Package elki.timeseries
Class OfflineChangePointDetectionAlgorithm.Instance
- java.lang.Object
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- elki.timeseries.OfflineChangePointDetectionAlgorithm.Instance
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- Enclosing class:
- OfflineChangePointDetectionAlgorithm
class OfflineChangePointDetectionAlgorithm.Instance extends java.lang.Object
Instance for a single data set.- Author:
- Erich Schubert
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Field Summary
Fields Modifier and Type Field Description (package private) double[]
bstrap
Temporary storage for bootstrap testing.(package private) double[]
column
Raw data column.(package private) int
columnnr
Current column number.(package private) DBIDArrayIter
iter
Iterator to reference data positions.(package private) ChangePoints
result
Result to output to.(package private) java.util.Random
rnd
Random generator.(package private) double[]
sums
Cumulative sum.
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Constructor Summary
Constructors Constructor Description Instance(java.util.Random rnd)
Constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description private double
bootstrapConfidence(int begin, int end, double thresh)
Calculates the confidence for the most probable change point of the given timer series.private int
multipleChangepointsWithConfidence(int begin, int end)
Performs multiple change point detection for a given time series.ChangePoints
run(Relation<DoubleVector> relation)
Run the change point detection algorithm on a data relation.
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Field Detail
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column
double[] column
Raw data column.
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sums
double[] sums
Cumulative sum.
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bstrap
double[] bstrap
Temporary storage for bootstrap testing.
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iter
DBIDArrayIter iter
Iterator to reference data positions.
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result
ChangePoints result
Result to output to.
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columnnr
int columnnr
Current column number.
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rnd
java.util.Random rnd
Random generator.
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Method Detail
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run
public ChangePoints run(Relation<DoubleVector> relation)
Run the change point detection algorithm on a data relation.- Parameters:
relation
- Data relation.- Returns:
- Change points
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multipleChangepointsWithConfidence
private int multipleChangepointsWithConfidence(int begin, int end)
Performs multiple change point detection for a given time series. This method uses a kind of divide and conquer approach- Parameters:
begin
- Interval beginend
- Interval end- Returns:
- Last change point position, or begin
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bootstrapConfidence
private double bootstrapConfidence(int begin, int end, double thresh)
Calculates the confidence for the most probable change point of the given timer series. Confidence is calculated with the help of bootstrapping.- Parameters:
begin
- Subset beginend
- Subset endthresh
- Threshold- Returns:
- confidence for most probable change point
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