Naive Bayes Gaussian classifier. More...

#include <config.h>
#include <stdlib.h>
#include <inttypes.h>
#include <jwsc/base/error.h>
#include <jwsc/vector/vector.h>
#include <jwsc/matrix/matrix.h>

Data Structures
struct	NB_gauss_model
	Naive Bayes Gaussian model. More...
struct	NB_gauss_model_node
	Naive Bayes Gaussian model tree node. More...
Typedefs
typedef struct NB_gauss_model_node	NB_gauss_model_node
	Naive Bayes Gaussian model tree node.
typedef NB_gauss_model_node	NB_gauss_model_tree
	Naive Bayes Gaussian model tree.
Functions
void	train_nb_gauss_model (NB_gauss_model *model_out, const Vector_u32 labels, const Matrix_i32 markers, const Vector_d priors)
	Trains a Naive Bayes Gaussian model.
Error *	predict_label_with_nb_gauss_model (uint32_t label_out, double confidence_out, const Vector_i32 markers, const NB_gauss_model model, uint32_t order)
	Predicts the label for a marker sample using a Naive Bayes Gaussian model.
Error *	predict_labels_with_nb_gauss_model (Vector_u32 labels_out, Vector_d confidence_out, const Matrix_i32 markers, const NB_gauss_model model)
	Predicts the labels for a set of marker samples using a Naive Bayes Gaussian model.
Error *	read_nb_gauss_model (NB_gauss_model *model_out, const char fname)
	Reads a Naive Bayes Gaussian model.
Error *	write_nb_gauss_model (NB_gauss_model model, const char fname)
	Writes a Naive Bayes Gaussian model.
void	free_nb_gauss_model (NB_gauss_model *model)
	Frees a Naive Bayes Gaussian model.
Error *	train_nb_gauss_model_tree (NB_gauss_model_tree *tree_out, const Vector_u32 labels, const Matrix_i32 markers, const char tree_xml_fname, const char *tree_dtd_fname)
	Trains a Naive Bayes Gaussian model tree .
Error *	predict_labels_with_nb_gauss_model_tree (Vector_u32 labels_out, Vector_d confidence_out, const Matrix_i32 markers, const NB_gauss_model_tree tree, uint32_t order)
	Predicts the labels for a set of marker samples using a Naive Bayes Gaussian model tree.
Error *	read_nb_gauss_model_tree (NB_gauss_model_tree *tree_out, const char tree_xml_fname, const char tree_dtd_fname, const char model_dirname)
	Reads a Naive Bayes Gaussian model tree from.
Error *	write_nb_gauss_model_tree (const NB_gauss_model_tree tree, const char model_dirname)
	Writes a Naive Bayes Gaussian model tree.
void	free_nb_gauss_model_tree (NB_gauss_model_tree *tree)
	Frees a Naive Bayes Gaussian model tree.

Detailed Description

Naive Bayes Gaussian classifier.

Author:: Joseph Schlecht

License:: Creative Commons BY-NC-SA 3.0

Definition in file nb_gauss.h.

Typedef Documentation

typedef struct NB_gauss_model_node NB_gauss_model_node

Naive Bayes Gaussian model tree node.

typedef NB_gauss_model_node NB_gauss_model_tree

Naive Bayes Gaussian model tree.

Definition at line 182 of file nb_gauss.h.

Function Documentation

void train_nb_gauss_model	(	NB_gauss_model **	model_out,
		const Vector_u32 *	labels,
		const Matrix_i32 *	markers,
		const Vector_d *	label_priors
	)

Trains a Naive Bayes Gaussian model.

Parameters:

model_out	Result parameter. If *model_out is NULL, a model is allocated; otherwise its space is re-used.
labels	Labels for training, with ith element as corresponding to the ith sample in markers.
markers	Markers for training, with ith row as a sample corresponding to the ith label in labels.
label_priors	Priors for each of the labels.

Definition at line 89 of file nb_gauss.c.

Error* predict_label_with_nb_gauss_model	(	uint32_t *	label_out,
		double *	confidence_out,
		const Vector_i32 *	markers,
		const NB_gauss_model *	model,
		uint32_t	order
	)

Predicts the label for a marker sample using a Naive Bayes Gaussian model.

Parameters:

label_out	Result parameter.
confidence_out	Result parameter.
markers	Marker data to predict.
model	Trained model to use for predicting.
order	Indicates which of the The kth best prediction values to return. Starts with 0 representing the overall best, 1 the next best and so on.

Returns:: On success, NULL is returned; otherwise an error is returned.

Definition at line 213 of file nb_gauss.c.

Error* predict_labels_with_nb_gauss_model	(	Vector_u32 **	labels_out,
		Vector_d **	confidence_out,
		const Matrix_i32 *	markers,
		const NB_gauss_model *	model
	)

Predicts the labels for a set of marker samples using a Naive Bayes Gaussian model.

Parameters:

labels_out	Result parameter. If *labels_out is NULL, it is allocated; otherwise its space is re-used.
confidence_out	Result parameter. If *confidence_out is NULL, it is allocated; otherwise its space is re-used.
markers	Marker data to predict. Each row is a sample for prediction, corresponding to an element in the result parameters.
model	Trained model to use for predicting.

Returns:: On success, NULL is returned; otherwise an error is returned, but the result parameters are not freed.

Definition at line 326 of file nb_gauss.c.

Error* read_nb_gauss_model	(	NB_gauss_model **	model_out,
		const char *	fname
	)

Reads a Naive Bayes Gaussian model.

Parameters:

model_out	Result parameter. If *model_out is NULL, a model is allocated; otherwise its space is re-used.
fname	File to read the model from.

Returns:: On success, NULL is returned; otherwise an error is returned but does not free result parameter.

Definition at line 383 of file nb_gauss.c.

Error* write_nb_gauss_model	(	NB_gauss_model *	model,
		const char *	fname
	)

Writes a Naive Bayes Gaussian model.

Parameters:

model	Model to write.
fname	File to write to.

Returns:: On success, NULL is returned; otherwise an error is returned.

Definition at line 446 of file nb_gauss.c.

void free_nb_gauss_model ( NB_gauss_model * model )

Frees a Naive Bayes Gaussian model.

Parameters:

model Model to free.

Definition at line 475 of file nb_gauss.c.

Error* train_nb_gauss_model_tree	(	NB_gauss_model_tree **	tree_out,
		const Vector_u32 *	labels,
		const Matrix_i32 *	markers,
		const char *	tree_xml_fname,
		const char *	tree_dtd_fname
	)

Trains a Naive Bayes Gaussian model tree .

Parameters:

tree_out	Result parameter.
labels	Sample group labels.
markers	Sample marker values.
tree_xml_fname	XML file containing the model tree information.
tree_dtd_fname	DTD file for validating the XML file, can be NULL.

Definition at line 806 of file nb_gauss.c.

Error* predict_labels_with_nb_gauss_model_tree	(	Vector_u32 **	labels_out,
		Vector_d **	confidence_out,
		const Matrix_i32 *	markers,
		const NB_gauss_model_tree *	tree,
		uint32_t	order
	)

Predicts the labels for a set of marker samples using a Naive Bayes Gaussian model tree.

Parameters:

labels_out	Result parameter. If *labels_out is NULL, it is allocated; otherwise its space is re-used.
confidence_out	Result parameter. If *confidence_out is NULL, it is allocated; otherwise its space is re-used.
markers	Marker data to predict. Each row is a sample for prediction, corresponding to an element in the result parameters.
tree	Trained model tree to use for predicting.
order	The kth order prediction value. Zero being the best prediction.

Returns:: On success, NULL is returned; otherwise an error is returned, but the result parameters are not freed.

Definition at line 914 of file nb_gauss.c.

Error* read_nb_gauss_model_tree	(	NB_gauss_model_tree **	tree_out,
		const char *	tree_xml_fname,
		const char *	tree_dtd_fname,
		const char *	model_dirname
	)

Reads a Naive Bayes Gaussian model tree from.

Parameters:

tree_out	Result parameter.
tree_xml_fname	XML file containing the model tree information.
tree_dtd_fname	DTD file for validating the XML file, can be NULL.
model_dirname	Directory prefixing each model file in the tree.

Definition at line 1004 of file nb_gauss.c.

Error* write_nb_gauss_model_tree	(	const NB_gauss_model_tree *	tree,
		const char *	model_dirname
	)

Writes a Naive Bayes Gaussian model tree.

Parameters:

tree	Model tree to write.
model_dirname	Directory to prefix to each model file in the tree.

Definition at line 1056 of file nb_gauss.c.

void free_nb_gauss_model_tree ( NB_gauss_model_tree * tree )

Frees a Naive Bayes Gaussian model tree.

Parameters:

tree	Model tree to free.

Definition at line 1091 of file nb_gauss.c.

Data Structures

Typedefs

Functions

Detailed Description

Typedef Documentation

Function Documentation