本文整理汇总了Java中org.apache.commons.math3.distribution.NormalDistribution.density方法的典型用法代码示例。如果您正苦于以下问题:Java NormalDistribution.density方法的具体用法?Java NormalDistribution.density怎么用?Java NormalDistribution.density使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math3.distribution.NormalDistribution
的用法示例。
在下文中一共展示了NormalDistribution.density方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: univarNormal
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
public RegDataSet univarNormal(){
NormalDistribution normalDistribution = new NormalDistribution(0,1);
RegDataSet dataSet = RegDataSetBuilder.getBuilder()
.numDataPoints(numDataPoints)
.numFeatures(1)
.dense(true)
.missingValue(false)
.build();
for (int i=0;i<numDataPoints;i++){
double featureValue = Sampling.doubleUniform(-1,1);
double label;
label = normalDistribution.density(featureValue);
label += noise.sample();
dataSet.setFeatureValue(i,0,featureValue);
dataSet.setLabel(i,label);
}
return dataSet;
}
开发者ID:cheng-li,项目名称:pyramid,代码行数:22,代码来源:RegressionSynthesizer.java示例2: loadGaussianDistrib
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
/**
* Define a symmetric pair of gaussian distributions around the origin.
* @param offset: mean of gaussian this number of bp upstream for Watson strand and this number of bp downstream for Crick strand
* @param gaussianSigma
*/
public void loadGaussianDistrib(double offsetWatson, double gaussianSigmaWatson, double offsetCrick, double gaussianSigmaCrick){
NormalDistribution wNorm = new NormalDistribution(offsetWatson, gaussianSigmaWatson);
NormalDistribution cNorm = new NormalDistribution(offsetCrick, gaussianSigmaCrick);
for(int i=left; i<=right; i++){
watsonData[i-left] = wNorm.density(i);
crickData[i-left] = cNorm.density(i);
}
makeProbabilities();
isGaussian = true;
gaussOffsetW = offsetWatson;
gaussOffsetC = offsetCrick;
gaussSigmaW = gaussianSigmaWatson;
gaussSigmaC = gaussianSigmaCrick;
}
开发者ID:seqcode,项目名称:chexmix,代码行数:20,代码来源:TagProbabilityDensity.java示例3: solveNormalNewsvendor
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
public static Newsvendor solveNormalNewsvendor (final double price, final double cost, final double mu, final double sigma) {
final NormalDistribution dist = new NormalDistribution();
return new Newsvendor(price,cost) {{
_safetyfactor = dist.inverseCumulativeProbability((price-cost)/price);
_quantity = mu+sigma*_safetyfactor;
_profit = (price-cost)*mu - price*sigma*dist.density(_safetyfactor);
}
@Override
public double getProfit(double quantity) {
double z = (quantity-mu)/sigma;
double lostSales = sigma*(dist.density(z)-z*(1-dist.cumulativeProbability(z)));
return _price*mu -_cost*quantity - _price*lostSales;
}
};
}
开发者ID:loehndorf,项目名称:scengen,代码行数:16,代码来源:Newsvendor.java示例4: createGaussianKernel
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
/**
*
* @param size Number of pixels from 3 standard deviations from one side of the Guassian to the other.
* @return
*/
public static float[][] createGaussianKernel(int size)
{
if (size == 0)
{
return new float[][] {{1f}};
}
// I want the edge of the kernel to be 3 standard deviations away from
// the middle. I also divide by 2 to get half of the size (the length from center to edge).
double sd = size / (2.0 * 3.0);
NormalDistribution dist = new NormalDistribution(0, sd);
int resultSize = (size * 2);
float[][] kernel = new float[resultSize][resultSize];
for (int x : new Range(resultSize))
{
double xDistanceFromCenter = Math.abs(resultSize / 2.0 - (x));
for (int y : new Range(resultSize))
{
double yDistanceFromCenter = Math.abs(resultSize / 2.0 - (y));
// Find the distance from the center (0,0).
double distanceFromCenter = Math.sqrt(xDistanceFromCenter
* xDistanceFromCenter + yDistanceFromCenter
* yDistanceFromCenter);
kernel[y][x] = (float) dist.density(distanceFromCenter);
}
}
normalize(kernel);
return kernel;
}
开发者ID:jeheydorn,项目名称:nortantis,代码行数:36,代码来源:ImageHelper.java示例5: calculateSpatialEntropyWeights
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
/**
* Calculate the max probability value applying the Gaussian functionon the
* probability distribution
* @param entropies : spatial entropy values of the terms
* @return max weight
*/
private static Map<String, Double> calculateSpatialEntropyWeights(
Map<String, Double> entropies){
double[] termSpatialEntropyValues = entropies
.values().stream().mapToDouble(d -> d).toArray();
NormalDistribution gd = new NormalDistribution( // Gaussian function for re-weighting
new Mean().evaluate(termSpatialEntropyValues),
new StandardDeviation().evaluate(termSpatialEntropyValues));
Double gdMax = 0.0;
Map<String, Double> weights = new HashMap<String, Double>();
for(Entry<String, Double> p:entropies.entrySet()){
double weight = gd.density(p.getValue());
weights.put(p.getKey(), weight);
if(gdMax < weight){
gdMax = weight;
}
}
for(Entry<String, Double> term:weights.entrySet()){
term.setValue(term.getValue()/gdMax);
}
return Utils.sortByValues(weights);
}
开发者ID:socialsensor,项目名称:multimedia-geotagging,代码行数:33,代码来源:Entropy.java示例6: TruncatedNormal
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
public TruncatedNormal(RandomGenerator rng, double mu, double sigma, double lowerBound, double upperBound) {
super(rng);
this.mu = mu;
this.sigma = sigma;
this.lowerBound = lowerBound;
this.upperBound = upperBound;
unnormalized = new NormalDistribution(mu, sigma);
if (upperBound < lowerBound) {
throw new IllegalArgumentException("upper bound must be no lower than lower bound");
}
lowerZ = unnormalized.cumulativeProbability(lowerBound);
upperZ = 1 - unnormalized.cumulativeProbability(upperBound);
reZ = 1 - (lowerZ + upperZ);
NormalDistribution standardNormal = new NormalDistribution();
double alpha = (lowerBound - mu) / sigma;
double beta = (upperBound - mu) / sigma;
double phiAlpha = standardNormal.density(alpha);
double phiBeta = standardNormal.density(beta);
double zPhiAlpha = standardNormal.cumulativeProbability(alpha);
double zPhiBeta = standardNormal.cumulativeProbability(beta);
double denom = (zPhiBeta - zPhiAlpha);
double c = (phiBeta - phiAlpha) / denom;
mean = mu - sigma * c;
double d = 1 - c * c;
if (phiBeta > 0.0) d -= beta * phiBeta / denom;
if (phiAlpha > 0.0) d += alpha * phiAlpha / denom;
variance = (sigma * sigma) * d;
}
开发者ID:mgormley,项目名称:pacaya,代码行数:30,代码来源:TruncatedNormal.java示例7: getNormalDistribution
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
public static List<FederationDatacenter> getNormalDistribution(int numOfDatacenters, int numHost)
{
Random r = new Random(13213);
int core_variance = maxNumOfCores - minNumOfCores;
int delta_cores = core_variance > 0 ? r.nextInt(core_variance) : 0;
List<FederationDatacenter> list = new ArrayList<FederationDatacenter>();
NormalDistribution nd = new NormalDistribution(numOfDatacenters / 2d, numOfDatacenters / 4d);
// System.out.println("Aa"+numHost);
for (int i=0; i<numOfDatacenters; i++)
{
// create the virtual processor (PE)
List<Pe> peList = new ArrayList<Pe>();
int mips = 25000;
for (int j=0; j<minNumOfCores + delta_cores; j++)
{
peList.add(new Pe(j, new PeProvisionerSimple(mips)));
}
// create the hosts
List<Host> hostList = new ArrayList<Host>();
HostProfile prof = HostProfile.getDefault();
prof.set(HostParams.RAM_AMOUNT_MB, 16*1024+"");
int num;
if (numOfDatacenters == 1)
{
num = numHost;
}
else
{
Double value = new Double(nd.density(i)) * numHost;
num = value.intValue();
}
if (num < 1)
num = 1;
for (int k=0; k<num; k++)
{
hostList.add(HostFactory.get(prof, peList));
}
// create the storage
List<Storage> storageList = new ArrayList<Storage>(); // if empty, no SAN attached
// create the datacenters
list.add(FederationDatacenterFactory.getDefault(hostList, storageList));
}
return list;
}
开发者ID:ecarlini,项目名称:smartfed,代码行数:58,代码来源:DatacenterFacilities.java示例8: probability
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
static
public double probability(GaussianDistribution gaussianDistribution, Number x){
NormalDistribution distribution = new NormalDistribution(gaussianDistribution.getMean(), Math.sqrt(gaussianDistribution.getVariance()));
return distribution.density(x.doubleValue());
}
开发者ID:jpmml,项目名称:jpmml-evaluator,代码行数:7,代码来源:DistributionUtil.java示例9: probability
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
static
private double probability(double mean, double variance, double x){
NormalDistribution distribution = new NormalDistribution(mean, Math.sqrt(variance));
return distribution.density(x);
}
开发者ID:jpmml,项目名称:jpmml-evaluator,代码行数:7,代码来源:TargetValueCountsTest.java示例10: calculateDensityProbabilityNumericAttribute
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
private double calculateDensityProbabilityNumericAttribute(RuleTerm ruleTerm, NormalDistribution normalDistribution){
// Cumulative probability for given class label
double DensityProbability = normalDistribution.density(ruleTerm.value);
return DensityProbability;
}
开发者ID:thienle2401,项目名称:G-eRules,代码行数:8,代码来源:GeRules.java示例11: findLowerUpperNumericAttributeForClassLabel
import org.apache.commons.math3.distribution.NormalDistribution; //导入方法依赖的package包/类
private double[] findLowerUpperNumericAttributeForClassLabel(NormalDistribution normalDistribution, Attribute attribute, ArrayList<Instance> instancesList){
// density probaility for each numeric value from the attribute from the dataset
TreeMap<Double, Double> densityProbabilitiesOfValues = new TreeMap<>();
for (Instance instance : instancesList) {
// get value of the attribute from an instance
double valueOfAttribute = instance.value(attribute);
// calculate density probability
double densityProbability = normalDistribution.density(valueOfAttribute);
// add to density probability map to find the best later
densityProbabilitiesOfValues.put(densityProbability, valueOfAttribute);
}
double valueWithBestDensityProbability = densityProbabilitiesOfValues.lastEntry().getValue();
TreeMap<Double, Double> smallerSideOfValueWithBestProbability = new TreeMap<>();
TreeMap<Double, Double> greaterSideOfValueWithBestProbability = new TreeMap<>();
// work out list of values and their corresponding density probability
for (Entry<Double, Double> probabilityValueEntry : densityProbabilitiesOfValues.entrySet()) {
// value greater than best value should be added to greater side
if(probabilityValueEntry.getValue() > valueWithBestDensityProbability){
greaterSideOfValueWithBestProbability.put(probabilityValueEntry.getKey(), probabilityValueEntry.getValue());
}
// otherwise, should be added to smaller side
else if (probabilityValueEntry.getValue() < valueWithBestDensityProbability){
smallerSideOfValueWithBestProbability.put(probabilityValueEntry.getKey(), probabilityValueEntry.getValue());
}
}
double[] boundsForAttribute = new double[2];
if(smallerSideOfValueWithBestProbability.isEmpty()){
boundsForAttribute[0] = valueWithBestDensityProbability;
}else{
boundsForAttribute[0] = smallerSideOfValueWithBestProbability.lastEntry().getValue();
}
if(greaterSideOfValueWithBestProbability.isEmpty()){
boundsForAttribute[1] = valueWithBestDensityProbability;
}else{
boundsForAttribute[1] = greaterSideOfValueWithBestProbability.lastEntry().getValue();
}
return boundsForAttribute;
}
开发者ID:thienle2401,项目名称:G-eRules,代码行数:62,代码来源:GeRules.java本文标签属性:
示例:示例英语
代码:代码转换器
java:java面试题
NormalDistribution:NormalDistribution
density:density中文翻译