the common functions written in PL/PGSQL shared by C4.5 and RF More...

Functions
bool	__is_valid_enc_table (text enc_tbl_name)

float8[]	__best_scv_sfunc (float8[] result, float8[] scv, int fid, float8 split_value)

float8[]	__best_scv_prefunc (float8[] sfunc1_result, float8[] sfunc2_result)

aggregate float8[]	__best_scv_aggr (float8[], int, float8)

float8[]	__scv_aggr_sfunc (float8[] result, int sc_code, boolean is_cont, int num_class, float8[] le_array, float8[] total_array, bigint true_total)

float8[]	__scv_aggr_prefunc (float8[] sfunc1_result, float8[] sfunc2_result)

float8[]	__scv_aggr_ffunc (float8[] internal_result)

aggregate float8[]	__scv_aggr (int, boolean, int, float8[], float8[], bigint)

int[]	__dt_get_node_split_fids (int4, int4, int4, int4[])

text	__get_features_of_nodes (text nid_table_name, text result_table_name, int num_chosen_fids, int total_num_fids, int verbosity)

__gen_acc_time	__gen_acc (text encoded_table_name, text metatable_name, text result_table_name, text tr_table_name, text sf_table_name, int num_featrue_try, int num_classes, boolean sampling_needed, int verbosity)

bigint[]	__rep_aggr_class_count_sfunc (bigint[] class_count_array, int classified_class, int original_class, int max_num_of_classes)

bigint[]	__bigint_array_add (bigint[], bigint[])

bigint[]	__rep_aggr_class_count_ffunc (bigint[] class_count_array)

aggregate bigint[]	__rep_aggr_class_count (int, int, int)

float8[]	__array_indexed_agg_sfunc (float8[] state, float8 elem, int8 elem_cnt, int8 elem_idx)

float8[]	__array_indexed_agg_prefunc (float8[], float8[])

float8[]	__array_indexed_agg_ffunc (float8[])

aggregate float8[]	__array_indexed_agg (float8, int8, int8)

bigint[]	__dt_acc_count_sfunc (bigint[] count_array, int num_of_class, bigint count, int class)

aggregate bigint[]	__dt_acc_count_aggr (int, bigint, int)

aggregate bigint[]	__bigint_array_sum (bigint[])

void	__find_best_split (text table_name, float confidence_level, text feature_table_name, int split_criterion, int continue_grow, text output_table, int h2hmv_routine_id, int num_classes)

void	__create_tree_tables (text result_tree_table_name)

void	__rep_prune_tree (text tree_table_name, text validation_table, int max_num_classes)

float8	__ebp_calc_errors (float8 total, float8 prob, float8 confidence_level)

void	__ebp_prune_tree (text tree_table_name)

void	__generate_final_tree (text result_tree_table_name)

set< bigint >	__sample_within_range (bigint num_of_samples, bigint low, bigint high)

void	__sample_with_replacement (int num_of_tree, bigint size_per_tree, text src_table, text target_table)

__train_result	__train_tree (text split_criterion, int num_trees, int features_per_node, text training_table_name, text training_table_meta, text result_tree_table_name, text validation_table_name, text id_col_name, text class_col_name, float confidence_level, int max_tree_depth, float sampling_percentage, float node_prune_threshold, float node_split_threshold, boolean sampling_needed, int h2hmv_routine_id, int verbosity)

text	__display_node_sfunc (text state, int depth, boolean is_cont, text feat_name, text curr_val, float8 split_value, float8 max_prob, text max_class, int num_of_samples)

set< text >	__treemodel_display_with_ordered_aggr (text tree_table, int[] tree_id, int max_depth)

text	__display_tree_no_ordered_aggr (text tree_table, int id, int feature_id, int depth, boolean is_cont, float split_value, text metatable_name, int max_depth, int tree_id)

set< text >	__treemodel_display_no_ordered_aggr (text tree_table, int[] tree_id, int max_depth)

void	__treemodel_get_vote_result (text src_table, text dst_table)

text[]	__treemodel_classify_internal (text classification_table_name, text tree_table_name, int verbosity)

text[]	__treemodel_classify_internal_serial (text classification_table_name, text tree_table_name, int verbosity)

float	__treemodel_score (text tree_table_name, text scoring_table_name, int verbosity)

void	__check_dt_common_params (text split_criterion, text training_table_name, text result_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, text how2handle_missing_value, int max_tree_depth, float node_prune_threshold, float node_split_threshold, int verbosity, text error_msg)

text[]	__gen_enc_meta_names (text result_table_name, text error_msg)

void	__check_training_table (text training_table_name, text[] continuous_feature_names, text[] feature_col_names, text id_col_name, text class_col_name, int features_per_node)

record	__encode_and_train (text dt_algo_name, text split_criterion, int num_trees, int features_per_node, text training_table_name, text validation_table_name, text tree_table_name, text continuous_feature_names, text feature_col_names, text id_col_name, text class_col_name, float8 confidence_level, text how2handle_missing_value, int max_tree_depth, float8 sampling_percentage, bool sampling_needed, float8 node_prune_threshold, float8 node_split_threshold, text error_msg, int verbosity)

Detailed Description

Date: April 5, 2012

Function Documentation

aggregate float8 [] __array_indexed_agg	(	float8	,
		int8	,
		int8
	)

float8 [] __array_indexed_agg_ffunc ( float8 [] )

float8 [] __array_indexed_agg_prefunc	(	float8	[],
		float8	[]
	)

float8 [] __array_indexed_agg_sfunc	(	float8[]	state,
		float8	elem,
		int8	elem_cnt,
		int8	elem_idx
	)

aggregate float8 [] __best_scv_aggr	(	float8	[],
		int	,
		float8
	)

float8 [] __best_scv_prefunc	(	float8[]	sfunc1_result,
		float8[]	sfunc2_result
	)

float8 [] __best_scv_sfunc	(	float8[]	result,
		float8[]	scv,
		int	fid,
		float8	split_value
	)

bigint [] __bigint_array_add	(	bigint	[],
		bigint	[]
	)

aggregate bigint [] __bigint_array_sum ( bigint [] )

void __check_dt_common_params	(	text	split_criterion,
		text	training_table_name,
		text	result_table_name,
		text	continuous_feature_names,
		text	feature_col_names,
		text	id_col_name,
		text	class_col_name,
		text	how2handle_missing_value,
		int	max_tree_depth,
		float	node_prune_threshold,
		float	node_split_threshold,
		int	verbosity,
		text	error_msg
	)

void __check_training_table	(	text	training_table_name,
		text[]	continuous_feature_names,
		text[]	feature_col_names,
		text	id_col_name,
		text	class_col_name,
		int	features_per_node
	)

void __create_tree_tables ( text result_tree_table_name )

text __display_node_sfunc	(	text	state,
		int	depth,
		boolean	is_cont,
		text	feat_name,
		text	curr_val,
		float8	split_value,
		float8	max_prob,
		text	max_class,
		int	num_of_samples
	)

text __display_tree_no_ordered_aggr	(	text	tree_table,
		int	id,
		int	feature_id,
		int	depth,
		boolean	is_cont,
		float	split_value,
		text	metatable_name,
		int	max_depth,
		int	tree_id
	)

aggregate bigint [] __dt_acc_count_aggr	(	int	,
		bigint	,
		int
	)

bigint [] __dt_acc_count_sfunc	(	bigint[]	count_array,
		int	num_of_class,
		bigint	count,
		int	class
	)

int [] __dt_get_node_split_fids	(	int4	,
		int4	,
		int4	,
		int4	[]
	)

float8 __ebp_calc_errors	(	float8	total,
		float8	prob,
		float8	confidence_level
	)

void __ebp_prune_tree ( text tree_table_name )

record __encode_and_train	(	text	dt_algo_name,
		text	split_criterion,
		int	num_trees,
		int	features_per_node,
		text	training_table_name,
		text	validation_table_name,
		text	tree_table_name,
		text	continuous_feature_names,
		text	feature_col_names,
		text	id_col_name,
		text	class_col_name,
		float8	confidence_level,
		text	how2handle_missing_value,
		int	max_tree_depth,
		float8	sampling_percentage,
		bool	sampling_needed,
		float8	node_prune_threshold,
		float8	node_split_threshold,
		text	error_msg,
		int	verbosity
	)

void __find_best_split	(	text	table_name,
		float	confidence_level,
		text	feature_table_name,
		int	split_criterion,
		int	continue_grow,
		text	output_table,
		int	h2hmv_routine_id,
		int	num_classes
	)

__gen_acc_time __gen_acc	(	text	encoded_table_name,
		text	metatable_name,
		text	result_table_name,
		text	tr_table_name,
		text	sf_table_name,
		int	num_featrue_try,
		int	num_classes,
		boolean	sampling_needed,
		int	verbosity
	)

text [] __gen_enc_meta_names	(	text	result_table_name,
		text	error_msg
	)

void __generate_final_tree ( text result_tree_table_name )

text __get_features_of_nodes	(	text	nid_table_name,
		text	result_table_name,
		int	num_chosen_fids,
		int	total_num_fids,
		int	verbosity
	)

bool __is_valid_enc_table ( text enc_tbl_name )

aggregate bigint [] __rep_aggr_class_count	(	int	,
		int	,
		int
	)

bigint [] __rep_aggr_class_count_ffunc ( bigint[] class_count_array )

bigint [] __rep_aggr_class_count_sfunc	(	bigint[]	class_count_array,
		int	classified_class,
		int	original_class,
		int	max_num_of_classes
	)

void __rep_prune_tree	(	text	tree_table_name,
		text	validation_table,
		int	max_num_classes
	)

void __sample_with_replacement	(	int	num_of_tree,
		bigint	size_per_tree,
		text	src_table,
		text	target_table
	)

set<bigint> __sample_within_range	(	bigint	num_of_samples,
		bigint	low,
		bigint	high
	)

aggregate float8 [] __scv_aggr	(	int	,
		boolean	,
		int	,
		float8	[],
		float8	[],
		bigint
	)

float8 [] __scv_aggr_ffunc ( float8[] internal_result )

float8 [] __scv_aggr_prefunc	(	float8[]	sfunc1_result,
		float8[]	sfunc2_result
	)

float8 [] __scv_aggr_sfunc	(	float8[]	result,
		int	sc_code,
		boolean	is_cont,
		int	num_class,
		float8[]	le_array,
		float8[]	total_array,
		bigint	true_total
	)

__train_result __train_tree	(	text	split_criterion,
		int	num_trees,
		int	features_per_node,
		text	training_table_name,
		text	training_table_meta,
		text	result_tree_table_name,
		text	validation_table_name,
		text	id_col_name,
		text	class_col_name,
		float	confidence_level,
		int	max_tree_depth,
		float	sampling_percentage,
		float	node_prune_threshold,
		float	node_split_threshold,
		boolean	sampling_needed,
		int	h2hmv_routine_id,
		int	verbosity
	)

text [] __treemodel_classify_internal	(	text	classification_table_name,
		text	tree_table_name,
		int	verbosity
	)

text [] __treemodel_classify_internal_serial	(	text	classification_table_name,
		text	tree_table_name,
		int	verbosity
	)

set<text> __treemodel_display_no_ordered_aggr	(	text	tree_table,
		int[]	tree_id,
		int	max_depth
	)

set<text> __treemodel_display_with_ordered_aggr	(	text	tree_table,
		int[]	tree_id,
		int	max_depth
	)

void __treemodel_get_vote_result	(	text	src_table,
		text	dst_table
	)

float __treemodel_score	(	text	tree_table_name,
		text	scoring_table_name,
		int	verbosity
	)

Functions

Detailed Description

Function Documentation