cern.jet.random.Normal Java Examples
The following examples show how to use
cern.jet.random.Normal.
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Example #1
| Source File: CrazyCharactersSample.java From micrometer with Apache License 2.0 | 6 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
String badCounterName = "\"\';^*()!~`_./?a{counter}:with123 weirdChars";
String badTagName = "\"\';^*()!~`_./?a{tag}:with123 weirdChars";
String badValueName = "\"\';^*()!~`_./?a{value}:with123 weirdChars";
Counter counter = registry.counter(badCounterName,
badTagName,
badValueName);
RandomEngine r = new MersenneTwister64(0);
Normal dist = new Normal(0, 1, r);
Flux.interval(Duration.ofMillis(10))
.doOnEach(d -> {
if (dist.nextDouble() + 0.1 > 0) {
counter.increment();
}
})
.blockLast();
}
Example #2
| Source File: CounterSample.java From micrometer with Apache License 2.0 | 6 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
Counter counter = registry.counter("counter", "method", "actual");
AtomicInteger n = new AtomicInteger(0);
registry.more().counter("counter", Tags.of("method", "function"), n);
RandomEngine r = new MersenneTwister64(0);
Normal dist = new Normal(0, 1, r);
Flux.interval(Duration.ofMillis(10))
.doOnEach(d -> {
if (dist.nextDouble() + 0.1 > 0) {
counter.increment();
n.incrementAndGet();
}
})
.blockLast();
}
Example #3
| Source File: IndependentNormalDistributionSampler.java From beast-mcmc with GNU Lesser General Public License v2.1 | 6 votes |
|
public IndependentNormalDistributionSampler(Variable variable, NormalDistributionModel model, double weight, boolean updateAllIndependently) {
this.variable = variable;
this.model = model;
this.updateAllIndependently = updateAllIndependently;
setWeight(weight);
if (TRY_COLT) {
randomEngine = new MersenneTwister(MathUtils.nextInt());
//create standard normal distribution, internal states will be bypassed anyway
coltNormal = new Normal(0.0, 1.0, randomEngine);
} else {
//no random draw with specified mean and stdev implemented in the normal distribution in BEAST (as far as I know)
throw new RuntimeException("Normal distribution in BEAST still needs a random sampler.");
}
}
Example #4
| Source File: GibbsIndependentNormalDistributionOperator.java From beast-mcmc with GNU Lesser General Public License v2.1 | 6 votes |
|
public GibbsIndependentNormalDistributionOperator(Variable variable, NormalDistributionModel model, double weight, boolean updateAllIndependently) {
this.variable = variable;
this.model = model;
this.updateAllIndependently = updateAllIndependently;
setWeight(weight);
if (TRY_COLT) {
randomEngine = new MersenneTwister(MathUtils.nextInt());
//create standard normal distribution, internal states will be bypassed anyway
//takes mean and standard deviation
coltNormal = new Normal(0.0, 1.0, randomEngine);
} else {
//no random draw with specified mean and stdev implemented in the normal distribution in BEAST (as far as I know)
throw new RuntimeException("Normal distribution in BEAST still needs a random sampler.");
}
}
Example #5
| Source File: GibbsIndependentJointNormalGammaOperator.java From beast-mcmc with GNU Lesser General Public License v2.1 | 6 votes |
|
public GibbsIndependentJointNormalGammaOperator(Variable mean, Variable precision, NormalDistributionModel model, GammaDistribution gamma, double weight, boolean updateAllIndependently) {
this.mean = mean;
this.precision = precision;
this.model = model;
this.gamma = gamma;
this.updateAllIndependently = updateAllIndependently;
setWeight(weight);
if (TRY_COLT) {
randomEngine = new MersenneTwister(MathUtils.nextInt());
//create standard normal distribution, internal states will be bypassed anyway
//takes mean and standard deviation
coltNormal = new Normal(0.0, 1.0, randomEngine);
//coltGamma = new Gamma(gamma.getShape(), 1.0/gamma.getScale(), randomEngine);
} else {
//no random draw with specified mean and stdev implemented in the normal distribution in BEAST (as far as I know)
throw new RuntimeException("Normal distribution in BEAST still needs a random sampler.");
}
}
Example #6
| Source File: Frugal2UTest.java From streaminer with Apache License 2.0 | 6 votes |
|
@Test
public void testOffer() throws QuantilesException {
System.out.println("offer");
double[] quantiles = new double[]{0.05, 0.25, 0.5, 0.75, 0.95};
Frugal2U instance = new Frugal2U(quantiles, 0);
ExactQuantilesAll<Integer> exact = new ExactQuantilesAll<Integer>();
RandomEngine r = new MersenneTwister64(0);
Normal dist = new Normal(100, 50, r);
int numSamples = 1000;
for(int i = 0; i < numSamples; ++i) {
int num = (int) Math.max(0, dist.nextDouble());
instance.offer(num);
exact.offer(num);
}
System.out.println("Q\tEst\tExact");
for (double q : quantiles) {
System.out.println(q + "\t" + instance.getQuantile(q) + "\t" + exact.getQuantile(q));
}
}
Example #7
| Source File: TimerSample.java From micrometer with Apache License 2.0 | 5 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
Timer timer = Timer.builder("timer")
.publishPercentileHistogram()
.publishPercentiles(0.5, 0.95, 0.99)
.serviceLevelObjectives(Duration.ofMillis(275), Duration.ofMillis(300), Duration.ofMillis(500))
.distributionStatisticExpiry(Duration.ofSeconds(10))
.distributionStatisticBufferLength(3)
.register(registry);
AtomicLong totalCount = new AtomicLong();
AtomicLong totalTime = new AtomicLong();
FunctionTimer.builder("ftimer", totalCount, t -> totalCount.get(), t -> totalTime.get(), TimeUnit.MILLISECONDS)
.register(registry);
RandomEngine r = new MersenneTwister64(0);
Normal incomingRequests = new Normal(0, 1, r);
Normal duration = new Normal(250, 50, r);
AtomicInteger latencyForThisSecond = new AtomicInteger(duration.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond.set(duration.nextInt()))
.subscribe();
// the potential for an "incoming request" every 10 ms
Flux.interval(Duration.ofMillis(10))
.doOnEach(d -> {
if (incomingRequests.nextDouble() + 0.4 > 0) {
// pretend the request took some amount of time, such that the time is
// distributed normally with a mean of 250ms
int latency = latencyForThisSecond.get();
timer.record(latency, TimeUnit.MILLISECONDS);
totalTime.addAndGet(latency);
totalCount.incrementAndGet();
}
})
.blockLast();
}
Example #8
| Source File: GaugeSample.java From micrometer with Apache License 2.0 | 5 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
AtomicLong n = new AtomicLong();
registry.gauge("gauge", Tags.of("k", "v"), n);
registry.gauge("gauge", Tags.of("k", "v1"), n, n2 -> n2.get() - 1);
RandomEngine r = new MersenneTwister64(0);
Normal dist = new Normal(0, 10, r);
Flux.interval(Duration.ofSeconds(5))
.doOnEach(d -> n.set(Math.abs(dist.nextInt())))
.blockLast();
}
Example #9
| Source File: LongTaskTimerSample.java From micrometer with Apache License 2.0 | 5 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
LongTaskTimer timer = registry.more().longTaskTimer("longTaskTimer");
RandomEngine r = new MersenneTwister64(0);
Normal incomingRequests = new Normal(0, 1, r);
Normal duration = new Normal(30, 50, r);
AtomicInteger latencyForThisSecond = new AtomicInteger(duration.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond.set(duration.nextInt()))
.subscribe();
final Map<LongTaskTimer.Sample, CountDownLatch> tasks = new ConcurrentHashMap<>();
// the potential for an "incoming request" every 10 ms
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> {
if (incomingRequests.nextDouble() + 0.4 > 0 && tasks.isEmpty()) {
int taskDur;
while ((taskDur = duration.nextInt()) < 0);
synchronized (tasks) {
tasks.put(timer.start(), new CountDownLatch(taskDur));
}
}
synchronized (tasks) {
for (Map.Entry<LongTaskTimer.Sample, CountDownLatch> e : tasks.entrySet()) {
e.getValue().countDown();
if (e.getValue().getCount() == 0) {
e.getKey().stop();
tasks.remove(e.getKey());
}
}
}
})
.blockLast();
}
Example #10
| Source File: TimerMaximumThroughputSample.java From micrometer with Apache License 2.0 | 5 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
Timer timer = Timer.builder("timer")
.publishPercentileHistogram()
// .publishPercentiles(0.5, 0.95, 0.99)
.serviceLevelObjectives(Duration.ofMillis(275), Duration.ofMillis(300), Duration.ofMillis(500))
.distributionStatisticExpiry(Duration.ofSeconds(10))
.distributionStatisticBufferLength(3)
.register(registry);
RandomEngine r = new MersenneTwister64(0);
Normal duration = new Normal(250, 50, r);
AtomicInteger latencyForThisSecond = new AtomicInteger(duration.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond.set(duration.nextInt()))
.subscribe();
Stream<Integer> infiniteStream = Stream.iterate(0, i -> (i + 1) % 1000);
Flux.fromStream(infiniteStream)
.parallel(4)
.runOn(Schedulers.parallel())
.doOnEach(d -> timer.record(latencyForThisSecond.get(), TimeUnit.MILLISECONDS))
.subscribe();
Flux.never().blockLast();
}
Example #11
| Source File: FunctionTimerSample.java From micrometer with Apache License 2.0 | 5 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
Timer timer = Timer.builder("timer")
.publishPercentiles(0.5, 0.95)
.register(registry);
Object placeholder = new Object();
AtomicLong totalTimeNanos = new AtomicLong(0);
AtomicLong totalCount = new AtomicLong(0);
FunctionTimer.builder("ftimer", placeholder, p -> totalCount.get(), p -> totalTimeNanos.get(), TimeUnit.NANOSECONDS)
.register(registry);
RandomEngine r = new MersenneTwister64(0);
Normal incomingRequests = new Normal(0, 1, r);
Normal duration = new Normal(250, 50, r);
AtomicInteger latencyForThisSecond = new AtomicInteger(duration.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond.set(duration.nextInt()))
.subscribe();
// the potential for an "incoming request" every 10 ms
Flux.interval(Duration.ofMillis(10))
.doOnEach(d -> {
if (incomingRequests.nextDouble() + 0.4 > 0) {
// pretend the request took some amount of time, such that the time is
// distributed normally with a mean of 250ms
timer.record(latencyForThisSecond.get(), TimeUnit.MILLISECONDS);
totalCount.incrementAndGet();
totalTimeNanos.addAndGet((long) TimeUtils.millisToUnit(latencyForThisSecond.get(), TimeUnit.NANOSECONDS));
}
})
.blockLast();
}
Example #12
| Source File: SimulatedEndpointInstrumentation.java From micrometer with Apache License 2.0 | 4 votes |
|
public static void main(String[] args) {
MeterRegistry registry = SampleConfig.myMonitoringSystem();
Timer e1Success = Timer.builder("http.server.requests")
.tags("uri", "/api/bar")
.tags("response", "200")
.publishPercentiles(0.5, 0.95)
.register(registry);
Timer e2Success = Timer.builder("http.server.requests")
.tags("uri", "/api/foo")
.tags("response", "200")
.publishPercentiles(0.5, 0.95)
.register(registry);
Timer e1Fail = Timer.builder("http.server.requests")
.tags("uri", "/api/bar")
.tags("response", "500")
.publishPercentiles(0.5, 0.95)
.register(registry);
Timer e2Fail = Timer.builder("http.server.requests")
.tags("uri", "/api/foo")
.tags("response", "500")
.publishPercentiles(0.5, 0.95)
.register(registry);
RandomEngine r = new MersenneTwister64(0);
Normal incomingRequests = new Normal(0, 1, r);
Normal successOrFail = new Normal(0, 1, r);
Normal duration = new Normal(250, 50, r);
Normal duration2 = new Normal(250, 50, r);
AtomicInteger latencyForThisSecond = new AtomicInteger(duration.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond.set(duration.nextInt()))
.subscribe();
AtomicInteger latencyForThisSecond2 = new AtomicInteger(duration2.nextInt());
Flux.interval(Duration.ofSeconds(1))
.doOnEach(d -> latencyForThisSecond2.set(duration2.nextInt()))
.subscribe();
// the potential for an "incoming request" every 10 ms
Flux.interval(Duration.ofMillis(10))
.doOnEach(d -> {
// are we going to receive a request for /api/foo?
if (incomingRequests.nextDouble() + 0.4 > 0) {
if (successOrFail.nextDouble() + 0.8 > 0) {
// pretend the request took some amount of time, such that the time is
// distributed normally with a mean of 250ms
e1Success.record(latencyForThisSecond.get(), TimeUnit.MILLISECONDS);
}
else {
e1Fail.record(latencyForThisSecond.get(), TimeUnit.MILLISECONDS);
}
}
})
.subscribe();
// the potential for an "incoming request" every 1 ms
Flux.interval(Duration.ofMillis(1))
.doOnEach(d -> {
// are we going to receive a request for /api/bar?
if (incomingRequests.nextDouble() + 0.4 > 0) {
if (successOrFail.nextDouble() + 0.8 > 0) {
// pretend the request took some amount of time, such that the time is
// distributed normally with a mean of 250ms
e2Success.record(latencyForThisSecond2.get(), TimeUnit.MILLISECONDS);
}
else {
e2Fail.record(latencyForThisSecond2.get(), TimeUnit.MILLISECONDS);
}
}
})
.blockLast();
}
Example #13
| 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 #14
| 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 #15
| 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 #16
| 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);
}
