PLearn 0.1
Public Member Functions | Protected Attributes
PLearn::MultinomialRandomVariable Class Reference

#include <RandomVar.h>

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List of all members.

Public Member Functions

 MultinomialRandomVariable (const RandomVar &log_probabilities)
 the parameters are unnormalized log-probabilities associated to each of the possible values of the observation
const RandomVarlog_probabilities ()
 convenience inline
bool learn_the_probabilities ()
virtual char * classname ()
Var logP (const Var &obs, const RVInstanceArray &RHS, RVInstanceArray *parameters_to_learn)
void setValueFromParentsValue ()
 sampling algorithm
void EMUpdate ()
void EMBprop (const Vec obs, real posterior)
void EMEpochInitialize ()
 Initialization of an individual EMEpoch.
bool isDiscrete ()
 most common default

Protected Attributes

Vec sum_posteriors
 temporaries used for EM:

Detailed Description

Y is discrete and takes values 0 to N-1. This class represents P(Y=i) = p_i where p=softmax(log_probabilities) and log_probabilities is the unique parent (which can be learned with "EM" in one iteration).

Definition at line 1472 of file RandomVar.h.


Constructor & Destructor Documentation

PLearn::MultinomialRandomVariable::MultinomialRandomVariable ( const RandomVar log_probabilities)

the parameters are unnormalized log-probabilities associated to each of the possible values of the observation

MultinomialRandomVariable.

Definition at line 2235 of file RandomVar.cc.


Member Function Documentation

virtual char* PLearn::MultinomialRandomVariable::classname ( ) [inline, virtual]

Implements PLearn::RandomVariable.

Definition at line 1483 of file RandomVar.h.

{ return "MultinomialRandomVariable"; }
void PLearn::MultinomialRandomVariable::EMBprop ( const Vec  obs,
real  posterior 
) [virtual]

************ EM STUFF ********** propagate posterior information to parents in order to perform an EMupdate at the end of an EMEpoch. In the case of mixture-like RVs and their components, the posterior is the probability of the component "this" given the observation "obs".

Implements PLearn::RandomVariable.

Definition at line 2261 of file RandomVar.cc.

References PLearn::TVec< T >::data(), learn_the_probabilities(), and sum_posteriors.

{
    if (learn_the_probabilities())
    {
        real *p = sum_posteriors.data();
        p[(int)obs[0]] += posterior;
    }
}

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void PLearn::MultinomialRandomVariable::EMEpochInitialize ( ) [virtual]

Initialization of an individual EMEpoch.

the default just propagates to the unmarked parents

Reimplemented from PLearn::RandomVariable.

Definition at line 2253 of file RandomVar.cc.

References PLearn::TVec< T >::clear(), PLearn::RandomVariable::EMmark, learn_the_probabilities(), and sum_posteriors.

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void PLearn::MultinomialRandomVariable::EMUpdate ( ) [virtual]

update the fixed (non-random) parameters using internal learning mechanism, at end of an EMEpoch. the default just propagates to the unmarked parents.

Reimplemented from PLearn::RandomVariable.

Definition at line 2270 of file RandomVar.cc.

References PLearn::apply(), PLearn::RandomVariable::EMmark, learn_the_probabilities(), log_probabilities(), PLearn::multiply(), PLearn::safeflog(), PLearn::sum(), sum_posteriors, and PLearn::RandomVariable::value.

{
    if (EMmark) return;
    EMmark=true;
    if (learn_the_probabilities())
    {
        real denom = sum(sum_posteriors);
        if (denom>0)
            // update probabilities
        {
            multiply(sum_posteriors,real(1.0/denom),sum_posteriors);
            apply(sum_posteriors,log_probabilities()->value->value,safeflog);
        }
        // maybe should WARN the user if denom==0 here
    }
}

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bool PLearn::MultinomialRandomVariable::isDiscrete ( ) [virtual]

most common default

Reimplemented from PLearn::StochasticRandomVariable.

Definition at line 2287 of file RandomVar.cc.

{
    return true;
}
bool PLearn::MultinomialRandomVariable::learn_the_probabilities ( ) [inline]

Definition at line 1481 of file RandomVar.h.

Referenced by EMBprop(), EMEpochInitialize(), and EMUpdate().

{ return learn_the_parameters[0]; }

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const RandomVar& PLearn::MultinomialRandomVariable::log_probabilities ( ) [inline]

convenience inline

Definition at line 1480 of file RandomVar.h.

Referenced by EMUpdate(), logP(), and setValueFromParentsValue().

{ return parents[0]; }

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Var PLearn::MultinomialRandomVariable::logP ( const Var obs,
const RVInstanceArray RHS,
RVInstanceArray parameters_to_learn 
) [virtual]

Construct a Var that computes logP(This = obs | RHS ). This function SHOULD NOT be used directly, but is called by the global function logP (same argument), which does proper massaging of the network before and after this call.

Implements PLearn::RandomVariable.

Definition at line 2241 of file RandomVar.cc.

References PLearn::RandomVariable::isMarked(), PLearn::log(), log_probabilities(), PLearn::marginalize(), PLearn::softmax(), and PLearn::RandomVariable::value.

{
    if (log_probabilities()->isMarked())
        return log(softmax(log_probabilities()->value))[obs];
    // else
    // probably not feasible..., but try in case we know a trick
    return PLearn::logP(ConditionalExpression
                        (RVInstance(marginalize(this,log_probabilities()),obs),
                         RHS),true,parameters_to_learn); 
}

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void PLearn::MultinomialRandomVariable::setValueFromParentsValue ( ) [virtual]

sampling algorithm

Implements PLearn::RandomVariable.

Definition at line 2292 of file RandomVar.cc.

References log_probabilities(), PLearn::softmax(), and PLearn::RandomVariable::value.

{
    value = 
        Var(new MultinomialSampleVariable(softmax(log_probabilities()->value)));
}

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Member Data Documentation

temporaries used for EM:

sum of posteriors over training data

Definition at line 1495 of file RandomVar.h.

Referenced by EMBprop(), EMEpochInitialize(), and EMUpdate().


The documentation for this class was generated from the following files:
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