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update gradient function for partial gradients calculation

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This commit is contained in:
wyq 2022-09-23 11:50:46 +08:00
parent 830f5972a1
commit 8a884699cb
3 changed files with 60 additions and 32 deletions
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6
meteoinfo-lab/milconfig.xml
View File

@ -1,7 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<MeteoInfo File="milconfig.xml" Type="configurefile">
<Path OpenPath="D:\Working\MIScript\Jython\mis\common_math\optimize">
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\others"/>
<Path OpenPath="D:\Temp\test\cs">
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\toolbox"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\toolbox\miml\test"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\toolbox\miml"/>
@ -16,6 +15,7 @@
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\array"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math"/>
<RecentFolder Folder="D:\Working\MIScript\Jython\mis\common_math\optimize"/>
<RecentFolder Folder="D:\Temp\test\cs"/>
</Path>
<File>
<OpenedFiles>
@ -36,5 +36,5 @@
</Font>
<LookFeel DockWindowDecorated="true" LafDecorated="true" Name="FlatDarkLaf"/>
<Figure DoubleBuffering="true"/>
<Startup MainFormLocation="-7,0" MainFormSize="1397,779"/>
<Startup MainFormLocation="-7,0" MainFormSize="1498,856"/>
</MeteoInfo>

2
meteoinfo-math/src/main/java/org/meteoinfo/math/interpolate/InterpUtil.java
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@ -1505,7 +1505,7 @@ public class InterpUtil {
}
/**
* Interpolate with nearest method
* Interpolate with the nearest method
*
* @param x_s scatter X array
* @param y_s scatter Y array

84
meteoinfo-math/src/main/java/org/meteoinfo/math/optimize/MyParametricUnivariateFunction.java
View File

@ -14,6 +14,7 @@ public class MyParametricUnivariateFunction implements ParametricUnivariateFunct
/** A number close to zero, between machine epsilon and its square root. */
double EPSILON = 1E-8;
boolean useDerivativeStructure;
/**
* Constructor
@ -22,9 +23,37 @@ public class MyParametricUnivariateFunction implements ParametricUnivariateFunct
* @param stepSize Step size for difference calculation
*/
public MyParametricUnivariateFunction(ParamUnivariateFunction function, int nbPoints, double stepSize) {
this(function, nbPoints, stepSize, true);
}
/**
* Constructor
* @param function ParamUnivariateFunction
* @param nbPoints Number of points for difference calculation
* @param stepSize Step size for difference calculation
*/
public MyParametricUnivariateFunction(ParamUnivariateFunction function, int nbPoints, double stepSize,
boolean useDerivativeStructure) {
this.function = function;
this.nbPoints = nbPoints;
this.stepSize = stepSize;
this.useDerivativeStructure = useDerivativeStructure;
}
/**
* Get whether using DerivativeStructure
* @return Whether using DerivativeStructure
*/
public boolean isUseDerivativeStructure() {
return this.useDerivativeStructure;
}
/**
* Set whether using DerivativeStructure
* @param value Whether using DerivativeStructure
*/
public void setUseDerivativeStructure(boolean value) {
this.useDerivativeStructure = value;
}
@Override
@ -41,29 +70,14 @@ public class MyParametricUnivariateFunction implements ParametricUnivariateFunct
@Override
public double[] gradient(double x, double ... parameters) {
function.setParameters(parameters);
double fx = function.value(x);
int n = parameters.length;
double[] gradients = new double[n];
for (int i = 0; i < n; i++) {
gradients[i] = Double.POSITIVE_INFINITY;
if (this.useDerivativeStructure) {
return gradientDerivativeStructure(x, parameters);
} else {
return gradientSimple(x, parameters);
}
FiniteDifferencesDifferentiator differentiator =
new FiniteDifferencesDifferentiator(5, 0.01);
for (int i = 0; i < n; i++) {
UnivariateFunction uf = new MyUnivariateFunction(function, x, i, parameters);
UnivariateDifferentiableFunction f = differentiator.differentiate(uf);
DerivativeStructure y = f.value(new DerivativeStructure(1, 1, 0, parameters[i]));
gradients[i] = y.getPartialDerivative(1);
}
function.setParameters(parameters);
return gradients;
}
public double[] gradient_bak(double x, double ... parameters) {
public double[] gradientSimple(double x, double ... parameters) {
function.setParameters(parameters);
double fx = function.value(x);
@ -92,15 +106,29 @@ public class MyParametricUnivariateFunction implements ParametricUnivariateFunct
function.setParameters(parameters);
return gradients;
}
/*final double a = parameters[0];
final double b = parameters[1];
final double c = parameters[2];
final double[] grad = new double[3];
grad[0] = Math.exp(-b * x);
grad[1] = -a * x * grad[0];
grad[2] = 1;
return grad;*/
public double[] gradientDerivativeStructure(double x, double ... parameters) {
function.setParameters(parameters);
double fx = function.value(x);
int n = parameters.length;
double[] gradients = new double[n];
for (int i = 0; i < n; i++) {
gradients[i] = Double.POSITIVE_INFINITY;
}
FiniteDifferencesDifferentiator differentiator =
new FiniteDifferencesDifferentiator(5, 0.01);
for (int i = 0; i < n; i++) {
UnivariateFunction uf = new MyUnivariateFunction(function, x, i, parameters);
UnivariateDifferentiableFunction f = differentiator.differentiate(uf);
DerivativeStructure y = f.value(new DerivativeStructure(1, 1, 0, parameters[i]));
gradients[i] = y.getPartialDerivative(1);
}
function.setParameters(parameters);
return gradients;
}
private class MyUnivariateFunction implements UnivariateFunction {