| 1 |
unit GikoBayesian; |
| 2 |
|
| 3 |
{! |
| 4 |
\file GikoBayesian.pas |
| 5 |
\brief ???ゃ?吾?≪?潟???c????/span> |
| 6 |
|
| 7 |
$Id$ |
| 8 |
} |
| 9 |
|
| 10 |
interface |
| 11 |
|
| 12 |
//================================================== |
| 13 |
uses |
| 14 |
//================================================== |
| 15 |
Classes, IniFiles; |
| 16 |
|
| 17 |
//================================================== |
| 18 |
type |
| 19 |
//================================================== |
| 20 |
|
| 21 |
{!*********************************************************** |
| 22 |
\brief ??茯???????????/span> |
| 23 |
************************************************************} |
| 24 |
TWordInfo = class( TObject ) |
| 25 |
private |
| 26 |
FNormalWord : Integer; //!< ??絽吾????茯????????糸?眼????????/span> |
| 27 |
FImportantWord : Integer; //!< 羈?????茯????????糸?眼????????/span> |
| 28 |
FNormalText : Integer; //!< ??絽吾????茯??????????障??????????腴?????/span> |
| 29 |
FImportantText : Integer; //!< 羈?????茯??????????障??????????腴?????/span> |
| 30 |
|
| 31 |
public |
| 32 |
property NormalWord : Integer read FNormalWord write FNormalWord; |
| 33 |
property ImportantWord : Integer read FImportantWord write FImportantWord; |
| 34 |
property NormalText : Integer read FNormalText write FNormalText; |
| 35 |
property ImportantText : Integer read FImportantText write FImportantText; |
| 36 |
end; |
| 37 |
|
| 38 |
{!*********************************************************** |
| 39 |
\brief 茹f??羝??水??茯???????????/span> |
| 40 |
************************************************************} |
| 41 |
TWordCountInfo = class( TObject ) |
| 42 |
private |
| 43 |
FWordCount : Integer; //!< ??茯???/span> |
| 44 |
|
| 45 |
public |
| 46 |
property WordCount : Integer read FWordCount write FWordCount; |
| 47 |
end; |
| 48 |
|
| 49 |
{!*********************************************************** |
| 50 |
\brief 茹f??羝??水??茯????鴻?? |
| 51 |
************************************************************} |
| 52 |
// TWordCount = class( THashedStringList ) // 羶??? |
| 53 |
TWordCount = class( TStringList ) // ?? |
| 54 |
public |
| 55 |
destructor Destroy; override; |
| 56 |
end; |
| 57 |
|
| 58 |
{!*********************************************************** |
| 59 |
\brief ???c???帥?≪???眼???冴?? |
| 60 |
************************************************************} |
| 61 |
TGikoBayesianAlgorithm = |
| 62 |
(gbaPaulGraham, gbaGaryRonbinson{, gbaGaryRonbinsonFisher}); |
| 63 |
|
| 64 |
{!*********************************************************** |
| 65 |
\brief ???ゃ?吾?≪?潟???c????/span> |
| 66 |
************************************************************} |
| 67 |
TGikoBayesian = class( THashedStringList ) |
| 68 |
private |
| 69 |
FFilePath : string; //!< 茯??粋昭???????<?ゃ??????/span> |
| 70 |
function GetObject( const name : string ) : TWordInfo; |
| 71 |
procedure SetObject( const name : string; value : TWordInfo ); |
| 72 |
|
| 73 |
public |
| 74 |
constructor Create; |
| 75 |
destructor Destroy; override; |
| 76 |
|
| 77 |
//! ???<?ゃ??????絖??絮ユ???茯??水?冴???障?? |
| 78 |
procedure LoadFromFile( const filePath : string ); |
| 79 |
|
| 80 |
//! ???<?ゃ?????膺?絮ユ???篆?絖????障?? |
| 81 |
procedure SaveToFile( const filePath : string ); |
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|
| 83 |
//! ???<?ゃ?????膺?絮ユ???篆?絖????障?? |
| 84 |
procedure Save; |
| 85 |
|
| 86 |
//! ??茯???????????宴????緇????障?? |
| 87 |
property Objects[ const name : string ] : TWordInfo |
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read GetObject write SetObject; default; |
| 89 |
|
| 90 |
//! ??腴??????障??????茯????????潟?????障?? |
| 91 |
procedure CountWord( |
| 92 |
const text : string; |
| 93 |
wordCount : TWordCount ); |
| 94 |
|
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{! |
| 96 |
\brief Paul Graham 羈????冴?ャ??????腴???絵??墾??羆阪????障?? |
| 97 |
\return ??腴???絵??墾 (羈??????ゃ?????? 0.0??1.0 羈??????鴻??) |
| 98 |
} |
| 99 |
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??) |
| 104 |
} |
| 105 |
function CalcGaryRobinson( wordCount : TWordCount ) : Extended; |
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|
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// 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? |
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\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???? |
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} |
| 118 |
function Parse( |
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const text : string; |
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wordCount : TWordCount; |
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algorithm : TGikoBayesianAlgorithm = gbaGaryRonbinson |
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) : Extended; |
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|
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{! |
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\brief 絖?????? |
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\param wordCount Parse ?цВ??????????茯????鴻?? |
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\param isImportant 羈??????鴻????腴???????????????? True |
| 128 |
} |
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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 |
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\warning 絖??羝??帥????腴???????????∈茯??堺?ャ?障??????<br> |
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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???? |
| 146 |
} |
| 147 |
procedure Forget( |
| 148 |
wordCount : TWordCount; |
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isImportant : Boolean ); |
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end; |
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|
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//================================================== |
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implementation |
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//================================================== |
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|
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uses |
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SysUtils, Math; |
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|
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const |
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GIKO_BAYESIAN_FILE_VERSION = '1.0'; |
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kYofKanji : TSysCharSet = [#$80..#$A0, #$E0..#$ff]; |
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|
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//************************************************************ |
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// misc |
| 165 |
//************************************************************ |
| 166 |
|
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//============================== |
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// RemoveToken |
| 169 |
//============================== |
| 170 |
function RemoveToken(var s: string;const delimiter: string): string; |
| 171 |
var |
| 172 |
p: Integer; |
| 173 |
begin |
| 174 |
p := AnsiPos(delimiter, s); |
| 175 |
if p = 0 then |
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Result := s |
| 177 |
else |
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Result := Copy(s, 1, p - 1); |
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s := Copy(s, Length(Result) + Length(delimiter) + 1, Length(s)); |
| 180 |
end; |
| 181 |
|
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//============================== |
| 183 |
// AbsSort |
| 184 |
//============================== |
| 185 |
function AbsSort( p1, p2 : Pointer ) : Integer; |
| 186 |
var |
| 187 |
v1, v2 : Single; |
| 188 |
begin |
| 189 |
|
| 190 |
v1 := Abs( Single( p1 ) - 0.5 ); |
| 191 |
v2 := Abs( Single( p2 ) - 0.5 ); |
| 192 |
if v1 > v2 then |
| 193 |
Result := -1 |
| 194 |
else if v1 = v2 then |
| 195 |
Result := 0 |
| 196 |
else |
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Result := 1; |
| 198 |
|
| 199 |
end; |
| 200 |
|
| 201 |
//************************************************************ |
| 202 |
// TWordCount class |
| 203 |
//************************************************************ |
| 204 |
destructor TWordCount.Destroy; |
| 205 |
var |
| 206 |
i : Integer; |
| 207 |
begin |
| 208 |
|
| 209 |
for i := Count - 1 downto 0 do |
| 210 |
if Objects[ i ] <> nil then |
| 211 |
Objects[ i ].Free; |
| 212 |
|
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inherited; |
| 214 |
|
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end; |
| 216 |
|
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//************************************************************ |
| 218 |
// TGikoBayesian class |
| 219 |
//************************************************************ |
| 220 |
|
| 221 |
//============================== |
| 222 |
// Create |
| 223 |
//============================== |
| 224 |
constructor TGikoBayesian.Create; |
| 225 |
begin |
| 226 |
|
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Duplicates := dupIgnore; |
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Sorted := True; |
| 229 |
|
| 230 |
end; |
| 231 |
|
| 232 |
//============================== |
| 233 |
// Destroy |
| 234 |
//============================== |
| 235 |
destructor TGikoBayesian.Destroy; |
| 236 |
var |
| 237 |
i : Integer; |
| 238 |
begin |
| 239 |
|
| 240 |
for i := Count - 1 downto 0 do |
| 241 |
if inherited Objects[ i ] <> nil then |
| 242 |
inherited Objects[ i ].Free; |
| 243 |
|
| 244 |
inherited; |
| 245 |
|
| 246 |
end; |
| 247 |
|
| 248 |
procedure TGikoBayesian.LoadFromFile( const filePath : string ); |
| 249 |
var |
| 250 |
i : Integer; |
| 251 |
sl : TStringList; |
| 252 |
s : string; |
| 253 |
name : string; |
| 254 |
info : TWordInfo; |
| 255 |
begin |
| 256 |
|
| 257 |
if not FileExists( filePath ) then |
| 258 |
Exit; |
| 259 |
|
| 260 |
sl := TStringList.Create; |
| 261 |
try |
| 262 |
sl.LoadFromFile( filePath ); |
| 263 |
|
| 264 |
for i := 1 to sl.Count - 1 do begin |
| 265 |
s := sl[ i ]; |
| 266 |
name := RemoveToken( s, #1 ); |
| 267 |
info := TWordInfo.Create; |
| 268 |
info.NormalWord := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 269 |
info.ImportantWord := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 270 |
info.NormalText := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 271 |
info.ImportantText := StrToIntDef( '$' + RemoveToken( s, #1 ), 0 ); |
| 272 |
|
| 273 |
AddObject( name, info ); |
| 274 |
end; |
| 275 |
finally |
| 276 |
sl.Free; |
| 277 |
end; |
| 278 |
|
| 279 |
end; |
| 280 |
|
| 281 |
procedure TGikoBayesian.SaveToFile( const filePath : string ); |
| 282 |
var |
| 283 |
i : Integer; |
| 284 |
sl : TStringList; |
| 285 |
s : string; |
| 286 |
info : TWordInfo; |
| 287 |
begin |
| 288 |
|
| 289 |
sl := TStringList.Create; |
| 290 |
try |
| 291 |
sl.BeginUpdate; |
| 292 |
sl.Add( GIKO_BAYESIAN_FILE_VERSION ); |
| 293 |
|
| 294 |
for i := 0 to Count - 1 do begin |
| 295 |
info := TWordInfo( inherited Objects[ i ] ); |
| 296 |
s := Strings[ i ] + #1 |
| 297 |
+ Format('%x', [info.NormalWord]) + #1 |
| 298 |
+ Format('%x', [info.ImportantWord]) + #1 |
| 299 |
+ Format('%x', [info.NormalText]) + #1 |
| 300 |
+ Format('%x', [info.ImportantText]); |
| 301 |
|
| 302 |
sl.Add(s); |
| 303 |
end; |
| 304 |
sl.EndUpdate; |
| 305 |
sl.SaveToFile( filePath ); |
| 306 |
finally |
| 307 |
sl.Free; |
| 308 |
end; |
| 309 |
|
| 310 |
end; |
| 311 |
|
| 312 |
procedure TGikoBayesian.Save; |
| 313 |
begin |
| 314 |
|
| 315 |
if FFilePath <> '' then |
| 316 |
SaveToFile( FFilePath ); |
| 317 |
|
| 318 |
end; |
| 319 |
|
| 320 |
//============================== |
| 321 |
// GetObject |
| 322 |
//============================== |
| 323 |
function TGikoBayesian.GetObject( const name : string ) : TWordInfo; |
| 324 |
var |
| 325 |
idx : Integer; |
| 326 |
begin |
| 327 |
|
| 328 |
idx := IndexOf( name ); |
| 329 |
if idx < 0 then |
| 330 |
Result := nil |
| 331 |
else |
| 332 |
Result := TWordInfo( inherited Objects[ idx ] ); |
| 333 |
|
| 334 |
end; |
| 335 |
|
| 336 |
//============================== |
| 337 |
// SetObject |
| 338 |
//============================== |
| 339 |
procedure TGikoBayesian.SetObject( const name : string; value : TWordInfo ); |
| 340 |
var |
| 341 |
idx : Integer; |
| 342 |
begin |
| 343 |
|
| 344 |
idx := IndexOf( name ); |
| 345 |
if idx < 0 then |
| 346 |
AddObject( name, value ) |
| 347 |
else |
| 348 |
inherited Objects[ idx ] := value; |
| 349 |
|
| 350 |
end; |
| 351 |
|
| 352 |
|
| 353 |
//============================== |
| 354 |
// CountWord |
| 355 |
//============================== |
| 356 |
procedure TGikoBayesian.CountWord( |
| 357 |
const text : string; |
| 358 |
wordCount : TWordCount ); |
| 359 |
type |
| 360 |
Modes = (ModeWhite, ModeGraph, ModeAlpha, ModeNum, ModeHanKana, |
| 361 |
ModeWGraph, ModeWAlpha, ModeWNum, |
| 362 |
ModeWHira, ModeWKata, ModeWKanji); |
| 363 |
var |
| 364 |
p, tail, last : PChar; |
| 365 |
mode, newMode : Modes; |
| 366 |
aWord : string; |
| 367 |
ch : Longword; |
| 368 |
chSize : Integer; |
| 369 |
delimiter : TStringList; |
| 370 |
delimited : Boolean; |
| 371 |
i, idx : Integer; |
| 372 |
countInfo : TWordCountInfo; |
| 373 |
const |
| 374 |
KAKUJOSI = '??' + #10 + '??#39; + #10 + '??' + #10 + '??#39; + #10 + '????' + |
| 375 |
#10 + '??#39; + #10 + '??#39; + #10 + '????' + #10 + '?障??#39;; |
| 376 |
begin |
| 377 |
|
| 378 |
delimiter := TStringList.Create; |
| 379 |
try |
| 380 |
//*** ??綺????鴻??筝?/span> |
| 381 |
wordCount.Duplicates := dupIgnore; |
| 382 |
wordCount.CaseSensitive := True; |
| 383 |
wordCount.Capacity := 1000; |
| 384 |
wordCount.Sorted := True; |
| 385 |
//*** |
| 386 |
|
| 387 |
mode := ModeWhite; |
| 388 |
delimiter.Text := KAKUJOSI; |
| 389 |
SetLength( aWord, 256 ); |
| 390 |
p := PChar( text ); |
| 391 |
tail := p + Length( text ); |
| 392 |
last := p; |
| 393 |
|
| 394 |
while p < tail do begin |
| 395 |
delimited := False; |
| 396 |
// ??絖????帥?ゃ?????ゅ??/span> |
| 397 |
// ?糸???鴻? ModeGraph ?????????у???ャ???綽?????????????? |
| 398 |
if p^ in kYofKanji then begin |
| 399 |
if p + 1 < tail then begin |
| 400 |
ch := (PByte( p )^ shl 8) or PByte( p + 1 )^; |
| 401 |
case ch of |
| 402 |
$8140: newMode := ModeWhite; |
| 403 |
$8141..$824e: newMode := ModeWGraph; |
| 404 |
$824f..$8258: newMode := ModeWNum; |
| 405 |
$8260..$829a: newMode := ModeWAlpha; |
| 406 |
$829f..$82f1: newMode := ModeWHira; |
| 407 |
$8340..$8396: newMode := ModeWKata; |
| 408 |
else newMode := ModeWKanji; |
| 409 |
end; |
| 410 |
end else begin |
| 411 |
newMode := ModeWhite; |
| 412 |
end; |
| 413 |
|
| 414 |
chSize := 2; |
| 415 |
|
| 416 |
// ?阪????????????絖?????????罎??祉???? |
| 417 |
if p + 3 < tail then begin // 3 = delimiter ????紊у?? - 1 |
| 418 |
for i := 0 to delimiter.Count - 1 do begin |
| 419 |
if CompareMem( |
| 420 |
p, PChar( delimiter[ i ] ), Length( delimiter[ i ] ) ) then begin |
| 421 |
delimited := True; |
| 422 |
chSize := Length( delimiter[ i ] ); |
| 423 |
Break; |
| 424 |
end; |
| 425 |
end; |
| 426 |
end; |
| 427 |
end else begin |
| 428 |
case p^ of |
| 429 |
#$0..#$20, #$7f: newMode := ModeWhite; |
| 430 |
'0'..'9': newMode := ModeNum; |
| 431 |
'a'..'z', 'A'..'Z': newMode := ModeAlpha; |
| 432 |
#$A6..#$DD: newMode := ModeHanKana; |
| 433 |
else newMode := ModeGraph; |
| 434 |
end; |
| 435 |
|
| 436 |
chSize := 1; |
| 437 |
end; |
| 438 |
|
| 439 |
if (mode <> newMode) or delimited then begin |
| 440 |
|
| 441 |
// ??絖????帥?ゃ????紊??眼?????? |
| 442 |
// ????????阪????????????絖??????????? |
| 443 |
if mode <> ModeWhite then begin |
| 444 |
aWord := Copy( last, 0, p - last ); // 羶??? |
| 445 |
// SetLength( aWord, p - last ); |
| 446 |
// CopyMemory( PChar( aWord ), last, p - last ); |
| 447 |
idx := wordCount.IndexOf( aWord ); // 羶??? |
| 448 |
if idx < 0 then begin |
| 449 |
countInfo := TWordCountInfo.Create; |
| 450 |
wordCount.AddObject( aWord, countInfo ); |
| 451 |
end else begin |
| 452 |
countInfo := TWordCountInfo( wordCount.Objects[ idx ] ); |
| 453 |
end; |
| 454 |
countInfo.WordCount := countInfo.WordCount + 1; |
| 455 |
end; |
| 456 |
|
| 457 |
last := p; |
| 458 |
mode := newMode; |
| 459 |
|
| 460 |
end; |
| 461 |
|
| 462 |
p := p + chSize; |
| 463 |
end; // while |
| 464 |
|
| 465 |
if mode <> ModeWhite then begin |
| 466 |
aWord := Copy( last, 0, p - last ); |
| 467 |
idx := wordCount.IndexOf( aWord ); |
| 468 |
if idx < 0 then begin |
| 469 |
countInfo := TWordCountInfo.Create; |
| 470 |
wordCount.AddObject( aWord, countInfo ); |
| 471 |
end else begin |
| 472 |
countInfo := TWordCountInfo( wordCount.Objects[ idx ] ); |
| 473 |
end; |
| 474 |
countInfo.WordCount := countInfo.WordCount + 1; |
| 475 |
end; |
| 476 |
finally |
| 477 |
delimiter.Free; |
| 478 |
end; |
| 479 |
|
| 480 |
end; |
| 481 |
|
| 482 |
//============================== |
| 483 |
// CalcPaulGraham |
| 484 |
//============================== |
| 485 |
function TGikoBayesian.CalcPaulGraham( wordCount : TWordCount ) : Extended; |
| 486 |
|
| 487 |
function p( const aWord : string ) : Single; |
| 488 |
var |
| 489 |
info : TWordInfo; |
| 490 |
begin |
| 491 |
info := Objects[ aWord ]; |
| 492 |
if info = nil then |
| 493 |
Result := 0.4 |
| 494 |
else if info.NormalWord = 0 then |
| 495 |
Result := 0.99 |
| 496 |
else if info.ImportantWord = 0 then |
| 497 |
Result := 0.01 |
| 498 |
else |
| 499 |
Result := ( info.ImportantWord / info.ImportantText ) / |
| 500 |
((info.NormalWord * 2 / info.NormalText ) + |
| 501 |
(info.ImportantWord / info.ImportantText)); |
| 502 |
end; |
| 503 |
|
| 504 |
var |
| 505 |
s, q : Extended; |
| 506 |
i : Integer; |
| 507 |
narray : TList; |
| 508 |
const |
| 509 |
SAMPLE_COUNT = 15; |
| 510 |
begin |
| 511 |
|
| 512 |
Result := 1; |
| 513 |
if wordCount.Count = 0 then |
| 514 |
Exit; |
| 515 |
|
| 516 |
narray := TList.Create; |
| 517 |
try |
| 518 |
for i := 0 to wordCount.Count - 1 do begin |
| 519 |
narray.Add( Pointer( p( wordCount[ i ] ) ) ); |
| 520 |
end; |
| 521 |
|
| 522 |
narray.Sort( AbsSort ); |
| 523 |
|
| 524 |
s := 1; |
| 525 |
q := 1; |
| 526 |
i := min( SAMPLE_COUNT, narray.Count ); |
| 527 |
while i > 0 do begin |
| 528 |
Dec( i ); |
| 529 |
s := s * Single( narray[ i ] ); |
| 530 |
q := q * (1 - Single( narray[ i ] )); |
| 531 |
end; |
| 532 |
|
| 533 |
Result := s / (s + q); |
| 534 |
finally |
| 535 |
narray.Free; |
| 536 |
end; |
| 537 |
|
| 538 |
end; |
| 539 |
|
| 540 |
//============================== |
| 541 |
// CalcGaryRobinson |
| 542 |
//============================== |
| 543 |
function TGikoBayesian.CalcGaryRobinson( wordCount : TWordCount ) : Extended; |
| 544 |
|
| 545 |
function p( const aWord : string ) : Single; |
| 546 |
var |
| 547 |
info : TWordInfo; |
| 548 |
begin |
| 549 |
info := Objects[ aWord ]; |
| 550 |
if info = nil then |
| 551 |
Result := 0.415 |
| 552 |
else if info.ImportantWord = 0 then |
| 553 |
Result := 0.0001 |
| 554 |
else if info.NormalWord = 0 then |
| 555 |
Result := 0.9999 |
| 556 |
else |
| 557 |
Result := ( info.ImportantWord / info.ImportantText ) / |
| 558 |
((info.NormalWord / info.NormalText ) + |
| 559 |
(info.ImportantWord / info.ImportantText)); |
| 560 |
end; |
| 561 |
|
| 562 |
function f( cnt : Integer; n, mean : Single ) : Extended; |
| 563 |
const |
| 564 |
k = 0.00001; |
| 565 |
begin |
| 566 |
Result := ( (k * mean) + (cnt * n) ) / (k + cnt); |
| 567 |
end; |
| 568 |
|
| 569 |
var |
| 570 |
n : Extended; |
| 571 |
narray : array of Single; |
| 572 |
mean : Extended; |
| 573 |
countInfo : TWordCountInfo; |
| 574 |
i : Integer; |
| 575 |
normal : Extended; |
| 576 |
important : Extended; |
| 577 |
cnt : Extended; |
| 578 |
begin |
| 579 |
|
| 580 |
if wordCount.Count = 0 then begin |
| 581 |
Result := 1; |
| 582 |
Exit; |
| 583 |
end; |
| 584 |
|
| 585 |
SetLength( narray, wordCount.Count ); |
| 586 |
mean := 0; |
| 587 |
for i := 0 to wordCount.Count - 1 do begin |
| 588 |
n := p( wordCount[ i ] ); |
| 589 |
narray[ i ] := n; |
| 590 |
mean := mean + n; |
| 591 |
end; |
| 592 |
mean := mean / wordCount.Count; |
| 593 |
|
| 594 |
cnt := 0; |
| 595 |
normal := 1; |
| 596 |
important := 1; |
| 597 |
for i := 0 to wordCount.Count - 1 do begin |
| 598 |
countInfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 599 |
n := f( countInfo.WordCount, narray[ i ], mean ); |
| 600 |
normal := normal * n; |
| 601 |
important := important * (1 - n); |
| 602 |
if countInfo <> nil then |
| 603 |
cnt := cnt + countInfo.WordCount; |
| 604 |
end; |
| 605 |
if cnt = 0 then |
| 606 |
cnt := 1; |
| 607 |
normal := 1 - Exp( Ln( normal ) * (1 / cnt) ); |
| 608 |
important := 1 - Exp( Ln( important ) * (1 / cnt) ); |
| 609 |
|
| 610 |
n := (important - normal+ 0.00001) / (important + normal + 0.00001); |
| 611 |
Result := (1 + n) / 2; |
| 612 |
|
| 613 |
end; |
| 614 |
|
| 615 |
//============================== |
| 616 |
// Parse |
| 617 |
//============================== |
| 618 |
function TGikoBayesian.Parse( |
| 619 |
const text : string; |
| 620 |
wordCount : TWordCount; |
| 621 |
algorithm : TGikoBayesianAlgorithm = gbaGaryRonbinson |
| 622 |
) : Extended; |
| 623 |
begin |
| 624 |
|
| 625 |
CountWord( text, wordCount ); |
| 626 |
case algorithm of |
| 627 |
gbaPaulGraham: Result := CalcPaulGraham( wordCount ); |
| 628 |
gbaGaryRonbinson: Result := CalcGaryRobinson( wordCount ); |
| 629 |
else Result := 0; |
| 630 |
end; |
| 631 |
|
| 632 |
end; |
| 633 |
|
| 634 |
//============================== |
| 635 |
// Learn |
| 636 |
//============================== |
| 637 |
procedure TGikoBayesian.Learn( |
| 638 |
wordCount : TWordCount; |
| 639 |
isImportant : Boolean ); |
| 640 |
var |
| 641 |
aWord : string; |
| 642 |
wordinfo : TWordInfo; |
| 643 |
countinfo : TWordCountInfo; |
| 644 |
i : Integer; |
| 645 |
begin |
| 646 |
|
| 647 |
for i := 0 to wordCount.Count - 1 do begin |
| 648 |
aWord := wordCount[ i ]; |
| 649 |
wordinfo := Objects[ aWord ]; |
| 650 |
if wordinfo = nil then begin |
| 651 |
wordinfo := TWordInfo.Create; |
| 652 |
Objects[ aWord ] := wordinfo; |
| 653 |
end; |
| 654 |
|
| 655 |
countinfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 656 |
if isImportant then begin |
| 657 |
wordinfo.ImportantWord := wordinfo.ImportantWord + countinfo.WordCount; |
| 658 |
wordinfo.ImportantText := wordinfo.ImportantText + 1; |
| 659 |
end else begin |
| 660 |
wordinfo.NormalWord := wordinfo.NormalWord + countinfo.WordCount; |
| 661 |
wordinfo.NormalText := wordinfo.NormalText + 1; |
| 662 |
end; |
| 663 |
end; |
| 664 |
|
| 665 |
end; |
| 666 |
|
| 667 |
//============================== |
| 668 |
// Forget |
| 669 |
//============================== |
| 670 |
procedure TGikoBayesian.Forget( |
| 671 |
wordCount : TWordCount; |
| 672 |
isImportant : Boolean ); |
| 673 |
var |
| 674 |
aWord : string; |
| 675 |
wordinfo : TWordInfo; |
| 676 |
countinfo : TWordCountInfo; |
| 677 |
i : Integer; |
| 678 |
begin |
| 679 |
|
| 680 |
for i := 0 to wordCount.Count - 1 do begin |
| 681 |
aWord := wordCount[ i ]; |
| 682 |
wordinfo := Objects[ aWord ]; |
| 683 |
if wordinfo = nil then |
| 684 |
Continue; |
| 685 |
|
| 686 |
countinfo := TWordCountInfo( wordCount.Objects[ i ] ); |
| 687 |
if isImportant then begin |
| 688 |
wordinfo.ImportantWord := wordinfo.ImportantWord - countinfo.WordCount; |
| 689 |
wordinfo.ImportantText := wordinfo.ImportantText - 1; |
| 690 |
end else begin |
| 691 |
wordinfo.NormalWord := wordinfo.NormalWord - countinfo.WordCount; |
| 692 |
wordinfo.NormalText := wordinfo.NormalText - 1; |
| 693 |
end; |
| 694 |
end; |
| 695 |
|
| 696 |
end; |
| 697 |
|
| 698 |
end. |