1 PACKAGE drvodm AUTHID current_user AS
2
3 TYPE CurType IS REF CURSOR;
4
5 /* type used for svm interface to odm containing training set */
6 TYPE trainsamp IS RECORD (
7 sequence_id NUMBER, -- document id
8 attribute_id NUMBER, -- feature id >=0
9 -- or -1 for rows that contain doc. assignment info
10 value NUMBER -- feature weight
11 -- or catid for rows for doc. assignment info
12 );
13
14 TYPE trainsamps IS TABLE OF trainsamp;
15
16 /* type used for feature definition */
17 TYPE feature IS RECORD (
18 text varchar2(160), -- feature text
19 type number(3), -- feature type with 0:single token,
20 -- 1:theme
21 -- 9:stem words
22 id number, -- feature id
23 score number -- statistic feature score which can be idf
24 -- if need to calculate idf for feature
25 -- weight in documents or simply document
26 -- frequency.
27 );
28 TYPE features IS TABLE OF feature;
29
30 TYPE suggestion IS RECORD (
31 docid number, -- document id
32 catid number, -- suggested category id
33 scr number -- associate score between doc and category
34 );
35 TYPE suggestions IS TABLE of suggestion;
36
37 TYPE kmeanmodel_rec IS RECORD (
38 id integer, -- cluster id
39 parent number, -- parent id
40 dispersion number, -- coherencen of the cluster
41 attribute_name varchar2(30)); -- attribute id
42 TYPE kmeanmodel is table of kmeanmodel_rec;
43
44 /* train SVM model */
45 PROCEDURE svm_train(
46 idx_owner varchar2,
47 idx_name varchar2,
48 doctab varchar2,
49 docid varchar2,
50 cattab varchar2,
51 catdocid varchar2,
52 catid varchar2,
53 restab varchar2,
54 prefid number
55 );
56
57 /* ----table function used for ODM to extract feature from training set ----*/
58 /* DESCRIPTION
59 The result table is either created by users before calling this function or
60 created in this program with the specified table name and under the current
61 user (if the specified table does not exist).
62 If user create the result table (which can support table schema for
63 different users),
64 the restab should have the following three columns with the exact column
65 names:
66 cat_id number
67 type number(3) not null
68 rule blob
69 */
70 FUNCTION feature_prep(
71 index_name varchar2, -- context index name on training document table.
72 docid varchar2, -- document id column name in document table
73 cattab varchar2, -- name of category table
74 catdocid varchar2, -- document id column name for category table
75 catid varchar2, -- category id column name for category table
76 restab varchar2, -- result table to write the model
77 preference varchar2 -- preference name
78 ) RETURN trainsamps PIPELINED;
79
80 /* -- table function used for ODM to get features for test set ----------*/
81 /* DESCRIPTION
82 This function is used in apply time. The <restab> is the result table
83 output from calling feature preparation function based on training set.
84 */
85 FUNCTION feature_prep(
86 index_name varchar2, -- context index name on training document table.
87 docid varchar2, -- document id column name in document table
88 restab varchar2 -- table storing the feature definition
89 ) RETURN trainsamps PIPELINED;
90
91 /* ----------------------- for ODM to explain features ------------------*/
92 /* DESCRIPTION
93 The <restab> is the result table output from calling feature preparation
94 function based on training set.
95 */
96 FUNCTION feature_explain(
97 restab varchar2 -- table storing the feature definition
98 ) RETURN features PIPELINED;
99
100
101
102 /*--------------------------------------------------------------------------*/
103 /* */
104 /*------------The following functions is internal functions ---------------*/
105 /* */
106 /*--------------------------------------------------------------------------*/
107
108 /* feature preparation table function without checking input parameter */
109 FUNCTION feature_prep_nc(
110 idx_owner varchar2,
111 idx_name varchar2,
112 doctab varchar2,
113 docid varchar2,
114 cattab varchar2,
115 catdocid varchar2,
116 catid varchar2,
117 restab varchar2,
118 prefid number,
119 temp_table varchar2
120 ) RETURN trainsamps PIPELINED;
121
122 /* start classify a document.Assume: The document feature vector has been
123 generated */
124 FUNCTION get_suggestions(model_name varchar2, dur number)
125 return suggestions PIPELINED;
126
127 /* type used for ODM setup */
128 TYPE settingdbrec_t IS RECORD (
129 id NUMBER,
130 value VARCHAR2(128)
131 );
132 TYPE setting_t IS TABLE of settingdbrec_t;
133
134 FUNCTION odmtrainset return setting_t PIPELINED;
135
136 FUNCTION get_features
137 RETURN trainsamps PIPELINED;
138
139 PROCEDURE fvstab(
140 fvstab OUT varchar2);
141
142 PROCEDURE fi2ttab(
143 fi2ttab OUT varchar2);
144
145
146 FUNCTION odmktrainset(p_srt in varchar2,
147 p_cluster_num in number) return setting_t PIPELINED;
148
149 -- generate model
150 PROCEDURE odm_genmodel(
151 p_fvstab in varchar2,
152 p_srtab in varchar2,
153 p_cluster_num in number,
154 o_modname OUT varchar2);
155
156 FUNCTION odm_readkmeanmodel(
157 p_modname varchar2) return kmeanmodel PIPELINED;
158
159 -- drop model
160 PROCEDURE odm_drpmodel(
161 p_modname IN varchar2);
162
163 END drvodm;
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