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 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 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,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());
}
}
public static void main(String[] args) throws IOException {
try (BufferedReader reader = new BufferedReader(new FileReader("matrix_test"))) {
// read table from file
List<String[]> rows =
reader.lines().map(line -> line.split(" ")).collect(Collectors.toList());
double[][] values = new double[rows.size()][rows.get(0).length];
for (int i = 0; i < values.length; i++) {
String[] row = rows.get(i);
for (int j = 0; j < values[0].length; j++) {
values[i][j] = Double.parseDouble(row[j]);
}
}
DoubleMatrix matrix = new DoubleMatrix(values);
Vector coordVector = new Vector(matrix.getRows()); // 000..00 (n-times)
while (coordVector.hasNextVector()) {
coordVector.nextVector(); // get next vector (eg 000..00 -> 000..01)
DoubleMatrix coord = coordVector.row();
DoubleMatrix codeWord = coord.mmul(matrix); // m * G
modi(codeWord, 2);
String prefix = "0 0 0 0 1 ";
String word = MatrixUtils.toString(codeWord);
if (word.startsWith(prefix)) {
System.out.println(word);
}
}
/*// find syndromes
Syndrome syndrome = new Syndrome(new DoubleMatrix(values));
syndrome.build();
// generate xls file with table
HSSFWorkbook wb = new HSSFWorkbook();
HSSFSheet sheet = wb.createSheet("Syndrome table");
int rowNumber = 0;
Vector syndromeVector = new Vector(values.length);
putValue(sheet, rowNumber++, syndromeVector.toString(),
syndrome.getErrorVector(syndromeVector.toString()));
while (syndromeVector.hasNextVector()) {
syndromeVector.nextVector();
putValue(sheet, rowNumber++, syndromeVector.toString(),
syndrome.getErrorVector(syndromeVector.toString()));
}
try (FileOutputStream fileOut = new FileOutputStream("syndrome_table.xls")) {
wb.write(fileOut);
}*/
}
}
/**
* 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;
}
private List<DoubleMatrix> countErrors(DoubleMatrix result, DoubleMatrix output) {
ArrayList<DoubleMatrix> errors = new ArrayList<DoubleMatrix>();
List<Layer> layers = getLayers();
DoubleMatrix err = result.sub(output);
errors.add(err);
for (int i = layers.size() - 1; i > 1; i--) {
DoubleMatrix previousErrors = errors.get(errors.size() - 1);
Layer layer = layers.get(i);
DoubleMatrix transposedWeights = layer.getWeights().transpose();
DoubleMatrix errorOfCurrentLayer = transposedWeights.mmul(previousErrors);
errors.add(errorOfCurrentLayer);
}
Collections.reverse(errors);
return errors;
}
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;
}
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();
}
/** 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………………");
}
public DoubleMatrix propdown(DoubleMatrix h) {
return MatrixMath.sigmoid(h.mmul(this.weights).addi(vbias.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…………");
}
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);
}
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 static void main(String[] args) {
DoubleMatrix A = readJamaMatrixFromFile(".\\src\\test\\resources\\10_10_A");
DoubleMatrix B = readJamaMatrixFromFile(".\\src\\test\\resources\\10_10_B");
Long start = System.currentTimeMillis();
DoubleMatrix result = A.mmul(B);
Long end = System.currentTimeMillis();
Long time = end - start;
System.out.println("Time for JBLAS multiply 10x10 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\100_100_M1");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\100_100_M2");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 100x100 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\200_200_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\200_200_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 200x200 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\300_300_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\300_300_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 300x300 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\500_500_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\500_500_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 500x500 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\1000_1000_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\1000_1000_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 1000x1000 is: " + time);
// Matrix Multiply x2
A = readJamaMatrixFromFile(".\\src\\test\\resources\\2_2_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\2_2_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 2x2 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\4_4_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\4_4_B");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 4x4 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\8_8_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\8_8_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 8x8 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\16_16_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\16_16_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 16x16 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\32_32_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\32_32_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 32x32 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\64_64_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\64_64_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 64x64 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\128_128_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\128_128_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 128x128 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\256_256_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\256_256_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 256x256 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\512_512_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\512_512_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 512x512 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\1024_1024_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\1024_1024_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 1024x1024 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\2048_2048_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\2048_2048_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 2048x2048 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\4096_4096_A");
B = readJamaMatrixFromFile(".\\src\\test\\resources\\4096_4096_A");
start = System.currentTimeMillis();
result = A.mmul(B);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS multiply 4096x4096 is: " + time);
// LU Decomp
A = readJamaMatrixFromFile(".\\src\\test\\resources\\2_2_A");
Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
Decompose.LUDecomposition<DoubleMatrix> resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 2x2 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\4_4_A");
// Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 4x4 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\8_8_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 8x8 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\16_16_A");
// Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 16x16 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\32_32_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 32x32 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\64_64_A");
// Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 64x64 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\128_128_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 128x128 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\256_256_A");
// Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 256x256 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\512_512_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 512x512 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\1024_1024_A");
// Decompose lucalc = new Decompose();
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 1024x1024 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\2048_2048_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 2048x2048 is: " + time);
A = readJamaMatrixFromFile(".\\src\\test\\resources\\4096_4096_A");
start = System.currentTimeMillis();
resultLu = lucalc.lu(A);
end = System.currentTimeMillis();
time = end - start;
System.out.println("Time for JBLAS LU 4096x4096 is: " + time);
}
