KernelDensityEstimate

java.lang.Object
- be.tarsos.util.KernelDensityEstimate

public class KernelDensityEstimate
extends java.lang.Object

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`KernelDensityEstimate.Cosine`
`static class`	`KernelDensityEstimate.GaussianKernel` A Gaussian kernel function.
`static interface`	`KernelDensityEstimate.KDECorrelation`
`static interface`	`KernelDensityEstimate.Kernel` Defines a kernel.
`static class`	`KernelDensityEstimate.Overlap`
`static class`	`KernelDensityEstimate.RectangularKernel` A rectangular kernel function.

Field Summary

Fields
Modifier and Type Field and Description

protected double[] accumulator

protected KernelDensityEstimate.Kernel kernel

Fields
Modifier and Type	Field and Description
`protected double[]`	`accumulator`
`protected KernelDensityEstimate.Kernel`	`kernel`

Constructor Summary

Constructors
Constructor and Description
`KernelDensityEstimate(KernelDensityEstimate.Kernel kernel, double[] accumulator)`
`KernelDensityEstimate(KernelDensityEstimate.Kernel kernel, int size)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`add(double value)` Add the kernel to an accumulator for each value.
`void`	`add(KernelDensityEstimate other)` Adds a KDE to this accumulator
`void`	`clear()` Clears the data in the accumulator.
`double`	`correlation(KernelDensityEstimate other, int positionsToShiftOther)` Calculate a correlation with another KernelDensityEstimate.
`double[]`	`getEstimate()` Returns the current estimate.
`double`	`getMaxElement()`
`double[]`	`getMedianFilteredEstimate(int medianFilterLength)`
`double`	`getSumFreq()` Returns the sum of all estimates in the accumulator.
`double`	`getValue(int index)` Return the value for the accumulator at a certain index.
`KernelDensityEstimate`	`map(int size)` Map the kernel density estimate to another size.
`void`	`max(KernelDensityEstimate other)` Takes the maximum of the value in the accumulator for two kde's.
`static double`	`median(double[] m)`
`void`	`normalize()` Sets the maximum value in accumulator to 1.0
`void`	`normalize(double newMaxvalue)` Sets a new maximum bin value.
`double`	`optimalCorrelation(KernelDensityEstimate.KDECorrelation correlationMeasure, KernelDensityEstimate other)` Calculates the optimal correlation between two Kernel Density Estimates by shifting and searching for optimal correlation.
`double`	`optimalCorrelation(KernelDensityEstimate other)` Calculates the optimal correlation between two Kernel Density Estimates by shifting and searching for optimal correlation.
`void`	`pdfify()` Sets the area under the curve to 1.0.
`void`	`remove(double value)` Remove a value from the kde, removes a kernel at the specified position.
`void`	`shift(int shift)` Shift the accumulator x positions.
`int`	`shiftForOptimalCorrelation(KernelDensityEstimate.KDECorrelation correlationMeasure, KernelDensityEstimate other)` Calculates how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
`int`	`shiftForOptimalCorrelation(KernelDensityEstimate other)` Calculates how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
`int`	`size()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - accumulator
```
protected final double[] accumulator
```
  - kernel
```
protected final KernelDensityEstimate.Kernel kernel
```
- Constructor Detail
  - KernelDensityEstimate
```
public KernelDensityEstimate(KernelDensityEstimate.Kernel kernel,
                             int size)
```
  - KernelDensityEstimate
```
public KernelDensityEstimate(KernelDensityEstimate.Kernel kernel,
                             double[] accumulator)
```
- Method Detail
  - add
```
public void add(double value)
```
    Add the kernel to an accumulator for each value. When a kernel with a width of 7 is added at 1 cents it has influence on the bins from 1200 - 7 * 10 + 1 to 1 + 7 * 10 so from 1131 to 71. To make the modulo calculation easy 1200 is added to each value: -69 % 1200 is -69, (-69 + 1200) % 1200 is the expected 1131. If you know what I mean. This algorithm computes O(width * n) sums with n the number of annotations and width the number of bins affected, rather efficient.
    
    Parameters:
    
    value - The value to add.
  - remove
```
public void remove(double value)
```
    Remove a value from the kde, removes a kernel at the specified position.
    
    Parameters:
    
    value - The value to remove.
  - shift
```
public void shift(int shift)
```
    Shift the accumulator x positions.
    
    Parameters:
    
    shift - The number of positions the accumulator should be shifted.
  - getEstimate
```
public double[] getEstimate()
```
    Returns the current estimate.
    
    Returns:
    
    The current estimate. To prevent unauthorized modification a clone of the array is returned. Please cache appropriately.
  - map
```
public KernelDensityEstimate map(int size)
```
    Map the kernel density estimate to another size. E.g. a KDE with 4 values mapped to two is done by iterating the 4 elements and adding them on modulo 2 places. Here 1 + 4 = 5, 2 + 9 = 11
```
 (1 2 4 9).map(2) = (5 11) 
 
```
    Parameters:
    
    size - The new size for the KDE.
    
    Returns:
    
    A new KDE with the contents of the original mapped to the new size.
  - getValue
```
public double getValue(int index)
```
    Return the value for the accumulator at a certain index.
    
    Parameters:
    
    index - The index.
    
    Returns:
    
    The value for the accumulator at a certain index.
  - size
```
public int size()
```
    Returns:
    
    The size of the accumulator.
  - getSumFreq
```
public double getSumFreq()
```
    Returns the sum of all estimates in the accumulator.
    
    Returns:
    
    The total sum of all estimates.
  - normalize
```
public void normalize()
```
    Sets the maximum value in accumulator to 1.0
  - normalize
```
public void normalize(double newMaxvalue)
```
    Sets a new maximum bin value.
    
    Parameters:
    
    newMaxvalue - The new maximum bin value.
  - getMaxElement
```
public double getMaxElement()
```
    Returns:
    
    the maximum element in the accumulator;
  - pdfify
```
public void pdfify()
```
    Sets the area under the curve to 1.0. In essence every value is divided by getSumFreq(). As per definition of a probability density function.
  - clear
```
public void clear()
```
    Clears the data in the accumulator.
  - max
```
public void max(KernelDensityEstimate other)
```
    Takes the maximum of the value in the accumulator for two kde's.
    
    Parameters:
    
    other - The other kde of the same size.
  - add
```
public void add(KernelDensityEstimate other)
```
    Adds a KDE to this accumulator
    
    Parameters:
    
    other - The other KDE of the same size.
  - correlation
```
public double correlation(KernelDensityEstimate other,
                          int positionsToShiftOther)
```
    Calculate a correlation with another KernelDensityEstimate. The index of the other estimates are shifted by a number which can be zero (or positive or negative). Beware: the index wraps around the edges.
    
    This and the other KernelDensityEstimate should have the same size.
    
    Parameters:
    
    other - The other estimate.
    
    positionsToShiftOther - The number of positions to shift the estimate.
    
    Returns:
    
    A value between 0 and 1 representing how similar both estimates are. 1 means total correlation, 0 no correlation.
  - shiftForOptimalCorrelation
```
public int shiftForOptimalCorrelation(KernelDensityEstimate other)
```
    Calculates how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
    
    Parameters:
    
    other - The other KernelDensityEstimate.
    
    Returns:
    
    A number between 0 (inclusive) and the size of the KernelDensityEstimate (exclusive) which represents how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
  - optimalCorrelation
```
public double optimalCorrelation(KernelDensityEstimate other)
```
    Calculates the optimal correlation between two Kernel Density Estimates by shifting and searching for optimal correlation.
    
    Parameters:
    
    other - The other KernelDensityEstimate.
    
    Returns:
    
    A value between 0 and 1 representing how similar both estimates are. 1 means total correlation, 0 no correlation.
  - optimalCorrelation
```
public double optimalCorrelation(KernelDensityEstimate.KDECorrelation correlationMeasure,
                                 KernelDensityEstimate other)
```
    Calculates the optimal correlation between two Kernel Density Estimates by shifting and searching for optimal correlation.
    
    Parameters:
    
    correlationMeasure -
    
    other - The other KernelDensityEstimate.
    
    Returns:
    
    A value between 0 and 1 representing how similar both estimates are. 1 means total correlation, 0 no correlation.
  - shiftForOptimalCorrelation
```
public int shiftForOptimalCorrelation(KernelDensityEstimate.KDECorrelation correlationMeasure,
                                      KernelDensityEstimate other)
```
    Calculates how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
    
    Parameters:
    
    correlationMeasure -
    
    other - The other KernelDensityEstimate.
    
    Returns:
    
    A number between 0 (inclusive) and the size of the KernelDensityEstimate (exclusive) which represents how much the other KernelDensityEstimate needs to be shifted for optimal correlation.
  - getMedianFilteredEstimate
```
public double[] getMedianFilteredEstimate(int medianFilterLength)
```
  - median
```
public static double median(double[] m)
```

Class KernelDensityEstimate

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

accumulator

kernel

Constructor Detail

KernelDensityEstimate

KernelDensityEstimate

Method Detail

add

remove

shift

getEstimate

map

getValue

size

getSumFreq

normalize

normalize

getMaxElement

pdfify

clear

max

add

correlation

shiftForOptimalCorrelation

optimalCorrelation

optimalCorrelation

shiftForOptimalCorrelation

getMedianFilteredEstimate

median