Java Code Examples for cern.jet.random.Normal#setMean()
The following examples show how to use
cern.jet.random.Normal#setMean() .
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Example 1
| Source File: VMPNormalTest.java From toolbox with Apache License 2.0 | 4 votes |
|
public static void test2() throws IOException, ClassNotFoundException{
Variables variables = new Variables();
Variable varA = variables.newGaussianVariable("A");
Variable varB = variables.newGaussianVariable("B");
DAG dag = new DAG(variables);
dag.getParentSet(varB).addParent(varA);
BayesianNetwork bn = new BayesianNetwork(dag);
Normal distA = bn.getConditionalDistribution(varA);
ConditionalLinearGaussian distB = bn.getConditionalDistribution(varB);
distA.setMean(1);
distA.setVariance(0.25);
distB.setIntercept(1);
//distB.setCoeffParents(new double[]{1});
distB.setCoeffForParent(varA, 1);
distB.setVariance(0.25);
if (Main.VERBOSE) System.out.println(bn.toString());
double meanPA = distA.getMean();
double sdPA = distA.getSd();
double b0PB = distB.getIntercept();
//double b1PB = distB.getCoeffParents()[0];
double b1PB = distB.getCoeffForParent(varA);
double sdPB = distB.getSd();
VMP vmp = new VMP();
vmp.setTestELBO(true);
vmp.setMaxIter(100);
vmp.setThreshold(0.0001);
vmp.setModel(bn);
EF_Normal qADist = ((EF_Normal) vmp.getNodes().get(0).getQDist());
EF_Normal qBDist = ((EF_Normal) vmp.getNodes().get(1).getQDist());
double meanQA= qADist.getMomentParameters().get(0);
double sdQA= Math.sqrt(qADist.getMomentParameters().get(1) - qADist.getMomentParameters().get(0) * qADist.getMomentParameters().get(0));
double meanQB= qBDist.getMomentParameters().get(0);
double sdQB= Math.sqrt(qBDist.getMomentParameters().get(1) - qBDist.getMomentParameters().get(0)*qBDist.getMomentParameters().get(0));
vmp.runInference();
Normal postA = vmp.getPosterior(varA);
if (Main.VERBOSE) System.out.println("P(A) = " + postA.toString());
Normal postB = ((Normal)vmp.getPosterior(varB));
if (Main.VERBOSE) System.out.println("P(B) = " + postB.toString());
boolean convergence = false;
double oldvalue = 0;
while(!convergence){
sdQA = Math.sqrt(Math.pow(b1PB*b1PB/(sdPB*sdPB) + 1.0/(sdPA*sdPA),-1));
meanQA = sdQA*sdQA*(b1PB*meanQB/(sdPB*sdPB) - b0PB*b1PB/(sdPB*sdPB) + meanPA/(sdPA*sdPA));
sdQB = sdPB;
meanQB = sdQB*sdQB*(b0PB/(sdPB*sdPB) + b1PB*meanQA/(sdPB*sdPB));
if (Math.abs(sdQA + meanQA + sdQB + meanQB - oldvalue) < 0.001) {
convergence = true;
}
oldvalue = sdQA + meanQA + sdQB + meanQB ;
}
if (Main.VERBOSE) System.out.println("Mean and Sd of A: " + meanQA +", " + sdQA );
if (Main.VERBOSE) System.out.println("Mean and Sd of B: " + meanQB +", " + sdQB );
Assert.assertEquals(postA.getMean(),meanQA,0.01);
Assert.assertEquals(postA.getSd(),sdQA,0.01);
Assert.assertEquals(postB.getMean(),meanQB,0.01);
Assert.assertEquals(postB.getSd(),sdQB,0.01);
}
Example 2
| Source File: VMPNormalTest.java From toolbox with Apache License 2.0 | 4 votes |
|
public static void test4() throws IOException, ClassNotFoundException{
Variables variables = new Variables();
Variable varA = variables.newGaussianVariable("A");
Variable varB = variables.newGaussianVariable("B");
Variable varC = variables.newGaussianVariable("C");
DAG dag = new DAG(variables);
dag.getParentSet(varC).addParent(varA);
dag.getParentSet(varC).addParent(varB);
BayesianNetwork bn = new BayesianNetwork(dag);
Normal distA = bn.getConditionalDistribution(varA);
Normal distB = bn.getConditionalDistribution(varB);
ConditionalLinearGaussian distC = bn.getConditionalDistribution(varC);
distA.setMean(1);
distA.setVariance(0.25);
distB.setMean(1.2);
distB.setVariance(0.64);
distC.setIntercept(1);
//distC.setCoeffParents(new double[]{1, 1});
distC.setCoeffForParent(varA, 1);
distC.setCoeffForParent(varB, 1);
distC.setVariance(0.25);
if (Main.VERBOSE) System.out.println(bn.toString());
double meanPA = distA.getMean();
double sdPA = distA.getSd();
double meanPB = distB.getMean();
double sdPB = distB.getSd();
double b0PC = distC.getIntercept();
//double b1PC = distC.getCoeffParents()[0];
//double b2PC = distC.getCoeffParents()[1];
double b1PC = distC.getCoeffForParent(varA);
double b2PC = distC.getCoeffForParent(varB);
double sdPC = distC.getSd();
VMP vmp = new VMP();
vmp.setTestELBO(true);
vmp.setMaxIter(100);
vmp.setThreshold(0.0001);
vmp.setModel(bn);
EF_Normal qADist = ((EF_Normal) vmp.getNodes().get(0).getQDist());
EF_Normal qBDist = ((EF_Normal) vmp.getNodes().get(1).getQDist());
EF_Normal qCDist = ((EF_Normal) vmp.getNodes().get(2).getQDist());
double meanQA= qADist.getMomentParameters().get(0);
double sdQA= Math.sqrt(qADist.getMomentParameters().get(1) - qADist.getMomentParameters().get(0) * qADist.getMomentParameters().get(0));
double meanQB= qBDist.getMomentParameters().get(0);
double sdQB= Math.sqrt(qBDist.getMomentParameters().get(1) - qBDist.getMomentParameters().get(0) * qBDist.getMomentParameters().get(0));
double meanQC= 0.7;
HashMapAssignment assignment = new HashMapAssignment(1);
assignment.setValue(varC, 0.7);
vmp.setEvidence(assignment);
vmp.runInference();
Normal postA = vmp.getPosterior(varA);
if (Main.VERBOSE) System.out.println("P(A) = " + postA.toString());
Normal postB = vmp.getPosterior(varB);
if (Main.VERBOSE) System.out.println("P(B) = " + postB.toString());
boolean convergence = false;
double oldvalue = 0;
while(!convergence){
sdQA = Math.sqrt(Math.pow(b1PC*b1PC/(sdPC*sdPC) + 1.0/(sdPA*sdPA),-1));
meanQA = sdQA*sdQA*(b1PC*meanQC/(sdPC*sdPC) - b0PC*b1PC/(sdPC*sdPC) - b1PC*b2PC*meanQB/(sdPC*sdPC) + meanPA/(sdPA*sdPA));
sdQB = Math.sqrt(Math.pow(b2PC*b2PC/(sdPC*sdPC) + 1.0/(sdPB*sdPB),-1));
meanQB = sdQB*sdQB*(b2PC*meanQC/(sdPC*sdPC) - b0PC*b2PC/(sdPC*sdPC) - b1PC*b2PC*meanQA/(sdPC*sdPC) + meanPB/(sdPB*sdPB));
if (Math.abs(sdQA + meanQA + sdQB + meanQB - oldvalue) < 0.001) {
convergence = true;
}
oldvalue = sdQA + meanQA + sdQB + meanQB;
}
if (Main.VERBOSE) System.out.println("Mean and Sd of A: " + meanQA +", " + sdQA );
if (Main.VERBOSE) System.out.println("Mean and Sd of B: " + meanQB +", " + sdQB );
Assert.assertEquals(postA.getMean(),meanQA,0.01);
Assert.assertEquals(postA.getSd(),sdQA,0.01);
Assert.assertEquals(postB.getMean(),meanQB,0.01);
Assert.assertEquals(postB.getSd(),sdQB,0.01);
}
Example 3
| Source File: VMPNormalTest.java From toolbox with Apache License 2.0 | 4 votes |
|
public static void test6() throws IOException, ClassNotFoundException{
Variables variables = new Variables();
Variable varA = variables.newGaussianVariable("A");
Variable varB = variables.newGaussianVariable("B");
Variable varC = variables.newGaussianVariable("C");
DAG dag = new DAG(variables);
dag.getParentSet(varA).addParent(varC);
dag.getParentSet(varB).addParent(varC);
BayesianNetwork bn = new BayesianNetwork(dag);
ConditionalLinearGaussian distA = bn.getConditionalDistribution(varA);
ConditionalLinearGaussian distB = bn.getConditionalDistribution(varB);
Normal distC = bn.getConditionalDistribution(varC);
distA.setIntercept(1);
//distA.setCoeffParents(new double[]{1});
distA.setCoeffForParent(varC, 1);
distA.setVariance(0.25);
distB.setIntercept(1.5);
//distB.setCoeffParents(new double[]{1});
distB.setCoeffForParent(varC, 1);
distB.setVariance(0.64);
distC.setMean(1);
distC.setVariance(0.25);
if (Main.VERBOSE) System.out.println(bn.toString());
double b0PA = distA.getIntercept();
//double b1PA = distA.getCoeffParents()[0];
double b1PA = distA.getCoeffForParent(varC);
double sdPA = distA.getSd();
double b0PB = distB.getIntercept();
//double b1PB = distB.getCoeffParents()[0];
double b1PB = distB.getCoeffForParent(varC);
double sdPB = distB.getSd();
double meanPC = distC.getMean();
double sdPC = distC.getSd();
VMP vmp = new VMP();
vmp.setTestELBO(true);
vmp.setMaxIter(100);
vmp.setThreshold(0.0001);
vmp.setModel(bn);
EF_Normal qADist = ((EF_Normal) vmp.getNodes().get(0).getQDist());
EF_Normal qBDist = ((EF_Normal) vmp.getNodes().get(1).getQDist());
EF_Normal qCDist = ((EF_Normal) vmp.getNodes().get(2).getQDist());
double meanQA= 0.7;
double meanQB= 0.2;
HashMapAssignment assignment = new HashMapAssignment(1);
assignment.setValue(varA, 0.7);
assignment.setValue(varB, 0.2);
double meanQC= qCDist.getMomentParameters().get(0);
double sdQC= Math.sqrt(qCDist.getMomentParameters().get(1) - qCDist.getMomentParameters().get(0)*qCDist.getMomentParameters().get(0));
vmp.setEvidence(assignment);
vmp.runInference();
Normal postC = ((Normal)vmp.getPosterior(varC));
if (Main.VERBOSE) System.out.println("P(C) = " + postC.toString());
boolean convergence = false;
double oldvalue = 0;
while(!convergence){
sdQC = Math.sqrt(Math.pow(b1PA*b1PA/(sdPA*sdPA) + b1PB*b1PB/(sdPB*sdPB) + 1.0/(sdPC*sdPC),-1));
meanQC = sdQC*sdQC*(b1PA*meanQA/(sdPA*sdPA) - b0PA*b1PA/(sdPA*sdPA) + b1PB*meanQB/(sdPB*sdPB) - b0PB*b1PB/(sdPB*sdPB) + meanPC/(sdPC*sdPC));
if (Math.abs(sdQC + meanQC - oldvalue) < 0.001) {
convergence = true;
}
oldvalue = sdQC + meanQC;
}
if (Main.VERBOSE) System.out.println("Mean and Sd of C: " + meanQC +", " + sdQC );
Assert.assertEquals(postC.getMean(),meanQC,0.01);
Assert.assertEquals(postC.getSd(),sdQC,0.01);
}
Example 4
| Source File: VMPNormalTest.java From toolbox with Apache License 2.0 | 4 votes |
|
public static void test8() throws IOException, ClassNotFoundException{
Variables variables = new Variables();
Variable varA = variables.newGaussianVariable("A");
Variable varB = variables.newGaussianVariable("B");
Variable varC = variables.newGaussianVariable("C");
DAG dag = new DAG(variables);
dag.getParentSet(varB).addParent(varA);
dag.getParentSet(varC).addParent(varB);
BayesianNetwork bn = new BayesianNetwork(dag);
Normal distA = bn.getConditionalDistribution(varA);
ConditionalLinearGaussian distB = bn.getConditionalDistribution(varB);
ConditionalLinearGaussian distC = bn.getConditionalDistribution(varC);
distA.setMean(1);
distA.setVariance(0.25);
distB.setIntercept(1);
//distB.setCoeffParents(new double[]{1});
distB.setCoeffForParent(varA, 1);
distB.setVariance(0.04);
distC.setIntercept(1);
//distC.setCoeffParents(new double[]{1});
distC.setCoeffForParent(varB, 1);
distC.setVariance(0.25);
if (Main.VERBOSE) System.out.println(bn.toString());
double meanPA = distA.getMean();
double sdPA = distA.getSd();
double b0PB = distB.getIntercept();
//double b1PB = distB.getCoeffParents()[0];
double b1PB = distB.getCoeffForParent(varA);
double sdPB = distB.getSd();
double b0PC = distC.getIntercept();
//double b1PC = distC.getCoeffParents()[0];
double b1PC = distC.getCoeffForParent(varB);
double sdPC = distC.getSd();
VMP vmp = new VMP();
vmp.setTestELBO(true);
vmp.setMaxIter(100);
vmp.setThreshold(0.0001);
vmp.setModel(bn);
EF_Normal qADist = ((EF_Normal) vmp.getNodes().get(0).getQDist());
EF_Normal qBDist = ((EF_Normal) vmp.getNodes().get(1).getQDist());
EF_Normal qCDist = ((EF_Normal) vmp.getNodes().get(2).getQDist());
double meanQA= qADist.getMomentParameters().get(0);
double sdQA= Math.sqrt(qADist.getMomentParameters().get(1) - qADist.getMomentParameters().get(0) * qADist.getMomentParameters().get(0));
double meanQC= qCDist.getMomentParameters().get(0);
double sdQC= Math.sqrt(qCDist.getMomentParameters().get(1) - qCDist.getMomentParameters().get(0)*qCDist.getMomentParameters().get(0));
double meanQB= 0.4;
HashMapAssignment assignment = new HashMapAssignment(1);
assignment.setValue(varB, 0.4);
vmp.setEvidence(assignment);
vmp.runInference();
Normal postA = vmp.getPosterior(varA);
if (Main.VERBOSE) System.out.println("P(A) = " + postA.toString());
Normal postC = ((Normal)vmp.getPosterior(varC));
if (Main.VERBOSE) System.out.println("P(C) = " + postC.toString());
boolean convergence = false;
double oldvalue = 0;
while(!convergence){
sdQA = Math.sqrt(Math.pow(b1PB*b1PB/(sdPB*sdPB) + 1.0/(sdPA*sdPA),-1));
meanQA = sdQA*sdQA*(b1PB*meanQB/(sdPB*sdPB) - b0PB*b1PB/(sdPB*sdPB) + meanPA/(sdPA*sdPA));
sdQC = sdPC;
meanQC = sdQC*sdQC*(b0PC/(sdPC*sdPC) + b1PC*meanQB/(sdPC*sdPC));
if (Math.abs(sdQA + meanQA + sdQC + meanQC - oldvalue) < 0.001) {
convergence = true;
}
oldvalue = sdQA + meanQA + + sdQC + meanQC;
}
if (Main.VERBOSE) System.out.println("Mean and Sd of A: " + meanQA +", " + sdQA );
if (Main.VERBOSE) System.out.println("Mean and Sd of C: " + meanQC +", " + sdQC );
Assert.assertEquals(postA.getMean(),meanQA,0.01);
Assert.assertEquals(postA.getSd(),sdQA,0.01);
Assert.assertEquals(postC.getMean(),meanQC,0.01);
Assert.assertEquals(postC.getSd(),sdQC,0.01);
}
