Package org.apache.spark.mllib.stat
Class KernelDensity
Object
org.apache.spark.mllib.stat.KernelDensity
- All Implemented Interfaces:
Serializable
,scala.Serializable
Kernel density estimation. Given a sample from a population, estimate its probability density
function at each of the given evaluation points using kernels. Only Gaussian kernel is supported.
Scala example:
val sample = sc.parallelize(Seq(0.0, 1.0, 4.0, 4.0))
val kd = new KernelDensity()
.setSample(sample)
.setBandwidth(3.0)
val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
- See Also:
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptiondouble[]
estimate
(double[] points) Estimates probability density function at the given array of points.static double
normPdf
(double mean, double standardDeviation, double logStandardDeviationPlusHalfLog2Pi, double x) Evaluates the PDF of a normal distribution.setBandwidth
(double bandwidth) Sets the bandwidth (standard deviation) of the Gaussian kernel (default:1.0
).Sets the sample to use for density estimation (for Java users).Sets the sample to use for density estimation.
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Constructor Details
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KernelDensity
public KernelDensity()
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Method Details
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normPdf
public static double normPdf(double mean, double standardDeviation, double logStandardDeviationPlusHalfLog2Pi, double x) Evaluates the PDF of a normal distribution. -
setBandwidth
Sets the bandwidth (standard deviation) of the Gaussian kernel (default:1.0
).- Parameters:
bandwidth
- (undocumented)- Returns:
- (undocumented)
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setSample
Sets the sample to use for density estimation.- Parameters:
sample
- (undocumented)- Returns:
- (undocumented)
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setSample
Sets the sample to use for density estimation (for Java users).- Parameters:
sample
- (undocumented)- Returns:
- (undocumented)
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estimate
public double[] estimate(double[] points) Estimates probability density function at the given array of points.- Parameters:
points
- (undocumented)- Returns:
- (undocumented)
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