| plAnonymousKernel | The {plAnonymousKernel} class implements a kernel having the user external function {function} as {compute} method |
| plArray | A {plArray} is a single type variable set containing the elements of an {n} dimensional array of unidimensional variables |
| plBayesianFilter | This class implements general purpose Bayesian Filters. Four types of filters are allowed: - Filters with action variables and non Idendity system model P(S S_ O | A) = P(S_) P(S | S_ A) P(O | S A). - Filters with no action variables and non Idendity system model P(S S_ O) = P(S_) P(S | S_) P(O | S). - Filters with action variables and Idendity system model (i.e P(S | S_) = Dirac(S, S_)) P(S O | A) = P(S) P(O | S A). - Filters with no action variables and Idendity system model P(S O) = P(S) P(O | S) |
| plBellShape | A {plBellShape} is a one-dimensional probability distribution on a single variable of {plIntegerType} type |
| plCKernel | The {plCKernel} class is an abstact class of all continuous kernel classes |
| plCndAnonymousKernel | The {plCndAnonymousKernel} class implements a conditional kernel having the user's external function {function} as {compute} method |
| plCndBellShape | A {plCndBellShape} is a "{\em plBellShape} family" in which mean and/or standard deviation are not constant but are {plVariablesConjunction}s or a external function of {plVariablesConjunction}s |
| plCndCKernel | The {plCndCKernel} class is an abstact class for all continuous conditional kernel classes |
| plCndDKernel | The {plCndDKernel} class is an abstact class of all discrete conditional kernel classes |
| plCndKernel | The {plCndKernel} class is the base class of all conditional probability (and density) distributions like {P(A | B) } |
| plCndLearnObject< T > | This template class implements conditional learning objects |
| plCndLogNormal | This class implements conditional lognormal distributions on one or multiple dimensional spaces |
| plCndNormal | This class implements conditional normal distributions on one or multiple dimensional spaces |
| plCndProbTable | This class implements a Conditional Probability Table |
| plCndUnknown | The {plCndUnknown} class permits to define an unknown distribution on a set of variables {left} knowing an other set of variables {right} |
| plComputableObject | A {computable object} on {Omega} is defined as an abstract object provided with a probability measure function {compute(Omega)} |
| plComputableObjectList | A {plComputableObjectList} is an slt-like list of {plComputableObject}s |
| plCUniform | The class plCUniform implements continuous uniform distributions |
| plData | A plData holds a single data |
| plDirac | The {plDirac} class implements the "dirac" or "Delta" function |
| plDKernel | A plDKernel is a Discrete Kernel |
| plEKF | This class implements the Extended Kalman Filter |
| plError | A {plError} permets to catch internal exceptions (errors) generated by inadequate use of OpenPL classes and methods |
| plExternalFunction | A function defined by the user, generaly used to define a {plFunctionalDirac} |
| plExternalProbFunction | A function defined by the user to be used as a probability or a density function |
| plFloatMatrix | A {plFloatMatrix} is an {m} x {n} matrix of elements of type {plFloat} |
| plFloatVector | A {plFloatVector} is a vector of {n} elements of type {plFloat} |
| plFunctionalDirac | PlFunctionalDirac is a conditional kernel defined from a user function |
| plGamma | This class implements the Gamma distribution |
| plIneqConstraint | The {plIneqConstraint} class defines a conditional kernel representing an {constraint_size}-dimensional inequality constraint on the Binary Variable {constraint_variable} |
| plIntegerType | The {plIntegerType} class is used to create integer types with particular interval [min,max] |
| plJointDistribution | The {plJointDistribution} class is used to describe a probabilistic model by providing a decomposition of the joint distribution of the whole model variables as a product of elementary conditional and non conditional distributions |
| plKalmanFilter | This class implements the Linear Kalman Filter |
| plKernel | The {plKernel} class is the base class of all probability (and density) distributions |
| plKernelDictionary | A {kernel dictionary} is a collection of kernels on $$ where each element is accessed by a key $$ |
| plKernelMap | A plKernelMap is a way to define a conditional kernel from a set of Computable Objects (Kernels and/or Conditional Kernels) having the same left variables than the building blocks |
| plKernelTable | A plKernelTable is a way to define a conditional kernel from a set of Computable Objects (Kernels and/or Conditional Kernels) having the same left variables than the building blocks |
| plLearn1dNormal | This class permits to learn one-dimensional Normal (Gaussian) distributions |
| plLearnDistribVector | This class permits to learn a set of conditional and non-conditional distributions in the same time |
| plLearnKHistogram | The class for learning n dimensionnal histograms with KNOWN variables definition sets |
| plLearnKLaplace | The class for learning n dimensionnal Laplace's distribution with KNOWN variables definition sets |
| plLearnKLidstone | The class for learning n dimensionnal Lidstone's distribution with KNOWN variables definition sets |
| plLearnNdNormal | This class permits to learn multi-dimensional Normal (Gaussian) distributions |
| plLearnObject | This is the base class of all learning objects |
| plLearnUnKHistogram | Class for learning histograms parametrical form for variables wich definition set is unknown |
| plLearnUnKLaplace | Class for learning Laplace's parametrical form for variables wich definition set is unknown |
| plLogNormal | A {plLogNormal} is a one-dimensional probability distribution on a single variable of {plRealType} type |
| plMutableCndKernel | A {plMutableCndKernel} is a conditional kernel whose shape can change dynamically |
| plMutableKernel | A {plMutableKernel} is a kernel whose shape can change dynamically |
| plNonCndLearnObject | This is the base class of all Non-conditional learning objects |
| plNormal | This class implements Normal distributions on one or multiple dimensional space |
| plObject | Used as the meta class of all ProBT API objects |
| plPoisson | This class implements the Poisson distribution |
| plProbability | This class implements specialized new arithmetics for probabilities |
| plProbTable | A plProbTable represents a table of probability values on integer and/or discretized real variables |
| plProduct | Creates a conditional or non conditional distribution as a product of a conditional and non conditional distributions |
| plRealType | The {plRealType} is used to create real types with a particular interval {[min,max[ } |
| plSparseType | Defines a type consisting in a set of dispersed values without constant distance between values |
| plSymbol | A {plSymbol} is a set containing one and only one unidimensional variable |
| plThread | Defines the number of threads and procesors to use in parallel execution |
| plType | A {plType} describes a variable type as well as its interval |
| plUniform | The {plUniform} class implements an uniform distribution on a given integer variable {variable} |
| plUnknown | The {plUnknown} class permits to define an unknown distribution on a set of variables {variable} |
| plValues | A plValues is an object storing the values of a set of variables |
| plVariable | A {plVariable} is a multiple type set of one-dimensional variables |
| plVariablesConjunction | The 'plVariablesConjunction' class implements the conjunction of a set of variables |
| plWarning | A plWarning permets to catch internal exceptions (warning) generated when using OpenPL classes |
1.4.1