Note

Functions/Classes with a link have been included into the python module. Tests have also been done.

List of AML functions of the STAT module

List of AML functions from STAT: alphabetic order

• AddAbsorbingRun()

• Cluster()

• Clustering()

• compare_histo() (distributions)

• compare_vectors() (vectors)

• compare_seq() (sequences)

• compare_markov() (Markovian models)

• ComparisonTest()

• Compound()

• ComputeCorrelation in aml/src/cpp/stat_funs5.cpp

• ComputePartialAutoCorrelation

• ComputeRankCorrelation

• ComputeSelfTransition

• ComputeStateSequences

• ComputeWhiteNoiseCorrelation

• ContingencyTable()

• Convolution()

• Cumulate

• Difference

• Display()

• Distribution

• Estimate (distributions)

• Estimate (renewal process)

• Estimate (Markovian models)

• Estimate (‘top’ parameters)

• ExtractData()

• ExtractDistribution()

• ExtractHistogram()

• ExtracVectors

• Fit()

• HiddenMarkov

• HiddenSemiMarkov

• Histogram()

• IndexSelect

• LengthSelect

• Load

• Markov

• Merge()

• MergeVariable()

• Mixture()

• ModelSelectionTest

• MovingAverage

• NbEventSelect

• Plot(), NewPlot

• RecurrenceTimeSequences

• Regression

• RemoveApicalInternodes

• RemoveRun

• Renewal

• Reverse

• Save()

• SegmentationExtract

• SelectIndividual()

• SelectVariable()

• SemiMarkov

• Sequences

• Shift()

• Simulate (distributions)

• Simulate (renewal process)

• Simulate (Markovian models)

• Simulate (‘topt’ parameters)

• Symmetrize

• TimeEvents

• TimeScaling

• TimeSelect

• ToDistanceMatrix()

• ToDistribution()

• ToHistogram()

• TopParameters

• Tops

• Transcode()

• TransformPosition

• ValueSelect()

• VariableScaling

• VarianceAnalysis()

• VectorDistance()

• Vectors

List of AML functions from STAT: by category

Input/output functions

• Compound() : construction d’un objet de type COMPOUND

• Convolution(): CONVOLUTION constructor,

• Distribution: DISTRIBUTION constructor,

• HiddenMarkov: HIDDEN_MARKOV constructor,

• HiddenSemiMarkov: HIDDEN_SEMI-MARKOV constructor,

• Histogram(): HISTOGRAM constructor,

• Markov: MARKOV constructor,

• Mixture: MIXTURE constructor,

• Renewal: RENEWAL constructor,

• SemiMarkov: SEMI-MARKOV constructor,

• Sequences: SEQUENCES constructor,

• TimeEvents: TIME_EVENTS constructor,

• TopParameters: TOP_PARAMETERS constructor,

• Tops: TOPS constructor,

• VectorDistance(): VECTOR_DISTANCE constructor,

• Vectors: VECTORS, constructor,

• Load: restoration of an object saved as a binary file

• Display(): ASCII output,

• Plot(): graphical output,

• Print(): ASCII print,

• Save(): save in a file.

Functions of data manipulation:

• Merge() merging of objects of the same ‘data’ type or merging of sample correlation functions,

• Cluster: clustering of values,

• Shift() shifting of values,

• Transcode(): transcoding of values,

• SelectIndividual() selection of individuals,

• ValueSelect() selection of individuals according to the values taken by a variable.

• MergeVariable() merging of variables,

• SelectVariable() selection of variables.

set of count data of type {time interval between two observation dates, number of events occurring between these two observation dates}:

• NbEventSelect: selection of data item according to a number of events criterion,

• TimeScaling: change of the time unit,

• TimeSelect: selection of data item according to a length of the observation period criterion.

set of sequences:

• AddAbsorbingRun: addition of a run of absorbing vectors at the end of sequences,

• Cumulate: sum of successive values along sequences,

• Difference: first-order differencing of sequences,

• IndexExtract: extraction of sub-sequences corresponding to a range of index parameters,

• LengthSelect: selection of sequences according to a length criterion,

• MovingAverage: extraction of trends or residuals using a symmetric smoothing filter,

• RecurrenceTimeSequences: computation of recurrence time sequences for a given value,

• RemoveRun: removal of the first or last run of a given value (for a given variable) in a sequence,

• Reverse: reversing of sequences or ‘tops’,

• SegmentationExtract: extraction of sub-sequences by segmentation,

• VariableScaling: change of the unit of a variable.

set of ‘tops’:

• RemoveApicalInternodes: removal of the apical internodes of the parent shoot of a ‘top’.

dissimilarity matrix:

• Symmetrize: symmetrization of a dissimilarity matrix.

Statistical functions:

• Clustering() application of clustering methods (either partitioning methods or hierarchical methods) to dissimilarity matrices between patterns,

• Compare() comparison of frequency distributions, vectors, sequences, Markovian models for sequences or Markovian models,

• ComparisonTest() test of comparison of frequency distributions,

• ComputeCorrelation: computation of sample autocorrelation or cross-correlation functions,

• ComputePartialAutoCorrelation: computation of sample partial autocorrelation functions,

• ComputeRankCorrelation: computation of a rank correlation matrix,

• ComputeStateSequences: computation of the optimal state sequences corresponding to the observed sequences using a hidden Markov chain or a hidden semi-Markov chain,

• ComputeWhiteNoiseAutoCorrelation: computation of the autocorrelation function induced on a white noise sequence by filtering,

• ContingencyTable(): computation of a contingency table,

• Estimate: estimation of distributions, renewal processes, Markovian models or ‘top’ parametres from data sample,

• Fit() fit of a frequency distribution by a theoretical distribution,

• ModelSelectionTest: test for selecting the order of a Markov chain or an aggregation of states of a Markov chain,

• Regression: simple (either linear or nonparametric) regression,

• Simulate: generation of random samples from distributions, renewal processes, Markovian models or ‘top’ parametres,

• VarianceAnalysis(): one-way variance analysis.

Miscellaneous functions

• ComputeSelfTransition: computation of the self-transition probabilities as a function of the index parameter from discrete sequences,

• ExtractData() extraction of the ‘data’ part of an object of type ‘model’,

• ExtractDistribution() extraction of a distribution from an object of type ‘model’,

• ExtractHistogram() extraction of a frequency distribution from an object of type ‘data’,

• ExtractVectors: extraction of vectors from global characteristics of sequences (length or counting characteristics),

• ToDistanceMatrix() cast of an object of type CLUSTERS into an object of type DISTANCE-MATRIX

• ToDistribution(): cast of an object of type HISTOGRAM into an object of type DISTRIBUTION

• ToHistogram(): cast of an object of type DISTRIBUTION into an object of type HISTOGRAM

• TransformPosition: discretization of inter-position intervals.

List by type

type clusters

• function returning an object of type CLUSTERS: - Load - Clustering()

• function taking as argument an object of type CLUSTERS: - Display() - Plot() - Print() - Save() - ToDistanceMatrix()