public class StochasticOutlierSelection$ extends Object implements WithParameters, scala.Serializable
Modifier and Type | Field and Description |
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static StochasticOutlierSelection$ |
MODULE$
Static reference to the singleton instance of this Scala object.
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Constructor and Description |
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StochasticOutlierSelection$() |
Modifier and Type | Method and Description |
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StochasticOutlierSelection |
apply() |
breeze.linalg.Vector<Object> |
binarySearch(breeze.linalg.Vector<Object> dissimilarityVector,
double logPerplexity,
int maxIterations,
double tolerance,
double beta,
double betaMin,
double betaMax,
int iteration)
Performs a binary search to get affinities in such a way that each conditional Gaussian has
the same perplexity.
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DataSet<StochasticOutlierSelection.BreezeLabeledVector> |
computeAffinity(DataSet<StochasticOutlierSelection.BreezeLabeledVector> dissimilarityVectors,
ParameterMap resultingParameters)
Approximate the affinity by fitting a Gaussian-like function
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DataSet<StochasticOutlierSelection.BreezeLabeledVector> |
computeBindingProbabilities(DataSet<StochasticOutlierSelection.BreezeLabeledVector> affinityVectors)
Normalizes the input vectors so each row sums up to one.
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DataSet<StochasticOutlierSelection.BreezeLabeledVector> |
computeDissimilarityVectors(DataSet<StochasticOutlierSelection.BreezeLabeledVector> inputVectors)
Compute pair-wise distance from each vector, to all other vectors.
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DataSet<scala.Tuple2<Object,Object>> |
computeOutlierProbability(DataSet<StochasticOutlierSelection.BreezeLabeledVector> bindingProbabilityVectors)
Compute the final outlier probability by taking the product of the column.
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Object |
transformLabeledVectors() |
<T extends Vector> |
transformVectors(BreezeVectorConverter<T> evidence$1,
TypeInformation<T> evidence$2,
scala.reflect.ClassTag<T> evidence$3)
TransformDataSetOperation applies the stochastic outlier selection algorithm on a
Vector which will transform the high-dimensionaly input to a single Double output. |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
parameters
public static final StochasticOutlierSelection$ MODULE$
public StochasticOutlierSelection apply()
public Object transformLabeledVectors()
public <T extends Vector> Object transformVectors(BreezeVectorConverter<T> evidence$1, TypeInformation<T> evidence$2, scala.reflect.ClassTag<T> evidence$3)
TransformDataSetOperation
applies the stochastic outlier selection algorithm on a
Vector
which will transform the high-dimensionaly input to a single Double output.
evidence$1
- (undocumented)evidence$2
- (undocumented)evidence$3
- (undocumented)TransformDataSetOperation
a single double which represents the oulierness of
the input vectors, where the output is in [0, 1]public DataSet<StochasticOutlierSelection.BreezeLabeledVector> computeDissimilarityVectors(DataSet<StochasticOutlierSelection.BreezeLabeledVector> inputVectors)
inputVectors
- The input vectors, will compare the vector to all other vectors based
on an distance method.BreezeLabeledVector
with dissimilarity vectorpublic DataSet<StochasticOutlierSelection.BreezeLabeledVector> computeAffinity(DataSet<StochasticOutlierSelection.BreezeLabeledVector> dissimilarityVectors, ParameterMap resultingParameters)
dissimilarityVectors
- The dissimilarity vectors which represents the distance to the
other vectors in the data set.resultingParameters
- The user defined parameters of the algorithmBreezeLabeledVector
with dissimilarity vectorpublic DataSet<StochasticOutlierSelection.BreezeLabeledVector> computeBindingProbabilities(DataSet<StochasticOutlierSelection.BreezeLabeledVector> affinityVectors)
affinityVectors
- The affinity vectors which is the quantification of the relationship
between the original vectors.BreezeLabeledVector
with represents the binding
probabilities, which is in fact the affinity where each row sums up to one.public DataSet<scala.Tuple2<Object,Object>> computeOutlierProbability(DataSet<StochasticOutlierSelection.BreezeLabeledVector> bindingProbabilityVectors)
bindingProbabilityVectors
- The binding probability vectors where the binding
probability is based on the affinity and represents the
probability of a vector binding with another vector.public breeze.linalg.Vector<Object> binarySearch(breeze.linalg.Vector<Object> dissimilarityVector, double logPerplexity, int maxIterations, double tolerance, double beta, double betaMin, double betaMax, int iteration)
dissimilarityVector
- The input dissimilarity vector which represents the current
vector distance to the other vectors in the data setlogPerplexity
- The log of the perplexity, which represents the probability of having
affinity with another vector.maxIterations
- The maximum iterations to limit the computational time.tolerance
- The allowed tolerance to sacrifice precision for decreased computational
time.beta:
- The current betabetaMin
- The lower bound of betabetaMax
- The upper bound of betaiteration
- The current iterationCopyright © 2014–2018 The Apache Software Foundation. All rights reserved.