1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
|
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<HTML>
<HEAD>
<TITLE>class ClassLLCriterion</TITLE>
<META NAME="GENERATOR" CONTENT="DOC++ 3.4.8">
</HEAD>
<BODY BGCOLOR="#ffffff">
<H2>class <A HREF="#DOC.DOCU">ClassLLCriterion</A></H2></H2><BLOCKQUOTE>This criterion can be used to train *in classification* a <TT>GradientMachine</TT> object using the <TT>GMTrainer</TT> trainer.</BLOCKQUOTE>
<HR>
<H2>Inheritance:</H2>
<APPLET CODE="ClassGraph.class" WIDTH=600 HEIGHT=155>
<param name=classes value="CObject,MObject.html,CMachine,MMachine.html,CGradientMachine,MGradientMachine.html,CCriterion,MCriterion.html,CClassLLCriterion,MClassLLCriterion.html">
<param name=before value="M,M,M,M,M">
<param name=after value="Md_SPSPSP,Md_SPSP,Md_SP,Md_,M">
<param name=indent value="0,1,2,3,4">
<param name=arrowdir value="down">
</APPLET>
<HR>
<DL>
<P><DL>
<DT><H3>Public Methods</H3><DD><DT>
<IMG ALT="[more]" BORDER=0 SRC=icon1.gif> <B><A HREF="#DOC.5.1">ClassLLCriterion</A></B>(<!1><A HREF="ClassFormat.html">ClassFormat</A>* <!1><A HREF="Boosting.html#DOC.2.2">class_format</A>)
<DD><I>The ClassFormat is needed just to know how the targets are encoded in the data</I>
</DL></P>
</DL>
<HR><H3>Inherited from <A HREF="Criterion.html">Criterion</A>:</H3>
<DL>
<P><DL>
<DT><H3>Public Fields</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif><!1><A HREF="DataSet.html">DataSet</A>* <B>data</B>
</DL></P>
<P><DL>
<DT><H3>Public Methods</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>setDataSet</B>(<!1><A HREF="DataSet.html">DataSet</A>* data_)
</DL></P>
</DL>
<HR><H3>Inherited from <A HREF="GradientMachine.html">GradientMachine</A>:</H3>
<DL>
<P><DL>
<DT><H3>Public Fields</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>bool <B>is_free</B>
<DT>
<IMG ALT="o" SRC=icon2.gif><!1><A HREF="List.html">List</A>* <B>params</B>
<DT>
<IMG ALT="o" SRC=icon2.gif><!1><A HREF="List.html">List</A>* <B>der_params</B>
<DT>
<IMG ALT="o" SRC=icon2.gif>int <B>n_params</B>
<DT>
<IMG ALT="o" SRC=icon2.gif>real* <B>beta</B>
</DL></P>
<P><DL>
<DT><H3>Public Methods</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>init</B>()
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual int <B>numberOfParams</B>()
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>iterInitialize</B>()
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>backward</B>(<!1><A HREF="List.html">List</A>* <!1><A HREF="SeqExample.html#DOC.107.3">inputs</A>, real* <!1><A HREF="QCMachine.html#DOC.40.5">alpha</A>)
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>allocateMemory</B>()
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>freeMemory</B>()
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>loadFILE</B>(FILE* <!1><A HREF="Measurer.html#DOC.30.2">file</A>)
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>saveFILE</B>(FILE* <!1><A HREF="Measurer.html#DOC.30.2">file</A>)
</DL></P>
</DL>
<HR><H3>Inherited from <A HREF="Machine.html">Machine</A>:</H3>
<DL>
<P><DL>
<DT><H3>Public Fields</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>int <B>n_inputs</B>
<DT>
<IMG ALT="o" SRC=icon2.gif>int <B>n_outputs</B>
<DT>
<IMG ALT="o" SRC=icon2.gif><!1><A HREF="List.html">List</A>* <B>outputs</B>
</DL></P>
<P><DL>
<DT><H3>Public Methods</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>forward</B>(<!1><A HREF="List.html">List</A>* <!1><A HREF="SeqExample.html#DOC.107.3">inputs</A>)
<DT>
<IMG ALT="o" SRC=icon2.gif>virtual void <B>reset</B>()
</DL></P>
</DL>
<HR><H3>Inherited from <A HREF="Object.html">Object</A>:</H3>
<DL>
<P><DL>
<DT><H3>Public Methods</H3><DD><DT>
<IMG ALT="o" SRC=icon2.gif>void <B>addOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, int size, void* <!1><A HREF="Vec.html#DOC.81.3">ptr</A>, const char* <!1><A HREF="CmdLine.html#DOC.7.3">help</A>="", bool is_allowed_after_init=false)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>addIOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, int* <!1><A HREF="Vec.html#DOC.81.3">ptr</A>, int init_value, const char* <!1><A HREF="CmdLine.html#DOC.7.3">help</A>="", bool is_allowed_after_init=false)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>addROption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, real* <!1><A HREF="Vec.html#DOC.81.3">ptr</A>, real init_value, const char* <!1><A HREF="CmdLine.html#DOC.7.3">help</A>="", bool is_allowed_after_init=false)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>addBOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, bool* <!1><A HREF="Vec.html#DOC.81.3">ptr</A>, bool init_value, const char* <!1><A HREF="CmdLine.html#DOC.7.3">help</A>="", bool is_allowed_after_init=false)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>setOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, void* <!1><A HREF="Vec.html#DOC.81.3">ptr</A>)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>setIOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, int option)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>setROption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, real option)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>setBOption</B>(const char* <!1><A HREF="SeqExample.html#DOC.107.9">name</A>, bool option)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>load</B>(const char* filename)
<DT>
<IMG ALT="o" SRC=icon2.gif>void <B>save</B>(const char* filename)
</DL></P>
</DL>
<A NAME="DOC.DOCU"></A>
<HR>
<H2>Documentation</H2>
<BLOCKQUOTE>This criterion can be used to train *in classification* a <TT>GradientMachine</TT>
object using the <TT>GMTrainer</TT> trainer. It then maximizes the log likelihood
of the data.
<P>If we write <IMG BORDER=0 SRC=g000001.gif> for the output <IMG BORDER=0 SRC=g000002.gif> of the <TT>GradientMachine</TT>, it supposes that
<UL>
<LI> the outputs <IMG BORDER=0 SRC=g000001.gif> are log-probabilities.
<LI> <IMG BORDER=0 SRC=g000003.gif> is the probability for the class <IMG BORDER=0 SRC=g000002.gif>
<LI> the predicted class follows a multinomial distribution with parameters
<IMG BORDER=0 SRC=g000004.gif>
</UL>
<P></BLOCKQUOTE>
<DL>
<A NAME="ClassLLCriterion"></A>
<A NAME="DOC.5.1"></A>
<DT><IMG ALT="o" BORDER=0 SRC=icon2.gif><TT><B> ClassLLCriterion(<!1><A HREF="ClassFormat.html">ClassFormat</A>* <!1><A HREF="Boosting.html#DOC.2.2">class_format</A>)</B></TT>
<DD>The ClassFormat is needed just to know how the targets are encoded in the data
<DL><DT><DD></DL><P></DL>
<HR><DL><DT><B>This class has no child classes.</B></DL>
<DL><DT><DT><B>Author:</B><DD>Ronan Collobert (collober@iro.umontreal.ca)
<DD></DL><P><P><I><A HREF="index.html">Alphabetic index</A></I> <I><A HREF="HIER.html">HTML hierarchy of classes</A> or <A HREF="HIERjava.html">Java</A></I></P><HR>
<BR>
This page was generated with the help of <A HREF="http://docpp.sourceforge.net">DOC++</A>.
</BODY>
</HTML>
|
