public static void DFS(
DoubleMatrix A, DoubleMatrix d, DoubleMatrix g, ArrayList[] V, int vertNum) {
int krawedzie = 0;
for (int i = 0; i <= vertNum; i++) {
krawedzie += V[i].size();
}
krawedzie /= 2;
// System.out.println("A wymiar: " + A.columns + " rows: "+A.rows);
int t = 0;
VertexState state[] = new VertexState[(d.rows - 1) * d.rows / 2]; // tyle stanów
for (int i = 0; i < d.rows - 1; i++) {
for (int j = i + 1; j < d.columns; j++) {
for (int x = 0; x < state.length; x++) {
state[x] = VertexState.White; // na początku białe
}
g.put(t, 0, d.get(i, j));
runDFS(i, j, state, d, V, vertNum);
for (int ii = 0; ii < krawedzie; ii++) {
if (state[ii] == VertexState.Black) {
A.put(t, ii, 1);
}
}
t++;
}
}
System.out.println(g);
System.out.println("t:" + t);
return;
}
/**
* 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;
}
/**
* Loads ground truth responses
*
* @param groundTruth is a Map from questions to responses
*/
public void setGroundTruth(Map<TypeQ, TypeR> groundTruth) {
log.debug("Setting ground truth...");
if (!(categToInt != null)) log.error("Result Vector not computed.");
assert categToInt != null : "Result Vector not computed.";
DoubleMatrix indicator = DoubleMatrix.zeros(sortedQuestions.size());
DoubleMatrix gtVec = DoubleMatrix.zeros(sortedQuestions.size());
int idx = 0;
for (TypeQ iter : sortedQuestions) {
if (groundTruth.containsKey(iter)) {
indicator.put(idx, 1.0d);
gtVec.put(idx, categToInt.get(groundTruth.get(iter)));
}
idx++;
}
groundTruthVector = new Pair<DoubleMatrix, DoubleMatrix>(gtVec, indicator);
}
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();
}
public DoubleMatrix getScoreMatrix(File file) {
Counter<String> docWords = new Counter<String>();
try {
LineIterator iter = FileUtils.lineIterator(file);
while (iter.hasNext()) {
Tokenizer t =
tokenizerFactory.create((new InputHomogenization(iter.nextLine()).transform()));
while (t.hasMoreTokens()) {
docWords.incrementCount(t.nextToken(), 1.0);
}
}
iter.close();
} catch (IOException e) {
throw new IllegalStateException("Unable to read file", e);
}
DoubleMatrix ret = new DoubleMatrix(1, currVocab.size());
for (int i = 0; i < currVocab.size(); i++) {
if (docWords.getCount(currVocab.get(i).toString()) > 0) {
ret.put(i, wordScores.getCount(currVocab.get(i).toString()));
}
}
return ret;
}
private static void modi(DoubleMatrix syndrome, int radix) {
for (int i = 0; i < syndrome.getRows(); i++) {
for (int j = 0; j < syndrome.getColumns(); j++) {
syndrome.put(i, j, ((int) syndrome.get(i, j)) % radix);
}
}
}
/**
* Loads tune responses
*
* @param tuneSet is a Map from questions to responses
*/
public void setTune(Map<TypeQ, TypeR> tuneSet) {
log.debug("Setting tune set...");
if (!(categToInt != null)) log.error("Result Vector not computed.");
assert categToInt != null : "Result Vector not computed.";
DoubleMatrix indicator = DoubleMatrix.zeros(sortedQuestions.size());
DoubleMatrix tuneVec = DoubleMatrix.zeros(sortedQuestions.size());
int idx = 0;
for (TypeQ iter : sortedQuestions) {
if (tuneSet.containsKey(iter)) {
indicator.put(idx, 1.0d);
tuneVec.put(idx, categToInt.get(tuneSet.get(iter)));
}
idx++;
}
tuneVector = new Pair<DoubleMatrix, DoubleMatrix>(tuneVec, indicator);
}
/**
* 对角线增加值
*
* @param matrix
* @throws Exception
*/
private void addPositiveValue(DoubleMatrix matrix) {
int m = matrix.rows;
if (matrix.columns != m) return;
for (int i = 0; i < m; i++) {
matrix.put(i, i, matrix.get(i, i) + Math.random());
}
}
/**
* 求逆矩阵
*
* @param mt
* @return
*/
public DoubleMatrix inverse(DoubleMatrix mt) {
int m = mt.rows;
DoubleMatrix E = DoubleMatrix.zeros(m, m);
for (int i = 0; i < m; i++) {
E.put(i, i, 1);
}
return Solve.solve(mt, E);
}
/**
* Transforms the matrix
*
* @param text
* @return
*/
@Override
public DoubleMatrix transform(String text) {
Tokenizer tokenizer = tokenizerFactory.create(text);
List<String> tokens = tokenizer.getTokens();
DoubleMatrix input = new DoubleMatrix(1, vocab.size());
for (int i = 0; i < tokens.size(); i++) {
int idx = vocab.indexOf(tokens.get(i));
if (vocab.indexOf(tokens.get(i)) >= 0) input.put(idx, wordCounts.getCount(tokens.get(i)));
}
return input;
}
public void reduce(
IntWritable sameNum,
Iterator<Text> data,
OutputCollector<Text, jBLASArrayWritable> output,
Reporter reporter)
throws IOException {
int totalBatchCount = exampleCount / batchSize;
DoubleMatrix weights = DoubleMatrix.randn(hiddenNodes, visibleNodes);
DoubleMatrix hbias = DoubleMatrix.zeros(hiddenNodes);
DoubleMatrix vbias = DoubleMatrix.zeros(visibleNodes);
DoubleMatrix label = DoubleMatrix.zeros(1);
DoubleMatrix hidden_chain = null;
DoubleMatrix vdata = DoubleMatrix.zeros(batchSize, visibleNodes);
ArrayList<DoubleMatrix> outputmatricies = new ArrayList<DoubleMatrix>();
outputmatricies.add(weights);
outputmatricies.add(hbias);
outputmatricies.add(vbias);
outputmatricies.add(label);
outputmatricies.add(vdata);
outputmatricies.add(hidden_chain);
int j;
for (int i = 0; i < totalBatchCount; i++) {
j = 0;
while (data.hasNext() && j < batchSize) {
j++;
StringTokenizer tk = new StringTokenizer(data.next().toString());
label.put(0, Double.parseDouble(tk.nextToken()));
String image = tk.nextToken();
for (int k = 0; k < image.length(); k++) {
Integer val = new Integer(image.charAt(k));
vdata.put(j, k, val.doubleValue());
}
dataArray = new jBLASArrayWritable(outputmatricies);
batchID.set("1\t" + i);
output.collect(batchID, dataArray);
}
}
}
public static DoubleMatrix conv2d(DoubleMatrix input, DoubleMatrix kernel, Type type) {
DoubleMatrix xShape = new DoubleMatrix(1, 2);
xShape.put(0, input.rows);
xShape.put(1, input.columns);
DoubleMatrix yShape = new DoubleMatrix(1, 2);
yShape.put(0, kernel.rows);
yShape.put(1, kernel.columns);
DoubleMatrix zShape = xShape.addi(yShape).subi(1);
int retRows = (int) zShape.get(0);
int retCols = (int) zShape.get(1);
ComplexDoubleMatrix fftInput = complexDisceteFourierTransform(input, retRows, retCols);
ComplexDoubleMatrix fftKernel = complexDisceteFourierTransform(kernel, retRows, retCols);
ComplexDoubleMatrix mul = fftKernel.muli(fftInput);
ComplexDoubleMatrix retComplex = complexInverseDisceteFourierTransform(mul);
DoubleMatrix ret = retComplex.getReal();
if (type == Type.VALID) {
DoubleMatrix validShape = xShape.subi(yShape).addi(1);
DoubleMatrix start = zShape.subi(validShape).divi(2);
DoubleMatrix end = start.addi(validShape);
if (start.get(0) < 1 || start.get(1) < 1)
throw new IllegalStateException("Illegal row index " + start);
if (end.get(0) < 1 || end.get(1) < 1)
throw new IllegalStateException("Illegal column index " + end);
ret =
ret.get(
RangeUtils.interval((int) start.get(0), (int) end.get(0)),
RangeUtils.interval((int) start.get(1), (int) end.get(1)));
}
return ret;
}
/**
* Vectorizes the passed in text treating it as one document
*
* @param text the text to vectorize
* @param label the label of the text
* @return a dataset with a applyTransformToDestination of weights(relative to impl; could be word
* counts or tfidf scores)
*/
@Override
public DataSet vectorize(String text, String label) {
Tokenizer tokenizer = tokenizerFactory.create(text);
List<String> tokens = tokenizer.getTokens();
DoubleMatrix input = new DoubleMatrix(1, vocab.size());
for (int i = 0; i < tokens.size(); i++) {
int idx = vocab.indexOf(tokens.get(i));
if (vocab.indexOf(tokens.get(i)) >= 0) input.put(idx, wordCounts.getCount(tokens.get(i)));
}
DoubleMatrix labelMatrix = MatrixUtil.toOutcomeVector(labels.indexOf(label), labels.size());
return new DataSet(input, labelMatrix);
}
/** Computes results as a vector of type DoubleMatrix */
public void computeComparableResultVector() {
log.info("Computing comparable result vector...");
if (!(sortedRePrPairs != null))
log.error("Attempted to compute results vector before results.");
assert sortedRePrPairs != null : "Attempted to compute results vector before results.";
categToInt = new HashMap<TypeR, Integer>();
resultVector = DoubleMatrix.zeros(sortedQuestions.size());
categoriesMat = DoubleMatrix.zeros(responseCategories.size());
int temp = 1;
for (TypeR iter : responseCategories) {
categToInt.put(iter, temp);
categoriesMat.put(temp - 1, temp);
temp++;
}
if (log.isDebugEnabled()) log.debug("Computed map from categories to integers:\n" + categToInt);
temp = 0;
for (Pair<TypeR, Double> iter : sortedRePrPairs) {
resultVector.put(temp, categToInt.get(iter.getFirst()));
temp++;
}
if (log.isDebugEnabled()) log.debug("Computed results vector:\n" + resultVector);
log.info("Computed result vector.");
}
public static DoubleMatrix readFileIntoMatrix(String filename, char delimiter)
throws IOException {
File csvData = new File(filename);
CSVFormat format = CSVFormat.DEFAULT.withHeader().withDelimiter(delimiter);
CSVParser parser = CSVParser.parse(csvData, StandardCharsets.UTF_8, format);
int row = 0;
List<CSVRecord> records = parser.getRecords();
DoubleMatrix x = new DoubleMatrix(records.size(), records.get(0).size());
for (CSVRecord csvRecord : records) {
for (int column = 0; column < csvRecord.size(); column++) {
String s = csvRecord.get(column);
x.put(row, column, Double.valueOf(s));
}
row++;
}
return x;
}
/** 文件载入矩阵 */
private DoubleMatrix loadMatrix(String trainSetPath, int rowNum, int colNum) throws IOException {
BufferedReader br = new BufferedReader(new FileReader(new File(trainSetPath)));
// 第一行为文件信息
DoubleMatrix matrix = new DoubleMatrix(rowNum, colNum);
String lineStr = "";
for (int i = 0; i < rowNum; i++) {
lineStr = br.readLine();
if (lineStr.startsWith("#") || "".equals(lineStr)) {
continue;
}
String[] strings = lineStr.split(",");
for (int j = 0; j < colNum; j++) {
matrix.put(i, j, Double.parseDouble(strings[j]));
}
}
br.close();
return matrix;
}
/** 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………………");
}
private void costantiniUnwrap() throws LPException {
final int ny = wrappedPhase.rows - 1; // start from Zero!
final int nx = wrappedPhase.columns - 1; // start from Zero!
if (wrappedPhase.isVector()) throw new IllegalArgumentException("Input must be 2D array");
if (wrappedPhase.rows < 2 || wrappedPhase.columns < 2)
throw new IllegalArgumentException("Size of input must be larger than 2");
// Default weight
DoubleMatrix w1 = DoubleMatrix.ones(ny + 1, 1);
w1.put(0, 0.5);
w1.put(w1.length - 1, 0.5);
DoubleMatrix w2 = DoubleMatrix.ones(1, nx + 1);
w2.put(0, 0.5);
w2.put(w2.length - 1, 0.5);
DoubleMatrix weight = w1.mmul(w2);
DoubleMatrix i, j, I_J, IP1_J, I_JP1;
DoubleMatrix Psi1, Psi2;
DoubleMatrix[] ROWS;
// Compute partial derivative Psi1, eqt (1,3)
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
IP1_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0].add(1), ROWS[1]);
Psi1 =
JblasUtils.getMatrixFromIdx(wrappedPhase, IP1_J)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi1 = UnwrapUtils.wrapDoubleMatrix(Psi1);
// Compute partial derivative Psi2, eqt (2,4)
i = intRangeDoubleMatrix(0, ny);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1].add(1));
Psi2 =
JblasUtils.getMatrixFromIdx(wrappedPhase, I_JP1)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi2 = UnwrapUtils.wrapDoubleMatrix(Psi2);
// Compute beq
DoubleMatrix beq = DoubleMatrix.zeros(ny, nx);
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1].add(1));
beq.addi(JblasUtils.getMatrixFromIdx(Psi1, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi1, I_J)));
I_J = JblasUtils.sub2ind(Psi2.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi2.rows, ROWS[0].add(1), ROWS[1]);
beq.subi(JblasUtils.getMatrixFromIdx(Psi2, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi2, I_J)));
beq.muli(-1 / (2 * Constants._PI));
for (int k = 0; k < beq.length; k++) {
beq.put(k, Math.round(beq.get(k)));
}
beq.reshape(beq.length, 1);
logger.debug("Constraint matrix");
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
DoubleMatrix ROW_I_J = JblasUtils.sub2ind(i.length, ROWS[0], ROWS[1]);
int nS0 = nx * ny;
// Use by S1p, S1m
DoubleMatrix[] COLS;
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
COLS = grid2D(i, j.add(1));
DoubleMatrix COL_I_JP1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
int nS1 = (nx + 1) * (ny);
// SOAPBinding.Use by S2p, S2m
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_2 = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
COLS = grid2D(i.add(1), j);
DoubleMatrix COL_IP1_J = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
int nS2 = nx * (ny + 1);
// Equality constraint matrix (Aeq)
/*
S1p = + sparse(ROW_I_J, COL_I_JP1,1,nS0,nS1) ...
- sparse(ROW_I_J, COL_IJ_1,1,nS0,nS1);
S1m = -S1p;
S2p = - sparse(ROW_I_J, COL_IP1_J,1,nS0,nS2) ...
+ sparse(ROW_I_J, COL_IJ_2,1,nS0,nS2);
S2m = -S2p;
*/
// ToDo: Aeq matrix should be sparse from it's initialization, look into JblasMatrix factory for
// howto
// ...otherwise even a data set of eg 40x40 pixels will exhaust heap:
// ... dimension of Aeq (equality constraints) matrix for 30x30 input is 1521x6240 matrix
// ... dimension of Aeq ( ) matrix for 50x50 input is 2401x9800
// ... dimension of Aeq ( ) matrix for 512x512 input is 261121x1046528
DoubleMatrix S1p =
JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_I_JP1)
.sub(JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_IJ_1));
DoubleMatrix S1m = S1p.neg();
DoubleMatrix S2p =
JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IP1_J)
.neg()
.add(JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IJ_2));
DoubleMatrix S2m = S2p.neg();
DoubleMatrix Aeq =
concatHorizontally(concatHorizontally(S1p, S1m), concatHorizontally(S2p, S2m));
final int nObs = Aeq.columns;
final int nUnkn = Aeq.rows;
DoubleMatrix c1 = JblasUtils.getMatrixFromRange(0, ny, 0, weight.columns, weight);
DoubleMatrix c2 = JblasUtils.getMatrixFromRange(0, weight.rows, 0, nx, weight);
c1.reshape(c1.length, 1);
c2.reshape(c2.length, 1);
DoubleMatrix cost =
DoubleMatrix.concatVertically(
DoubleMatrix.concatVertically(c1, c1), DoubleMatrix.concatVertically(c2, c2));
logger.debug("Minimum network flow resolution");
StopWatch clockLP = new StopWatch();
LinearProgram lp = new LinearProgram(cost.data);
lp.setMinProblem(true);
boolean[] integerBool = new boolean[nObs];
double[] lowerBound = new double[nObs];
double[] upperBound = new double[nObs];
for (int k = 0; k < nUnkn; k++) {
lp.addConstraint(new LinearEqualsConstraint(Aeq.getRow(k).toArray(), beq.get(k), "cost"));
}
for (int k = 0; k < nObs; k++) {
integerBool[k] = true;
lowerBound[k] = 0;
upperBound[k] = 99999;
}
// setup bounds and integer nature
lp.setIsinteger(integerBool);
lp.setUpperbound(upperBound);
lp.setLowerbound(lowerBound);
LinearProgramSolver solver = SolverFactory.newDefault();
// double[] solution;
// solution = solver.solve(lp);
DoubleMatrix solution = new DoubleMatrix(solver.solve(lp));
clockLP.stop();
logger.debug("Total GLPK time: {} [sec]", (double) (clockLP.getElapsedTime()) / 1000);
// Displatch the LP solution
int offset;
int[] idx1p = JblasUtils.intRangeIntArray(0, nS1 - 1);
DoubleMatrix x1p = solution.get(idx1p);
x1p.reshape(ny, nx + 1);
offset = idx1p[nS1 - 1] + 1;
int[] idx1m = JblasUtils.intRangeIntArray(offset, offset + nS1 - 1);
DoubleMatrix x1m = solution.get(idx1m);
x1m.reshape(ny, nx + 1);
offset = idx1m[idx1m.length - 1] + 1;
int[] idx2p = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2p = solution.get(idx2p);
x2p.reshape(ny + 1, nx);
offset = idx2p[idx2p.length - 1] + 1;
int[] idx2m = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2m = solution.get(idx2m);
x2m.reshape(ny + 1, nx);
// Compute the derivative jumps, eqt (20,21)
DoubleMatrix k1 = x1p.sub(x1m);
DoubleMatrix k2 = x2p.sub(x2m);
// (?) Round to integer solution
if (roundK == true) {
for (int idx = 0; idx < k1.length; idx++) {
k1.put(idx, FastMath.floor(k1.get(idx)));
}
for (int idx = 0; idx < k2.length; idx++) {
k2.put(idx, FastMath.floor(k2.get(idx)));
}
}
// Sum the jumps with the wrapped partial derivatives, eqt (10,11)
k1.reshape(ny, nx + 1);
k2.reshape(ny + 1, nx);
k1.addi(Psi1.div(Constants._TWO_PI));
k2.addi(Psi2.div(Constants._TWO_PI));
// Integrate the partial derivatives, eqt (6)
// cumsum() method in JblasTester -> see cumsum_demo() in JblasTester.cumsum_demo()
DoubleMatrix k2_temp = DoubleMatrix.concatHorizontally(DoubleMatrix.zeros(1), k2.getRow(0));
k2_temp = JblasUtils.cumsum(k2_temp, 1);
DoubleMatrix k = DoubleMatrix.concatVertically(k2_temp, k1);
k = JblasUtils.cumsum(k, 1);
// Unwrap - final solution
unwrappedPhase = k.mul(Constants._TWO_PI);
}
public void model(
final SLCImage master, final SLCImage slave, Orbit masterOrbit, Orbit slaveOrbit)
throws Exception {
if (!masterOrbit.isInterpolated()) {
logger.debug("Baseline cannot be computed, master orbit not initialized.");
throw new Exception("Baseline.model_parameters: master orbit not initialized");
} else if (!slaveOrbit.isInterpolated()) {
logger.debug("Baseline cannot be computed, slave orbit not initialized.");
throw new Exception("Baseline.model_parameters: slave orbit not initialized");
}
if (isInitialized) {
logger.warn("baseline already isInitialized??? (returning)");
return;
}
masterWavelength = master.getRadarWavelength();
// Model r = nearRange + drange_dp*p -- p starts at 1
nearRange = master.pix2range(1.0);
drange_dp = master.pix2range(2.0) - master.pix2range(1.0);
nearRange -= drange_dp; // -- p starts at 1
// Set min/maxima for normalization
linMin = master.currentWindow.linelo; // also used during polyval...
linMax = master.currentWindow.linehi;
pixMin = master.currentWindow.pixlo;
pixMax = master.currentWindow.pixhi;
hMin = 0.0;
hMax = 5000.0;
// Loop counters and sampling
int cnt = 0; // matrix index
final double deltaPixels = master.currentWindow.pixels() / N_POINTS_RNG;
final double deltaLines = master.currentWindow.lines() / N_POINTS_AZI;
final double deltaHeight = (hMax - hMin) / N_HEIGHTS;
// Declare matrices for modeling Bperp
// Note: for stability of normalmatrix, fill aMatrix with normalized line, etc.
// perpendicular baseline
DoubleMatrix bPerpMatrix = new DoubleMatrix(N_POINTS_AZI * N_POINTS_RNG * N_HEIGHTS, 1);
// parallel baseline
DoubleMatrix bParMatrix = new DoubleMatrix(N_POINTS_AZI * N_POINTS_RNG * N_HEIGHTS, 1);
// viewing angle
DoubleMatrix thetaMatrix = new DoubleMatrix(N_POINTS_AZI * N_POINTS_RNG * N_HEIGHTS, 1);
// incidence angle
DoubleMatrix thetaIncMatrix = new DoubleMatrix(N_POINTS_AZI * N_POINTS_RNG * N_HEIGHTS, 1);
// design matrix
DoubleMatrix aMatrix = new DoubleMatrix(N_POINTS_AZI * N_POINTS_RNG * N_HEIGHTS, numCoeffs);
// Loop over heights(k), lines(i), pixels(j) to estimate baseline
// height levels
for (long k = 0; k < N_HEIGHTS; ++k) {
final double height = hMin + k * deltaHeight;
// azimuth direction
for (long i = 0; i < N_POINTS_AZI; ++i) {
final double line = master.currentWindow.linelo + i * deltaLines;
Point pointOnEllips; // point, returned by lp2xyz
double sTazi, sTrange;
// Azimuth time for this line
final double mTazi = master.line2ta(line);
// xyz for master satellite from time
final Point pointOnMasterOrb = masterOrbit.getXYZ(mTazi);
// Continue looping in range direction
for (long j = 0; j < N_POINTS_RNG; ++j) {
final double pixel = master.currentWindow.pixlo + j * deltaPixels;
// ______ Range time for this pixel ______
// final double m_trange = master.pix2tr(pixel);
pointOnEllips = masterOrbit.lph2xyz(line, pixel, height, master);
// Compute xyz for slave satellite from pointOnEllips
Point pointTime = slaveOrbit.xyz2t(pointOnEllips, slave);
sTazi = pointTime.y;
sTrange = pointTime.x;
// Slave position
final Point pointOnSlaveOrb = slaveOrbit.getXYZ(sTazi);
// Compute angle between near parallel orbits
final Point velOnMasterOrb = masterOrbit.getXYZDot(mTazi);
final Point velOnSlaveOrb = slaveOrbit.getXYZDot(sTazi);
final double angleOrbits = velOnMasterOrb.angle(velOnSlaveOrb);
logger.debug(
"Angle between orbits master-slave (at l,p= "
+ line
+ ","
+ pixel
+ ") = "
+ rad2deg(angleOrbits)
+ " [deg]");
// Note: convergence assumed constant!
orbitConvergence = angleOrbits;
// final heading = angle(velOnMasterOrb,[1 0 0]) //?
// orbitHeading = 0.0; // not yet used
// The baseline parameters, derived from the positions (x,y,z)
// alpha is angle counterclockwize(b, plane with normal=rho1=rho2)
// theta is angle counterclockwize(rho1 = pointOnMasterOrb, r1 = pointOnMasterOrb -
// pointOnEllips, r2 = pointOnSlaveOrb - pointOnEllips)
// construct helper class
BaselineComponents baselineComponents = new BaselineComponents().invoke();
compute_B_Bpar_Bperp_Theta(
baselineComponents, pointOnEllips, pointOnMasterOrb, pointOnSlaveOrb);
final double b = baselineComponents.getB();
final double bPar = baselineComponents.getBpar();
final double bPerp = baselineComponents.getBperp();
final double theta = baselineComponents.getTheta();
final double thetaInc = computeIncAngle(pointOnMasterOrb, pointOnEllips); // [rad]!!!
// Modelling of bPerp(l,p) = a00 + a10*l + a01*p
bPerpMatrix.put(cnt, 0, bPerp);
bParMatrix.put(cnt, 0, bPar);
thetaMatrix.put(cnt, 0, theta);
thetaIncMatrix.put(cnt, 0, thetaInc);
// --- b(l,p,h) = a000 +
// a100*l + a010*p + a001*h +
// a110*l*p + a101*l*h + a011*p*h +
// a200*l^2 + a020*p^2 + a002*h^2
aMatrix.put(cnt, 0, 1.0);
aMatrix.put(cnt, 1, normalize2(line, linMin, linMax));
aMatrix.put(cnt, 2, normalize2(pixel, pixMin, pixMax));
aMatrix.put(cnt, 3, normalize2(height, hMin, hMax));
aMatrix.put(cnt, 4, normalize2(line, linMin, linMax) * normalize2(pixel, pixMin, pixMax));
aMatrix.put(cnt, 5, normalize2(line, linMin, linMax) * normalize2(height, hMin, hMax));
aMatrix.put(cnt, 6, normalize2(pixel, pixMin, pixMax) * normalize2(height, hMin, hMax));
aMatrix.put(cnt, 7, sqr(normalize2(line, linMin, linMax)));
aMatrix.put(cnt, 8, sqr(normalize2(pixel, pixMin, pixMax)));
aMatrix.put(cnt, 9, sqr(normalize2(height, hMin, hMax)));
cnt++;
// b/alpha representation of baseline
final double alpha =
(bPar == 0 && bPerp == 0)
? Double.NaN
: theta - Math.atan2(bPar, bPerp); // sign ok atan2
// horizontal/vertical representation of baseline
final double bH = b * Math.cos(alpha); // sign ok
final double bV = b * Math.sin(alpha); // sign ok
// TODO: check sign of infinity!!!
// Height ambiguity: [h] = -lambda/4pi * (r1sin(theta)/bPerp) * phi==2pi
final double hAmbiguity =
(bPerp == 0)
? Double.POSITIVE_INFINITY
: -master.getRadarWavelength()
* (pointOnMasterOrb.min(pointOnEllips)).norm()
* Math.sin(theta)
/ (2.0 * bPerp);
// Some extra info if in DEBUG unwrapMode
logger.debug(
"The baseline parameters for (l,p,h) = " + line + ", " + pixel + ", " + height);
logger.debug("\talpha (deg), BASELINE: \t" + rad2deg(alpha) + " \t" + b);
logger.debug("\tbPar, bPerp: \t" + bPar + " \t" + bPerp);
logger.debug("\tbH, bV: \t" + bH + " \t" + bV);
logger.debug("\tHeight ambiguity: \t" + hAmbiguity);
logger.debug("\ttheta (deg): \t" + rad2deg(theta));
logger.debug("\tthetaInc (deg): \t" + rad2deg(thetaInc));
logger.debug("\tpointOnMasterOrb (x,y,z) = " + pointOnMasterOrb.toString());
logger.debug("\tpointOnSlaveOrb (x,y,z) = " + pointOnSlaveOrb.toString());
logger.debug("\tpointOnEllips (x,y,z) = " + pointOnEllips.toString());
} // loop pixels
} // loop lines
} // loop heights
// Model all Baselines as 2d polynomial of degree 1
DoubleMatrix nMatrix = matTxmat(aMatrix, aMatrix);
DoubleMatrix rhsBperp = matTxmat(aMatrix, bPerpMatrix);
DoubleMatrix rhsBpar = matTxmat(aMatrix, bParMatrix);
DoubleMatrix rhsTheta = matTxmat(aMatrix, thetaMatrix);
DoubleMatrix rhsThetaInc = matTxmat(aMatrix, thetaIncMatrix);
// DoubleMatrix Qx_hat = nMatrix;
final DoubleMatrix Qx_hat =
LinearAlgebraUtils.invertChol(Decompose.cholesky(nMatrix).transpose());
// TODO: refactor to _internal_ cholesky decomposition
// choles(Qx_hat); // Cholesky factorisation normalmatrix
// solvechol(Qx_hat,rhsBperp); // Solution Bperp coefficients in rhsB
// solvechol(Qx_hat,rhsBpar); // Solution Theta coefficients in rhsTheta
// solvechol(Qx_hat,rhsTheta); // Solution Theta coefficients in rhsTheta
// solvechol(Qx_hat,rhsThetaInc); // Solution Theta_inc coefficients in rhsThetaInc
// invertchol(Qx_hat); // Covariance matrix of normalized unknowns
rhsBperp = Solve.solvePositive(nMatrix, rhsBperp);
rhsBpar = Solve.solvePositive(nMatrix, rhsBpar);
rhsTheta = Solve.solvePositive(nMatrix, rhsTheta);
rhsThetaInc = Solve.solvePositive(nMatrix, rhsThetaInc);
// Info on inversion, normalization is ok______
final DoubleMatrix yHatBperp = aMatrix.mmul(rhsBperp);
final DoubleMatrix eHatBperp = bPerpMatrix.sub(yHatBperp);
// DoubleMatrix Qe_hat = Qy - Qy_hat;
// DoubleMatrix y_hatT = aMatrix * rhsTheta;
// DoubleMatrix e_hatT = thetaMatrix - y_hatT;
// Copy estimated coefficients to private fields
bperpCoeffs = rhsBperp;
bparCoeffs = rhsBpar;
thetaCoeffs = rhsTheta;
thetaIncCoeffs = rhsThetaInc;
// Test inverse -- repair matrix!!!
for (int i = 0; i < Qx_hat.rows; i++) {
for (int j = 0; j < i; j++) {
Qx_hat.put(j, i, Qx_hat.get(i, j));
}
}
final double maxDev = abs(nMatrix.mmul(Qx_hat).sub(DoubleMatrix.eye(Qx_hat.rows))).max();
logger.debug("BASELINE: max(abs(nMatrix*inv(nMatrix)-I)) = " + maxDev);
if (maxDev > .01) {
logger.warn(
"BASELINE: max. deviation nMatrix*inv(nMatrix) from unity = "
+ maxDev
+ ". This is larger than .01: do not use this!");
} else if (maxDev > .001) {
logger.warn(
"BASELINE: max. deviation nMatrix*inv(nMatrix) from unity = "
+ maxDev
+ ". This is between 0.01 and 0.001 (maybe not use it)");
}
// Output solution and give max error
// --- B(l,p,h) = a000 +
// a100*l + a010*p + a001*h +
// a110*l*p + a101*l*h + a011*p*h +
// a200*l^2 + a020*p^2 + a002*h^2
logger.debug("--------------------");
logger.debug("Result of modeling: Bperp(l,p) = a000 + a100*l + a010*p + a001*h + ");
logger.debug(" a110*l*p + a101*l*h + a011*p*h + a200*l^2 + a020*p^2 + a002*h^2");
logger.debug("l,p,h in normalized coordinates [-2:2].");
logger.debug("Bperp_a000 = " + rhsBperp.get(0, 0));
logger.debug("Bperp_a100 = " + rhsBperp.get(1, 0));
logger.debug("Bperp_a010 = " + rhsBperp.get(2, 0));
logger.debug("Bperp_a001 = " + rhsBperp.get(3, 0));
logger.debug("Bperp_a110 = " + rhsBperp.get(4, 0));
logger.debug("Bperp_a101 = " + rhsBperp.get(5, 0));
logger.debug("Bperp_a011 = " + rhsBperp.get(6, 0));
logger.debug("Bperp_a200 = " + rhsBperp.get(7, 0));
logger.debug("Bperp_a020 = " + rhsBperp.get(8, 0));
logger.debug("Bperp_a002 = " + rhsBperp.get(9, 0));
double maxerr = (abs(eHatBperp)).max();
if (maxerr > 2.00) //
{
logger.warn("Max. error bperp modeling at 3D datapoints: " + maxerr + "m");
} else {
logger.info("Max. error bperp modeling at 3D datapoints: " + maxerr + "m");
}
logger.debug("--------------------");
logger.debug("Range: r(p) = r0 + dr*p");
logger.debug("l and p in un-normalized, absolute, coordinates (1:nMatrix).");
final double range1 = master.pix2range(1.0);
final double range5000 = master.pix2range(5000.0);
final double drange = (range5000 - range1) / 5000.0;
logger.debug("range = " + (range1 - drange) + " + " + drange + "*p");
// orbit initialized
isInitialized = true;
}
// TODO: DOUBLECHECK EVERYTHING
@Override
public void map(Text key, jBLASArrayWritable input, GibbsSampling.Context context)
throws IOException, InterruptedException {
/* *******************************************************************/
/* initialize all memory we're going to use during the process */
long start_time = System.nanoTime();
ArrayList<DoubleMatrix> data = input.getData();
label = data.get(4);
v_data = data.get(5);
// check to see if we are in the first layer or there are layers beneath us we must sample
// from
if (data.size() > 6) {
int prelayer = (data.size() - 6) / 3;
DoubleMatrix[] preWeights = new DoubleMatrix[prelayer],
preHbias = new DoubleMatrix[prelayer],
preVbias = new DoubleMatrix[prelayer];
for (int i = 0; i < prelayer; i++) {
preWeights[i] = data.get(6 + i * 3);
preHbias[i] = data.get(7 + i * 3);
preVbias[i] = data.get(8 + i * 3);
}
DoubleMatrix vnew = null;
for (int i = 0; i < prelayer; i++) {
weights = preWeights[i];
vbias = preVbias[i];
hbias = preHbias[i];
vnew = sample_h_from_v(i == 0 ? v_data : vnew);
}
v_data = vnew;
}
weights = data.get(0);
hbias = data.get(1);
hiddenChain = data.get(2);
vbias = data.get(3);
// check if we need to attach labels to the observed variables
if (vbias.columns != v_data.columns) {
DoubleMatrix labels = DoubleMatrix.zeros(1, classCount);
int labelNum = (new Double(label.get(0))).intValue();
labels.put(labelNum, 1.0);
v_data = DoubleMatrix.concatHorizontally(v_data, labels);
}
w1 = DoubleMatrix.zeros(weights.rows, weights.columns);
hb1 = DoubleMatrix.zeros(hbias.rows, hbias.columns);
vb1 = DoubleMatrix.zeros(vbias.rows, vbias.columns);
/* ********************************************************************/
// sample hidden state to get positive phase
// if empty, use it as the start of the chain
// or use persistent hidden state from pCD
DoubleMatrix phaseSample = sample_h_from_v(v_data);
h1_data = new DoubleMatrix();
v1_data = new DoubleMatrix();
if (hiddenChain == null) {
data.set(2, new DoubleMatrix(hbias.rows, hbias.columns));
hiddenChain = data.get(2);
hiddenChain.copy(phaseSample);
h1_data.copy(phaseSample);
} else {
h1_data.copy(hiddenChain);
}
// run Gibbs chain for k steps
for (int j = 0; j < gibbsSteps; j++) {
v1_data.copy(sample_v_from_h(h1_data));
h1_data.copy(sample_h_from_v(v1_data));
}
DoubleMatrix hprob = propup(v1_data);
weight_contribution(hiddenChain, v_data, hprob, v1_data);
hiddenChain.copy(h1_data);
data.get(0).copy(w1);
data.get(1).copy(hb1);
data.get(2).copy(hiddenChain);
data.get(3).copy(vb1);
jBLASArrayWritable outputmatrix = new jBLASArrayWritable(data);
context.write(key, outputmatrix);
log.info("Job completed in: " + (System.nanoTime() - start_time) / (1E6) + " ms");
}
public static void main(String[] args) {
Scanner s = new Scanner(System.in);
int n = s.nextInt();
int nn = n;
DoubleMatrix d = DoubleMatrix.zeros(2 * n, 2 * n);
DoubleMatrix d2 = DoubleMatrix.zeros(n, n);
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
d.put(i, j, s.nextDouble());
d2.put(i, j, d.get(i, j));
}
}
// d2 = new DoubleMatrix(d.data);
System.out.println(d);
System.out.println(d);
List<Integer> L = new ArrayList<Integer>(2 * n);
List<Double> R = new ArrayList<Double>(2 * n);
for (int i = 0; i < 2 * n; i++) {
L.add(0);
R.add(0.0);
}
// inicjalizacja lisci - jako etykiety kolejne liczby od 0
for (int i = 0; i < n; i++) {
L.set(i, i);
}
// V - drzewo addytywne, które tworzymy
ArrayList[] V = new ArrayList[2 * n];
for (int i = 0; i < V.length; i++) {
V[i] = new ArrayList<Integer>();
}
double suma, rmin, rr;
int i, j, vertNum = n;
while (n > 3) {
// wyznaczanie r dla każdego liścia
for (int a = 0; a < n; a++) {
suma = 0;
for (int b = 0; b < n; b++) {
suma = suma + d.get(L.get(a), L.get(b));
}
suma = suma / (n - 2);
R.set(a, suma);
}
// wyznaczania sąsiadów na podstawie r
i = 0;
j = 1;
rmin = d.get(L.get(0), L.get(1)) - (R.get(0) + R.get(1));
for (int a = 0; a < n - 1; a++) {
for (int b = a + 1; b < n; b++) {
rr = d.get(L.get(a), L.get(b)) - (R.get(a) + R.get(b));
if (rr < rmin) {
rmin = rr;
i = a;
j = b;
}
}
}
// usuniecie ze zbioru lisci i,j oraz dodanie k
L.set(n, vertNum);
vertNum++;
i = L.remove(i);
j = L.remove(j - 1);
n = n - 1;
// uaktualnienie d dla każdego pozostałego liścia
for (int l = 0; l < n - 1; l++) {
double value = (d.get(i, L.get(l)) + d.get(j, L.get(l)) - d.get(i, j)) / 2;
d.put(L.get(n - 1), L.get(l), value);
d.put(L.get(l), L.get(n - 1), value);
}
// dodanie odpowiednich krawędzi do tworzonego drzewa
V[i].add((vertNum - 1));
V[j].add((vertNum - 1));
V[vertNum - 1].add(i);
V[vertNum - 1].add(j);
// wyznaczenie odległości między nowym wierzchołkiem oraz i,j
double value = (d.get(i, j) + d.get(i, L.get(0)) - d.get(j, L.get(0))) / 2;
d.put(i, vertNum - 1, value);
d.put(vertNum - 1, i, value);
d.put(j, vertNum - 1, d.get(i, j) - d.get(i, vertNum - 1));
d.put(vertNum - 1, j, d.get(i, j) - d.get(i, vertNum - 1));
}
// 3 elementowe drzewo
double value;
value = (d.get(L.get(0), L.get(1)) + d.get(L.get(0), L.get(2)) - d.get(L.get(1), L.get(2))) / 2;
d.put(L.get(0), vertNum, value);
d.put(vertNum, L.get(0), value);
value = (d.get(L.get(0), L.get(1)) + d.get(L.get(1), L.get(2)) - d.get(L.get(0), L.get(2))) / 2;
d.put(L.get(1), vertNum, value);
d.put(vertNum, L.get(1), value);
value = (d.get(L.get(0), L.get(2)) + d.get(L.get(1), L.get(2)) - d.get(L.get(0), L.get(1))) / 2;
d.put(L.get(2), vertNum, value);
d.put(vertNum, L.get(2), value);
V[vertNum].add(L.get(0));
V[vertNum].add(L.get(1));
V[vertNum].add(L.get(2));
V[L.get(0)].add(vertNum);
V[L.get(1)].add(vertNum);
V[L.get(2)].add(vertNum);
// wypisanie wyników
System.out.println(d);
// DoubleMatrix w2 = DoubleMatrix.zeros(2*n, 2*n);
ArrayList w = new ArrayList<Integer>();
for (int a = 0; a <= vertNum; a++) {
System.out.print(a);
System.out.print(" : ");
for (int b = 0; b < V[a].size(); b++) {
System.out.print(V[a].get(b));
System.out.print(" ");
// w2.put(a,b,Integer.parseInt(V[a].get(b).toString()));
w.add(V[a].get(b));
}
System.out.println("");
}
DoubleMatrix A = DoubleMatrix.zeros((nn * (nn - 1)) / 2, vertNum);
DoubleMatrix g = DoubleMatrix.zeros((nn * (nn - 1)) / 2, 1);
double blad = nk(A, d2, g, V, vertNum); // wrzucam to do siebie - mkd
System.out.println(A);
DoubleMatrix p = (new DoubleMatrix(A.rows, A.columns, A.data)).transpose();
System.out.println(p.rows + " " + p.columns);
DoubleMatrix p2 =
(new DoubleMatrix(p.rows, p.columns, p.data))
.mmul((new DoubleMatrix(A.rows, A.columns, A.data)));
System.out.println("p2: " + p2);
DoubleMatrix p3 = MatrixFunctions.pow(p2, -1);
System.out.println("p3: " + p3);
DoubleMatrix p4 = p3.mmul(p);
DoubleMatrix b = p4.mmul(g);
// DoubleMatrix b = MatrixFunctions.pow(A.transpose().mmul(A), -1).mmul(A.transpose()).mmul(g);
System.out.println(g);
System.out.println(b);
System.out.println("Kwadrat bledu wynosi " + blad);
}
public static DoubleMatrix polyval(
final DoubleMatrix x, final DoubleMatrix y, final DoubleMatrix coeff, int degree) {
if (!x.isColumnVector()) {
logger.warn("polyValGrid: require (x) standing data vectors!");
throw new IllegalArgumentException("polyval functions require (x) standing data vectors!");
}
if (!y.isColumnVector()) {
logger.warn("polyValGrid: require (y) standing data vectors!");
throw new IllegalArgumentException("polyval functions require (y) standing data vectors!");
}
if (!coeff.isColumnVector()) {
logger.warn("polyValGrid: require (coeff) standing data vectors!");
throw new IllegalArgumentException(
"polyval functions require (coeff) standing data vectors!");
}
if (degree < -1) {
logger.warn("polyValGrid: degree < -1 ????");
}
if (x.length > y.length) {
logger.warn("polValGrid: x larger than y, while optimized for y larger x");
}
if (degree == -1) {
degree = degreeFromCoefficients(coeff.length);
}
// evaluate polynomial //
DoubleMatrix result = new DoubleMatrix(x.length, y.length);
int i;
int j;
double c00,
c10,
c01,
c20,
c11,
c02,
c30,
c21,
c12,
c03,
c40,
c31,
c22,
c13,
c04,
c50,
c41,
c32,
c23,
c14,
c05;
switch (degree) {
case 0:
result.put(0, 0, coeff.get(0, 0));
break;
case 1:
c00 = coeff.get(0, 0);
c10 = coeff.get(1, 0);
c01 = coeff.get(2, 0);
for (j = 0; j < result.columns; j++) {
double c00pc01y1 = c00 + c01 * y.get(j, 0);
for (i = 0; i < result.rows; i++) {
result.put(i, j, c00pc01y1 + c10 * x.get(i, 0));
}
}
break;
case 2:
c00 = coeff.get(0, 0);
c10 = coeff.get(1, 0);
c01 = coeff.get(2, 0);
c20 = coeff.get(3, 0);
c11 = coeff.get(4, 0);
c02 = coeff.get(5, 0);
for (j = 0; j < result.columns; j++) {
double y1 = y.get(j, 0);
double c00pc01y1 = c00 + c01 * y1;
double c02y2 = c02 * Math.pow(y1, 2);
double c11y1 = c11 * y1;
for (i = 0; i < result.rows; i++) {
double x1 = x.get(i, 0);
result.put(i, j, c00pc01y1 + c10 * x1 + c20 * Math.pow(x1, 2) + c11y1 * x1 + c02y2);
}
}
break;
case 3:
c00 = coeff.get(0, 0);
c10 = coeff.get(1, 0);
c01 = coeff.get(2, 0);
c20 = coeff.get(3, 0);
c11 = coeff.get(4, 0);
c02 = coeff.get(5, 0);
c30 = coeff.get(6, 0);
c21 = coeff.get(7, 0);
c12 = coeff.get(8, 0);
c03 = coeff.get(9, 0);
for (j = 0; j < result.columns; j++) {
double y1 = y.get(j, 0);
double y2 = Math.pow(y1, 2);
double c00pc01y1 = c00 + c01 * y1;
double c02y2 = c02 * y2;
double c11y1 = c11 * y1;
double c21y1 = c21 * y1;
double c12y2 = c12 * y2;
double c03y3 = c03 * y1 * y2;
for (i = 0; i < result.rows; i++) {
double x1 = x.get(i, 0);
double x2 = Math.pow(x1, 2);
result.put(
i,
j,
c00pc01y1
+ c10 * x1
+ c20 * x2
+ c11y1 * x1
+ c02y2
+ c30 * x1 * x2
+ c21y1 * x2
+ c12y2 * x1
+ c03y3);
}
}
break;
case 4:
c00 = coeff.get(0, 0);
c10 = coeff.get(1, 0);
c01 = coeff.get(2, 0);
c20 = coeff.get(3, 0);
c11 = coeff.get(4, 0);
c02 = coeff.get(5, 0);
c30 = coeff.get(6, 0);
c21 = coeff.get(7, 0);
c12 = coeff.get(8, 0);
c03 = coeff.get(9, 0);
c40 = coeff.get(10, 0);
c31 = coeff.get(11, 0);
c22 = coeff.get(12, 0);
c13 = coeff.get(13, 0);
c04 = coeff.get(14, 0);
for (j = 0; j < result.columns; j++) {
double y1 = y.get(j, 0);
double y2 = Math.pow(y1, 2);
double c00pc01y1 = c00 + c01 * y1;
double c02y2 = c02 * y2;
double c11y1 = c11 * y1;
double c21y1 = c21 * y1;
double c12y2 = c12 * y2;
double c03y3 = c03 * y1 * y2;
double c31y1 = c31 * y1;
double c22y2 = c22 * y2;
double c13y3 = c13 * y2 * y1;
double c04y4 = c04 * y2 * y2;
for (i = 0; i < result.rows; i++) {
double x1 = x.get(i, 0);
double x2 = Math.pow(x1, 2);
result.put(
i,
j,
c00pc01y1
+ c10 * x1
+ c20 * x2
+ c11y1 * x1
+ c02y2
+ c30 * x1 * x2
+ c21y1 * x2
+ c12y2 * x1
+ c03y3
+ c40 * x2 * x2
+ c31y1 * x2 * x1
+ c22y2 * x2
+ c13y3 * x1
+ c04y4);
}
}
break;
case 5:
c00 = coeff.get(0, 0);
c10 = coeff.get(1, 0);
c01 = coeff.get(2, 0);
c20 = coeff.get(3, 0);
c11 = coeff.get(4, 0);
c02 = coeff.get(5, 0);
c30 = coeff.get(6, 0);
c21 = coeff.get(7, 0);
c12 = coeff.get(8, 0);
c03 = coeff.get(9, 0);
c40 = coeff.get(10, 0);
c31 = coeff.get(11, 0);
c22 = coeff.get(12, 0);
c13 = coeff.get(13, 0);
c04 = coeff.get(14, 0);
c50 = coeff.get(15, 0);
c41 = coeff.get(16, 0);
c32 = coeff.get(17, 0);
c23 = coeff.get(18, 0);
c14 = coeff.get(19, 0);
c05 = coeff.get(20, 0);
for (j = 0; j < result.columns; j++) {
double y1 = y.get(j, 0);
double y2 = Math.pow(y1, 2);
double y3 = y2 * y1;
double c00pc01y1 = c00 + c01 * y1;
double c02y2 = c02 * y2;
double c11y1 = c11 * y1;
double c21y1 = c21 * y1;
double c12y2 = c12 * y2;
double c03y3 = c03 * y3;
double c31y1 = c31 * y1;
double c22y2 = c22 * y2;
double c13y3 = c13 * y3;
double c04y4 = c04 * y2 * y2;
double c41y1 = c41 * y1;
double c32y2 = c32 * y2;
double c23y3 = c23 * y3;
double c14y4 = c14 * y2 * y2;
double c05y5 = c05 * y3 * y2;
for (i = 0; i < result.rows; i++) {
double x1 = x.get(i, 0);
double x2 = Math.pow(x1, 2);
double x3 = x1 * x2;
result.put(
i,
j,
c00pc01y1
+ c10 * x1
+ c20 * x2
+ c11y1 * x1
+ c02y2
+ c30 * x3
+ c21y1 * x2
+ c12y2 * x1
+ c03y3
+ c40 * x2 * x2
+ c31y1 * x3
+ c22y2 * x2
+ c13y3 * x1
+ c04y4
+ c50 * x3 * x2
+ c41y1 * x2 * x2
+ c32y2 * x3
+ c23y3 * x2
+ c14y4 * x1
+ c05y5);
}
}
break;
// TODO: solve up to 5 efficiently, do rest in loop
default:
for (j = 0; j < result.columns; j++) {
double yy = y.get(j, 0);
for (i = 0; i < result.rows; i++) {
double xx = x.get(i, 0);
result.put(i, j, polyval(xx, yy, coeff, degree));
}
}
} // switch degree
return result;
}
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();
}
}
/**
* 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…………");
}
