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49 changes: 49 additions & 0 deletions
49
src/main/java/net/haesleinhuepf/clijx/incubator/optimize/GradientDescentOptimizer.java
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| package net.haesleinhuepf.clijx.incubator.optimize; | ||
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| import org.apache.commons.math3.analysis.MultivariateFunction; | ||
| import org.apache.commons.math3.optim.InitialGuess; | ||
| import org.apache.commons.math3.optim.MaxEval; | ||
| import org.apache.commons.math3.optim.PointValuePair; | ||
| import org.apache.commons.math3.optim.SimpleValueChecker; | ||
| import org.apache.commons.math3.optim.nonlinear.scalar.*; | ||
| import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer; | ||
| import org.apache.commons.math3.random.GaussianRandomGenerator; | ||
| import org.apache.commons.math3.random.JDKRandomGenerator; | ||
| import org.apache.commons.math3.random.RandomVectorGenerator; | ||
| import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator; | ||
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| import java.util.Arrays; | ||
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| import static net.haesleinhuepf.clijx.incubator.optimize.OptimizationUtilities.range; | ||
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| public class GradientDescentOptimizer implements Optimizer { | ||
| int iterations = 6; | ||
| public GradientDescentOptimizer() { | ||
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| } | ||
| public GradientDescentOptimizer(int iterations) { | ||
| this.iterations = iterations; | ||
| } | ||
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| @Override | ||
| public double[] optimize(double[] current, Workflow workflow, int[] parameter_index_map, MultivariateFunction fitness) { | ||
| GradientMultivariateOptimizer underlying = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-10, 1e-10)); | ||
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| for (int i = 0; i < iterations; i++) { | ||
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| double[] stdDev = range(current.length, workflow.getNumericParameterNames(), parameter_index_map, Math.pow(2, iterations / 2 - i - 1)); | ||
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| RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(current, stdDev, new GaussianRandomGenerator(new JDKRandomGenerator())); | ||
| int nbStarts = 10; | ||
| MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, nbStarts, generator); | ||
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| PointValuePair solution = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(fitness), new ObjectiveFunctionGradient(new GradientOfMultivariateFunction(fitness, stdDev)), GoalType.MINIMIZE, new InitialGuess(current)); | ||
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| current = solution.getKey(); | ||
| System.out.println("Intermediate optimum: " + Arrays.toString(current)); | ||
| } | ||
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| return current; | ||
| } | ||
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| } |
33 changes: 33 additions & 0 deletions
33
src/main/java/net/haesleinhuepf/clijx/incubator/optimize/GradientOfMultivariateFunction.java
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| package net.haesleinhuepf.clijx.incubator.optimize; | ||
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| import org.apache.commons.math3.analysis.MultivariateFunction; | ||
| import org.apache.commons.math3.analysis.MultivariateVectorFunction; | ||
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| public class GradientOfMultivariateFunction implements MultivariateVectorFunction { | ||
| private MultivariateFunction function; | ||
| private double[] steps; | ||
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| public GradientOfMultivariateFunction(MultivariateFunction function, double[] steps) { | ||
| this.function = function; | ||
| this.steps = steps; | ||
| } | ||
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| @Override | ||
| public double[] value(double[] point) throws IllegalArgumentException { | ||
| double[] result = new double[point.length]; | ||
| for (int i = 0; i < result.length; i++) { | ||
| double[] input = new double[point.length]; | ||
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| System.arraycopy(point, 0, input, 0, input.length); | ||
| input[i] -= steps[i]; | ||
| double a = function.value(input); | ||
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| System.arraycopy(point, 0, input, 0, input.length); | ||
| input[i] += steps[i]; | ||
| double b = function.value(input); | ||
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| result[i] = (a - b) / steps[i] / 2; | ||
| } | ||
| return result; | ||
| } | ||
| } |
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7 changes: 7 additions & 0 deletions
7
src/main/java/net/haesleinhuepf/clijx/incubator/optimize/Optimizer.java
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,7 @@ | ||
| package net.haesleinhuepf.clijx.incubator.optimize; | ||
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| import org.apache.commons.math3.analysis.MultivariateFunction; | ||
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| public interface Optimizer { | ||
| double[] optimize(double[] current, Workflow workflow, int[] parameter_index_map, MultivariateFunction fitness); | ||
| } |
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53 changes: 53 additions & 0 deletions
53
src/main/java/net/haesleinhuepf/clijx/incubator/optimize/SimplexOptimizer.java
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| Original file line number | Diff line number | Diff line change |
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| package net.haesleinhuepf.clijx.incubator.optimize; | ||
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| import net.imagej.ops.Op; | ||
| import org.apache.commons.math3.analysis.MultivariateFunction; | ||
| import org.apache.commons.math3.optim.InitialGuess; | ||
| import org.apache.commons.math3.optim.MaxEval; | ||
| import org.apache.commons.math3.optim.PointValuePair; | ||
| import org.apache.commons.math3.optim.nonlinear.scalar.GoalType; | ||
| import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction; | ||
| import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex; | ||
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| import java.util.Arrays; | ||
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| import static net.haesleinhuepf.clijx.incubator.optimize.OptimizationUtilities.range; | ||
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| public class SimplexOptimizer implements Optimizer { | ||
| int iterations = 6; | ||
| public SimplexOptimizer() { | ||
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| } | ||
| public SimplexOptimizer(int iterations) { | ||
| this.iterations = iterations; | ||
| } | ||
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| @Override | ||
| public double[] optimize(double[] current, Workflow workflow, int[] parameter_index_map, MultivariateFunction fitness) { | ||
| org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer optimizer = new org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer(-1, 1e-5); | ||
| for (int i = 0; i < iterations; i++) { | ||
| NelderMeadSimplex simplex = makeSimplexOptimizer(current.length, workflow.getNumericParameterNames(), parameter_index_map, Math.pow(2, iterations / 2 - i - 1)); | ||
| //double[] lowerBounds = new double[simplex.getDimension()]; | ||
| //double[] upperBounds = new double[simplex.getDimension()]; | ||
| //for (int b = 0; b < upperBounds.length; b++) { | ||
| // upperBounds[b] = Double.MAX_VALUE; | ||
| //} | ||
| //, new SimpleBounds(lowerBounds, upperBounds) | ||
| PointValuePair solution = optimizer.optimize(new MaxEval(1000), new InitialGuess(current), simplex, new ObjectiveFunction(fitness), GoalType.MINIMIZE); | ||
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| current = solution.getKey(); | ||
| System.out.println("Intermediate optimum: " + Arrays.toString(current)); | ||
| } | ||
| return current; | ||
| } | ||
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| private static NelderMeadSimplex makeSimplexOptimizer(int numDimensions, String[] numericParameterNames, int[] parameter_index_map, double factor) { | ||
| double[] steps = range(numDimensions, numericParameterNames, parameter_index_map, factor); | ||
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| System.out.println("Step lengths: " + Arrays.toString(steps) ); | ||
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| NelderMeadSimplex simplex = new NelderMeadSimplex(steps); | ||
| return simplex; | ||
| } | ||
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| } |
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