Java Code Examples for org.apache.commons.math3.linear.RealMatrix#scalarMultiply()
The following examples show how to use
org.apache.commons.math3.linear.RealMatrix#scalarMultiply() .
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
| Source File: Arja_0028_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 2
| Source File: Arja_0079_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 3
| Source File: Arja_0079_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 4
| Source File: Arja_00166_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 5
| Source File: CMAESOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
// XXX Line below to replace the current one in 4.0 (MATH-879).
// throw new NotStrictlyPositiveException(lambda);
lambda = 4 + (int) (3 * Math.log(dimension));
}
// initialize sigma
final double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
// XXX Line below to replace the current one in 4.0 (MATH-868).
// sigmaArray[i][0] = inputSigma[i];
sigmaArray[i][0] = inputSigma == null ? 0.3 : inputSigma[i];
}
final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1 / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4 + mueff / dimension) /
(dimension + 4 + 2 * mueff / dimension);
cs = (mueff + 2) / (dimension + mueff + 3.);
damps = (1 + 2 * Math.max(0, Math.sqrt((mueff - 1) /
(dimension + 1)) - 1)) *
Math.max(0.3,
1 - dimension / (1e-6 + maxIterations)) + cs; // minor increment
ccov1 = 2 / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2 * (mueff - 2 + 1 / mueff) /
((dimension + 2) * (dimension + 2) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3);
chiN = Math.sqrt(dimension) *
(1 - 1 / ((double) 4 * dimension) + 1 / ((double) 21 * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective variables
diagD = insigma.scalarMultiply(1 / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3 * 10 * dimension / (double) lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 6
| Source File: Cardumen_00163_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 7
| Source File: KalmanFilterTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testConstantAcceleration() {
// simulates a vehicle, accelerating at a constant rate (0.1 m/s)
// discrete time interval
double dt = 0.1d;
// position measurement noise (meter)
double measurementNoise = 10d;
// acceleration noise (meter/sec^2)
double accelNoise = 0.2d;
// A = [ 1 dt ]
// [ 0 1 ]
RealMatrix A = new Array2DRowRealMatrix(new double[][] { { 1, dt }, { 0, 1 } });
// B = [ dt^2/2 ]
// [ dt ]
RealMatrix B = new Array2DRowRealMatrix(
new double[][] { { FastMath.pow(dt, 2d) / 2d }, { dt } });
// H = [ 1 0 ]
RealMatrix H = new Array2DRowRealMatrix(new double[][] { { 1d, 0d } });
// x = [ 0 0 ]
RealVector x = new ArrayRealVector(new double[] { 0, 0 });
RealMatrix tmp = new Array2DRowRealMatrix(
new double[][] { { FastMath.pow(dt, 4d) / 4d, FastMath.pow(dt, 3d) / 2d },
{ FastMath.pow(dt, 3d) / 2d, FastMath.pow(dt, 2d) } });
// Q = [ dt^4/4 dt^3/2 ]
// [ dt^3/2 dt^2 ]
RealMatrix Q = tmp.scalarMultiply(FastMath.pow(accelNoise, 2));
// P0 = [ 1 1 ]
// [ 1 1 ]
RealMatrix P0 = new Array2DRowRealMatrix(new double[][] { { 1, 1 }, { 1, 1 } });
// R = [ measurementNoise^2 ]
RealMatrix R = new Array2DRowRealMatrix(
new double[] { FastMath.pow(measurementNoise, 2) });
// constant control input, increase velocity by 0.1 m/s per cycle
RealVector u = new ArrayRealVector(new double[] { 0.1d });
ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
MeasurementModel mm = new DefaultMeasurementModel(H, R);
KalmanFilter filter = new KalmanFilter(pm, mm);
Assert.assertEquals(1, filter.getMeasurementDimension());
Assert.assertEquals(2, filter.getStateDimension());
assertMatrixEquals(P0.getData(), filter.getErrorCovariance());
// check the initial state
double[] expectedInitialState = new double[] { 0.0, 0.0 };
assertVectorEquals(expectedInitialState, filter.getStateEstimation());
RandomGenerator rand = new JDKRandomGenerator();
RealVector tmpPNoise = new ArrayRealVector(
new double[] { FastMath.pow(dt, 2d) / 2d, dt });
// iterate 60 steps
for (int i = 0; i < 60; i++) {
filter.predict(u);
// Simulate the process
RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());
// x = A * x + B * u + pNoise
x = A.operate(x).add(B.operate(u)).add(pNoise);
// Simulate the measurement
double mNoise = measurementNoise * rand.nextGaussian();
// z = H * x + m_noise
RealVector z = H.operate(x).mapAdd(mNoise);
filter.correct(z);
// state estimate shouldn't be larger than the measurement noise
double diff = FastMath.abs(x.getEntry(0) - filter.getStateEstimation()[0]);
Assert.assertTrue(Precision.compareTo(diff, measurementNoise, 1e-6) < 0);
}
// error covariance of the velocity should be already very low (< 0.1)
Assert.assertTrue(Precision.compareTo(filter.getErrorCovariance()[1][1],
0.1d, 1e-6) < 0);
}
Example 8
| Source File: CMAESOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 9
| Source File: Arja_00181_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 10
| Source File: Elixir_0021_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 11
| Source File: CMAESOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
// XXX Line below to replace the current one in 4.0 (MATH-879).
// throw new NotStrictlyPositiveException(lambda);
lambda = 4 + (int) (3 * FastMath.log(dimension));
}
// initialize sigma
final double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
// XXX Line below to replace the current one in 4.0 (MATH-868).
// sigmaArray[i][0] = inputSigma[i];
sigmaArray[i][0] = inputSigma == null ? 0.3 : inputSigma[i];
}
final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = FastMath.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1 / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4 + mueff / dimension) /
(dimension + 4 + 2 * mueff / dimension);
cs = (mueff + 2) / (dimension + mueff + 3.);
damps = (1 + 2 * FastMath.max(0, FastMath.sqrt((mueff - 1) /
(dimension + 1)) - 1)) *
FastMath.max(0.3,
1 - dimension / (1e-6 + maxIterations)) + cs; // minor increment
ccov1 = 2 / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = FastMath.min(1 - ccov1, 2 * (mueff - 2 + 1 / mueff) /
((dimension + 2) * (dimension + 2) + mueff));
ccov1Sep = FastMath.min(1, ccov1 * (dimension + 1.5) / 3);
ccovmuSep = FastMath.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3);
chiN = FastMath.sqrt(dimension) *
(1 - 1 / ((double) 4 * dimension) + 1 / ((double) 21 * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective variables
diagD = insigma.scalarMultiply(1 / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3 * 10 * dimension / (double) lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 12
| Source File: Cardumen_00213_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 13
| Source File: Arja_00104_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 14
| Source File: Math_6_CMAESOptimizer_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
throw new NotStrictlyPositiveException(lambda);
}
// initialize sigma
final double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
sigmaArray[i][0] = inputSigma[i];
}
final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1 / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4 + mueff / dimension) /
(dimension + 4 + 2 * mueff / dimension);
cs = (mueff + 2) / (dimension + mueff + 3.);
damps = (1 + 2 * Math.max(0, Math.sqrt((mueff - 1) /
(dimension + 1)) - 1)) *
Math.max(0.3,
1 - dimension / (1e-6 + maxIterations)) + cs; // minor increment
ccov1 = 2 / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2 * (mueff - 2 + 1 / mueff) /
((dimension + 2) * (dimension + 2) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3);
chiN = Math.sqrt(dimension) *
(1 - 1 / ((double) 4 * dimension) + 1 / ((double) 21 * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective variables
diagD = insigma.scalarMultiply(1 / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3 * 10 * dimension / (double) lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 15
| Source File: KalmanFilterTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
@Test
public void testConstantAcceleration() {
// simulates a vehicle, accelerating at a constant rate (0.1 m/s)
// discrete time interval
double dt = 0.1d;
// position measurement noise (meter)
double measurementNoise = 10d;
// acceleration noise (meter/sec^2)
double accelNoise = 0.2d;
// A = [ 1 dt ]
// [ 0 1 ]
RealMatrix A = new Array2DRowRealMatrix(new double[][] { { 1, dt }, { 0, 1 } });
// B = [ dt^2/2 ]
// [ dt ]
RealMatrix B = new Array2DRowRealMatrix(
new double[][] { { Math.pow(dt, 2d) / 2d }, { dt } });
// H = [ 1 0 ]
RealMatrix H = new Array2DRowRealMatrix(new double[][] { { 1d, 0d } });
// x = [ 0 0 ]
RealVector x = new ArrayRealVector(new double[] { 0, 0 });
RealMatrix tmp = new Array2DRowRealMatrix(
new double[][] { { Math.pow(dt, 4d) / 4d, Math.pow(dt, 3d) / 2d },
{ Math.pow(dt, 3d) / 2d, Math.pow(dt, 2d) } });
// Q = [ dt^4/4 dt^3/2 ]
// [ dt^3/2 dt^2 ]
RealMatrix Q = tmp.scalarMultiply(Math.pow(accelNoise, 2));
// P0 = [ 1 1 ]
// [ 1 1 ]
RealMatrix P0 = new Array2DRowRealMatrix(new double[][] { { 1, 1 }, { 1, 1 } });
// R = [ measurementNoise^2 ]
RealMatrix R = new Array2DRowRealMatrix(
new double[] { Math.pow(measurementNoise, 2) });
// constant control input, increase velocity by 0.1 m/s per cycle
RealVector u = new ArrayRealVector(new double[] { 0.1d });
ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
MeasurementModel mm = new DefaultMeasurementModel(H, R);
KalmanFilter filter = new KalmanFilter(pm, mm);
Assert.assertEquals(1, filter.getMeasurementDimension());
Assert.assertEquals(2, filter.getStateDimension());
assertMatrixEquals(P0.getData(), filter.getErrorCovariance());
// check the initial state
double[] expectedInitialState = new double[] { 0.0, 0.0 };
assertVectorEquals(expectedInitialState, filter.getStateEstimation());
RandomGenerator rand = new JDKRandomGenerator();
RealVector tmpPNoise = new ArrayRealVector(
new double[] { Math.pow(dt, 2d) / 2d, dt });
RealVector mNoise = new ArrayRealVector(1);
// iterate 60 steps
for (int i = 0; i < 60; i++) {
filter.predict(u);
// Simulate the process
RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());
// x = A * x + B * u + pNoise
x = A.operate(x).add(B.operate(u)).add(pNoise);
// Simulate the measurement
mNoise.setEntry(0, measurementNoise * rand.nextGaussian());
// z = H * x + m_noise
RealVector z = H.operate(x).add(mNoise);
filter.correct(z);
// state estimate shouldn't be larger than the measurement noise
double diff = Math.abs(x.getEntry(0) - filter.getStateEstimation()[0]);
Assert.assertTrue(Precision.compareTo(diff, measurementNoise, 1e-6) < 0);
}
// error covariance of the velocity should be already very low (< 0.1)
Assert.assertTrue(Precision.compareTo(filter.getErrorCovariance()[1][1],
0.1d, 1e-6) < 0);
}
Example 16
| Source File: Arja_0055_s.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 17
| Source File: CMAESOptimizer.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
throw new NotStrictlyPositiveException(lambda);
}
// initialize sigma
final double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
sigmaArray[i][0] = inputSigma[i];
}
final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = FastMath.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1 / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4 + mueff / dimension) /
(dimension + 4 + 2 * mueff / dimension);
cs = (mueff + 2) / (dimension + mueff + 3.);
damps = (1 + 2 * FastMath.max(0, FastMath.sqrt((mueff - 1) /
(dimension + 1)) - 1)) *
FastMath.max(0.3,
1 - dimension / (1e-6 + maxIterations)) + cs; // minor increment
ccov1 = 2 / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = FastMath.min(1 - ccov1, 2 * (mueff - 2 + 1 / mueff) /
((dimension + 2) * (dimension + 2) + mueff));
ccov1Sep = FastMath.min(1, ccov1 * (dimension + 1.5) / 3);
ccovmuSep = FastMath.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3);
chiN = FastMath.sqrt(dimension) *
(1 - 1 / ((double) 4 * dimension) + 1 / ((double) 21 * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective variables
diagD = insigma.scalarMultiply(1 / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3 * 10 * dimension / (double) lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 18
| Source File: GaussianDPMM.java From DPMM-Clustering with GNU General Public License v3.0 | 4 votes |
|
private RealMatrix calculateMeanError(RealMatrix Psi, int kappa, int nu) {
//Reference: page 18, equation 228 at http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf
return Psi.scalarMultiply(1.0/(kappa*(nu-dimensionality+1.0)));
}
Example 19
| Source File: 1_CMAESOptimizer.java From SimFix with GNU General Public License v2.0 | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
Example 20
| Source File: JGenProg2017_00111_t.java From coming with MIT License | 4 votes |
|
/**
* Initialization of the dynamic search parameters
*
* @param guess Initial guess for the arguments of the fitness function.
*/
private void initializeCMA(double[] guess) {
if (lambda <= 0) {
lambda = 4 + (int) (3. * Math.log(dimension));
}
// initialize sigma
double[][] sigmaArray = new double[guess.length][1];
for (int i = 0; i < guess.length; i++) {
final double range = (boundaries == null) ? 1.0 : boundaries[1][i] - boundaries[0][i];
sigmaArray[i][0] = ((inputSigma == null) ? 0.3 : inputSigma[i]) / range;
}
RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);
sigma = max(insigma); // overall standard deviation
// initialize termination criteria
stopTolUpX = 1e3 * max(insigma);
stopTolX = 1e-11 * max(insigma);
stopTolFun = 1e-12;
stopTolHistFun = 1e-13;
// initialize selection strategy parameters
mu = lambda / 2; // number of parents/points for recombination
logMu2 = Math.log(mu + 0.5);
weights = log(sequence(1, mu, 1)).scalarMultiply(-1.).scalarAdd(logMu2);
double sumw = 0;
double sumwq = 0;
for (int i = 0; i < mu; i++) {
double w = weights.getEntry(i, 0);
sumw += w;
sumwq += w * w;
}
weights = weights.scalarMultiply(1. / sumw);
mueff = sumw * sumw / sumwq; // variance-effectiveness of sum w_i x_i
// initialize dynamic strategy parameters and constants
cc = (4. + mueff / dimension) /
(dimension + 4. + 2. * mueff / dimension);
cs = (mueff + 2.) / (dimension + mueff + 3.);
damps = (1. + 2. * Math.max(0, Math.sqrt((mueff - 1.) /
(dimension + 1.)) - 1.)) *
Math.max(0.3, 1. - dimension /
(1e-6 + Math.min(maxIterations, getMaxEvaluations() /
lambda))) + cs; // minor increment
ccov1 = 2. / ((dimension + 1.3) * (dimension + 1.3) + mueff);
ccovmu = Math.min(1 - ccov1, 2. * (mueff - 2. + 1. / mueff) /
((dimension + 2.) * (dimension + 2.) + mueff));
ccov1Sep = Math.min(1, ccov1 * (dimension + 1.5) / 3.);
ccovmuSep = Math.min(1 - ccov1, ccovmu * (dimension + 1.5) / 3.);
chiN = Math.sqrt(dimension) *
(1. - 1. / (4. * dimension) + 1 / (21. * dimension * dimension));
// intialize CMA internal values - updated each generation
xmean = MatrixUtils.createColumnRealMatrix(guess); // objective
// variables
diagD = insigma.scalarMultiply(1. / sigma);
diagC = square(diagD);
pc = zeros(dimension, 1); // evolution paths for C and sigma
ps = zeros(dimension, 1); // B defines the coordinate system
normps = ps.getFrobeniusNorm();
B = eye(dimension, dimension);
D = ones(dimension, 1); // diagonal D defines the scaling
BD = times(B, repmat(diagD.transpose(), dimension, 1));
C = B.multiply(diag(square(D)).multiply(B.transpose())); // covariance
historySize = 10 + (int) (3. * 10. * dimension / lambda);
fitnessHistory = new double[historySize]; // history of fitness values
for (int i = 0; i < historySize; i++) {
fitnessHistory[i] = Double.MAX_VALUE;
}
}
