Java Code Examples for org.apache.commons.math3.linear.MatrixUtils#createColumnRealMatrix()
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org.apache.commons.math3.linear.MatrixUtils#createColumnRealMatrix() .
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Example 1
| Source File: OLSTrendLine.java From hortonmachine with GNU General Public License v3.0 | 6 votes |
|
@Override
public void setValues( double[] y, double[] x ) {
if (x.length != y.length) {
throw new IllegalArgumentException(
String.format("The numbers of y and x values must be equal (%d != %d)", y.length, x.length));
}
double[][] xData = new double[x.length][];
for( int i = 0; i < x.length; i++ ) {
// the implementation determines how to produce a vector of predictors from a single x
xData[i] = xVector(x[i]);
}
if (logY()) { // in some models we are predicting ln y, so we replace each y with ln y
y = Arrays.copyOf(y, y.length); // user might not be finished with the array we were
// given
for( int i = 0; i < x.length; i++ ) {
y[i] = Math.log(y[i]);
}
}
ols = new OLSMultipleLinearRegression();
ols.setNoIntercept(true); // let the implementation include a constant in xVector if desired
ols.newSampleData(y, xData); // provide the data to the model
coef = MatrixUtils.createColumnRealMatrix(ols.estimateRegressionParameters());
}
Example 2
| Source File: Arja_00177_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 3
| Source File: Arja_00181_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_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 5
| Source File: Arja_0028_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 6
| 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 7
| Source File: Math_6_CMAESOptimizer_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) {
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 8
| Source File: Cardumen_0037_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 9
| Source File: Arja_00154_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: Arja_00177_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 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 * 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 12
| Source File: Math_18_CMAESOptimizer_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 13
| Source File: 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) {
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: 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 = 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: 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 16
| Source File: Cardumen_00161_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 = (getMaxEvaluations()) / (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: Cardumen_00213_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 18
| Source File: Cardumen_0037_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 19
| Source File: Cardumen_00211_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 20
| Source File: Cardumen_0035_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;
}
}
