PLearn 0.1
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | Static Protected Member Functions | Protected Attributes | Private Types | Private Member Functions
PLearn::UniformDistribution Class Reference

#include <UniformDistribution.h>

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List of all members.

Public Member Functions

 UniformDistribution ()
 Default constructor.
virtual void build ()
 Simply call inherited::build() then build_().
virtual void makeDeepCopyFromShallowCopy (CopiesMap &copies)
 Transform a shallow copy into a deep copy.
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual UniformDistributiondeepCopy (CopiesMap &copies) const
virtual real log_density (const Vec &x) const
 Return log of probability density log(p(x)).
virtual real survival_fn (const Vec &x) const
 Return survival fn = P(X>x).
virtual real cdf (const Vec &x) const
 Return survival fn = P(X<x).
virtual void expectation (Vec &mu) const
 Return E[X].
virtual void variance (Mat &cov) const
 Return Var[X].
virtual void resetGenerator (long g_seed)
 Reset the random number generator used by generate() using the given seed.
virtual void generate (Vec &x) const
 Return a pseudo-random sample generated from the distribution.

Static Public Member Functions

static string _classname_ ()
static OptionList_getOptionList_ ()
static RemoteMethodMap_getRemoteMethodMap_ ()
static Object_new_instance_for_typemap_ ()
static bool _isa_ (const Object *o)
static void _static_initialize_ ()
static const PPathdeclaringFile ()

Public Attributes

Vec max
Vec min
int mesh_size
int n_dim

Static Public Attributes

static StaticInitializer _static_initializer_

Static Protected Member Functions

static void declareOptions (OptionList &ol)
 Declare this class' options.

Protected Attributes

int counter

Private Types

typedef UnconditionalDistribution inherited

Private Member Functions

void build_ ()
 This does the actual building.

Detailed Description

Definition at line 52 of file UniformDistribution.h.


Member Typedef Documentation

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 57 of file UniformDistribution.h.


Constructor & Destructor Documentation

PLearn::UniformDistribution::UniformDistribution ( )

Default constructor.

Definition at line 51 of file UniformDistribution.cc.

References max, min, and PLearn::TVec< T >::resize().

                                        :
    counter(0),
    mesh_size(-1),
    n_dim(-1)
{
    // Default = generate points uniformly in [0,1].
    min.resize(1);
    max.resize(1);
    min[0] = 0;
    max[0] = 1;
}

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Member Function Documentation

string PLearn::UniformDistribution::_classname_ ( ) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

OptionList & PLearn::UniformDistribution::_getOptionList_ ( ) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

RemoteMethodMap & PLearn::UniformDistribution::_getRemoteMethodMap_ ( ) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

bool PLearn::UniformDistribution::_isa_ ( const Object o) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

Object * PLearn::UniformDistribution::_new_instance_for_typemap_ ( ) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

StaticInitializer UniformDistribution::_static_initializer_ & PLearn::UniformDistribution::_static_initialize_ ( ) [static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

void PLearn::UniformDistribution::build ( ) [virtual]

Simply call inherited::build() then build_().

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 102 of file UniformDistribution.cc.

References PLearn::UnconditionalDistribution::build(), and build_().

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void PLearn::UniformDistribution::build_ ( ) [private]

This does the actual building.

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 111 of file UniformDistribution.cc.

References PLearn::TVec< T >::append(), PLearn::UnconditionalDistribution::build(), i, PLearn::PLearner::inputsize_, PLearn::TVec< T >::length(), max, min, n_dim, PLASSERT, and PLERROR.

Referenced by build().

{
    // ### This method should do the real building of the object,
    // ### according to set 'options', in *any* situation.
    // ### Typical situations include:
    // ###  - Initial building of an object from a few user-specified options
    // ###  - Building of a "reloaded" object: i.e. from the complete set of all serialised options.
    // ###  - Updating or "re-building" of an object after a few "tuning" options have been modified.
    // ### You should assume that the parent class' build_() has already been called.

    // Check consistency of intervals.
    if (min.length() != max.length()) {
        PLERROR("In UniformDistribution::build_ - 'min' and 'max' should have the same size");
    }

    if (n_dim == -1)
        n_dim = min.length();

    if (n_dim != min.length()) {
        if (min.length() == 1) {
            real min_val = min[0];
            real max_val = max[0];
            for (int i = 0; i < n_dim - 1; i++) {
                min.append(min_val);
                max.append(max_val);
            }
        } else
            PLERROR("In UniformDistribution::build_ - The value of 'n_dim' "
                    "does not match the length of the 'min' vector");
    }
    PLASSERT( n_dim == min.length() );
    for (int i = 0; i < n_dim; i++) {
        if (min[i] > max[i]) {
            PLERROR("In UniformDistribution::build_ - 'min' should be always <= 'max'");
        }
    }
    inputsize_ = n_dim;
    // We need to re-build the parent classes, because the inputsize has
    // changed.
    inherited::build();
}

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real PLearn::UniformDistribution::cdf ( const Vec x) const [virtual]

Return survival fn = P(X<x).

Reimplemented from PLearn::PDistribution.

Definition at line 156 of file UniformDistribution.cc.

References PLERROR.

{
    PLERROR("cdf not implemented for UniformDistribution"); return 0;
}
string PLearn::UniformDistribution::classname ( ) const [virtual]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

void PLearn::UniformDistribution::declareOptions ( OptionList ol) [static, protected]

Declare this class' options.

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 74 of file UniformDistribution.cc.

References PLearn::OptionBase::buildoption, counter, PLearn::declareOption(), PLearn::UnconditionalDistribution::declareOptions(), PLearn::OptionBase::learntoption, max, mesh_size, min, and n_dim.

{
    declareOption(ol, "min", &UniformDistribution::min, OptionBase::buildoption,
        "The inferior bound for all intervals.");

    declareOption(ol, "max", &UniformDistribution::max, OptionBase::buildoption,
        "The superior bound for all intervals.");

    declareOption(ol, "mesh_size", &UniformDistribution::mesh_size, OptionBase::buildoption,
        "If set to a value > 0, this distribution will generate points deterministically\n"
        "so as to form a mesh of 'mesh_size'^d points equally spaced.");

    declareOption(ol, "n_dim", &UniformDistribution::n_dim,
                               OptionBase::buildoption,
        "Optionally, the number of dimensions. Provide this option only if\n"
        "you want to generate 'n_dim' dimensions all in [min,max] (in which\n"
        "case the length of 'min' and 'max' should be 1).");

    declareOption(ol, "counter", &UniformDistribution::counter, OptionBase::learntoption,
                  "Counts the number of points generated (necessary when 'mesh_size' is used).");

    // Now call the parent class' declareOptions().
    inherited::declareOptions(ol);
}

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static const PPath& PLearn::UniformDistribution::declaringFile ( ) [inline, static]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 114 of file UniformDistribution.h.

UniformDistribution * PLearn::UniformDistribution::deepCopy ( CopiesMap copies) const [virtual]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

void PLearn::UniformDistribution::expectation ( Vec mu) const [virtual]

Return E[X].

Reimplemented from PLearn::PDistribution.

Definition at line 164 of file UniformDistribution.cc.

References PLERROR.

{
    PLERROR("expectation not implemented for UniformDistribution");
}
void PLearn::UniformDistribution::generate ( Vec x) const [virtual]

Return a pseudo-random sample generated from the distribution.

Reimplemented from PLearn::PDistribution.

Definition at line 172 of file UniformDistribution.cc.

References counter, i, max, mesh_size, min, n_dim, PLearn::PLearner::random_gen, and PLearn::TVec< T >::resize().

{
    x.resize(n_dim);
    if (mesh_size > 0) {
        int val = counter;
        int coord;
        for (int i = 0; i < n_dim; i++) {
            coord = val % mesh_size;
            val /= mesh_size;
            x[i] = min[i] + (max[i] - min[i]) * coord / real(mesh_size) + (max[i] - min[i]) / (2 * real(mesh_size));
        }
        counter++;
    } else {
        for (int i = 0; i < n_dim; i++) {
            x[i] = random_gen->bounded_uniform(min[i], max[i]);
        }
    }
}

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OptionList & PLearn::UniformDistribution::getOptionList ( ) const [virtual]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

OptionMap & PLearn::UniformDistribution::getOptionMap ( ) const [virtual]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

RemoteMethodMap & PLearn::UniformDistribution::getRemoteMethodMap ( ) const [virtual]

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 69 of file UniformDistribution.cc.

real PLearn::UniformDistribution::log_density ( const Vec x) const [virtual]

Return log of probability density log(p(x)).

Reimplemented from PLearn::PDistribution.

Definition at line 194 of file UniformDistribution.cc.

References i, max, min, n_dim, pl_log, and PLearn::sum().

{
    real sum = 0;
    for (int i = 0; i < n_dim; i++) {
        if (x[i] > max[i] || x[i] < min[i])
            return -INFINITY;
        sum += pl_log(max[i] - min[i]);
    }
    return -sum;
}

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void PLearn::UniformDistribution::makeDeepCopyFromShallowCopy ( CopiesMap copies) [virtual]

Transform a shallow copy into a deep copy.

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 208 of file UniformDistribution.cc.

References PLearn::UnconditionalDistribution::makeDeepCopyFromShallowCopy(), and PLERROR.

{
    inherited::makeDeepCopyFromShallowCopy(copies);

    // ### Call deepCopyField on all "pointer-like" fields
    // ### that you wish to be deepCopied rather than
    // ### shallow-copied.
    // ### ex:
    // deepCopyField(trainvec, copies);

    // ### Remove this line when you have fully implemented this method.
    PLERROR("UniformDistribution::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!");
}

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void PLearn::UniformDistribution::resetGenerator ( long  g_seed) [virtual]

Reset the random number generator used by generate() using the given seed.

Reimplemented from PLearn::PDistribution.

Definition at line 225 of file UniformDistribution.cc.

References counter, and PLearn::PDistribution::resetGenerator().

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real PLearn::UniformDistribution::survival_fn ( const Vec x) const [virtual]

Return survival fn = P(X>x).

Reimplemented from PLearn::PDistribution.

Definition at line 234 of file UniformDistribution.cc.

References PLERROR.

{
    PLERROR("survival_fn not implemented for UniformDistribution"); return 0;
}
void PLearn::UniformDistribution::variance ( Mat cov) const [virtual]

Return Var[X].

Reimplemented from PLearn::PDistribution.

Definition at line 242 of file UniformDistribution.cc.

References PLERROR.

{
    PLERROR("variance not implemented for UniformDistribution");
}

Member Data Documentation

Reimplemented from PLearn::UnconditionalDistribution.

Definition at line 114 of file UniformDistribution.h.

Definition at line 65 of file UniformDistribution.h.

Referenced by declareOptions(), generate(), and resetGenerator().

Definition at line 77 of file UniformDistribution.h.

Referenced by declareOptions(), and generate().

Definition at line 78 of file UniformDistribution.h.

Referenced by build_(), declareOptions(), generate(), and log_density().


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