我正在尝试使用Commons Math版本3.6.1制作一个4参数Hill方程。到2018年6月20日，我也尝试使用4.0-SNAPSHOT版本。我得到了相同的结果。我有一个简单的测试运行，并没有抛出异常。然而，更复杂的数据位失败。我从一些处理Hill / Sigmoidal拟合的网站中搜集了数据。我不知道接下来要做什么来解决这个问题，有什么建议吗？
我得到了这个：
org.apache.commons.math3.exception.ConvergenceException: illegal state: unable to perform Q.R decomposition on the 9x4 jacobian matrix
at org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer.qrDecomposition(LevenbergMarquardtOptimizer.java:975)
at org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer.optimize(LevenbergMarquardtOptimizer.java:342)
at org.apache.commons.math3.fitting.AbstractCurveFitter.fit(AbstractCurveFitter.java:63)
at com.adarza.curve.fit.FourParamHillFitterTest.largerDataTest(FourParamHillFitterTest.java:42)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57)
at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:68)
at com.intellij.rt.execution.junit.IdeaTestRunner$Repeater.startRunnerWithArgs(IdeaTestRunner.java:47)
at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:242)
at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:70)
我的代码如下。
初始参数：
import lombok.Data;
@Data
public class FourParamHillEqInitParams {
double initialHighVarD = Double.MIN_VALUE;
double initialLowVarA = Double.MAX_VALUE;
double midInflectionPointVarC = 0.0;
double initialHillSlopeVarB = 0.0;
}
曲线钳工：
import org.apache.commons.math3.fitting.AbstractCurveFitter;
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresBuilder;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem;
import org.apache.commons.math3.linear.DiagonalMatrix;
import java.util.*;
public class FourParamHillFitter extends AbstractCurveFitter {
protected LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points) {
final int len = points.size();
final double[] target = new double[len];
final double[] weights = new double[len];
FourParamHillEqInitParams initialGuesses = guessInitialCoefficents(points);
final double[] initialGuess = { initialGuesses.initialLowVarA,
initialGuesses.initialHillSlopeVarB,
initialGuesses.midInflectionPointVarC,
initialGuesses.initialHighVarD };
System.out.println("Initial Guesses: " + Arrays.toString(initialGuess));
int i = 0;
for(WeightedObservedPoint point : points) {
target[i] = point.getY();
weights[i] = point.getWeight();
i += 1;
}
final AbstractCurveFitter.TheoreticalValuesFunction model = new
AbstractCurveFitter.TheoreticalValuesFunction(new FourParamHillFunction(), points);
return new LeastSquaresBuilder().
maxEvaluations(Integer.MAX_VALUE).
maxIterations(Integer.MAX_VALUE).
start(initialGuess).
target(target).
weight(new DiagonalMatrix(weights)).
model(model.getModelFunction(), model.getModelFunctionJacobian()).
build();
}
private FourParamHillEqInitParams guessInitialCoefficents(Collection<WeightedObservedPoint> points) {
FourParamHillEqInitParams initParams = new FourParamHillEqInitParams();
double sum = 0.0;
for (Iterator<WeightedObservedPoint> iterator = points.iterator(); iterator.hasNext(); ) {
WeightedObservedPoint p = iterator.next();
if (p.getY() > initParams.initialHighVarD) {
initParams.initialHighVarD = p.getY();
}
if (p.getY() < initParams.initialLowVarA){
initParams.initialLowVarA = p.getY();
}
sum += p.getY();
}
initParams.midInflectionPointVarC = sum / points.size(); // mean
initParams.initialHillSlopeVarB = 25.0;
return initParams;
}
}
功能：
import org.apache.commons.math3.analysis.ParametricUnivariateFunction;
import org.apache.commons.math3.analysis.differentiation.DerivativeStructure;
public class FourParamHillFunction implements ParametricUnivariateFunction {
public double value(double x, double... parm) {
// return parameters[0] * Math.pow(t, parameters[1]) * Math.exp(-parameters[2] * t);
double a = parm[0];
double b = parm[1];
double c = parm[2];
double d = parm[3];
return d+ ((a-d)/ (1 + Math.pow( (x/c), b)));
}
// Jacobian matrix of the above. In this case, this is just an array of
// partial derivatives of the above function, with one element for each parameter.
public double[] gradient(double t, double... parameters) {
final double a = parameters[0];
final double b = parameters[1];
final double c = parameters[2];
final double d = parameters[3];
// Jacobian Matrix Edit
// Using Derivative Structures...
// constructor takes 4 arguments - the number of parameters in your
// equation to be differentiated (4 in this case), the order of
// differentiation for the DerivativeStructure, the index of the
// parameter represented by the DS, and the value of the parameter itself
DerivativeStructure aDev = new DerivativeStructure(4, 1, 0, a);
DerivativeStructure bDev = new DerivativeStructure(4, 1, 1, b);
DerivativeStructure cDev = new DerivativeStructure(4, 1, 2, c);
DerivativeStructure dDev = new DerivativeStructure(4, 1, 3, d);
// define the equation to be differentiated using another DerivativeStructure
// DerivativeStructure y = aDev.multiply(DerivativeStructure.pow(t, bDev))
// .multiply(cDev.negate().multiply(t).exp());
//y = d+(a-d)/(1+(x/c)^b)
DerivativeStructure numerator = aDev.subtract(dDev);
DerivativeStructure xPart = cDev.reciprocal().multiply(t).pow(bDev);
DerivativeStructure denominator = xPart.add(1.0);
DerivativeStructure y = dDev.add( numerator.divide(denominator) );
// then return the partial derivatives required
// notice the format, 4 arguments for the method since 4 parameters were
// specified first order derivative of the first parameter, then the second,
// then the third
return new double[] {
y.getPartialDerivative(1, 0, 0, 0),
y.getPartialDerivative(0, 1, 0, 0),
y.getPartialDerivative(0, 0, 1, 0),
y.getPartialDerivative(0, 0, 0, 1)
};
}
}
测试：
import org.apache.commons.math3.fitting.WeightedObservedPoint;
import org.junit.Test;
import java.util.ArrayList;
import java.util.Arrays;
import static org.junit.Assert.assertEquals;
public class FourParamHillFitterTest {
@Test
public void basicTest() {
FourParamHillFitter fitter = new FourParamHillFitter();
double[] xValues = { 1.0, 2.0, 3.0, 4.0, 5.0 };
double[] yValues = { 1.0, 1.2, 3.0, 7.0, 7.0 };
ArrayList<WeightedObservedPoint> points = createPointsFromArray(xValues, yValues);
double coeffs[] = fitter.fit(points);
System.out.println(Arrays.toString(coeffs));
assertEquals(4, coeffs.length);
assertEquals(1.099995, coeffs[0], 0.1);
assertEquals(31.03071, coeffs[1], 0.01);
assertEquals(3.072862, coeffs[2], 0.01);
assertEquals(7.000825, coeffs[3], 0.01);
}
@Test
public void largerDataTest() {
FourParamHillFitter fitter = new FourParamHillFitter();
double[] xValues = { 0, 1.3, 2.8, 5, 10.2, 16.5, 21.3, 31.8, 52.2};
double[] yValues = { 0.1, 0.5, 0.9, 2.6, 7.1, 12.3, 15.3, 20.4, 24.4 };
ArrayList<WeightedObservedPoint> points = createPointsFromArray(xValues, yValues);
final double coeffs[] = fitter.fit(points);
System.out.println(Arrays.toString(coeffs));
assertEquals(4, coeffs.length);
assertEquals(0.1536, coeffs[0], 0.01);
assertEquals(1.7718, coeffs[1], 0.01);
assertEquals(19.3494, coeffs[2], 0.01);
assertEquals(28.4479, coeffs[3], 0.01);
}
public ArrayList<WeightedObservedPoint> createPointsFromArray(double[] xs, double[] ys){
ArrayList<WeightedObservedPoint> points = new ArrayList<WeightedObservedPoint>();
for(int i=0; i < xs.length; i++){
WeightedObservedPoint point = new WeightedObservedPoint(0, xs[i], ys[i]);
points.add(point);
}
return points;
}
}
答案 0 :(得分：2)
不确定，但我想是因为，如果您计算函数的梯度，则：
d+((a-d)/(1 + Math.pow( (x/c), b)))
b的偏导数包含一个Log()（自然对数）表达式
-(((a - d)*(x/c)^b*Log(x/c))/((x/c)^b + 1)^2)
Log(0)是-Infinity。
因此，请避免x值等于0。像0这样的0.0001附近的x值可能会有所帮助。
在我自己的项目中，我在FindFit函数中实现了符号渐变，这也可以改善结果。