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DAGTrainSM
).Storable
.
Sequence
s.DataSet
from a StringExtractor
using the given AlphabetContainer
.
DataSet
from a StringExtractor
using the given AlphabetContainer
and all overlapping windows of
length subsequenceLength
.
DataSet
from a StringExtractor
using the given AlphabetContainer
and a delimiter
delim
.
DataSet
from a StringExtractor
using the given AlphabetContainer
, the given delimiter
delim
and all overlapping windows of length
subsequenceLength
.
DataSet
from a given DataSet
and a given
length subsequenceLength
.DataSet
.
DataSet
from an array of Sequence
s and a
given annotation.StatisticalModel.emitDataSet(int, int...)
DataSet
from a Collection
of Sequence
s and a
given annotation.
DataSet
.DataSet.ElementEnumerator
on the given DataSet
data
.
enum
defines different partition methods for a
DataSet
.Sequence
s that occur more
than once in one or more DataSet
s.DataSet.WeightedDataSetFactory
on the given
DataSet
(s) with DataSet.WeightedDataSetFactory.SortOperation
sort
.
DataSet.WeightedDataSetFactory
on the given
DataSet
and an array of weights
with
DataSet.WeightedDataSetFactory.SortOperation
sort
.
DataSet.WeightedDataSetFactory
on the given
DataSet
and an array of weights
with a given
length
and DataSet.WeightedDataSetFactory.SortOperation
sort
.
DataSet.WeightedDataSetFactory
on the given array of
DataSet
s and an array of weights
with a given
length
and DataSet.WeightedDataSetFactory.SortOperation
sort
.
enum
defines the different types of sort operations
that can be performed while creating a DataSet.WeightedDataSetFactory
.RecyclableSequenceEnumerator
of Sequence
s that enumerates all k-mers that exist in a given DataSet
, optionally ignoring reverse complements.DataSet
data
by extracting all k-mers.
Result
that contains a DataSet
.DataSetResult
from a DataSet
with the
annotation name
and comment
.
Storable
.
SequenceIterator
from the DataSet
sample
preserving as much annotation as possible.
enum
defines a number of data types that can be used for
Parameter
s and Result
s.FileFilter
that accepts File
s that were modified after the date that is given in the constructor.File
s that were modified after the given year, month, ...
File
s that were modified after d
.
ClassifierAssessment
that
is used as a super-class of all implemented methodologies of
an assessment to assess classifiers.Classifier
s that are based on SequenceScore
s.DifferentiableStatisticalModel
s by
a unified generative-discriminative learning principle.DifferentiableStatisticalModel
s either
by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL).AbstractScoreBasedClassifier
s that are based on
SamplingDifferentiableStatisticalModel
s
and that sample parameters using the Metropolis-Hastings algorithm.Classifier
s that are based on TrainableStatisticalModel
s.Alphabet
and AlphabetContainer
for representing alphabets,
Sequence
and its sub-classes to represent continuous and discrete sequences, and
DataSet
to represent data sets comprising a set of sequences.DNAAlphabet
for the most common case of DNA-sequences.DiscreteSequence
s prepared for alphabets of different sizes, and ArbitrarySequence
s that may
contain continuous values as well.Sequence
s.Sequence
s using a number of pre-defined annotation types, or additional
implementations of the SequenceAnnotation
class.Storable
s to an XML-representation.Parameter
-interface.Parameter
values.SequenceScore
s, which can be used to score a Sequence
, typically using some model assumptions.StatisticalModel
s, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.StatisticalModel
s can be further differentiated into TrainableStatisticalModel
s,
which can be learned from a single input DataSet
, and DifferentiableStatisticalModel
s,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScore
s.DifferentiableStatisticalModel
s, which can compute the gradient with
respect to their parameters for a given input Sequence
.DifferentiableStatisticalModel
s that are directed graphical models.BayesianNetworkDiffSM
.BayesianNetworkDiffSM
as
a Bayesian tree using a number of measures to define a rating of structures.BayesianNetworkDiffSM
as
a permuted Markov model using a number of measures to define a rating of structures.DifferentiableStatisticalModel
s that are homogeneous, i.e.DifferentiableSequenceScore
s that are mixtures of other DifferentiableSequenceScore
s.TrainableStatisticalModel
s, which can
be learned from a single DataSet
.AbstractHMM
.OutputStream
.
ProgressUpdater
and prints the
percentage of iterations that is already done on the screen.DefaultProgressUpdater
.
AbstractMixtureDiffSM.isNormalized()
.
ParameterSet
for any parameter set of
a DiscreteGraphicalTrainSM
.Storable
.
length
.
DataSet
data
and
the DataSet
s samples
.
HigherOrderHMM
and a DifferentiableStatisticalModel
by implementing some of the declared methods.Storable
.
ScoreClassifier
.DifferentiableSequenceScore
s.Storable
.
Optimizer
.OptimizableFunction
s that are based on DifferentiableSequenceScore
s.Exception
s depending on wrong dimensions of vectors
for a given function.DimensionException
with standard error message
("The vector has wrong dimension for this function.").
DimensionException
with a more detailed error
message.
enum
defines physicochemical, conformational, and letter-based dinucleotide properties of nucleotide sequences.DinucleotideProperty.MeanSmoothing
that averages over windows of width width
.
DinucleotideProperty.MedianSmoothing
that computes the median over windows of width width
.
DinucleotideProperty.Smoothing
that conducts no smoothing.String
s.Storable
.
InstantiableFromParameterSet
interface.
DiscreteAlphabet
from a minimal and a maximal
value, i.e.
DiscreteAlphabet
from a given alphabet as a
String
array.
ParameterSet
of a
DiscreteAlphabet
.DiscreteAlphabet
.
DiscreteAlphabet.DiscreteAlphabetParameterSet
with empty values.
DiscreteAlphabet.DiscreteAlphabetParameterSet
from an alphabet
given as a String
array.
DiscreteAlphabet.DiscreteAlphabetParameterSet
from an alphabet
of symbols given as a char
array.
Storable
.
DiscreteAlphabet
.DiscreteAlphabetMapping
.
Storable
.
DiscreteEmission
based on the equivalent sample size.
DiscreteEmission
defining the individual hyper parameters.
DiscreteEmission
from its XML representation.
Storable
.
DataSet
s for discrete inhomogeneous models by a naive implementation.Sequence
s of a specific
AlphabetContainer
and length.DiscreteSequenceEnumerator
from a given
AlphabetContainer
and a length.
pos
of the
Sequence
.
BNDiffSMParameter
, which is defined not to be free.
DoubleList
s are used during the parallel computation of the gradient.
DoubleList
s that are used while
computing the partial derivation.
DNAAlphabet
.AlphabetContainer
that can be used for DNA.ParameterSet
of a DNAAlphabetContainer
.DataSet
s of DNA Sequence
s.fName
.
fName
.
fName
using the given parser
.
Sequence
seq
fulfills all
requirements defined in the BNDiffSMParameter.context
.
LogPrior
that does not penalize any parameter.DifferentiableSequenceScore
is a MutableMotifDiscoverer
.
train
-method
Comparator
of double arrays.double
.DoubleList
with
initial length 10.
DoubleList
with
initial length size
.
Storable
.
DoubleSymbolException
is thrown if a symbol occurred more than once
in an alphabet.DoubleSymbolException
that takes the symbol
that occurs more than once in the error message.
contrast
and
endIdx-startIdx
distributions drawn from a Dirichlet density centered around contrast
, i.e.
contrast[i]
each weighted by weights[i]
kls.length
distributions drawn from a Dirichlet density centered around contrast
, i.e.
ess
(equivalent sample size)
as hyperparameters.
Storable
.
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