~sgould/darwin/classdrwnRandomForest.html

// load training dataset

drwnClassifierDataset dataset;

dataset.read("training_data.bin");


// train the classifier

const int nFeatures = dataset.numFeatures();

const int nClasses = dataset.maxTarget() + 1;

drwnRandomForest model(nFeatures, nClasses);

model.train(dataset);


// load evaluation set

dataset.read("testing_data.bin", false);


// predict labels

vector<int> predictions;

model.getClassifications(dataset.features, predictions);
Public Member Functions
	drwnRandomForest ()
	default constructor

	drwnRandomForest (unsigned n, unsigned k=2)
	construct a random forest classifier for `k` classes and `n` features

	drwnRandomForest (const drwnRandomForest &c)
	copy constructor

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

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

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

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

virtual bool	load (drwnXMLNode &node)
	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 void	getClassScores (const vector< double > &features, vector< double > &outputScores) const
	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 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 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 int	NUM_TREES = 100
	default number of trees in the forest (used during construction)

static int	MAX_DEPTH = 2
	default depth of each tree (used during construction)

static int	MAX_FEATURES = 10
	maximum number of features to use at each iteration (used during construction)
Protected Attributes
int	_numTrees
	number of trees to learn

int	_maxDepth
	maximum depth of each decision tree

int	_maxFeatures
	maximum number of features to use at each iteration

vector< drwnDecisionTree * >	_forest
	forest

vector< double >	_alphas
	weight for each weak tree

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
Additional Inherited Members
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)