
/**
* W11 W12 W13(第一个隐含神经元对应各输入的权值) W21 W22 W23(第二个隐含神经元对应各输入的权值) ………………………………(第N个隐含神经元对应各输入的权值)
*/
InputWeight = DoubleMatrix.rand(NumberofHiddenNeurons, NumberofInputNeurons);
// 初始化阈值
BiasofHiddenNeurons = DoubleMatrix.rand(NumberofHiddenNeurons, 1);
printInfo("隐含层神经元权重与阈值初始化完毕………………开始计算输入权重");
DoubleMatrix tmpH = new DoubleMatrix(NumberofHiddenNeurons, train_data_Count);
tmpH = InputWeight.mmul(P);
printInfo("输入矩阵计算权重完毕………………开始计算输入权重");
// 加阈值
// 注意横向问题
for (int i = 0; i < NumberofHiddenNeurons; i++) {
for (int j = 0; j < train_data_Count; j++) {
tmpH.put(i, j, tmpH.get(i, j) + BiasofHiddenNeurons.get(i, 0));
}
}
printInfo("输入矩阵计算阈值完毕………………开始计算输入激活函数");
// 输出矩阵
DoubleMatrix H = new DoubleMatrix(NumberofHiddenNeurons, train_data_Count);
if (func.startsWith("sig")) {
for (int i = 0; i < NumberofHiddenNeurons; i++) {
for (int j = 0; j < train_data_Count; j++) {
double tmp = tmpH.get(i, j);
tmp = 1.0f / (1 + Math.exp(-tmp));
H.put(i, j, tmp);
}
}
} else {
throw new IllegalArgumentException("不支持的激活函数类型");
}
printInfo("输出矩阵计算激活函数完毕，开始求解广义逆………………矩阵规模" + H.columns + "x" + H.rows);
// 将H转置
DoubleMatrix Ht = H.transpose();
// 求广义逆
DoubleMatrix Ht_MP = getMPMatrix(Ht);
printInfo("输出矩阵广义逆求解完毕………………开始求解输出矩阵权重");
// 隐含层输出权重矩阵= Ht_MP * Tt
OutputWeight = Ht_MP.mmul(T.transpose());
printInfo("输出矩阵权重求解完毕………………");
long end_time_train = System.currentTimeMillis();
TrainingTime = (end_time_train - start_time_train) * 1.0f / 1000;
printInfo("模型训练完毕………………开始评估模型");
// 误差评估
DoubleMatrix Yt = new DoubleMatrix(train_data_Count, NumberofOutputNeurons);
Yt = Ht.mmul(OutputWeight);
double MSE = 0;
printInfo("共有" + NumberofOutputNeurons + "个输出值");
for (int out = 0; out < NumberofOutputNeurons; out++) {
printInfo("计算第" + (out + 1) + "个输出值");
double mseTem = 0.0;
for (int i = 0; i < train_data_Count; i++) {
System.out.println(Yt.get(i, out) + " " + T.get(out, i));
mseTem += (Yt.get(i, out) - T.get(out, i)) * (Yt.get(i, out) - T.get(out, i));
}
printInfo(
"第" + NumberofOutputNeurons + "个输出值计算完毕,MSE=" + Math.sqrt(mseTem / train_data_Count));
MSE += mseTem;
}
TrainingAccuracy = Math.sqrt(MSE / train_data_Count);
printInfo("模型评估完毕………………");
}
