| 1 |
unit GikoBayesian; |
| 2 |
|
| 3 |
{! |
| 4 |
\file GikoBayesian.pas |
| 5 |
\brief ???ゃ?吾?≪?潟???c????/span> |
| 6 |
|
| 7 |
$Id: GikoBayesian.pas,v 1.17.4.1 2005/07/10 04:16:46 h677 Exp $ |
| 8 |
} |
| 9 |
|
| 10 |
//! 綛割皿????莨??吾?????????? |
| 11 |
{$DEFINE GIKO_BAYESIAN_NO_HIRAGANA_DIC} |
| 12 |
|
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interface |
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|
| 15 |
//================================================== |
| 16 |
uses |
| 17 |
//================================================== |
| 18 |
Classes; |
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|
| 20 |
//================================================== |
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type |
| 22 |
//================================================== |
| 23 |
|
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{!*********************************************************** |
| 25 |
\brief ??茯???????????/span> |
| 26 |
************************************************************} |
| 27 |
TWordInfo = class( TObject ) |
| 28 |
private |
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FNormalWord : Integer; //!< ??絽吾????茯????????糸?眼????????/span> |
| 30 |
FImportantWord : Integer; //!< 羈?????茯????????糸?眼????????/span> |
| 31 |
FNormalText : Integer; //!< ??絽吾????茯??????????障??????????腴?????/span> |
| 32 |
FImportantText : Integer; //!< 羈?????茯??????????障??????????腴?????/span> |
| 33 |
|
| 34 |
public |
| 35 |
property NormalWord : Integer read FNormalWord write FNormalWord; |
| 36 |
property ImportantWord : Integer read FImportantWord write FImportantWord; |
| 37 |
property NormalText : Integer read FNormalText write FNormalText; |
| 38 |
property ImportantText : Integer read FImportantText write FImportantText; |
| 39 |
end; |
| 40 |
|
| 41 |
{!*********************************************************** |
| 42 |
\brief 茹f??羝??水??茯???????????/span> |
| 43 |
************************************************************} |
| 44 |
TWordCountInfo = class( TObject ) |
| 45 |
private |
| 46 |
FWordCount : Integer; //!< ??茯???/span> |
| 47 |
|
| 48 |
public |
| 49 |
property WordCount : Integer read FWordCount write FWordCount; |
| 50 |
end; |
| 51 |
|
| 52 |
{!*********************************************************** |
| 53 |
\brief 茹f??羝??水??茯????鴻?? |
| 54 |
************************************************************} |
| 55 |
// TWordCount = class( THashedStringList ) // 羶??? |
| 56 |
TWordCount = class( TStringList ) |
| 57 |
public |
| 58 |
constructor Create; |
| 59 |
destructor Destroy; override; |
| 60 |
end; |
| 61 |
|
| 62 |
{!*********************************************************** |
| 63 |
\brief ???c???帥?≪???眼???冴?? |
| 64 |
************************************************************} |
| 65 |
TGikoBayesianAlgorithm = |
| 66 |
(gbaPaulGraham, gbaGaryRobinson, gbaGaryRobinsonFisher); |
| 67 |
|
| 68 |
{!*********************************************************** |
| 69 |
\brief ???ゃ?吾?≪?潟???c????/span> |
| 70 |
************************************************************} |
| 71 |
// TGikoBayesian = class( THashedStringList ) // 羶??? |
| 72 |
TGikoBayesian = class( TStringList ) |
| 73 |
private |
| 74 |
FFilePath : string; //!< 茯??粋昭???????<?ゃ??????/span> |
| 75 |
function GetObject( const name : string ) : TWordInfo; |
| 76 |
procedure SetObject( const name : string; value : TWordInfo ); |
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|
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public |
| 79 |
constructor Create; |
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destructor Destroy; override; |
| 81 |
|
| 82 |
//! ???<?ゃ??????絖??絮ユ???茯??水?冴???障?? |
| 83 |
procedure LoadFromFile( const filePath : string ); |
| 84 |
|
| 85 |
//! ???<?ゃ?????膺?絮ユ???篆?絖????障?? |
| 86 |
procedure SaveToFile( const filePath : string ); |
| 87 |
|
| 88 |
//! ???<?ゃ?????膺?絮ユ???篆?絖????障?? |
| 89 |
procedure Save; |
| 90 |
|
| 91 |
//! ??茯???????????宴????緇????障?? |
| 92 |
property Objects[ const name : string ] : TWordInfo |
| 93 |
read GetObject write SetObject; default; |
| 94 |
|
| 95 |
//! ??腴??????障??????茯????????潟?????障?? |
| 96 |
procedure CountWord( |
| 97 |
const text : string; |
| 98 |
wordCount : TWordCount ); |
| 99 |
|
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{! |
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\brief Paul Graham 羈????冴?ャ??????腴???絵??墾??羆阪????障?? |
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\return ??腴???絵??墾 (羈??????ゃ?????? 0.0??1.0 羈??????鴻??) |
| 103 |
} |
| 104 |
function CalcPaulGraham( wordCount : TWordCount ) : Extended; |
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|
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{! |
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\brief GaryRobinson 羈????冴?ャ??????腴???絵??墾??羆阪????障?? |
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\return ??腴???絵??墾 (羈??????ゃ?????? 0.0??1.0 羈??????鴻??) |
| 109 |
} |
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function CalcGaryRobinson( wordCount : TWordCount ) : Extended; |
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|
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{! |
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\brief GaryRobinson-Fisher 羈????冴?ャ??????腴???絵??墾??羆阪????障?? |
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\return ??腴???絵??墾 (羈??????ゃ?????? 0.0??1.0 羈??????鴻??) |
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} |
| 116 |
function CalcGaryRobinsonFisher( wordCount : TWordCount ) : Extended; |
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|
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{! |
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\brief ??腴???茹f?? |
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\param text 茹f????????腴? |
| 121 |
\param wordCount 茹f??????????茯????鴻????菴??? |
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\param algorithm 羈???墾??浦絎??????????≪???眼???冴??????絎????障?? |
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\return ??腴???絵??墾 (羈??????ゃ?????? 0.0??1.0 羈??????鴻??) |
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|
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CountWord ? Calcxxxxx ???障???????茵??????????с???? |
| 126 |
} |
| 127 |
function Parse( |
| 128 |
const text : string; |
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wordCount : TWordCount; |
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algorithm : TGikoBayesianAlgorithm = gbaGaryRobinsonFisher |
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) : Extended; |
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|
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{! |
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\brief 絖?????? |
| 135 |
\param wordCount Parse ?цВ??????????茯????鴻?? |
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\param isImportant 羈??????鴻????腴???????????????? True |
| 137 |
} |
| 138 |
procedure Learn( |
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wordCount : TWordCount; |
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isImportant : Boolean ); |
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|
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{! |
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\brief 絖??腟?????綽????? |
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\param wordCount Parse ?цВ??????????茯????鴻?? |
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\param isImportant 羈??????鴻????腴???????????????????????? True |
| 146 |
\warning 絖??羝??帥????腴???????????∈茯??堺?ャ?障??????<br> |
| 147 |
Learn ????????????腴??? isImportant ???????c????????腴??? |
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Forget ?????????若?帥???若?鴻???贋?????障????<br> |
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絖??羝??帥???????????????????????????????? |
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|
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???????膺?腟??????????≪????????с???????障??????<br> |
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wordCount ??緇?????腴? (Parse ? text 綣??? ???膺?腟??????帥?????≪???障????<br><br> |
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|
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筝祉??絵????腴?????羈?????腴????????帥????????? Forget -> Learn ?????т戎?????障???? |
| 155 |
} |
| 156 |
procedure Forget( |
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wordCount : TWordCount; |
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isImportant : Boolean ); |
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end; |
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|
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//================================================== |
| 162 |
implementation |
| 163 |
//================================================== |
| 164 |
|
| 165 |
uses |
| 166 |
SysUtils, Math, Windows, |
| 167 |
MojuUtils; |
| 168 |
|
| 169 |
const |
| 170 |
GIKO_BAYESIAN_FILE_VERSION = '1.0'; |
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{ |
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Modes = (ModeWhite, ModeGraph, ModeAlpha, ModeHanKana, ModeNum, |
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ModeWGraph, ModeWAlpha, ModeWNum, |
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ModeWHira, ModeWKata, ModeWKanji); |
| 175 |
} |
| 176 |
CharMode1 : array [ 0..255 ] of Byte = |
| 177 |
( |
| 178 |
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, |
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2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, |
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1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, |
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3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, |
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1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, |
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3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 0, |
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|
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, |
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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, |
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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, |
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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 |
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); |
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|
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//************************************************************ |
| 198 |
// misc |
| 199 |
//************************************************************ |
| 200 |
|
| 201 |
//============================== |
| 202 |
// RemoveToken |
| 203 |
//============================== |
| 204 |
function RemoveToken(var s: string;const delimiter: string): string; |
| 205 |
var |
| 206 |
p: Integer; |
| 207 |
begin |
| 208 |
p := AnsiPos(delimiter, s); |
| 209 |
if p = 0 then |
| 210 |
Result := s |
| 211 |
else |
| 212 |
Result := Copy(s, 1, p - 1); |
| 213 |
s := Copy(s, Length(Result) + Length(delimiter) + 1, Length(s)); |
| 214 |
end; |
| 215 |
|
| 216 |
//============================== |
| 217 |
// AbsSort |
| 218 |
//============================== |
| 219 |
function AbsSort( p1, p2 : Pointer ) : Integer; |
| 220 |
var |
| 221 |
v1, v2 : Single; |
| 222 |
begin |
| 223 |
|
| 224 |
v1 := Abs( Single( p1 ) - 0.5 ); |
| 225 |
v2 := Abs( Single( p2 ) - 0.5 ); |
| 226 |
if v1 > v2 then |
| 227 |
Result := -1 |
| 228 |
else if v1 = v2 then |
| 229 |
Result := 0 |
| 230 |
else |
| 231 |
Result := 1; |
| 232 |
|
| 233 |
end; |
| 234 |
|
| 235 |
//************************************************************ |
| 236 |
// TWordCount class |
| 237 |
//************************************************************ |
| 238 |
constructor TWordCount.Create; |
| 239 |
begin |
| 240 |
|
| 241 |
Duplicates := dupIgnore; |
| 242 |
CaseSensitive := True; |
| 243 |
Sorted := True; |
| 244 |
|
| 245 |
end; |
| 246 |
|
| 247 |
destructor TWordCount.Destroy; |
| 248 |
var |
| 249 |
i : Integer; |
| 250 |
begin |
| 251 |
|
| 252 |
for i := Count - 1 downto 0 do |
| 253 |
if Objects[ i ] <> nil then |
| 254 |
Objects[ i ].Free; |
| 255 |
|
| 256 |
inherited; |
| 257 |
|
| 258 |
end; |
| 259 |
|
| 260 |
//************************************************************ |
| 261 |
// TGikoBayesian class |
| 262 |
//************************************************************ |
| 263 |
|
| 264 |
//============================== |
| 265 |
// Create |
| 266 |
//============================== |
| 267 |
constructor TGikoBayesian.Create; |
| 268 |
begin |
| 269 |
|
| 270 |
Duplicates := dupIgnore; |
| 271 |
CaseSensitive := True; |
| 272 |
Sorted := True; |
| 273 |
|
| 274 |
end; |
| 275 |
|
| 276 |
//============================== |
| 277 |
// Destroy |
| 278 |
//============================== |
| 279 |
destructor TGikoBayesian.Destroy; |
| 280 |
var |
| 281 |
i : Integer; |
| 282 |
begin |
| 283 |
|
| 284 |
for i := Count - 1 downto 0 do |
| 285 |
if inherited Objects[ i ] <> nil then |
| 286 |
inherited Objects[ i ].Free; |
| 287 |
|
| 288 |
inherited; |
| 289 |
|
| 290 |
end; |
| 291 |
|
| 292 |
procedure TGikoBayesian.LoadFromFile( const filePath : string ); |
| 293 |
var |
| 294 |
i : Integer; |
| 295 |
sl : TStringList; |
| 296 |
s : string; |
| 297 |
name : string; |
| 298 |
info : TWordInfo; |
| 299 |
begin |
| 300 |
|
| 301 |
FFilePath := filePath; |
| 302 |
|
| 303 |
if not FileExists( filePath ) then |
| 304 |
Exit; |
| 305 |
|
| 306 |
sl := TStringList.Create; |
| 307 |
try |
| 308 |
sl.LoadFromFile( filePath ); |
| 309 |
|
| 310 |
for i := 1 to sl.Count - 1 do begin |
| 311 |
s := sl[ i ]; |
| 312 |
name := RemoveToken( s, #1 ); |
| 313 |
info := TWordInfo.Create; |
| 314 |
info.NormalWord := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 315 |
info.ImportantWord := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 316 |
info.NormalText := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 317 |
info.ImportantText := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 318 |
|
| 319 |
AddObject( name, info ); |
| 320 |
end; |
| 321 |
finally |
| 322 |
sl.Free; |
| 323 |
end; |
| 324 |
|
| 325 |
end; |
| 326 |
|
| 327 |
procedure TGikoBayesian.SaveToFile( const filePath : string ); |
| 328 |
var |
| 329 |
i : Integer; |
| 330 |
sl : TStringList; |
| 331 |
s : string; |
| 332 |
info : TWordInfo; |
| 333 |
begin |
| 334 |
|
| 335 |
FFilePath := filePath; |
| 336 |
|
| 337 |
sl := TStringList.Create; |
| 338 |
try |
| 339 |
sl.BeginUpdate; |
| 340 |
sl.Add( GIKO_BAYESIAN_FILE_VERSION ); |
| 341 |
|
| 342 |
for i := 0 to Count - 1 do begin |
| 343 |
info := TWordInfo( inherited Objects[ i ] ); |
| 344 |
s := Strings[ i ] + #1 |
| 345 |
+ Format('%x', [info.NormalWord]) + #1 |
| 346 |
+ Format('%x', [info.ImportantWord]) + #1 |
| 347 |
+ Format('%x', [info.NormalText]) + #1 |
| 348 |
+ Format('%x', [info.ImportantText]); |
| 349 |
|
| 350 |
sl.Add(s); |
| 351 |
end; |
| 352 |
sl.EndUpdate; |
| 353 |
sl.SaveToFile( filePath ); |
| 354 |
finally |
| 355 |
sl.Free; |
| 356 |
end; |
| 357 |
|
| 358 |
end; |
| 359 |
|
| 360 |
procedure TGikoBayesian.Save; |
| 361 |
begin |
| 362 |
|
| 363 |
if FFilePath <> '' then |
| 364 |
SaveToFile( FFilePath ); |
| 365 |
|
| 366 |
end; |
| 367 |
|
| 368 |
//============================== |
| 369 |
// GetObject |
| 370 |
//============================== |
| 371 |
function TGikoBayesian.GetObject( const name : string ) : TWordInfo; |
| 372 |
var |
| 373 |
idx : Integer; |
| 374 |
begin |
| 375 |
|
| 376 |
if Find( name, idx ) then |
| 377 |
Result := TWordInfo( inherited Objects[ idx ] ) |
| 378 |
else |
| 379 |
Result := nil; |
| 380 |
|
| 381 |
end; |
| 382 |
|
| 383 |
//============================== |
| 384 |
// SetObject |
| 385 |
//============================== |
| 386 |
procedure TGikoBayesian.SetObject( const name : string; value : TWordInfo ); |
| 387 |
var |
| 388 |
idx : Integer; |
| 389 |
begin |
| 390 |
|
| 391 |
if Find( name, idx ) then |
| 392 |
inherited Objects[ idx ] := value |
| 393 |
else |
| 394 |
AddObject( name, value ); |
| 395 |
|
| 396 |
end; |
| 397 |
|
| 398 |
|
| 399 |
//============================== |
| 400 |
// CountWord |
| 401 |
//============================== |
| 402 |
procedure TGikoBayesian.CountWord( |
| 403 |
const text : string; |
| 404 |
wordCount : TWordCount ); |
| 405 |
type |
| 406 |
Modes = (ModeWhite, ModeGraph, ModeAlpha, ModeNum, ModeHanKana, |
| 407 |
ModeWGraph, ModeWAlpha, ModeWNum, |
| 408 |
ModeWHira, ModeWKata, ModeWKanji); |
| 409 |
var |
| 410 |
p, tail, last : PChar; |
| 411 |
mode, newMode : Modes; |
| 412 |
ch : Longword; |
| 413 |
chSize : Integer; |
| 414 |
wHiraDelimiter : TStringList; |
| 415 |
wHiraFinalDelimiter : TStringList; |
| 416 |
wKanjiDelimiter : TStringList; |
| 417 |
words : TStringList; |
| 418 |
aWord : string; |
| 419 |
countInfo : TWordCountInfo; |
| 420 |
|
| 421 |
function cutBoth( _aWord : string; _delim : TStringList ) : string; |
| 422 |
var |
| 423 |
_i : Integer; |
| 424 |
begin |
| 425 |
for _i := 0 to _delim.Count - 1 do begin |
| 426 |
_aWord := CustomStringReplace( |
| 427 |
_aWord, |
| 428 |
_delim[ _i ], |
| 429 |
#10 + _delim[ _i ] + #10, False ); |
| 430 |
end; |
| 431 |
Result := _aWord; |
| 432 |
end; |
| 433 |
|
| 434 |
function cutFirst( _aWord : string; _delim : TStringList ) : string; |
| 435 |
var |
| 436 |
_i : Integer; |
| 437 |
begin |
| 438 |
for _i := 0 to _delim.Count - 1 do begin |
| 439 |
_aWord := CustomStringReplace( |
| 440 |
_aWord, |
| 441 |
_delim[ _i ], |
| 442 |
#10 + _delim[ _i ], False ); |
| 443 |
end; |
| 444 |
Result := _aWord; |
| 445 |
end; |
| 446 |
|
| 447 |
function cutFinal( _aWord : string; _delim : TStringList ) : string; |
| 448 |
var |
| 449 |
_i : Integer; |
| 450 |
begin |
| 451 |
for _i := 0 to _delim.Count - 1 do begin |
| 452 |
_aWord := CustomStringReplace( |
| 453 |
_aWord, |
| 454 |
_delim[ _i ], |
| 455 |
_delim[ _i ] + #10, False ); |
| 456 |
end; |
| 457 |
Result := _aWord; |
| 458 |
end; |
| 459 |
|
| 460 |
procedure addWord( _dst : TWordCount; _words : TStringList ); |
| 461 |
var |
| 462 |
_aWord : string; |
| 463 |
_i, _idx : Integer; |
| 464 |
_countInfo : TWordCountInfo; |
| 465 |
begin |
| 466 |
for _i := 0 to _words.Count - 1 do begin |
| 467 |
_aWord := _words[ _i ]; |
| 468 |
if Length( _aWord ) > 0 then begin |
| 469 |
if _dst.Find( _aWord, _idx ) then begin |
| 470 |
_countInfo := TWordCountInfo( _dst.Objects[ _idx ] ); |
| 471 |
end else begin |
| 472 |
_countInfo := TWordCountInfo.Create; |
| 473 |
_dst.AddObject( _aWord, _countInfo ); |
| 474 |
end; |
| 475 |
_countInfo.WordCount := _countInfo.WordCount + 1; |
| 476 |
end; |
| 477 |
end; |
| 478 |
end; |
| 479 |
|
| 480 |
function changeMode( _aWord : string; _mode : Modes ) : string; |
| 481 |
var |
| 482 |
_i : Integer; |
| 483 |
_aWord2 : string; |
| 484 |
_pWord, _pWord2 : PChar; |
| 485 |
_pWordTail, _pFound : PChar; |
| 486 |
const |
| 487 |
_delim : string = #10; |
| 488 |
begin |
| 489 |
{$IFDEF GIKO_BAYESIAN_NO_HIRAGANA_DIC} |
| 490 |
if mode = ModeWHira then begin |
| 491 |
Result := ''; |
| 492 |
Exit; |
| 493 |
end; |
| 494 |
{$ENDIF} |
| 495 |
if Ord( _mode ) >= Ord( ModeWGraph ) then begin |
| 496 |
// ?ユ??? |
| 497 |
// ?鴻???若?鴻??荅違???? |
| 498 |
_aWord := CustomStringReplace( _aWord, ' ', '', False ); |
| 499 |
_aWord := CustomStringReplace( _aWord, '??', '', False ); |
| 500 |
|
| 501 |
// ???????帥?у??茯????? |
| 502 |
case mode of |
| 503 |
ModeWHira: |
| 504 |
begin |
| 505 |
_aWord := cutFinal( _aWord, wHiraFinalDelimiter ); |
| 506 |
Result := cutBoth( _aWord, wHiraDelimiter ); |
| 507 |
end; |
| 508 |
|
| 509 |
ModeWKanji: |
| 510 |
begin |
| 511 |
// ???????帥?у??茯????? |
| 512 |
_aWord := cutBoth( _aWord, wKanjiDelimiter ); |
| 513 |
// 4 byte (2 絖?) ???ゃ?у??茯????? |
| 514 |
_pWord := PChar( _aWord ); |
| 515 |
_i := Length( _aWord ); |
| 516 |
_pWordTail := _pWord + _i; |
| 517 |
SetLength( _aWord2, _i + (_i shr 2) ); |
| 518 |
_pWord2 := PChar( _aWord2 ); |
| 519 |
|
| 520 |
while _pWord < _pWordTail do begin |
| 521 |
_pFound := AnsiStrPos( _pWord, PChar( _delim ) ); |
| 522 |
if _pFound = nil then |
| 523 |
_pFound := _pWordTail; |
| 524 |
_pFound := _pFound - 3; |
| 525 |
|
| 526 |
while _pWord <= _pFound do begin |
| 527 |
CopyMemory( _pWord2, _pWord, 4 ); _pWord2[ 4 ] := #10; |
| 528 |
_pWord2 := _pWord2 + 5; _pWord := _pWord + 4; |
| 529 |
end; |
| 530 |
_i := _pFound + 4 - _pWord; // 4 = 3 + #10 |
| 531 |
CopyMemory( _pWord2, _pWord, _i ); |
| 532 |
_pWord2 := _pWord2 + _i; _pWord := _pWord + _i; |
| 533 |
end; |
| 534 |
if _pWord < _pWordTail then begin |
| 535 |
_i := _pWordTail - _pWord; |
| 536 |
CopyMemory( _pWord2, _pWord, _i ); |
| 537 |
_pWord2 := _pWord2 + _i; |
| 538 |
end; |
| 539 |
SetLength( _aWord2, _pWord2 - PChar( _aWord2 ) ); |
| 540 |
|
| 541 |
Result := _aWord2; |
| 542 |
end; |
| 543 |
|
| 544 |
else |
| 545 |
Result := _aWord; |
| 546 |
end; |
| 547 |
end else begin |
| 548 |
Result := _aWord; |
| 549 |
end; |
| 550 |
end; |
| 551 |
const |
| 552 |
WHIRA_DELIMITER = '??' + #10 + '??#39; + #10 + '??' + #10 + '??#39; + #10 + '????' |
| 553 |
+ #10 + '??#39; + #10 + '????' + #10 + '?障??#39;+ #10 + '??#39; |
| 554 |
+ #10 + '????' + #10 + '????' + #10 + '????' |
| 555 |
+ #10 + '????' + #10 + '????' + #10 + '????' + #10 + '????' |
| 556 |
+ #10 + '????#39; + #10 + '????#39; + #10 + '????#39; + #10 + '????#39; |
| 557 |
+ #10 + '????' + #10 + '????' + #10 + '????' + #10 + '????' |
| 558 |
+ #10 + '??????#39; + #10 + '??????#39; + #10 + '??????#39; + #10 + '??????#39; |
| 559 |
+ #10 + '????' + #10 + '????#39; + #10 + '????' + #10 + '????' |
| 560 |
+ #10 + '????' + #10 + '??????' |
| 561 |
+ #10 + '?с??' + #10 + '?障??' + #10 + '?障????' |
| 562 |
+ #10 + '?с????' + #10 + '?障????' |
| 563 |
+ #10 + '????' + #10 + '??????' + #10 + '??????' + #10 + '????????' |
| 564 |
; |
| 565 |
WKANJI_DELIMITER = '??' + #10 + '??#39; + #10 + '綣?39; + #10 + '??' + #10 + '羈?' |
| 566 |
+ #10 + '筝?' + #10 + '??#39; + #10 + '??' + #10 + '??' |
| 567 |
; |
| 568 |
WHIRA_FINAL_DELIMITER = '?c??' + #10 + '?c??#39; |
| 569 |
;{ |
| 570 |
+ #10 + '???c??#39; + #10 + '???????c??#39; + #10 + '??????#39; |
| 571 |
+ #10 + '??????' + #10 + '?с??????' |
| 572 |
+ #10 + '?障??' |
| 573 |
+ #10 + '??????' + #10 + '????' + #10 + '????#39; + #10 + '??????#39; |
| 574 |
+ #10 + '??????' + #10 + '???c?宴??' |
| 575 |
+ #10 + '?с??' + #10 + '????' |
| 576 |
+ #10 + '????' + #10 + '??????' + #10 + '????' + #10 + '??????' |
| 577 |
;} |
| 578 |
// '??#39; ?? '??????????' ???? |
| 579 |
HA_LINE = '?????????????障????????????違?宴????#39;; |
| 580 |
HI_LINE = '???????<???蚊?帥?????????潟?眼??'; |
| 581 |
HU_LINE = '??????ゃ???泣?????????吟?激??'; |
| 582 |
HE_LINE = '???????????吾?????????鴻?冴??'; |
| 583 |
HO_LINE = '???????????祉???????????若?純??'; |
| 584 |
KA_LINE = '?≪???泣?帥??????ゃ???????吟???????<?泣??#39;; |
| 585 |
KI_LINE = '?ゃ???激???????????違???吾??????#39;; |
| 586 |
KU_LINE = '?????鴻?????????????違?????ャ??#39;; |
| 587 |
KE_LINE = '???宴?祉???????<???宴?蚊?????с??#39;; |
| 588 |
KO_LINE = '???潟?純???????≪?????蚊?眼??????#39;; |
| 589 |
kKanji = [$80..$A0, $E0..$ff]; |
| 590 |
begin |
| 591 |
|
| 592 |
wHiraDelimiter := TStringList.Create; |
| 593 |
wHiraFinalDelimiter := TStringList.Create; |
| 594 |
wKanjiDelimiter := TStringList.Create; |
| 595 |
words := TStringList.Create; |
| 596 |
try |
| 597 |
mode := ModeWhite; |
| 598 |
{$IFNDEF GIKO_BAYESIAN_NO_HIRAGANA_DIC} |
| 599 |
wHiraDelimiter.Text := WHIRA_DELIMITER; |
| 600 |
wHiraFinalDelimiter.Text := WHIRA_FINAL_DELIMITER; |
| 601 |
{$ENDIF} |
| 602 |
wKanjiDelimiter.Text := WKANJI_DELIMITER; |
| 603 |
p := PChar( text ); |
| 604 |
tail := p + Length( text ); |
| 605 |
last := p; |
| 606 |
|
| 607 |
while p < tail do begin |
| 608 |
// ??絖????帥?ゃ?????ゅ??/span> |
| 609 |
// ?糸???鴻? ModeGraph ?????????у???ャ???綽?????????????? |
| 610 |
// if Byte(Byte( p^ ) - $a1) < $5e then begin |
| 611 |
if Byte( p^ ) in kKanji then begin |
| 612 |
if p + 1 < tail then begin |
| 613 |
ch := (PByte( p )^ shl 8) or PByte( p + 1 )^; |
| 614 |
case ch of |
| 615 |
// ?鴻???若?鴻?у??茯???????????????? |
| 616 |
//$8140: newMode := ModeWhite; |
| 617 |
$8141..$824e: newMode := ModeWGraph; |
| 618 |
$824f..$8258: newMode := ModeWNum; |
| 619 |
$8260..$829a: newMode := ModeWAlpha; |
| 620 |
$829f..$82f1: newMode := ModeWHira; |
| 621 |
$8340..$8396: newMode := ModeWKata; |
| 622 |
else newMode := ModeWKanji; |
| 623 |
end; |
| 624 |
// '??????#39; ??抗篁??????障???????帥?????????障???? |
| 625 |
if (mode = ModeWHira) or (mode = ModeWKata) then |
| 626 |
if (ch = $814a) or (ch = $814b) or (ch = $815b) then |
| 627 |
newMode := mode; |
| 628 |
end else begin |
| 629 |
newMode := ModeWhite; |
| 630 |
end; |
| 631 |
|
| 632 |
chSize := 2; |
| 633 |
end else begin |
| 634 |
newMode := Modes( CharMode1[ Byte( p^ ) ] ); |
| 635 |
if (p^ = ' ') and (Ord( mode ) >= Ord( ModeWGraph )) then begin |
| 636 |
// 篁??障?ф?ユ????т??鴻???若??/span> |
| 637 |
// ??茯???膵???????с?鴻???若?鴻??荅違???? |
| 638 |
// ?糸??茹?????????絽吾?鴻???若?鴻?у?阪??????????????荅違?????? |
| 639 |
newMode := mode; |
| 640 |
end; |
| 641 |
|
| 642 |
chSize := 1; |
| 643 |
end; |
| 644 |
|
| 645 |
if mode <> newMode then begin |
| 646 |
|
| 647 |
// ??絖????帥?ゃ????紊??眼?????? |
| 648 |
if mode <> ModeWhite then begin |
| 649 |
SetLength( aWord, p - last ); |
| 650 |
CopyMemory( PChar( aWord ), last, p - last ); |
| 651 |
|
| 652 |
words.Text := changeMode( aWord, mode ); |
| 653 |
|
| 654 |
// ??茯??脂??/span> |
| 655 |
addWord( wordCount, words ); |
| 656 |
end; |
| 657 |
|
| 658 |
last := p; |
| 659 |
mode := newMode; |
| 660 |
|
| 661 |
end; |
| 662 |
|
| 663 |
p := p + chSize; |
| 664 |
end; // while |
| 665 |
|
| 666 |
if mode <> ModeWhite then begin |
| 667 |
SetLength( aWord, p - last ); |
| 668 |
CopyMemory( PChar( aWord ), last, p - last ); |
| 669 |
|
| 670 |
words.Text := changeMode( aWord, mode ); |
| 671 |
|
| 672 |
// ??茯??脂??/span> |
| 673 |
addWord( wordCount, words ); |
| 674 |
end; |
| 675 |
finally |
| 676 |
words.Free; |
| 677 |
wKanjiDelimiter.Free; |
| 678 |
wHiraFinalDelimiter.Free; |
| 679 |
wHiraDelimiter.Free; |
| 680 |
end; |
| 681 |
|
| 682 |
end; |
| 683 |
|
| 684 |
//============================== |
| 685 |
// CalcPaulGraham |
| 686 |
//============================== |
| 687 |
function TGikoBayesian.CalcPaulGraham( wordCount : TWordCount ) : Extended; |
| 688 |
|
| 689 |
function p( const aWord : string ) : Single; |
| 690 |
var |
| 691 |
info : TWordInfo; |
| 692 |
begin |
| 693 |
info := Objects[ aWord ]; |
| 694 |
if info = nil then |
| 695 |
Result := 0.415 |
| 696 |
else if info.NormalWord = 0 then |
| 697 |
Result := 0.99 |
| 698 |
else if info.ImportantWord = 0 then |
| 699 |
Result := 0.01 |
| 700 |
else if info.ImportantWord + info.NormalWord * 2 < 5 then |
| 701 |
Result := 0.5 |
| 702 |
else begin |
| 703 |
try |
| 704 |
Result := ( info.ImportantWord / info.ImportantText ) / |
| 705 |
((info.NormalWord * 2 / info.NormalText ) + |
| 706 |
(info.ImportantWord / info.ImportantText)); |
| 707 |
except |
| 708 |
on EZeroDivide do Result := 0.99; |
| 709 |
end; |
| 710 |
end; |
| 711 |
end; |
| 712 |
|
| 713 |
var |
| 714 |
s, q : Extended; |
| 715 |
i : Integer; |
| 716 |
narray : TList; |
| 717 |
const |
| 718 |
SAMPLE_COUNT = 15; |
| 719 |
begin |
| 720 |
|
| 721 |
Result := 1; |
| 722 |
if wordCount.Count = 0 then |
| 723 |
Exit; |
| 724 |
|
| 725 |
narray := TList.Create; |
| 726 |
try |
| 727 |
for i := 0 to wordCount.Count - 1 do begin |
| 728 |
narray.Add( Pointer( p( wordCount[ i ] ) ) ); |
| 729 |
end; |
| 730 |
|
| 731 |
narray.Sort( AbsSort ); |
| 732 |
|
| 733 |
s := 1; |
| 734 |
q := 1; |
| 735 |
i := min( SAMPLE_COUNT, narray.Count ); |
| 736 |
while i > 0 do begin |
| 737 |
Dec( i ); |
| 738 |
|
| 739 |
s := s * Single( narray[ i ] ); |
| 740 |
q := q * (1 - Single( narray[ i ] )); |
| 741 |
end; |
| 742 |
try |
| 743 |
Result := s / (s + q); |
| 744 |
except |
| 745 |
Result := 0.5; |
| 746 |
end; |
| 747 |
finally |
| 748 |
narray.Free; |
| 749 |
end; |
| 750 |
|
| 751 |
end; |
| 752 |
|
| 753 |
//============================== |
| 754 |
// CalcGaryRobinson |
| 755 |
//============================== |
| 756 |
function TGikoBayesian.CalcGaryRobinson( wordCount : TWordCount ) : Extended; |
| 757 |
|
| 758 |
function p( const aWord : string ) : Single; |
| 759 |
var |
| 760 |
info : TWordInfo; |
| 761 |
begin |
| 762 |
info := Objects[ aWord ]; |
| 763 |
if info = nil then |
| 764 |
Result := 0.415 |
| 765 |
else if info.ImportantWord = 0 then |
| 766 |
Result := 0.01 |
| 767 |
else if info.NormalWord = 0 then |
| 768 |
Result := 0.99 |
| 769 |
else |
| 770 |
{ |
| 771 |
Result := ( info.ImportantWord / info.ImportantText ) / |
| 772 |
((info.NormalWord / info.NormalText ) + |
| 773 |
(info.ImportantWord / info.ImportantText)); |
| 774 |
} |
| 775 |
try |
| 776 |
Result := (info.ImportantWord * info.NormalText) / |
| 777 |
(info.NormalWord * info.ImportantText + |
| 778 |
info.ImportantWord * info.NormalText); |
| 779 |
except |
| 780 |
Result := 0.5; |
| 781 |
end; |
| 782 |
end; |
| 783 |
|
| 784 |
function f( cnt : Integer; n, mean : Single ) : Extended; |
| 785 |
const |
| 786 |
k = 0.001; |
| 787 |
begin |
| 788 |
Result := ( (k * mean) + (cnt * n) ) / (k + cnt); |
| 789 |
end; |
| 790 |
|
| 791 |
var |
| 792 |
n : Extended; |
| 793 |
narray : array of Single; |
| 794 |
mean : Extended; |
| 795 |
countInfo : TWordCountInfo; |
| 796 |
i : Integer; |
| 797 |
P1, Q1, R1 : Extended; |
| 798 |
cnt : Extended; |
| 799 |
begin |
| 800 |
|
| 801 |
if wordCount.Count = 0 then begin |
| 802 |
Result := 1; |
| 803 |
Exit; |
| 804 |
end; |
| 805 |
|
| 806 |
SetLength( narray, wordCount.Count ); |
| 807 |
mean := 0; |
| 808 |
for i := 0 to wordCount.Count - 1 do begin |
| 809 |
n := p( wordCount[ i ] ); |
| 810 |
narray[ i ] := n; |
| 811 |
mean := mean + n; |
| 812 |
end; |
| 813 |
mean := mean / wordCount.Count; |
| 814 |
|
| 815 |
P1 := 1; |
| 816 |
Q1 := 1; |
| 817 |
for i := 0 to wordCount.Count - 1 do begin |
| 818 |
countInfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 819 |
n := f( countInfo.WordCount, narray[ i ], mean ); |
| 820 |
P1 := P1 * ( 1 - n ); |
| 821 |
Q1 := Q1 * n; |
| 822 |
end; |
| 823 |
cnt := wordCount.Count; |
| 824 |
if cnt = 0 then |
| 825 |
cnt := 1; |
| 826 |
try |
| 827 |
P1 := 1 - Power( P1, 1 / cnt ); |
| 828 |
except |
| 829 |
end; |
| 830 |
try |
| 831 |
Q1 := 1 - Power( Q1, 1 / cnt ); |
| 832 |
except |
| 833 |
end; |
| 834 |
|
| 835 |
if P1 + Q1 = 0 then begin |
| 836 |
Result := 0.5 |
| 837 |
end else begin |
| 838 |
n := (P1 - Q1) / (P1 + Q1); |
| 839 |
Result := (1 + n) / 2; |
| 840 |
end; |
| 841 |
|
| 842 |
end; |
| 843 |
|
| 844 |
//============================== |
| 845 |
// CalcGaryRobinsonFisher |
| 846 |
//============================== |
| 847 |
function TGikoBayesian.CalcGaryRobinsonFisher( |
| 848 |
wordCount : TWordCount |
| 849 |
) : Extended; |
| 850 |
|
| 851 |
function p( const aWord : string ) : Single; |
| 852 |
var |
| 853 |
info : TWordInfo; |
| 854 |
begin |
| 855 |
info := Objects[ aWord ]; |
| 856 |
if info = nil then |
| 857 |
Result := 0.415 |
| 858 |
else if info.ImportantWord = 0 then |
| 859 |
Result := 0.01 |
| 860 |
else if info.NormalWord = 0 then |
| 861 |
Result := 0.99 |
| 862 |
else |
| 863 |
{ |
| 864 |
Result := ( info.ImportantWord / info.ImportantText ) / |
| 865 |
((info.NormalWord / info.NormalText ) + |
| 866 |
(info.ImportantWord / info.ImportantText)); |
| 867 |
} |
| 868 |
Result := (info.ImportantWord * info.NormalText) / |
| 869 |
(info.NormalWord * info.ImportantText + |
| 870 |
info.ImportantWord * info.NormalText); |
| 871 |
end; |
| 872 |
|
| 873 |
function f( cnt : Integer; n, mean : Single ) : Extended; |
| 874 |
const |
| 875 |
k = 0.001; |
| 876 |
begin |
| 877 |
Result := ( (k * mean) + (cnt * n) ) / (k + cnt); |
| 878 |
end; |
| 879 |
|
| 880 |
function prbx( x2, degree : Extended ) : Extended; |
| 881 |
begin |
| 882 |
|
| 883 |
Result := 0.5; |
| 884 |
|
| 885 |
end; |
| 886 |
|
| 887 |
var |
| 888 |
n : Extended; |
| 889 |
narray : array of Single; |
| 890 |
mean : Extended; |
| 891 |
countInfo : TWordCountInfo; |
| 892 |
i : Integer; |
| 893 |
normal : Extended; |
| 894 |
important : Extended; |
| 895 |
P1, Q1 : Extended; |
| 896 |
cnt : Extended; |
| 897 |
begin |
| 898 |
|
| 899 |
if wordCount.Count = 0 then begin |
| 900 |
Result := 1; |
| 901 |
Exit; |
| 902 |
end; |
| 903 |
|
| 904 |
SetLength( narray, wordCount.Count ); |
| 905 |
mean := 0; |
| 906 |
for i := 0 to wordCount.Count - 1 do begin |
| 907 |
n := p( wordCount[ i ] ); |
| 908 |
narray[ i ] := n; |
| 909 |
mean := mean + n; |
| 910 |
end; |
| 911 |
mean := mean / wordCount.Count; |
| 912 |
|
| 913 |
P1 := 1; |
| 914 |
Q1 := 1; |
| 915 |
for i := 0 to wordCount.Count - 1 do begin |
| 916 |
countInfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 917 |
n := f( countInfo.WordCount, narray[ i ], mean ); |
| 918 |
P1 := P1 * ( 1 - n ); |
| 919 |
Q1 := Q1 * n; |
| 920 |
end; |
| 921 |
cnt := wordCount.Count; |
| 922 |
if cnt = 0 then |
| 923 |
cnt := 1; |
| 924 |
try |
| 925 |
P1 := Power( P1, 1 / cnt ); |
| 926 |
except |
| 927 |
end; |
| 928 |
try |
| 929 |
Q1 := Power( Q1, 1 / cnt ); |
| 930 |
except |
| 931 |
end; |
| 932 |
|
| 933 |
P1 := 1 - prbx( -2 * Ln( P1 ), 2 * cnt ); |
| 934 |
Q1 := 1 - prbx( -2 * Ln( Q1 ), 2 * cnt ); |
| 935 |
|
| 936 |
Result := (1 + P1 - Q1) / 2; |
| 937 |
|
| 938 |
end; |
| 939 |
|
| 940 |
//============================== |
| 941 |
// Parse |
| 942 |
//============================== |
| 943 |
function TGikoBayesian.Parse( |
| 944 |
const text : string; |
| 945 |
wordCount : TWordCount; |
| 946 |
algorithm : TGikoBayesianAlgorithm |
| 947 |
) : Extended; |
| 948 |
begin |
| 949 |
|
| 950 |
CountWord( text, wordCount ); |
| 951 |
case algorithm of |
| 952 |
gbaPaulGraham: Result := CalcPaulGraham( wordCount ); |
| 953 |
gbaGaryRobinson: Result := CalcGaryRobinson( wordCount ); |
| 954 |
gbaGaryRobinsonFisher: |
| 955 |
Result := CalcGaryRobinsonFisher( wordCount ); |
| 956 |
else Result := 0; |
| 957 |
end; |
| 958 |
|
| 959 |
end; |
| 960 |
|
| 961 |
//============================== |
| 962 |
// Learn |
| 963 |
//============================== |
| 964 |
procedure TGikoBayesian.Learn( |
| 965 |
wordCount : TWordCount; |
| 966 |
isImportant : Boolean ); |
| 967 |
var |
| 968 |
aWord : string; |
| 969 |
wordinfo : TWordInfo; |
| 970 |
countinfo : TWordCountInfo; |
| 971 |
i : Integer; |
| 972 |
begin |
| 973 |
|
| 974 |
for i := 0 to wordCount.Count - 1 do begin |
| 975 |
aWord := wordCount[ i ]; |
| 976 |
wordinfo := Objects[ aWord ]; |
| 977 |
countinfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 978 |
if wordinfo = nil then begin |
| 979 |
wordinfo := TWordInfo.Create; |
| 980 |
Objects[ aWord ] := wordinfo; |
| 981 |
end; |
| 982 |
|
| 983 |
if isImportant then begin |
| 984 |
wordinfo.ImportantWord := wordinfo.ImportantWord + countinfo.WordCount; |
| 985 |
wordinfo.ImportantText := wordinfo.ImportantText + 1; |
| 986 |
end else begin |
| 987 |
wordinfo.NormalWord := wordinfo.NormalWord + countinfo.WordCount; |
| 988 |
wordinfo.NormalText := wordinfo.NormalText + 1; |
| 989 |
end; |
| 990 |
end; |
| 991 |
|
| 992 |
end; |
| 993 |
|
| 994 |
//============================== |
| 995 |
// Forget |
| 996 |
//============================== |
| 997 |
procedure TGikoBayesian.Forget( |
| 998 |
wordCount : TWordCount; |
| 999 |
isImportant : Boolean ); |
| 1000 |
var |
| 1001 |
aWord : string; |
| 1002 |
wordinfo : TWordInfo; |
| 1003 |
countinfo : TWordCountInfo; |
| 1004 |
i : Integer; |
| 1005 |
begin |
| 1006 |
|
| 1007 |
for i := 0 to wordCount.Count - 1 do begin |
| 1008 |
aWord := wordCount[ i ]; |
| 1009 |
wordinfo := Objects[ aWord ]; |
| 1010 |
if wordinfo = nil then |
| 1011 |
Continue; |
| 1012 |
|
| 1013 |
countinfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 1014 |
if isImportant then begin |
| 1015 |
if wordInfo.ImportantText > 0 then begin |
| 1016 |
wordinfo.ImportantText := wordinfo.ImportantText - 1; |
| 1017 |
wordinfo.ImportantWord := wordinfo.ImportantWord - countinfo.WordCount; |
| 1018 |
end; |
| 1019 |
end else begin |
| 1020 |
if wordinfo.NormalText > 0 then begin |
| 1021 |
wordinfo.NormalText := wordinfo.NormalText - 1; |
| 1022 |
wordinfo.NormalWord := wordinfo.NormalWord - countinfo.WordCount; |
| 1023 |
end; |
| 1024 |
end; |
| 1025 |
end; |
| 1026 |
|
| 1027 |
end; |
| 1028 |
|
| 1029 |
end. |