Modifier and Type | Method and Description |
---|---|
List<IndependentPair<String,Class<?>>> |
FieldGetter.getStringValues() |
List<IndependentPair<String,Class<?>>> |
MapGetter.getStringValues() |
List<IndependentPair<String,Class<?>>> |
Getter.getStringValues()
A
Getter object has an implicit knowledge about the property it's setting,
and the instance of the option bean. |
List<IndependentPair<String,Class<?>>> |
MultiValueFieldGetter.getStringValues() |
Modifier and Type | Method and Description |
---|---|
static List<IndependentPair<Integer,Double>> |
MusicUtils.parseABCNotes(String abc)
Parses the music notation from ABC files (without the header).
|
Modifier and Type | Method and Description |
---|---|
List<IndependentPair<Double,Double>> |
IREvalResult.getInterpolatedPRData() |
List<IndependentPair<String,Number>> |
IREvalResult.getSummaryData() |
Modifier and Type | Method and Description |
---|---|
static <T,I extends Image<T,I>> |
MatchingUtilities.drawMatches(I image,
List<IndependentPair<Point2d,Point2d>> matches,
T col)
Draw matches between two images in the given colour.
|
Modifier and Type | Method and Description |
---|---|
abstract IndependentPair<String,List<URL>> |
InputMode.Parser.parse(String data)
Parse a record into a key and list of potential URLs.
|
Modifier and Type | Class and Description |
---|---|
class |
TokenPairCount
A pair of strings with 2 distinct counts:
number of times the pair appears together in a document
|
class |
TokenPairUnaryCount
A Pair count with a unary count for each item of the pair.
|
Modifier and Type | Method and Description |
---|---|
static IndependentPair<Long,TokenPairCount> |
TokenPairCount.parseTimeTokenID(String string)
Given a string, extract the time and TokenPairCount assuming the format:
time + TokenPairCount#TIMESPLIT +
TokenPairCount.identifier() |
Modifier and Type | Method and Description |
---|---|
static IndependentPair<Long,Double> |
PMIPairSort.parseTimeBinary(byte[] bytes)
read time and pmi from a byte array.
|
static IndependentPair<Long,Double> |
PMIPairSort.parseTimeBinary(byte[] bytes,
int start,
int len)
use a
ByteArrayInputStream and a DataInputStream to read a byte[] |
Constructor and Description |
---|
CorrelateWordTimeSeries(String financelocation,
IndependentPair<Long,Long> startend) |
Modifier and Type | Field and Description |
---|---|
LinkedHashMap<Long,IndependentPair<Long,Long>> |
WordTimeValue.timeIndex |
LinkedHashMap<String,IndependentPair<Long,Long>> |
WordTimeValue.wordIndex |
Modifier and Type | Method and Description |
---|---|
static LinkedHashMap<Long,IndependentPair<Long,Long>> |
TimeIndex.readTimeCountLines(String path)
from a report output path get the words
|
static LinkedHashMap<String,IndependentPair<Long,Long>> |
WordIndex.readWordCountLines(String path) |
static LinkedHashMap<String,IndependentPair<Long,Long>> |
WordIndex.readWordCountLines(String path,
String ext)
from a report output path get the words
|
Modifier and Type | Method and Description |
---|---|
static LinkedHashMap<String,WordDFIDFTimeSeries> |
Values.readWordDFIDF(String path,
LinkedHashMap<Long,IndependentPair<Long,Long>> timeIndex,
LinkedHashMap<String,IndependentPair<Long,Long>> wordIndex)
Construct a time series per word
|
static LinkedHashMap<String,WordDFIDFTimeSeries> |
Values.readWordDFIDF(String path,
LinkedHashMap<Long,IndependentPair<Long,Long>> timeIndex,
LinkedHashMap<String,IndependentPair<Long,Long>> wordIndex)
Construct a time series per word
|
Modifier and Type | Field and Description |
---|---|
protected List<List<? extends IndependentPair<? extends Point2d,? extends Point2d>>> |
CameraCalibrationZhang.points |
Constructor and Description |
---|
CameraCalibration(List<List<? extends IndependentPair<? extends Point2d,? extends Point2d>>> points,
int width,
int height) |
CameraCalibrationZhang(List<List<? extends IndependentPair<? extends Point2d,? extends Point2d>>> points,
int width,
int height)
Calibrate a camera using Zhang's method based on the given model-image
point pairs across a number of images.
|
Modifier and Type | Method and Description |
---|---|
void |
SuzukiNeighborStrategy.directedContour(FImage image,
Pixel ij,
Pixel i2j2,
Operation<IndependentPair<Pixel,boolean[]>> operation)
Directed contour following.
|
void |
MooreNeighborStrategy.directedContour(FImage image,
Pixel start,
Pixel from,
Operation<IndependentPair<Pixel,org.openimaj.image.contour.Direction>> operation)
Directed contour following.
|
Modifier and Type | Method and Description |
---|---|
void |
FisherImages.train(List<? extends IndependentPair<?,FImage>> data) |
Modifier and Type | Method and Description |
---|---|
static <I extends Image<?,I> & SinglebandImageProcessor.Processable<Float,FImage,I>> |
MultiResolutionActiveShapeModel.trainModel(int numLevels,
PrincipalComponentAnalysis.ComponentSelector selector,
List<IndependentPair<PointList,I>> data,
PointDistributionModel.Constraint constraint,
LandmarkModelFactory<I> factory)
Train a new
MultiResolutionActiveShapeModel from the given
data. |
static <I extends Image<?,I>> |
ActiveShapeModel.trainModel(PrincipalComponentAnalysis.ComponentSelector selector,
List<IndependentPair<PointList,I>> data,
PointDistributionModel.Constraint constraint,
LandmarkModelFactory<I> factory)
Train a new
ActiveShapeModel using the given data and parameters. |
Modifier and Type | Method and Description |
---|---|
static <IMAGE extends Image<?,IMAGE>> |
ShapeModelDatasets.create(List<IndependentPair<PointList,IMAGE>> data,
PointListConnections connections)
Create a dataset with the given data.
|
Modifier and Type | Method and Description |
---|---|
boolean |
PatchClassificationModel.estimate(List<? extends IndependentPair<T,FImage>> data) |
Modifier and Type | Method and Description |
---|---|
boolean |
PixelClassificationModel.estimate(List<? extends IndependentPair<T,FImage>> data) |
Modifier and Type | Method and Description |
---|---|
void |
FisherFaceFeature.Extractor.train(List<? extends IndependentPair<?,T>> data) |
Modifier and Type | Method and Description |
---|---|
List<IndependentPair<FACE,List<ScoredAnnotation<PERSON>>>> |
FaceRecognitionEngine.recognise(FImage image)
Detect and recognise the faces in the given image, returning a list of
potential people for each face.
|
List<IndependentPair<FACE,List<ScoredAnnotation<PERSON>>>> |
FaceRecognitionEngine.recognise(FImage image,
Set<PERSON> restrict)
Detect and recognise the faces in the given image, returning a list of
potential people for each face.
|
List<IndependentPair<FACE,ScoredAnnotation<PERSON>>> |
FaceRecognitionEngine.recogniseBest(FImage image)
Detect and recognise the faces in the given image, returning the most
likely person for each face.
|
List<IndependentPair<FACE,ScoredAnnotation<PERSON>>> |
FaceRecognitionEngine.recogniseBest(FImage image,
Set<PERSON> restrict)
Detect and recognise the faces in the given image, returning the most
likely person for each face.
|
Modifier and Type | Method and Description |
---|---|
List<IndependentPair<Point2d,Point2d>> |
LiuSamarabanduTextExtractorBasic.calculateHomography(Polygon p)
Calculates the point pairing for a given distorted polygon into
orthogonal space.
|
Map<Rectangle,IndependentPair<T,String>> |
TextExtractor.getText()
Get text that can be extracted from an image.
|
Modifier and Type | Method and Description |
---|---|
static IndependentPair<org.apache.http.HttpEntity,ByteArrayInputStream> |
HttpUtils.readURLAsByteArrayInputStream(URL u,
boolean followRedirects)
Read the contents of the given
URL as a
ByteArrayInputStream (i.e. |
static IndependentPair<org.apache.http.HttpEntity,ByteArrayInputStream> |
HttpUtils.readURLAsByteArrayInputStream(URL url,
int connectionTimeout,
int readTimeout,
org.apache.http.client.RedirectStrategy redirectStrategy,
String userAgent)
Read the contents of the given
URL as a
ByteArrayInputStream (i.e. |
static IndependentPair<org.apache.http.HttpEntity,ByteArrayInputStream> |
HttpUtils.readURLAsByteArrayInputStream(URL u,
org.apache.http.client.RedirectStrategy strategy)
Read the contents of the given
URL as a
ByteArrayInputStream (i.e. |
Modifier and Type | Class and Description |
---|---|
class |
ReadWritableIndependentPair<A,B>
Base class for writing any independent pair.
|
Constructor and Description |
---|
ReadWritableIndependentPair(IndependentPair<A,B> pair)
initialise with an existing pair
|
Modifier and Type | Method and Description |
---|---|
IndependentPair<Jama.Matrix,double[]> |
PointDistributionModel.fitModel(PointList observed)
Determine the best parameters of the PDM for the given model.
|
IndependentPair<Jama.Matrix,Double> |
Ellipse.secondMomentsAndScale() |
Modifier and Type | Method and Description |
---|---|
static IndependentPair<Point2d,Point2d> |
TransformUtilities.normalise(IndependentPair<Point2d,Point2d> data,
Pair<Jama.Matrix> normalisations)
Normalise the data, returning a normalised copy.
|
Modifier and Type | Method and Description |
---|---|
static List<? extends IndependentPair<Point2d,Point2d>> |
TransformUtilities.normalise(List<? extends IndependentPair<Point2d,Point2d>> data,
Pair<Jama.Matrix> normalisations)
Normalise the data, returning a normalised copy.
|
Modifier and Type | Method and Description |
---|---|
double |
FundamentalModel.Fundamental8PtResidual.computeResidual(IndependentPair<Point2d,Point2d> data) |
double |
FundamentalModel.EpipolarResidual.computeResidual(IndependentPair<Point2d,Point2d> data) |
double |
FundamentalModel.SampsonGeometricResidual.computeResidual(IndependentPair<Point2d,Point2d> data) |
static IndependentPair<Point2d,Point2d> |
TransformUtilities.normalise(IndependentPair<Point2d,Point2d> data,
Pair<Jama.Matrix> normalisations)
Normalise the data, returning a normalised copy.
|
Modifier and Type | Method and Description |
---|---|
static Jama.Matrix |
TransformUtilities.affineMatrix(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Construct an affine transform using a least-squares fit of the provided
point pairs.
|
static Jama.Matrix |
TransformUtilities.affineMatrixND(List<? extends IndependentPair<? extends Coordinate,? extends Coordinate>> data)
Find the affine transform between pairs of matching points in
n-dimensional space.
|
abstract double |
HomographyRefinement.computeError(Jama.Matrix h,
List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Compute the error value being optimised between the two point sets.
|
boolean |
AffineTransformModel.estimate(List<? extends IndependentPair<Point2d,Point2d>> data) |
boolean |
FundamentalModel.estimate(List<? extends IndependentPair<Point2d,Point2d>> data) |
boolean |
HomographyModel.estimate(List<? extends IndependentPair<Point2d,Point2d>> data)
DLT estimation of least-squares solution of 3D homogeneous homography
|
boolean |
RigidTransformModel3d.estimate(List<? extends IndependentPair<Point3d,Point3d>> data) |
boolean |
AffineTransformModel3d.estimate(List<? extends IndependentPair<Point3d,Point3d>> data) |
boolean |
NullModel.estimate(List<? extends IndependentPair<T,T>> data) |
static Jama.Matrix |
TransformUtilities.fundamentalMatrix8Pt(List<? extends IndependentPair<Point2d,Point2d>> data)
The un-normalised 8-point algorithm for estimation of the Fundamental
matrix.
|
static Jama.Matrix |
TransformUtilities.fundamentalMatrix8PtNorm(List<? extends IndependentPair<Point2d,Point2d>> data)
The normalised 8-point algorithm for estimating the Fundamental matrix
|
protected abstract org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction |
FundamentalRefinement.getFunctions(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data,
FundamentalRefinement.Parameterisation p) |
protected abstract org.apache.commons.math3.analysis.MultivariateMatrixFunction |
HomographyRefinement.getJacobianFunction(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
static Pair<Jama.Matrix> |
TransformUtilities.getNormalisations(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Generates the data for normalisation of the points such that each matched
point is centered about the origin and also scaled be be within
Math.sqrt(2) of the origin.
|
protected abstract org.apache.commons.math3.analysis.MultivariateVectorFunction |
HomographyRefinement.getValueFunction(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
static Jama.Matrix |
TransformUtilities.homographyMatrix(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Compute the least-squares estimate (the normalised Direct Linear
Transform approach) of the homography between a set of matching data
points.
|
static Jama.Matrix |
TransformUtilities.homographyMatrixNorm(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Compute the least-squares estimate (the normalised Direct Linear
Transform approach) of the homography between a set of matching data
points.
|
static List<? extends IndependentPair<Point2d,Point2d>> |
TransformUtilities.normalise(List<? extends IndependentPair<Point2d,Point2d>> data,
Pair<Jama.Matrix> normalisations)
Normalise the data, returning a normalised copy.
|
abstract Jama.Matrix |
HomographyRefinement.refine(Jama.Matrix initial,
List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Refine an initial guess at the homography that takes the first points in
data to the second using non-linear Levenberg Marquardt optimisation.
|
Jama.Matrix |
FundamentalRefinement.refine(Jama.Matrix initial,
List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)
Refine an initial guess at the homography that takes the first points in
data to the second using non-linear Levenberg Marquardt optimisation.
|
static Jama.Matrix |
TransformUtilities.rigidMatrix(List<? extends IndependentPair<? extends Coordinate,? extends Coordinate>> data)
Compute the least-squares rigid alignment between two sets of matching
points in N-dimensional space.
|
Constructor and Description |
---|
Base(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F12Epipolar(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F12Sampson(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F13Epipolar(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F13Sampson(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F23Epipolar(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
F23Sampson(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) |
Modifier and Type | Method and Description |
---|---|
List<? extends IndependentPair<Point2d,Point2d>> |
RobustAffineTransformEstimator.getInliers() |
List<? extends IndependentPair<Point2d,Point2d>> |
RobustHomographyEstimator.getInliers() |
List<? extends IndependentPair<Point2d,Point2d>> |
RobustFundamentalEstimator.getInliers() |
List<? extends IndependentPair<Point2d,Point2d>> |
RobustAffineTransformEstimator.getOutliers() |
List<? extends IndependentPair<Point2d,Point2d>> |
RobustHomographyEstimator.getOutliers() |
List<? extends IndependentPair<Point2d,Point2d>> |
RobustFundamentalEstimator.getOutliers() |
Modifier and Type | Method and Description |
---|---|
boolean |
RobustAffineTransformEstimator.fitData(List<? extends IndependentPair<Point2d,Point2d>> data) |
boolean |
RobustHomographyEstimator.fitData(List<? extends IndependentPair<Point2d,Point2d>> data) |
boolean |
RobustFundamentalEstimator.fitData(List<? extends IndependentPair<Point2d,Point2d>> data) |
Modifier and Type | Method and Description |
---|---|
List<IndependentPair<Point2d,Point2d>> |
BucketingSampler2d.sample(int nItems) |
Modifier and Type | Method and Description |
---|---|
void |
BucketingSampler2d.setCollection(Collection<? extends IndependentPair<Point2d,Point2d>> collection) |
Modifier and Type | Method and Description |
---|---|
double |
SingleImageTransferResidual2d.computeResidual(IndependentPair<Point2d,Point2d> data) |
double |
TransformedSITR2d.computeResidual(IndependentPair<Point2d,Point2d> data) |
double |
SymmetricTransferResidual2d.computeResidual(IndependentPair<Point2d,Point2d> data) |
double |
AlgebraicResidual2d.computeResidual(IndependentPair<Point2d,Point2d> data) |
Modifier and Type | Method and Description |
---|---|
void |
SingleImageTransferResidual2d.computeResiduals(List<? extends IndependentPair<Point2d,Point2d>> data,
double[] errors) |
void |
TransformedSITR2d.computeResiduals(List<? extends IndependentPair<Point2d,Point2d>> data,
double[] errors) |
void |
SymmetricTransferResidual2d.computeResiduals(List<? extends IndependentPair<Point2d,Point2d>> data,
double[] residuals) |
void |
AlgebraicResidual2d.computeResiduals(List<? extends IndependentPair<Point2d,Point2d>> data,
double[] residuals) |
Modifier and Type | Method and Description |
---|---|
static IndependentPair<no.uib.cipr.matrix.DenseMatrix,no.uib.cipr.matrix.DenseVector> |
GeneralisedEigenvalueProblem.symmetricGeneralisedEigenvectors(no.uib.cipr.matrix.DenseMatrix A,
no.uib.cipr.matrix.DenseMatrix B)
Solve the general problem A x = L B x.
|
static IndependentPair<Jama.Matrix,double[]> |
GeneralisedEigenvalueProblem.symmetricGeneralisedEigenvectors(Jama.Matrix A,
Jama.Matrix B)
Solve the general problem A x = L B x.
|
static IndependentPair<Jama.Matrix,double[]> |
GeneralisedEigenvalueProblem.symmetricGeneralisedEigenvectorsSorted(Jama.Matrix A,
Jama.Matrix B)
Solve the general problem A x = L B x.
|
static IndependentPair<Jama.Matrix,double[]> |
GeneralisedEigenvalueProblem.symmetricGeneralisedEigenvectorsSorted(Jama.Matrix A,
Jama.Matrix B,
int numVecs)
Solve the general problem A x = L B x.
|
Modifier and Type | Method and Description |
---|---|
void |
LinearDiscriminantAnalysis.learnBasisIP(List<? extends IndependentPair<?,double[]>> data)
Learn the LDA basis.
|
Modifier and Type | Field and Description |
---|---|
protected List<IndependentPair<String,Point2d>> |
MultidimensionalScaling.points |
Modifier and Type | Method and Description |
---|---|
List<IndependentPair<String,Point2d>> |
MultidimensionalScaling.getPoints()
Get a list of the 2-D coordinates learned by the MDS algorithm for each
element in the input similarity matrix.
|
Modifier and Type | Method and Description |
---|---|
boolean |
GaussianVectorNaiveBayesModel.estimate(List<? extends IndependentPair<double[],T>> data) |
boolean |
UnivariateGaussianNaiveBayesModel.estimate(List<? extends IndependentPair<Double,T>> data) |
boolean |
EstimatableModel.estimate(List<? extends IndependentPair<I,D>> data)
Estimates the model from the observations in the list of data.
|
boolean |
LeastSquaresLinearModel.estimate(List<? extends IndependentPair<Integer,Integer>> data)
Using standard vertical linear regression as outlined here:
http://mathworld.wolfram.com/LeastSquaresFitting.html
calculate the m and c of a line of best fit given the data.
|
Modifier and Type | Field and Description |
---|---|
protected List<IndependentPair<I,D>> |
RANSAC.bestModelInliers |
protected List<IndependentPair<I,D>> |
RANSAC.bestModelOutliers |
protected List<IndependentPair<I,D>> |
RANSAC.inliers |
protected List<IndependentPair<I,D>> |
LMedS.inliers |
protected List<? extends IndependentPair<I,D>> |
RANSAC.modelConstructionData |
protected List<IndependentPair<I,D>> |
RANSAC.outliers |
protected List<IndependentPair<I,D>> |
LMedS.outliers |
protected CollectionSampler<IndependentPair<I,D>> |
RANSAC.sampler |
protected CollectionSampler<IndependentPair<I,D>> |
LMedS.sampler |
Modifier and Type | Method and Description |
---|---|
List<? extends IndependentPair<I,D>> |
RobustModelFitting.getInliers() |
List<? extends IndependentPair<I,D>> |
RANSAC.getInliers() |
List<? extends IndependentPair<I,D>> |
LMedS.getInliers() |
List<? extends IndependentPair<I,D>> |
SimpleModelFitting.getInliers() |
List<? extends IndependentPair<I,D>> |
RANSAC.getModelConstructionData() |
List<? extends IndependentPair<I,D>> |
RobustModelFitting.getOutliers() |
List<? extends IndependentPair<I,D>> |
RANSAC.getOutliers() |
List<? extends IndependentPair<I,D>> |
LMedS.getOutliers() |
List<? extends IndependentPair<I,D>> |
SimpleModelFitting.getOutliers() |
Modifier and Type | Method and Description |
---|---|
boolean |
RANSAC.fitData(List<? extends IndependentPair<I,D>> data) |
boolean |
LMedS.fitData(List<? extends IndependentPair<I,D>> data) |
boolean |
SimpleModelFitting.fitData(List<? extends IndependentPair<I,D>> data) |
boolean |
ModelFitting.fitData(List<? extends IndependentPair<I,D>> data)
Attempt to fit the given data to the model.
|
void |
RANSAC.setModelConstructionData(List<? extends IndependentPair<I,D>> modelConstructionData)
Set the data used to construct the model
|
Constructor and Description |
---|
LMedS(M model,
ResidualCalculator<I,D,M> residualEstimator,
boolean impEst,
CollectionSampler<IndependentPair<I,D>> sampler)
Construct with the given model and residual calculator.
|
LMedS(M model,
ResidualCalculator<I,D,M> residualEstimator,
double outlierProportion,
boolean impEst,
CollectionSampler<IndependentPair<I,D>> sampler)
Construct with the given model, residual calculator and estimated
proportion of outliers.
|
LMedS(M model,
ResidualCalculator<I,D,M> residualEstimator,
double outlierProportion,
double inlierNoiseLevel,
double degreesOfFreedom,
boolean impEst,
CollectionSampler<IndependentPair<I,D>> sampler)
Construct with the given model, residual calculator and estimated
proportion of outliers.
|
RANSAC(M model,
ResidualCalculator<I,D,M> errorModel,
DistanceCheck dc,
int nIterations,
RANSAC.StoppingCondition stoppingCondition,
boolean impEst,
CollectionSampler<IndependentPair<I,D>> sampler)
Create a RANSAC object
|
RANSAC(M model,
ResidualCalculator<I,D,M> errorModel,
double errorThreshold,
int nIterations,
RANSAC.StoppingCondition stoppingCondition,
boolean impEst,
CollectionSampler<IndependentPair<I,D>> sampler)
Create a RANSAC object
|
Modifier and Type | Method and Description |
---|---|
double |
ResidualCalculator.computeResidual(IndependentPair<I,D> data)
Compute the residual for a single point
|
double |
DistanceComparatorResidual.computeResidual(IndependentPair<I,D> data) |
Modifier and Type | Method and Description |
---|---|
void |
ResidualCalculator.computeResiduals(List<? extends IndependentPair<I,D>> data,
double[] residuals)
Compute the residual for a set of data points
|
void |
AbstractResidualCalculator.computeResiduals(List<? extends IndependentPair<I,D>> data,
double[] errors) |
Modifier and Type | Method and Description |
---|---|
IndependentPair<double[],double[][]> |
MixtureOfGaussians.scoreSamples(double[][] samples)
Compute the posterior distribution of the samples, and the overall log
probability of each sample as belonging to the model.
|
Modifier and Type | Method and Description |
---|---|
IndependentPair<int[],DISTANCES> |
SoftAssigner.assignWeighted(DATATYPE data)
Assign a single point to some clusters.
|
Modifier and Type | Method and Description |
---|---|
IndependentPair<int[],float[]> |
HierarchicalBytePathAssigner.assignWeighted(byte[] data) |
IndependentPair<int[],float[]> |
ByteKNNAssigner.assignWeighted(byte[] data) |
IndependentPair<int[],double[]> |
DoubleKNNAssigner.assignWeighted(double[] data) |
IndependentPair<int[],double[]> |
HierarchicalDoublePathAssigner.assignWeighted(double[] data) |
IndependentPair<int[],float[]> |
FloatKNNAssigner.assignWeighted(float[] data) |
IndependentPair<int[],float[]> |
HierarchicalFloatPathAssigner.assignWeighted(float[] data) |
IndependentPair<int[],float[]> |
HierarchicalIntPathAssigner.assignWeighted(int[] data) |
IndependentPair<int[],float[]> |
IntKNNAssigner.assignWeighted(int[] data) |
IndependentPair<int[],double[]> |
HierarchicalLongPathAssigner.assignWeighted(long[] data) |
IndependentPair<int[],double[]> |
LongKNNAssigner.assignWeighted(long[] data) |
IndependentPair<int[],float[]> |
ShortKNNAssigner.assignWeighted(short[] data) |
IndependentPair<int[],float[]> |
HierarchicalShortPathAssigner.assignWeighted(short[] data) |
Modifier and Type | Method and Description |
---|---|
protected IndependentPair<double[],double[][]> |
PreparedSpectralClustering.bestCols(Eigenvalues eig) |
IndependentPair<double[],double[][]> |
SpectralIndexedClusters.getValVect() |
Modifier and Type | Method and Description |
---|---|
SpatialClusterer<? extends SpatialClusters<DATATYPE>,DATATYPE> |
SpectralClusteringConf.DefaultClustererFunction.apply(IndependentPair<double[],double[][]> in) |
protected SpectralIndexedClusters |
PreparedSpectralClustering.eigenspaceCluster(IndependentPair<double[],double[][]> lowestCols) |
Modifier and Type | Method and Description |
---|---|
boolean |
StoppingCondition.stop(List<IndependentPair<double[],double[][]>> answers)
Called once at the beggining of each full iteration
|
boolean |
StoppingCondition.HardCoded.stop(List<IndependentPair<double[],double[][]>> answers) |
Constructor and Description |
---|
SpectralIndexedClusters(IndexClusters c,
IndependentPair<double[],double[][]> valvects) |
Modifier and Type | Method and Description |
---|---|
IndependentPair<Map<String,Map<String,Double>>,Map<String,Double>> |
BiconvexIncrementalDataGenerator.generate() |
IndependentPair<X,Y> |
FixedDataGenerator.generate() |
IndependentPair<double[],PerceptronClass> |
LinearPerceptronDataGenerator.generate() |
IndependentPair<I,D> |
DataGenerator.generate() |
Constructor and Description |
---|
FixedDataGenerator(List<IndependentPair<X,Y>> d) |
Modifier and Type | Method and Description |
---|---|
Double |
LinearVectorKernel.apply(IndependentPair<double[],double[]> in) |
Modifier and Type | Method and Description |
---|---|
Pair<gov.sandia.cognition.math.matrix.Matrix> |
IncrementalBilinearSparseOnlineLearner.asMatrixPair(IndependentPair<Map<String,Map<String,Double>>,Map<String,Double>> in) |
Pair<gov.sandia.cognition.math.matrix.Matrix> |
IncrementalBilinearSparseOnlineLearner.asMatrixPair(IndependentPair<Map<String,Map<String,Double>>,Map<String,Double>> xy,
int nfeatures,
int nusers,
int ntasks)
Given a sparse pair of user/words and value construct a pair of matricies
using the current mappings of words and users to matrix rows.
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Modifier and Type | Method and Description |
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protected double |
SimplePerceptron.calculateError(List<? extends IndependentPair<double[],Integer>> pts) |
boolean |
SimplePerceptron.estimate(List<? extends IndependentPair<double[],Integer>> data) |
Modifier and Type | Method and Description |
---|---|
boolean |
LinearRegression.estimate(List<? extends IndependentPair<double[],double[]>> data) |
Constructor and Description |
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WindowedLinearRegressionAggregator(String ydataName,
boolean autoregressive,
IndependentPair<Integer,Integer>... windowOffsets)
Perform regression s.t.
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Modifier and Type | Method and Description |
---|---|
Iterator<IndependentPair<Long,Map<String,SINGLEINPUT>>> |
TimeSeriesCollection.iterator() |
Modifier and Type | Method and Description |
---|---|
Iterator<IndependentPair<Long,DATA>> |
ConcreteTimeSeries.iterator() |
Iterator<IndependentPair<Long,Double>> |
DoubleTimeSeries.iterator() |
Iterator<IndependentPair<Long,Map<String,Double>>> |
DoubleSynchronisedTimeSeriesCollection.iterator() |
Constructor and Description |
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DoubleSynchronisedTimeSeriesCollection(IndependentPair<String,DoubleTimeSeries>... series)
create a synchronised series from a bunch of pairs
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Modifier and Type | Method and Description |
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List<IndependentPair<URL,MBFImage>> |
StatusConsumer.urlToImage(URL url)
First, try all the
SiteSpecificConsumer instances loaded into
StatusConsumer.siteSpecific . |
Modifier and Type | Method and Description |
---|---|
static Collection<? extends IndependentPair<URL,Map<String,String>>> |
WebpageUtils.allURLs(org.jsoup.nodes.Document doc,
String entity,
String attribute) |
Set<IndependentPair<URL,Map<String,String>>> |
FlickrWebpageImageCollection.prepareURLs(URL url) |
abstract Set<IndependentPair<URL,Map<String,String>>> |
AbstractWebpageImageCollection.prepareURLs(URL url) |
Set<IndependentPair<URL,Map<String,String>>> |
AbstractWebpageImageCollection.Generic.prepareURLs(URL url) |
Constructor and Description |
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URLImageIterator(Set<IndependentPair<URL,Map<String,String>>> imageList2,
ImageCollectionEntrySelection<MBFImage> selection) |
Modifier and Type | Method and Description |
---|---|
protected FImage |
MDS.render(List<IndependentPair<String,Point2d>> pts,
int sz) |
Modifier and Type | Class and Description |
---|---|
class |
Pair<T>
Pair represents a generic pair of objects.
|
Modifier and Type | Method and Description |
---|---|
static <T,Q> IndependentPair<T,Q> |
IndependentPair.pair(T t,
Q q)
Create a pair from the given objects.
|
IndependentPair<B,A> |
IndependentPair.swap()
Create a new
IndependentPair from this one with the elements
swapped |
Modifier and Type | Method and Description |
---|---|
static <T,Q> Function<IndependentPair<T,Q>,T> |
IndependentPair.getFirstFunction()
Get the function that returns the first object from the pair
|
static <T,Q> Function<IndependentPair<T,Q>,Q> |
IndependentPair.getSecondFunction()
Get the function that returns the second object from the pair
|
static <T,Q> List<IndependentPair<T,Q>> |
IndependentPair.pairList(List<T> t,
List<Q> q)
Create a pair list from the given objects.
|
static <T,Q> List<IndependentPair<? extends Q,? extends T>> |
IndependentPair.swapList(List<? extends IndependentPair<? extends T,? extends Q>> data)
Swap the order of the pairs
|
Modifier and Type | Method and Description |
---|---|
static <T,Q> List<T> |
IndependentPair.getFirst(Iterable<? extends IndependentPair<T,Q>> data)
Extract the first objects from a list of pairs.
|
static <T,Q> List<Q> |
IndependentPair.getSecond(Iterable<? extends IndependentPair<T,Q>> data)
Extract the second objects from a list of pairs.
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static <T,Q> List<IndependentPair<? extends Q,? extends T>> |
IndependentPair.swapList(List<? extends IndependentPair<? extends T,? extends Q>> data)
Swap the order of the pairs
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Modifier and Type | Method and Description |
---|---|
IndependentPair<AOUT,BOUT> |
CombinedStreamFunction.apply(IndependentPair<AIN,BIN> in) |
IndependentPair<A,B> |
StreamCombiner.next() |
Modifier and Type | Method and Description |
---|---|
IndependentPair<AOUT,BOUT> |
CombinedStreamFunction.apply(IndependentPair<AIN,BIN> in) |
Modifier and Type | Method and Description |
---|---|
MetaPayload<IndependentPair<AOUT,BOUT>,IndependentPair<AM,BM>> |
CombinedMetaPayloadFunction.apply(MetaPayload<IndependentPair<AIN,BIN>,IndependentPair<AM,BM>> inaggr) |
MetaPayload<IndependentPair<AOUT,BOUT>,IndependentPair<AM,BM>> |
CombinedMetaPayloadFunction.apply(MetaPayload<IndependentPair<AIN,BIN>,IndependentPair<AM,BM>> inaggr) |
MetaPayload<IndependentPair<AP,BP>,IndependentPair<AM,BM>> |
MetaPayloadStreamCombiner.next() |
MetaPayload<IndependentPair<AP,BP>,IndependentPair<AM,BM>> |
MetaPayloadStreamCombiner.next() |
Modifier and Type | Method and Description |
---|---|
MetaPayload<IndependentPair<AOUT,BOUT>,IndependentPair<AM,BM>> |
CombinedMetaPayloadFunction.apply(MetaPayload<IndependentPair<AIN,BIN>,IndependentPair<AM,BM>> inaggr) |
MetaPayload<IndependentPair<AOUT,BOUT>,IndependentPair<AM,BM>> |
CombinedMetaPayloadFunction.apply(MetaPayload<IndependentPair<AIN,BIN>,IndependentPair<AM,BM>> inaggr) |
Modifier and Type | Method and Description |
---|---|
<I extends Image<?,I>,O> |
ObjectTimeFinder.trackObject(ObjectTracker<O,I> objectTracker,
Video<I> video,
VideoTimecode keyframeTime,
Rectangle bounds,
ObjectTimeFinder.TimeFinderListener<O,I> listener)
Given a video, a keyframe (timecode) and a region of the image,
this method will attempt to track the the contents of the rectangle
from the given frame, back and forth to find the place
at which the object appears and disappears from the video.
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Modifier and Type | Method and Description |
---|---|
static void |
AudioFramePlot.drawChart(int numFrames,
boolean colouredFrames,
List<IndependentPair<AudioStream,String>> streams)
Draws the first n frames of the audio streams on to a chart mapping the names given
to each stream into the legend.
|