Common functionality for drwnMultiClassLogistic. More...

Inheritance diagram for drwnMultiClassLogisticBase:

Public Member Functions
	drwnMultiClassLogisticBase ()
	default constructor

	drwnMultiClassLogisticBase (unsigned n, unsigned k=2)
	construct a `k-class` logistic classifier for data of dimension `n`

	drwnMultiClassLogisticBase (const drwnMultiClassLogisticBase &c)
	copy constructor

virtual const char *	type () const
	returns object type as a string (e.g., Foo::type() { return "Foo"; })

virtual bool	save (drwnXMLNode &xml) const
	write object to XML node (see also write)

virtual bool	load (drwnXMLNode &xml)
	read object from XML node (see also read)

virtual double	train (const drwnClassifierDataset &dataset)
	train the parameters of the classifier from a drwnClassifierDataset object

virtual double	train (const vector< vector< double > > &features, const vector< int > &targets)
	train the parameters of the classifier from a set of features and corresponding labels

virtual double	train (const vector< vector< double > > &features, const vector< int > &targets, const vector< double > &weights)
	train the parameters of the classifier from a weighted set of features and corresponding labels

virtual void	getClassScores (const vector< double > &features, vector< double > &outputScores) const =0
	compute the unnormalized log-probability for a single feature vector

Public Member Functions inherited from drwnClassifier
	drwnClassifier ()
	default constructor

	drwnClassifier (unsigned n, unsigned k=2)
	construct a classifer with `n` features and `k` classes

	drwnClassifier (const drwnClassifier &c)
	copy constructor

int	numFeatures () const
	returns the number of features expected by the classifier object

int	numClasses () const
	returns the number of classes predicted by the classifier object

virtual bool	valid () const
	returns true if the classifier is valid (has been initialized and trained)

virtual void	initialize (unsigned n, unsigned k=2)
	initialize the classifier object for `n` features and `k` classes

virtual drwnClassifier *	clone () const =0
	returns a copy of the class usually implemented as virtual Foo* clone() { return new Foo(*this); }

virtual double	train (const char *filename)
	train the parameters of the classifier from data stored in `filename`

virtual void	getClassScores (const vector< vector< double > > &features, vector< vector< double > > &outputScores) const
	compute the unnormalized log-probability for a set of feature vectors

virtual void	getClassMarginals (const vector< double > &features, vector< double > &outputMarginals) const
	compute the class marginal probabilities for a single feature vector

virtual void	getClassMarginals (const vector< vector< double > > &features, vector< vector< double > > &outputMarginals) const
	compute the class marginal probabilities for a set of feature vectors

virtual int	getClassification (const vector< double > &features) const
	return the most likely class label for a single feature vector

virtual void	getClassifications (const vector< vector< double > > &features, vector< int > &outputLabels) const
	compute the most likely class labels for a set of feature vector

Public Member Functions inherited from drwnWriteable
bool	write (const char *filename) const
	write object to file (calls save)

bool	read (const char *filename)
	read object from file (calls load)

void	dump () const
	print object's current state to standard output (for debugging)

Public Member Functions inherited from drwnProperties
unsigned	numProperties () const

bool	hasProperty (const string &name) const

bool	hasProperty (const char *name) const

unsigned	findProperty (const string &name) const

unsigned	findProperty (const char *name) const

void	setProperty (unsigned indx, bool value)

void	setProperty (unsigned indx, int value)

void	setProperty (unsigned indx, double value)

void	setProperty (unsigned indx, const string &value)

void	setProperty (unsigned indx, const char *value)

void	setProperty (unsigned indx, const Eigen::VectorXd &value)

void	setProperty (unsigned indx, const Eigen::MatrixXd &value)

void	setProperty (const char *name, bool value)

void	setProperty (const char *name, int value)

void	setProperty (const char *name, double value)

void	setProperty (const char *name, const string &value)

void	setProperty (const char name, const char value)

void	setProperty (const char *name, const Eigen::VectorXd &value)

void	setProperty (const char *name, const Eigen::MatrixXd &value)

string	getPropertyAsString (unsigned indx) const

drwnPropertyType	getPropertyType (unsigned indx) const

bool	isReadOnly (unsigned indx) const

const drwnPropertyInterface *	getProperty (unsigned indx) const

const drwnPropertyInterface *	getProperty (const char *name) const

bool	getBoolProperty (unsigned indx) const

int	getIntProperty (unsigned indx) const

double	getDoubleProperty (unsigned indx) const

const string &	getStringProperty (unsigned indx) const

const list< string > &	getListProperty (unsigned indx) const

int	getSelectionProperty (unsigned indx) const

const Eigen::VectorXd &	getVectorProperty (unsigned indx) const

const Eigen::MatrixXd &	getMatrixProperty (unsigned indx) const

const string &	getPropertyName (unsigned indx) const

vector< string >	getPropertyNames () const

void	readProperties (drwnXMLNode &xml, const char *tag="property")

void	writeProperties (drwnXMLNode &xml, const char *tag="property") const

void	printProperties (ostream &os) const

Static Public Attributes
static double	REG_STRENGTH = 1.0e-9
	default strength of regularizer (used during construction)

static int	MAX_ITERATIONS = 1000
	maximum number of training iterations

Protected Member Functions
double	objective (const double *x) const
	returns value of objective function at point x

void	gradient (const double x, double df) const
	populates gradient of objective function at point x

virtual double	objectiveAndGradient (const double x, double df) const =0
	returns value of objective function and populates gradient df at point x

Protected Member Functions inherited from drwnProperties
void	declareProperty (const string &name, drwnPropertyInterface *optif)

void	undeclareProperty (const string &name)

void	exposeProperties (drwnProperties *opts, const string &prefix=string(""), bool bSerializable=false)

virtual void	propertyChanged (const string &name)

Protected Member Functions inherited from drwnOptimizer
	drwnOptimizer ()
	default constructor

	drwnOptimizer (unsigned n)
	construct a problem with dimension n

	drwnOptimizer (const drwnOptimizer &o)
	copy constructor

void	initialize (unsigned n, const double *x=NULL)
	initialize an optimization problem of size n possibly with feasible starting point x $\mathbb{R}^n$ (or zero)

void	initialize (const double *x=NULL)
	initialize an optimization problem at feasible starting point x in $\mathbb{R}^n$ (or zero)

double	solve (unsigned maxiter, bool bMonitor=false)
	Solve the optimization problem for up to maxiter iterations to precision set by EPSF, EPSG, and EPSX static variables. Calls monitor function after each iteration if bMonitor is true.

unsigned	size () const
	dimension of optimization problem

double	operator[] (unsigned i) const
	returns the i-th component of the current solution

double &	operator[] (unsigned i)
	returns a reference to the i-th component of the current solution

virtual void	monitor (unsigned iter, double objValue)
	callback for each iteration during optimization (if bMonitor is true)

Protected Attributes
VectorXd	_theta
	joint feature map weights

int	_regularizer
	regularization option

double	_lambda
	regularization strength

const vector< vector< double > > *	_features

const vector< int > *	_targets

const vector< double > *	_weights

Protected Attributes inherited from drwnClassifier
int	_nFeatures
	number of features

int	_nClasses
	number of classes

bool	_bValid
	true if the classifier has been trained or loaded

Protected Attributes inherited from drwnOptimizer
unsigned	_n
	dimension of optimization problem (i.e., $\mathbb{R}^n$ )

double *	_x
	current feasible solution in $\mathbb{R}^n$

double *	_df
	gradient at _x in $\mathbb{R}^n$

Additional Inherited Members
Static Protected Attributes inherited from drwnOptimizer
static double	EPSF = 1.0e-6
	default tolerance on function convergence

static double	EPSG = 1.0e-3
	deafult tolerance on gradient convergence

static double	EPSX = 1.0e-6
	default tolerance on solution convergence

Detailed Description

Common functionality for drwnMultiClassLogistic.

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

drwnMultiClassLogistic.h
drwnMultiClassLogistic.cpp

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description