List of usage examples for org.apache.commons.math3.random UncorrelatedRandomVectorGenerator UncorrelatedRandomVectorGenerator
public UncorrelatedRandomVectorGenerator(int dimension, NormalizedRandomGenerator generator)
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
/** * Item parameters and true results from Kolen's STUIRT program. *//*from ww w . ja va 2 s .c o m*/ @Test public void haebaraTest1() { System.out.println("Haebara Test 1: Actual Distribution"); LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); irmX.put("i1", new Irm3PL(0.4551, -0.7101, 0.2087, 1.7)); irmX.put("i2", new Irm3PL(0.5839, -0.8567, 0.2038, 1.7)); irmX.put("i3", new Irm3PL(0.7544, 0.0212, 0.1600, 1.7)); irmX.put("i4", new Irm3PL(0.6633, 0.0506, 0.1240, 1.7)); irmX.put("i5", new Irm3PL(1.0690, 0.9610, 0.2986, 1.7)); irmX.put("i6", new Irm3PL(0.9672, 0.1950, 0.0535, 1.7)); irmX.put("i7", new Irm3PL(0.3479, 2.2768, 0.1489, 1.7)); irmX.put("i8", new Irm3PL(1.4579, 1.0241, 0.2453, 1.7)); irmX.put("i9", new Irm3PL(1.8811, 1.4062, 0.1992, 1.7)); irmX.put("i10", new Irm3PL(0.7020, 2.2401, 0.0853, 1.7)); irmX.put("i11", new Irm3PL(1.4080, 1.5556, 0.0789, 1.7)); irmX.put("i12", new Irm3PL(1.2993, 2.1589, 0.1075, 1.7)); irmY.put("i1", new Irm3PL(0.4416, -1.3349, 0.1559, 1.7)); irmY.put("i2", new Irm3PL(0.5730, -1.3210, 0.1913, 1.7)); irmY.put("i3", new Irm3PL(0.5987, -0.7098, 0.1177, 1.7)); irmY.put("i4", new Irm3PL(0.6041, -0.3539, 0.0818, 1.7)); irmY.put("i5", new Irm3PL(0.9902, 0.5320, 0.3024, 1.7)); irmY.put("i6", new Irm3PL(0.8081, -0.1156, 0.0648, 1.7)); irmY.put("i7", new Irm3PL(0.4140, 2.5538, 0.2410, 1.7)); irmY.put("i8", new Irm3PL(1.3554, 0.5811, 0.2243, 1.7)); irmY.put("i9", new Irm3PL(1.0417, 0.9392, 0.1651, 1.7)); irmY.put("i10", new Irm3PL(0.6336, 1.8960, 0.0794, 1.7)); irmY.put("i11", new Irm3PL(1.1347, 1.0790, 0.0630, 1.7)); irmY.put("i12", new Irm3PL(0.9255, 2.1337, 0.1259, 1.7)); double[] points = { -4.0000, -3.1110, -2.2220, -1.3330, -0.4444, 0.4444, 1.3330, 2.2220, 3.1110, 4.0000 }; double[] xDensity = { 0.0001008, 0.002760, 0.03021, 0.1420, 0.3149, 0.3158, 0.1542, 0.03596, 0.003925, 0.0001862 }; double[] yDensity = { 0.0001173, 0.003242, 0.03449, 0.1471, 0.3148, 0.3110, 0.1526, 0.03406, 0.002510, 0.0001116 }; UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1Q2); hb.setPrecision(6); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] hbCoefficients = optimum.getPoint(); hb.setIntercept(hbCoefficients[0]); hb.setScale(hbCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + hbCoefficients[0] + " A = " + hbCoefficients[1]); assertEquals(" Intercept test", -0.471281, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.067800, hb.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.IrtScaleLinking.java
public void computeCoefficients() { raschFamily = checkRaschModel();//from w w w . j a v a 2 s . c o m ms.setPrecision(precision); mm.setPrecision(precision); hb.setPrecision(precision); sl.setPrecision(precision); if (raschFamily) { double[] sv = { mm.getIntercept() }; UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14); JDKRandomGenerator g = new JDKRandomGenerator(); //Haebara method MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 5, g);//Five random starts to Brent optimizer. UnivariatePointValuePair hbPair = optimizer.optimize(new MaxEval(500), new UnivariateObjectiveFunction(hb), GoalType.MINIMIZE, new SearchInterval(-4, 4), new InitialGuess(sv)); hb.setIntercept(hbPair.getPoint()); hb.setScale(1.0); fHB = hbPair.getValue(); //Stocking-Lord method UnivariatePointValuePair slPair = optimizer.optimize(new MaxEval(500), new UnivariateObjectiveFunction(sl), GoalType.MINIMIZE, new SearchInterval(-4, 4), new InitialGuess(sv)); sl.setIntercept(slPair.getPoint()); sl.setScale(1.0); fSL = slPair.getValue(); } else { double[] hbStartValues = { mm.getIntercept(), mm.getScale() }; double[] slStartValues = { mm.getIntercept(), mm.getScale() }; if (useUncmin) { DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer(); try { optimizer.minimize(hb, hbStartValues); double[] param = optimizer.getParameters(); fHB = optimizer.getFunctionValue(); hb.setIntercept(param[0]); if (param.length > 1) { hb.setScale(param[1]); } else { hb.setScale(1.0);//Rasch family of models } optimizer.minimize(sl, slStartValues); param = optimizer.getParameters(); fSL = optimizer.getFunctionValue(); sl.setIntercept(param[0]); if (param.length > 1) { sl.setScale(param[1]); } else { sl.setScale(1.0);//Rasch family of models } } catch (UncminException ex) { ex.printStackTrace(); } } else { int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair hbOptimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(hbStartValues)); double[] hbCoefficients = hbOptimum.getPoint(); hb.setIntercept(hbCoefficients[0]); hb.setScale(hbCoefficients[1]); fHB = hbOptimum.getValue(); PointValuePair slOptimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(slStartValues)); double[] slCoefficients = slOptimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); fSL = slOptimum.getValue(); } } }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
/** * Item parameters, quadrature points and weights, and "true" linking coefficients were obtained * from example1 in Kolen's STUIRT program. However, the Stocking-Lord procedure was changed in * the STUIRT example to produce symmetric optimization of the criterion function (i.e. SY BI BI * instead of SY BI NO).//from w ww. java 2s. co m * * This test runs the Stocking-Lord procedure twice: One with the UNCMIN optimizer and once with * the BOBYQA optimizer. Results from each are compared to teh STUIRT results and to each other. * */ @Test public void stockingLordTest0() { System.out.println("StockingLordMethod Test 0: Actual Distribution"); LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); irmX.put("i1", new Irm3PL(0.4551, -0.7101, 0.2087, 1.7)); irmX.put("i2", new Irm3PL(0.5839, -0.8567, 0.2038, 1.7)); irmX.put("i3", new Irm3PL(0.7544, 0.0212, 0.1600, 1.7)); irmX.put("i4", new Irm3PL(0.6633, 0.0506, 0.1240, 1.7)); irmX.put("i5", new Irm3PL(1.0690, 0.9610, 0.2986, 1.7)); irmX.put("i6", new Irm3PL(0.9672, 0.1950, 0.0535, 1.7)); irmX.put("i7", new Irm3PL(0.3479, 2.2768, 0.1489, 1.7)); irmX.put("i8", new Irm3PL(1.4579, 1.0241, 0.2453, 1.7)); irmX.put("i9", new Irm3PL(1.8811, 1.4062, 0.1992, 1.7)); irmX.put("i10", new Irm3PL(0.7020, 2.2401, 0.0853, 1.7)); irmX.put("i11", new Irm3PL(1.4080, 1.5556, 0.0789, 1.7)); irmX.put("i12", new Irm3PL(1.2993, 2.1589, 0.1075, 1.7)); irmY.put("i1", new Irm3PL(0.4416, -1.3349, 0.1559, 1.7)); irmY.put("i2", new Irm3PL(0.5730, -1.3210, 0.1913, 1.7)); irmY.put("i3", new Irm3PL(0.5987, -0.7098, 0.1177, 1.7)); irmY.put("i4", new Irm3PL(0.6041, -0.3539, 0.0818, 1.7)); irmY.put("i5", new Irm3PL(0.9902, 0.5320, 0.3024, 1.7)); irmY.put("i6", new Irm3PL(0.8081, -0.1156, 0.0648, 1.7)); irmY.put("i7", new Irm3PL(0.4140, 2.5538, 0.2410, 1.7)); irmY.put("i8", new Irm3PL(1.3554, 0.5811, 0.2243, 1.7)); irmY.put("i9", new Irm3PL(1.0417, 0.9392, 0.1651, 1.7)); irmY.put("i10", new Irm3PL(0.6336, 1.8960, 0.0794, 1.7)); irmY.put("i11", new Irm3PL(1.1347, 1.0790, 0.0630, 1.7)); irmY.put("i12", new Irm3PL(0.9255, 2.1337, 0.1259, 1.7)); double[] points = { -4.0000, -3.1110, -2.2220, -1.3330, -0.4444, 0.4444, 1.3330, 2.2220, 3.1110, 4.0000 }; double[] xDensity = { 0.0001008, 0.002760, 0.03021, 0.1420, 0.3149, 0.3158, 0.1542, 0.03596, 0.003925, 0.0001862 }; double[] yDensity = { 0.0001173, 0.003242, 0.03449, 0.1471, 0.3148, 0.3110, 0.1526, 0.03406, 0.002510, 0.0001116 }; UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1Q2); sl.setPrecision(6); double[] startValues = { 0, 1 }; double f = 0; //Run test with UNCMIN optimizer DefaultUncminOptimizer optimizer = new DefaultUncminOptimizer(); double[] param1 = null; double[] param2 = null; try { optimizer.minimize(sl, startValues); param1 = optimizer.getParameters(); f = optimizer.getFunctionValue(); sl.setIntercept(param1[0]); sl.setScale(param1[1]); } catch (UncminException ex) { ex.printStackTrace(); } //Check UNCMIN values against results from STUIRT. System.out.println(" UNCMIN Optimization"); System.out.println(" Iterations: "); System.out.println(" fmin: " + f); System.out.println(" B = " + sl.getIntercept() + " A = " + sl.getScale()); assertEquals(" Intercept test", -0.487619, sl.getIntercept(), 1e-6); assertEquals(" Scale test", 1.083417, sl.getScale(), 1e-6); //Run test with BOBYQA optimizer int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer2 = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer2.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); param2 = optimum.getPoint(); sl.setIntercept(param2[0]); sl.setScale(param2[1]); //Check BOBYQA values against results from STUIRT. System.out.println(); System.out.println(" BOBYQA Optimization"); System.out.println(" Iterations: " + optimizer2.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + param2[0] + " A = " + param2[1]); assertEquals(" Intercept test", -0.487619, sl.getIntercept(), 1e-6); assertEquals(" Scale test", 1.083417, sl.getScale(), 1e-6); //Compare results from each optimizer assertEquals(" Intercept test", param1[0], param2[0], 1e-6); assertEquals(" Scale test", param1[1], param2[1], 1e-6); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
@Test public void haearaTest2() { System.out.println("Haebara Test 2: Uniform Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/*from w ww. j a v a2s . c om*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UniformDistributionApproximation uniform = new UniformDistributionApproximation(-4, 4, 10); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, uniform, uniform, EquatingCriterionType.Q1Q2); hb.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); hb.setIntercept(slCoefficients[0]); hb.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4303, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0894, hb.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest1() { System.out.println("StockingLordMethod Test 1: Actual Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;//from w ww .j ava 2 s.c o m for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4788, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0911, sl.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
@Test public void haebaraTest3() { System.out.println("Haebara Test 3: Normal Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/*from ww w.jav a2 s. c o m*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } NormalDistributionApproximation normal = new NormalDistributionApproximation(0, 1, -4, 4, 10); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, normal, normal, EquatingCriterionType.Q1Q2); hb.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); hb.setIntercept(slCoefficients[0]); hb.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4658, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0722, hb.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest2() { System.out.println("StockingLordMethod Test 2: Uniform Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;//from ww w .j ava 2s . co m for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UniformDistributionApproximation uniform = new UniformDistributionApproximation(-4, 4, 10); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, uniform, uniform, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4532, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0962, sl.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
@Test public void haebaraTest4() { System.out.println("Haebara Test 4: Actual Distribution -backwards"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;//from ww w .j ava 2 s .com for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1); hb.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] hbCoefficients = optimum.getPoint(); hb.setIntercept(hbCoefficients[0]); hb.setScale(hbCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + hbCoefficients[0] + " A = " + hbCoefficients[1]); assertEquals(" Intercept test", -0.4710, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0798, hb.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest3() { System.out.println("StockingLordMethod Test 3: Normal Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/*from w w w.j a v a 2s . c o m*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } NormalDistributionApproximation normal = new NormalDistributionApproximation(0, 1, -4, 4, 10); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, normal, normal, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4788, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0901, sl.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest4() { System.out.println("StockingLordMethod Test 4: Actual Distribution -backwards"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/* w w w.j av a2 s. c o m*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4834, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0967, sl.getScale(), 1e-4); System.out.println(); }