├── README.md
├── Tokenizer.php
├── classify.py
├── config.php
├── confusion_matrix.py
├── result
    ├── predict_result.txt
    ├── predict_value.txt
    ├── tapcmNor.png
    └── testdata_y.txt
├── sarderrors.png
└── tap.py


/README.md:
--------------------------------------------------------------------------------
 1 | # TAP
 2 | 
 3 | ## Research paper
 4 | 
 5 | We present the findings of this work in the following research paper:
 6 | 
 7 | **TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology**
 8 | Yong Fang,Shengjun Han,Cheng Huang ,Runpu Wu
 9 | Published: November 18, 2019
10 | [View](https://doi.org/10.1371/journal.pone.0225196)
11 | 
12 | ## Introduction
13 | 
14 | With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There are some models for solving this problem. However, most of them rely on complex graphs generated from source code or regex patterns based on expert experience. In this paper, TAP, which is based on token mechanism and deep learning technology, was proposed as an analysis model to discover the vulnerabilities of PHP: Hypertext Preprocessor (PHP) Web programs conveniently and easily. Based on the token mechanism of PHP language, a custom tokenizer was designed, and it unifies tokens, supports some features of PHP and optimizes the parsing. Besides, the tokenizer also implements parameter iteration to achieve data flow analysis. On the Software Assurance Reference Dataset(SARD) and SQLI-LABS dataset, we trained the deep learning model of TAP by combining the word2vec model with Long Short-Term Memory (LSTM) network algorithm. According to the experiment on the dataset of CWE-89, TAP not only achieves the 0.9941 Area Under the Curve(AUC), which is better than other models, but also achieves the highest accuracy: 0.9787. Further, compared with RIPS, TAP shows much better in multiclass classification with 0.8319 Kappa and 0.0840 hamming distance.
15 | 
16 | 
17 | ## Reference
18 | 
19 | If you use TAP in a scientific publication, we would appreciate citations using this Bibtex entry:
20 | 
21 | ```
22 | @article{fang2019tap,
23 |   title={TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology},
24 |   author={Fang, Yong and Han, Shengjun and Huang, Cheng and Wu, Runpu},
25 |   journal={PloS one},
26 |   volume={14},
27 |   number={11},
28 |   year={2019},
29 |   publisher={Public Library of Science}
30 | }
31 | ```
32 | 
33 | # TAP Demo
34 | 
35 | TAP: A Static Analysis Model for PHP Vulnerabilities Based on Token and Deep Learning Technology
36 | 
37 | 
38 | Data download from
39 | ```
40 | https://samate.nist.gov/SARD/index.php
41 | ```
42 | 
43 | Unzip 
44 | ```
45 | SARD-testsuite-103.zip
46 | ```
47 |  
48 | Classify samples.
49 | ```
50 | python classify.py
51 | ```
52 | 
53 | Delete three subsets of less than 10 samples, which are
54 | ```
55 | CWE-209: Information Exposure Through an Error Message, 
56 | CWE-311: Missing Encryption of Sensitive Data 
57 | CWE-327: Use of a Broken or Risky Cryptographic Algorithm.
58 | ```
59 | 
60 | Correct some errors of SARD manually. 
61 | ![Errors](./sarderrors.png) 
62 | 
63 | Get tokens by our tokenizer.
64 | ```
65 | php Tokenizer.php
66 | ```
67 | 
68 | Run TAP.
69 | ```
70 | python tap.py
71 | ```
72 | 
73 | Evaluation.
74 | ```
75 | python confusion_matrix.py
76 | ```
77 | 
78 | Result.
79 | ![Results](./result/tapcmNor.png)
80 | 


--------------------------------------------------------------------------------
/Tokenizer.php:
--------------------------------------------------------------------------------
  1 | <?php
  2 | // error_reporting(0);
  3 | include("./config.php");
  4 | 
  5 | $safePath = "./safe_samples/";
  6 | $unsafePath = "./unsafe_samples/";
  7 | $safeTokenFile = "./safe_tokens.txt";
  8 | $unsafeTokenFile = "./unsafe_tokens.txt";
  9 | 
 10 | unlink('unsafe_y.txt');
 11 | xxxasdfasdfsdfs_getAllTokens($safePath,$safeTokenFile);
 12 | xxxasdfasdfsdfs_getAllTokens($unsafePath,$unsafeTokenFile);
 13 | // xxxasdfasdfsdfs_test();
 14 | 
 15 | 
 16 | // ()
 17 | $bracketNum = 0;
 18 | // {}
 19 | $bracesNum = 0;
 20 | $senEnd = 0;
 21 | $varValue = '';
 22 | 
 23 | function isSenEnd($char)
 24 | {
 25 | 	global $bracketNum;
 26 | 	global $bracesNum;
 27 | 	global $senEnd;
 28 | 	
 29 | 	if('(' == $char)
 30 | 	{
 31 | 		$bracketNum++;
 32 | 	}elseif('{' == $char)
 33 | 	{
 34 | 		$bracesNum++;
 35 | 	}
 36 | 	
 37 | 	if(';' == $char)
 38 | 	{
 39 | 		$senEnd = 1;
 40 | 		return 1;
 41 | 	}elseif(')' == $char)
 42 | 	{
 43 | 		$bracketNum--;
 44 | 		if(0 == $bracketNum)
 45 | 		{
 46 | 			$senEnd = 1;
 47 | 			return 1;
 48 | 		}
 49 | 	}elseif('}' == $char) 
 50 | 	{
 51 | 		$bracesNum--;
 52 | 		if(0 == $bracesNum)
 53 | 		{
 54 | 			$senEnd = 1;
 55 | 			return 1;
 56 | 		}
 57 | 	}
 58 | 
 59 | 	return 0;
 60 | 	
 61 | }
 62 | 
 63 | 
 64 | function xxxasdfasdfsdfs_oneTokenize(&$n,&$tokens,&$cleanTokens,&$allVars,&$assignment,&$remain)
 65 | {
 66 | 
 67 | 	global $T_IGNORE;
 68 | 	global $T_INCLUDES;
 69 | 	global $T_ASSIGNMENT;
 70 | 	global $T_ECHO;
 71 | 	global $V_ECHO;
 72 | 	global $V_EXEC;
 73 | 	global $V_SQL;
 74 | 	global $V_PREG;
 75 | 	global $V_INPUT;
 76 | 	global $senEnd;
 77 | 
 78 | 	
 79 | 	
 80 | 	if (is_array($tokens[$n])) {
 81 | 
 82 | 			if(in_array($tokens[$n][0],$T_IGNORE)){	
 83 | 				// return 'T_IGNORE';
 84 | 				return '';
 85 | 			}elseif(in_array($tokens[$n][0],$T_INCLUDES)){
 86 | 				$remain = 1;
 87 | 				return 'T_INCLUDES';
 88 | 			// }elseif(in_array($tokens[$n][0],$T_ASSIGNMENT)){
 89 | 				// return 'T_ASSIGNMENT';
 90 | 			}elseif(in_array($tokens[$n][0],$T_ECHO)){
 91 | 				$remain = 1;
 92 | 				return 'T_ECHO';
 93 | 			}elseif(in_array($tokens[$n][1],$V_ECHO)){
 94 | 				$remain = 1;
 95 | 				return 'T_ECHO';
 96 | 			}elseif(in_array($tokens[$n][1],$V_EXEC)){
 97 | 				$remain = 1;
 98 | 				return 'T_EXEC';
 99 | 			}elseif(in_array($tokens[$n][1],$V_SQL)){
100 | 				$remain = 1;
101 | 				return 'T_SQL';
102 | 			}elseif(in_array($tokens[$n][1],$V_PREG)){
103 | 				$remain = 1;
104 | 				return 'T_PREG';
105 | 			}elseif(in_array($tokens[$n][1],$V_INPUT)){
106 | 				return 'T_INPUT';
107 | 				
108 | 			}elseif(in_array($tokens[$n][0],$T_ASSIGNMENT)){
109 | 				$assignment = 1;
110 | 				$T_OP = token_name($tokens[$n][0]);
111 | 				$operation = $tokens[$n][1];
112 | 				// $varValue = array_pop($cleanTokens);
113 | 				$varValue = end($cleanTokens);
114 | 				$varName = array_search($varValue,$allVars);
115 | 				// var_dump($varValue);
116 | 				$t = '';
117 | 				// $n++;
118 | 				// while(is_array($tokens[$n]) || $tokens[$n]!=';')
119 | 				
120 | 				do{
121 | 					$n++;
122 | 					$t .= xxxasdfasdfsdfs_oneTokenize($n,$tokens,$cleanTokens,$allVars,$assignment,$remain).' ';
123 | 					
124 | 				}while(!$senEnd
125 | 					&& (is_array($tokens[$n])
126 | 					|| !isSenEnd(substr($tokens[$n],-1,1)))
127 | 					);
128 | 				
129 | 				$assignment = 0;
130 | 				// eval('$varValue'.$operation.'$t;');
131 | 				// $varValue = substr($varValue,0,-1);
132 | 				$allVars[$varName] =  $varValue.$T_OP.$t;
133 | 				return $T_OP.$t.' ;';
134 | 				
135 | 			}elseif(
136 | 			// remain system function
137 | 			(function_exists($tokens[$n][1]))	
138 | 			// remain user-defined function
139 | 			|| (T_STRING == $tokens[$n][0])
140 | 			){			
141 | 				$remain = 1;
142 | 				return $tokens[$n][1];
143 | 			}elseif(// remain INT				
144 | 			(T_LNUMBER == $tokens[$n][0])){
145 | 				return $tokens[$n][1];
146 | 			}elseif(T_CONSTANT_ENCAPSED_STRING == $tokens[$n][0]){
147 | 				$tokenvalue = trim($tokens[$n][1]);
148 | 				$v = 'T_CONSTANT_ENCAPSED_STRING';
149 | 				$spChar = array('"','\'');
150 | 				// is special symbols?
151 | 				$startChar = substr($tokenvalue,1,1);
152 | 				$endChar = substr($tokenvalue,-2,1);
153 | 				if(in_array($startChar,$spChar))
154 | 				{
155 | 					$v = $startChar.' '.$v;
156 | 				}elseif(in_array($endChar,$spChar))
157 | 				{
158 | 					$v = $v.' '.$endChar;
159 | 				}else{
160 | 				}
161 | 				return $v;
162 | 				
163 | 			}elseif(T_ENCAPSED_AND_WHITESPACE == $tokens[$n][0]){
164 | 				$v = 'T_CONSTANT_ENCAPSED_STRING';
165 | 				// var_dump($allVars);
166 | 				global $varValue;
167 | 				if('"' == substr($varValue,-2,1)
168 | 				|| "'" == substr($varValue,-2,1))
169 | 				{
170 | 					$varValue = substr($varValue,0,-2);
171 | 				}
172 | 
173 | 				$tokenvalue = trim($tokens[$n][1]);
174 | 				$spChar = array('"','\'');
175 | 				$startChar = substr($tokenvalue,0,1);
176 | 				$endChar = substr($tokenvalue,-1,1);
177 | 				if(in_array($startChar,$spChar))
178 | 				{
179 | 					$v = $startChar.' '.$v;
180 | 				}elseif(in_array($endChar,$spChar))
181 | 				{
182 | 					$v = $v.' '.$endChar;
183 | 				}else{
184 | 				}
185 | 				
186 | 				try{
187 | 					if('"' == $tokens[$n+1]
188 | 					|| "'" == $tokens[$n+1])
189 | 					{
190 | 						$n++;
191 | 					}
192 | 				}catch(Exception $e){}
193 | 				return $v;
194 | 				
195 | 			}elseif(T_VARIABLE == $tokens[$n][0]){
196 | 				//deal array
197 | 				$varName = $tokens[$n][1];
198 | 				
199 | 				if('[' == $tokens[$n+1]){
200 | 					$varName .= '[';
201 | 					$t = $n+1;
202 | 					$n += 2;
203 | 					while(']'!=$tokens[$n]){
204 | 						$varName .= xxxasdfasdfsdfs_oneTokenize($n,$tokens,$cleanTokens,$allVars,$assignment,$remain);
205 | 						$n++;
206 | 					}
207 | 				
208 | 					$varName .= ']';
209 | 					
210 | 					if(1 == $n-$t){
211 | 						$i = 0;
212 | 						
213 | 						while(array_key_exists((substr($varName,0,-1).$i.']'),$allVars))
214 | 						{
215 | 							$i++;
216 | 						}
217 | 						$varName = substr($varName,0,-1).$i.']';
218 | 					}
219 | 				
220 | 		
221 | 				}
222 | 				
223 | 				if(array_key_exists($varName,$allVars))
224 | 				{
225 | 					return $allVars[$varName];
226 | 				}elseif($assignment)
227 | 				{
228 | 					$allVars[$varName] = 'T_UNDEFINED_VAR';
229 | 					return 'T_UNDEFINED_VAR';
230 | 					
231 | 				}else{
232 | 					return $varName;
233 | 				}
234 | 											
235 | 			}else{
236 | 				return token_name($tokens[$n][0]);
237 | 			}
238 | 		}elseif('=' == $tokens[$n]){
239 | 			
240 | 			$assignment = 1;
241 | 	
242 | 			// $varName = array_pop($cleanTokens);
243 | 			$varName = end($cleanTokens);
244 | 			
245 | 			$t = array_search($varName,$allVars);
246 | 			if($t)
247 | 			{
248 | 				$varName = $t;
249 | 			}else{
250 | 				
251 | 			}
252 | 			// var_dump($cleanTokens);
253 | 			global $varValue;
254 | 			$varValue = '';
255 | 			$n++;
256 | 			// while(!$senEnd || !isSenEnd($tokens[$n]))
257 | 			while(!$senEnd && !isSenEnd($tokens[$n]))
258 | 			{
259 | 				$varValue .= xxxasdfasdfsdfs_oneTokenize($n,$tokens,$cleanTokens,$allVars,$assignment,$remain).' ';
260 | 				$n++;
261 | 			}
262 | 			// $varValue = substr($varValue,0,-1);
263 | 			$assignment = 0;
264 | 			$allVars[$varName] =  $varValue;
265 | 	
266 | 			return $varValue.' '.$tokens[$n];
267 | 	
268 | 		}else{
269 | 			return $tokens[$n];
270 | 		}
271 | 		
272 | }	
273 | 	
274 | function xxxasdfasdfsdfs_tokenize($data)
275 | {
276 | 	global $senEnd;
277 | 	$tokens = token_get_all($data);
278 | 	// var_dump($tokens);
279 | 	$cleanTokens = array();
280 | 
281 | 	$allVars = array();
282 | 	// global $allVars;
283 | 	$tokenNum = count($tokens);
284 | 	$assignment = 0;
285 | 	$remain = 0;
286 | 	for($n=0;$n<$tokenNum;$n++)
287 | 	{
288 | 		$token = xxxasdfasdfsdfs_oneTokenize($n,$tokens,$oneLine,$allVars,$assignment,$remain);
289 | 
290 | 		// var_dump($token);
291 | 		if($token){
292 | 			if(!$senEnd
293 | 			&& !isSenEnd(substr($token,-1,1)) ){
294 | 				$oneLine[] = $token;
295 | 				if(1 == $remain){
296 | 					// array_filter($oneLine);
297 | 					$cleanTokens[] = $token;
298 | 				}
299 | 			}elseif($remain){
300 | 				$oneLine[] = $token;
301 | 				// array_filter($oneLine);
302 | 				// $cleanTokens = array_merge($cleanTokens,$oneLine);
303 | 				$cleanTokens[] = $token;
304 | 				$senEnd = 0;
305 | 				$remain = 0;
306 | 			}else{
307 | 				$senEnd = 0;
308 | 				$oneLine = array();
309 | 			}
310 | 		}
311 | 	
312 | 				
313 | 		
314 | 	}	
315 | 	// var_dump( $allVars);
316 | 
317 | 	$cleanTokens = implode(' ',$cleanTokens);
318 | 	$cleanTokens = preg_replace('/[\s]+/',' ',$cleanTokens);
319 | 	return $cleanTokens;
320 | }
321 | 
322 | 
323 | 
324 | function xxxasdfasdfsdfs_getAllTokens($path,$dstFile)
325 | {
326 | 	$log = __DIR__."\\log.txt";
327 | 	$tokens = "";
328 | 	// Global $parser;
329 | 	
330 | 	$files = scandir($path);
331 | 	// remove ./ and ../
332 | 	unset($files[0]);
333 | 	unset($files[1]);
334 | 	
335 | 
336 | 	foreach ($files as $file)
337 | 	{
338 | 
339 | 		preg_match('/unsafe/',$path,$isUnsafe);
340 | 		if(!empty($isUnsafe))
341 | 		{			
342 | 			preg_match("/CWE_(\d+)_/",$file,$label);
343 | 			$label = 'CWE-'.$label[1]."\r\n";
344 | 			file_put_contents('./unsafe_y.txt',$label,FILE_APPEND);
345 | 		}
346 | 	
347 | 		$data = file_get_contents($path.$file);
348 | 		
349 | 		// echo $file."\n\r";
350 | 		// file_put_contents($log,$path.$file."\n\r",FILE_APPEND);
351 | 		
352 | 		$t = xxxasdfasdfsdfs_tokenize($data);
353 | 		
354 | 			$tokens .= $t;
355 | 			$tokens .= "\r\n";
356 | 		
357 | 	}
358 | 	
359 | 	file_put_contents($dstFile,$tokens);
360 | }
361 | 
362 | function xxxasdfasdfsdfs_test()
363 | {
364 | 	$data = <<<'END'
365 | <!-- 
366 | Safe sample
367 | input : get the $_GET['userData'] in an array
368 | sanitize : cast in float
369 | File : use of untrusted data in a simple quote attribute
370 | -->
371 | 
372 | <!DOCTYPE html>
373 | <html>
374 | <head/>
375 | <body>
376 | <?php
377 | $array = array();
378 | $array[] = 'safe' ;
379 | $array[] = $_GET['userData'] ;
380 | $array[] = 'safe' ;
381 | $tainted = $array[1] ;
382 | 
383 | $tainted = (float) $tainted ;
384 | 
385 | 
386 | echo "<div id='".  $tainted ."'>content</div>" ;
387 | ?>
388 | <h1>Hello World!</h1>
389 | </body>
390 | </html>
391 | END;
392 | 
393 | 	print(xxxasdfasdfsdfs_tokenize($data));
394 | }
395 | 
396 | 
397 | 
398 | 
399 | 
400 | 
401 | 
402 | 


--------------------------------------------------------------------------------
/classify.py:
--------------------------------------------------------------------------------
 1 | #coding = utf-8
 2 | 
 3 | import os,shutil
 4 | 
 5 | safefilepath = "./safe_samples"
 6 | unsafefilepath = "./unsafe_samples"
 7 | sum = 0
 8 | numsafe = 0
 9 | numunsafe = 0
10 | 
11 | 
12 | try:
13 |     os.mkdir(safefilepath)
14 |     os.mkdir(unsafefilepath)
15 | except:
16 |     pass
17 | 
18 | for root, dirs, files in os.walk("./testcases/000/"):
19 |     for file in files:
20 |         filepath = root+"/"+file
21 |         sum += 1
22 |         with open(filepath,'r') as f:
23 |             for line in f.readlines()[:7]:
24 |                 if "Unsafe sample" in line:
25 |                     shutil.copyfile(filepath,unsafefilepath+"/"+file)
26 |                     numunsafe += 1
27 |                     break
28 |                 elif "Safe sample" in line:
29 |                     shutil.copyfile(filepath, safefilepath+"/"+file)
30 |                     numsafe += 1
31 |                     break
32 |                 # elif:
33 |                 #     print "Unknown file ",filepath
34 | 
35 | 
36 | print('Total:',sum,'\n\rSafe sample:',numsafe,'\n\rUnsafe sample:',numunsafe)
37 | 
38 | 


--------------------------------------------------------------------------------
/config.php:
--------------------------------------------------------------------------------
  1 | <?php
  2 | 
  3 | 
  4 | // tokens to ignore while scanning
  5 | $T_IGNORE = array(
  6 | 	// T_BAD_CHARACTER,
  7 | 	T_DOC_COMMENT,
  8 | 	T_COMMENT,
  9 | 	// T_ML_COMMENT,
 10 | 	T_INLINE_HTML,
 11 | 	T_WHITESPACE,
 12 | 	T_OPEN_TAG,
 13 | 	T_CLOSE_TAG
 14 | );
 15 | 	
 16 | 
 17 | 	
 18 | // variable assignment tokens
 19 | $T_ASSIGNMENT = array(
 20 | 	T_AND_EQUAL,
 21 | 	T_CONCAT_EQUAL,
 22 | 	T_DIV_EQUAL,
 23 | 	T_MINUS_EQUAL,
 24 | 	T_MOD_EQUAL,
 25 | 	T_MUL_EQUAL,
 26 | 	T_OR_EQUAL,
 27 | 	T_PLUS_EQUAL,
 28 | 	T_SL_EQUAL,
 29 | 	T_SR_EQUAL,
 30 | 	T_XOR_EQUAL
 31 | );
 32 | 	
 33 | 
 34 | 	
 35 | // condition operators
 36 | // 暂不用
 37 | $T_COMPARISON = array(
 38 | 	T_IS_EQUAL,
 39 | 	T_IS_GREATER_OR_EQUAL,
 40 | 	T_IS_IDENTICAL,
 41 | 	T_IS_NOT_EQUAL,
 42 | 	T_IS_NOT_IDENTICAL,
 43 | 	T_IS_SMALLER_OR_EQUAL
 44 | );
 45 | 	
 46 | 
 47 | 	
 48 | // including operation tokens
 49 | $T_INCLUDES = array(
 50 | 	T_INCLUDE,
 51 | 	T_INCLUDE_ONCE,
 52 | 	T_REQUIRE,
 53 | 	T_REQUIRE_ONCE
 54 | );
 55 | 	
 56 | // XSS affected operation tokens
 57 | $T_ECHO = array(
 58 | 	T_PRINT,
 59 | 	T_ECHO,
 60 | 	T_OPEN_TAG_WITH_ECHO,
 61 | 	T_EXIT
 62 | );
 63 | 		
 64 | $V_ECHO = array(
 65 | 	'print_r',
 66 | 	'printf',
 67 | 	'vprintf',
 68 | 	'trigger_error',
 69 | 	'user_error',
 70 | 	'odbc_result_all',
 71 | 	'ifx_htmltbl_result'
 72 | );
 73 | 	
 74 | 
 75 | $V_INPUT = array(
 76 | 		'$_GET',
 77 | 		'$_POST',
 78 | 		'$_COOKIE',
 79 | 		'$_REQUEST',
 80 | 		'$_FILES',
 81 | 		'$_SERVER',
 82 | 		'$HTTP_GET_VARS',
 83 | 		'$HTTP_POST_VARS',
 84 | 		'$HTTP_COOKIE_VARS',  
 85 | 		'$HTTP_REQUEST_VARS', 
 86 | 		'$HTTP_POST_FILES',
 87 | 		'$HTTP_SERVER_VARS',
 88 | 		'$HTTP_RAW_POST_DATA',
 89 | 		'$argc',
 90 | 		'$argv',
 91 | 		'get_headers',
 92 | 		'getallheaders',
 93 | 		'get_browser',
 94 | 		#'getenv',
 95 | 		#'gethostbyaddr',
 96 | 		// 'runkit_superglobals',
 97 | 		'import_request_variables'
 98 | 	);
 99 | 	
100 | $V_PREG = array(
101 | 		'preg_filter',
102 | 		'preg_grep',
103 | 		'preg_last_error',
104 | 		'preg_match_all',
105 | 		'preg_match',
106 | 		'preg_quote',
107 | 		'preg_replace_callback',
108 | 		'preg_replace',
109 | 		'ereg_replace',
110 | 		'ereg',
111 | 		'eregi_replace',
112 | 		'eregi',
113 | 	);
114 | 	
115 | $V_EXEC = array(
116 | 		'backticks',
117 | 		'exec',
118 | 		'expect_popen',
119 | 		'passthru',
120 | 		'pcntl_exec',
121 | 		'popen',
122 | 		'proc_open',
123 | 		'shell_exec',
124 | 		'system',
125 | 		'eval',
126 | 		);
127 | 
128 | $V_SQL = array(
129 | 	// Abstraction Layers
130 | 	// 'dba_open',
131 | 	// 'dba_popen', 
132 | 	'dba_insert',
133 | 	'dba_fetch', 
134 | 	'dba_delete', 
135 | 	'dbx_query', 
136 | 	'odbc_do',
137 | 	'odbc_exec',
138 | 	'odbc_execute',
139 | 	'db2_exec' ,
140 | 	'db2_execute',
141 | 	'fbsql_db_query',
142 | 	'fbsql_query', 
143 | 	'ibase_query', 
144 | 	'ibase_execute', 
145 | 	'ifx_query', 
146 | 	'ifx_do',
147 | 	'ingres_query',
148 | 	'ingres_execute',
149 | 	'ingres_unbuffered_query',
150 | 	'msql_db_query', 
151 | 	'msql_query',
152 | 	// 'msql', 
153 | 	'mssql_query', 
154 | 	'mssql_execute',
155 | 	'mysql_db_query',  
156 | 	'mysql_query', 
157 | 	'mysql_unbuffered_query', 
158 | 	'mysqli_stmt_execute',
159 | 	'mysqli_query',
160 | 	'mysqli_real_query',
161 | 	'mysqli_master_query',
162 | 	'oci_execute',
163 | 	'ociexecute',
164 | 	'ovrimos_exec',
165 | 	'ovrimos_execute',
166 | 	'ora_do', 
167 | 	'ora_exec', 
168 | 	'pg_query',
169 | 	'pg_send_query',
170 | 	'pg_send_query_params',
171 | 	'pg_send_prepare',
172 | 	'pg_prepare',
173 | 	'sqlite_open',
174 | 	'sqlite_popen',
175 | 	'sqlite_array_query',
176 | 	'arrayQuery',
177 | 	'singleQuery',
178 | 	'sqlite_query',
179 | 	'sqlite_exec',
180 | 	'sqlite_single_query',
181 | 	'sqlite_unbuffered_query',
182 | 	'sybase_query', 
183 | 	'sybase_unbuffered_query'
184 | );
185 | 
186 | ?>	


--------------------------------------------------------------------------------
/confusion_matrix.py:
--------------------------------------------------------------------------------
 1 | # -*-coding:utf-8-*-
 2 | 
 3 | from sklearn.metrics import confusion_matrix
 4 | import matplotlib.pyplot as plt
 5 | import numpy as np
 6 | from sklearn.utils.multiclass import unique_labels
 7 | 
 8 | # translate true y
 9 | y = []
10 | with open('./testdata_y.txt') as f:
11 | 	for line in f:
12 | 		y.append((line.index('1')-1)//3)
13 | 
14 | y = np.array(y)
15 | predict_y = np.loadtxt('./predict_result.txt')
16 | labels = ["Safe","CWE-78","CWE-79","CWE-89","CWE-90","CWE-91","CWE-95","CWE-98","CWE-601","CWE-862",]
17 | 
18 | classes = np.array(labels)
19 | 
20 | 
21 | 
22 | def plot_confusion_matrix(y_true, y_pred, classes,
23 | 						  normalize=False,
24 | 						  title=None,
25 | 						  cmap=plt.cm.Blues):
26 | 	"""
27 | 	This function prints and plots the confusion matrix.
28 | 	Normalization can be applied by setting `normalize=True`.
29 | 	"""
30 | 	if not title:
31 | 		if normalize:
32 | 			title = 'Normalized confusion matrix'
33 | 		else:
34 | 			title = 'Confusion matrix, without normalization'
35 | 
36 | 	# Compute confusion matrix
37 | 	cm = confusion_matrix(y_true, y_pred)
38 | 	# Only use the labels that appear in the data
39 | 	classes = classes[unique_labels(y_true, y_pred)]
40 | 	if normalize:
41 | 		cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
42 | 		print("Normalized confusion matrix")
43 | 	else:
44 | 		print('Confusion matrix, without normalization')
45 | 
46 | 	# print(cm.sum(axis=1)[:, np.newaxis])
47 | 	print(cm)
48 | 
49 | 	fig, ax = plt.subplots()
50 | 	im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
51 | 	ax.figure.colorbar(im, ax=ax)
52 | 	# We want to show all ticks...
53 | 	ax.set(xticks=np.arange(cm.shape[1]),
54 | 		   yticks=np.arange(cm.shape[0]),
55 | 		   # ... and label them with the respective list entries
56 | 		   xticklabels=classes, yticklabels=classes,
57 | 		   # title=title,
58 | 		   ylabel='True label',
59 | 		   xlabel='Predicted label')
60 | 
61 | 	# Rotate the tick labels and set their alignment.
62 | 	plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
63 | 			 rotation_mode="anchor")
64 | 
65 | 	# Loop over data dimensions and create text annotations.
66 | 	fmt = '.2f' if normalize else 'd'
67 | 	thresh = cm.max() / 2.
68 | 	
69 | 	print(cm.sum(axis=1))
70 | 	
71 | 	for i in range(cm.shape[0]):
72 | 		for j in range(cm.shape[1]):
73 | 			ax.text(j, i, format(cm[i, j], fmt),
74 | 					ha="center", va="center",
75 | 					color="white" if cm[i, j] > thresh else "black")
76 | 	fig.tight_layout()
77 | 	return ax
78 | 
79 | 
80 | plot_confusion_matrix(y,predict_y,classes, normalize=True,title="Normalized confusion matrix of TAP")
81 | plt.savefig('tapcmNor.png',dpi=300)
82 | plt.show()
83 | 
84 | from sklearn.metrics import cohen_kappa_score
85 | kappa = cohen_kappa_score(y,predict_y)
86 | print("Kappa: ",kappa)
87 | 
88 | from sklearn.metrics import hamming_loss
89 | ham_distance = hamming_loss(y,predict_y)
90 | print("ham_distance: ",ham_distance)
91 | 
92 | 
93 | from sklearn.metrics import jaccard_similarity_score
94 | jaccrd_score = jaccard_similarity_score(y,predict_y,normalize = True)
95 | print("jaccrd_score: ",jaccrd_score)
96 | 
97 | 


--------------------------------------------------------------------------------
/result/predict_result.txt:
--------------------------------------------------------------------------------
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1137 | 2
1138 | 2
1139 | 2
1140 | 2
1141 | 2
1142 | 2
1143 | 2
1144 | 2
1145 | 2
1146 | 0
1147 | 0
1148 | 2
1149 | 2
1150 | 2
1151 | 2
1152 | 2
1153 | 2
1154 | 2
1155 | 2
1156 | 2
1157 | 2
1158 | 0
1159 | 0
1160 | 0
1161 | 0
1162 | 0
1163 | 2
1164 | 2
1165 | 2
1166 | 2
1167 | 2
1168 | 2
1169 | 2
1170 | 2
1171 | 2
1172 | 2
1173 | 2
1174 | 2
1175 | 2
1176 | 2
1177 | 2
1178 | 2
1179 | 2
1180 | 2
1181 | 2
1182 | 2
1183 | 2
1184 | 2
1185 | 2
1186 | 2
1187 | 2
1188 | 2
1189 | 2
1190 | 2
1191 | 2
1192 | 2
1193 | 2
1194 | 2
1195 | 0
1196 | 2
1197 | 2
1198 | 0
1199 | 2
1200 | 0
1201 | 2
1202 | 0
1203 | 2
1204 | 2
1205 | 0
1206 | 0
1207 | 2
1208 | 2
1209 | 2
1210 | 2
1211 | 2
1212 | 2
1213 | 2
1214 | 2
1215 | 2
1216 | 2
1217 | 0
1218 | 0
1219 | 2
1220 | 2
1221 | 2
1222 | 2
1223 | 2
1224 | 2
1225 | 2
1226 | 0
1227 | 0
1228 | 2
1229 | 2
1230 | 0
1231 | 2
1232 | 2
1233 | 2
1234 | 2
1235 | 2
1236 | 2
1237 | 2
1238 | 2
1239 | 2
1240 | 2
1241 | 2
1242 | 2
1243 | 2
1244 | 2
1245 | 2
1246 | 2
1247 | 2
1248 | 2
1249 | 2
1250 | 2
1251 | 2
1252 | 2
1253 | 2
1254 | 2
1255 | 2
1256 | 2
1257 | 2
1258 | 2
1259 | 2
1260 | 0
1261 | 2
1262 | 2
1263 | 2
1264 | 2
1265 | 2
1266 | 2
1267 | 0
1268 | 0
1269 | 2
1270 | 2
1271 | 2
1272 | 0
1273 | 2
1274 | 2
1275 | 2
1276 | 2
1277 | 2
1278 | 2
1279 | 2
1280 | 2
1281 | 2
1282 | 2
1283 | 2
1284 | 2
1285 | 2
1286 | 2
1287 | 2
1288 | 2
1289 | 2
1290 | 2
1291 | 2
1292 | 2
1293 | 0
1294 | 0
1295 | 2
1296 | 0
1297 | 2
1298 | 2
1299 | 0
1300 | 2
1301 | 2
1302 | 2
1303 | 2
1304 | 2
1305 | 2
1306 | 2
1307 | 2
1308 | 2
1309 | 2
1310 | 2
1311 | 2
1312 | 2
1313 | 2
1314 | 2
1315 | 2
1316 | 2
1317 | 2
1318 | 2
1319 | 2
1320 | 2
1321 | 2
1322 | 2
1323 | 2
1324 | 2
1325 | 2
1326 | 2
1327 | 2
1328 | 2
1329 | 2
1330 | 2
1331 | 2
1332 | 2
1333 | 2
1334 | 2
1335 | 2
1336 | 2
1337 | 2
1338 | 2
1339 | 2
1340 | 2
1341 | 2
1342 | 2
1343 | 0
1344 | 2
1345 | 2
1346 | 2
1347 | 2
1348 | 2
1349 | 2
1350 | 0
1351 | 0
1352 | 2
1353 | 2
1354 | 2
1355 | 2
1356 | 2
1357 | 0
1358 | 0
1359 | 0
1360 | 0
1361 | 2
1362 | 2
1363 | 2
1364 | 2
1365 | 0
1366 | 2
1367 | 2
1368 | 2
1369 | 2
1370 | 2
1371 | 2
1372 | 2
1373 | 2
1374 | 2
1375 | 2
1376 | 2
1377 | 2
1378 | 2
1379 | 2
1380 | 2
1381 | 2
1382 | 2
1383 | 2
1384 | 2
1385 | 2
1386 | 2
1387 | 2
1388 | 2
1389 | 2
1390 | 2
1391 | 2
1392 | 2
1393 | 2
1394 | 2
1395 | 2
1396 | 2
1397 | 2
1398 | 2
1399 | 2
1400 | 2
1401 | 2
1402 | 2
1403 | 2
1404 | 0
1405 | 2
1406 | 2
1407 | 2
1408 | 2
1409 | 2
1410 | 2
1411 | 0
1412 | 2
1413 | 2
1414 | 2
1415 | 2
1416 | 2
1417 | 2
1418 | 2
1419 | 0
1420 | 2
1421 | 2
1422 | 2
1423 | 0
1424 | 2
1425 | 2
1426 | 2
1427 | 2
1428 | 2
1429 | 2
1430 | 2
1431 | 2
1432 | 2
1433 | 0
1434 | 2
1435 | 2
1436 | 2
1437 | 2
1438 | 2
1439 | 2
1440 | 2
1441 | 2
1442 | 2
1443 | 2
1444 | 2
1445 | 2
1446 | 0
1447 | 2
1448 | 0
1449 | 2
1450 | 2
1451 | 0
1452 | 0
1453 | 2
1454 | 2
1455 | 2
1456 | 2
1457 | 2
1458 | 2
1459 | 2
1460 | 2
1461 | 2
1462 | 2
1463 | 2
1464 | 2
1465 | 2
1466 | 2
1467 | 2
1468 | 2
1469 | 2
1470 | 2
1471 | 2
1472 | 2
1473 | 2
1474 | 2
1475 | 2
1476 | 2
1477 | 2
1478 | 2
1479 | 2
1480 | 2
1481 | 2
1482 | 2
1483 | 2
1484 | 2
1485 | 2
1486 | 2
1487 | 2
1488 | 2
1489 | 0
1490 | 0
1491 | 2
1492 | 2
1493 | 2
1494 | 2
1495 | 2
1496 | 2
1497 | 2
1498 | 2
1499 | 2
1500 | 2
1501 | 2
1502 | 2
1503 | 2
1504 | 2
1505 | 2
1506 | 2
1507 | 9
1508 | 9
1509 | 9
1510 | 9
1511 | 9
1512 | 9
1513 | 9
1514 | 9
1515 | 0
1516 | 0
1517 | 0
1518 | 9
1519 | 9
1520 | 9
1521 | 0
1522 | 9
1523 | 9
1524 | 0
1525 | 0
1526 | 0
1527 | 0
1528 | 3
1529 | 3
1530 | 3
1531 | 3
1532 | 3
1533 | 3
1534 | 3
1535 | 3
1536 | 3
1537 | 3
1538 | 3
1539 | 3
1540 | 3
1541 | 3
1542 | 3
1543 | 3
1544 | 3
1545 | 3
1546 | 3
1547 | 3
1548 | 3
1549 | 3
1550 | 3
1551 | 3
1552 | 3
1553 | 3
1554 | 3
1555 | 3
1556 | 0
1557 | 3
1558 | 3
1559 | 3
1560 | 3
1561 | 3
1562 | 3
1563 | 3
1564 | 3
1565 | 3
1566 | 3
1567 | 3
1568 | 3
1569 | 3
1570 | 3
1571 | 3
1572 | 0
1573 | 3
1574 | 3
1575 | 3
1576 | 3
1577 | 3
1578 | 3
1579 | 3
1580 | 3
1581 | 3
1582 | 3
1583 | 0
1584 | 0
1585 | 0
1586 | 3
1587 | 3
1588 | 3
1589 | 3
1590 | 3
1591 | 3
1592 | 3
1593 | 3
1594 | 3
1595 | 3
1596 | 3
1597 | 3
1598 | 3
1599 | 3
1600 | 3
1601 | 3
1602 | 3
1603 | 3
1604 | 3
1605 | 3
1606 | 3
1607 | 3
1608 | 3
1609 | 3
1610 | 3
1611 | 3
1612 | 3
1613 | 3
1614 | 3
1615 | 3
1616 | 3
1617 | 3
1618 | 0
1619 | 0
1620 | 3
1621 | 3
1622 | 3
1623 | 3
1624 | 0
1625 | 0
1626 | 3
1627 | 0
1628 | 0
1629 | 3
1630 | 0
1631 | 3
1632 | 0
1633 | 3
1634 | 3
1635 | 3
1636 | 3
1637 | 3
1638 | 3
1639 | 3
1640 | 0
1641 | 0
1642 | 3
1643 | 3
1644 | 3
1645 | 3
1646 | 3
1647 | 0
1648 | 0
1649 | 3
1650 | 3
1651 | 3
1652 | 3
1653 | 3
1654 | 3
1655 | 3
1656 | 3
1657 | 0
1658 | 0
1659 | 0
1660 | 3
1661 | 3
1662 | 3
1663 | 3
1664 | 3
1665 | 3
1666 | 3
1667 | 3
1668 | 3
1669 | 3
1670 | 3
1671 | 3
1672 | 3
1673 | 3
1674 | 3
1675 | 3
1676 | 3
1677 | 3
1678 | 3
1679 | 3
1680 | 3
1681 | 3
1682 | 3
1683 | 3
1684 | 3
1685 | 3
1686 | 3
1687 | 3
1688 | 3
1689 | 3
1690 | 3
1691 | 3
1692 | 3
1693 | 3
1694 | 3
1695 | 3
1696 | 3
1697 | 3
1698 | 3
1699 | 3
1700 | 3
1701 | 3
1702 | 0
1703 | 0
1704 | 3
1705 | 0
1706 | 0
1707 | 0
1708 | 4
1709 | 4
1710 | 4
1711 | 4
1712 | 0
1713 | 4
1714 | 4
1715 | 0
1716 | 4
1717 | 4
1718 | 4
1719 | 0
1720 | 4
1721 | 4
1722 | 4
1723 | 4
1724 | 0
1725 | 4
1726 | 4
1727 | 4
1728 | 4
1729 | 0
1730 | 0
1731 | 4
1732 | 4
1733 | 4
1734 | 4
1735 | 4
1736 | 4
1737 | 4
1738 | 0
1739 | 0
1740 | 4
1741 | 4
1742 | 4
1743 | 4
1744 | 4
1745 | 4
1746 | 4
1747 | 4
1748 | 4
1749 | 4
1750 | 4
1751 | 4
1752 | 4
1753 | 4
1754 | 4
1755 | 4
1756 | 4
1757 | 4
1758 | 4
1759 | 4
1760 | 4
1761 | 4
1762 | 4
1763 | 4
1764 | 4
1765 | 4
1766 | 4
1767 | 4
1768 | 4
1769 | 4
1770 | 4
1771 | 4
1772 | 4
1773 | 4
1774 | 4
1775 | 4
1776 | 4
1777 | 0
1778 | 4
1779 | 0
1780 | 4
1781 | 4
1782 | 4
1783 | 4
1784 | 4
1785 | 4
1786 | 0
1787 | 0
1788 | 4
1789 | 0
1790 | 4
1791 | 4
1792 | 4
1793 | 4
1794 | 0
1795 | 4
1796 | 0
1797 | 0
1798 | 0
1799 | 4
1800 | 4
1801 | 4
1802 | 0
1803 | 0
1804 | 0
1805 | 0
1806 | 0
1807 | 4
1808 | 4
1809 | 4
1810 | 4
1811 | 4
1812 | 4
1813 | 4
1814 | 4
1815 | 4
1816 | 4
1817 | 4
1818 | 4
1819 | 4
1820 | 4
1821 | 4
1822 | 4
1823 | 4
1824 | 4
1825 | 4
1826 | 4
1827 | 4
1828 | 4
1829 | 4
1830 | 4
1831 | 4
1832 | 4
1833 | 4
1834 | 4
1835 | 4
1836 | 4
1837 | 4
1838 | 4
1839 | 4
1840 | 4
1841 | 4
1842 | 4
1843 | 4
1844 | 4
1845 | 4
1846 | 4
1847 | 4
1848 | 4
1849 | 4
1850 | 4
1851 | 4
1852 | 4
1853 | 4
1854 | 4
1855 | 4
1856 | 4
1857 | 4
1858 | 4
1859 | 4
1860 | 4
1861 | 4
1862 | 4
1863 | 4
1864 | 4
1865 | 4
1866 | 0
1867 | 0
1868 | 0
1869 | 4
1870 | 4
1871 | 0
1872 | 4
1873 | 0
1874 | 4
1875 | 4
1876 | 4
1877 | 4
1878 | 0
1879 | 0
1880 | 4
1881 | 4
1882 | 4
1883 | 4
1884 | 4
1885 | 4
1886 | 4
1887 | 4
1888 | 4
1889 | 4
1890 | 4
1891 | 4
1892 | 4
1893 | 4
1894 | 4
1895 | 4
1896 | 4
1897 | 4
1898 | 4
1899 | 4
1900 | 4
1901 | 4
1902 | 4
1903 | 4
1904 | 4
1905 | 4
1906 | 4
1907 | 4
1908 | 4
1909 | 4
1910 | 4
1911 | 4
1912 | 4
1913 | 4
1914 | 4
1915 | 0
1916 | 4
1917 | 4
1918 | 0
1919 | 0
1920 | 4
1921 | 4
1922 | 4
1923 | 4
1924 | 0
1925 | 4
1926 | 4
1927 | 4
1928 | 4
1929 | 0
1930 | 4
1931 | 0
1932 | 0
1933 | 4
1934 | 0
1935 | 0
1936 | 0
1937 | 4
1938 | 4
1939 | 4
1940 | 4
1941 | 4
1942 | 4
1943 | 0
1944 | 4
1945 | 4
1946 | 4
1947 | 4
1948 | 4
1949 | 4
1950 | 0
1951 | 4
1952 | 0
1953 | 0
1954 | 4
1955 | 4
1956 | 4
1957 | 0
1958 | 4
1959 | 4
1960 | 4
1961 | 4
1962 | 4
1963 | 4
1964 | 4
1965 | 4
1966 | 4
1967 | 4
1968 | 4
1969 | 4
1970 | 4
1971 | 4
1972 | 4
1973 | 4
1974 | 4
1975 | 4
1976 | 4
1977 | 4
1978 | 4
1979 | 4
1980 | 4
1981 | 4
1982 | 4
1983 | 4
1984 | 4
1985 | 4
1986 | 4
1987 | 4
1988 | 4
1989 | 4
1990 | 4
1991 | 4
1992 | 4
1993 | 4
1994 | 4
1995 | 4
1996 | 4
1997 | 4
1998 | 4
1999 | 4
2000 | 4
2001 | 4
2002 | 4
2003 | 4
2004 | 4
2005 | 4
2006 | 4
2007 | 4
2008 | 4
2009 | 4
2010 | 4
2011 | 4
2012 | 4
2013 | 4
2014 | 4
2015 | 4
2016 | 4
2017 | 4
2018 | 4
2019 | 4
2020 | 4
2021 | 4
2022 | 4
2023 | 4
2024 | 4
2025 | 4
2026 | 4
2027 | 0
2028 | 4
2029 | 4
2030 | 4
2031 | 4
2032 | 4
2033 | 0
2034 | 4
2035 | 4
2036 | 4
2037 | 0
2038 | 4
2039 | 0
2040 | 0
2041 | 5
2042 | 5
2043 | 5
2044 | 5
2045 | 5
2046 | 5
2047 | 5
2048 | 5
2049 | 5
2050 | 5
2051 | 5
2052 | 5
2053 | 5
2054 | 5
2055 | 5
2056 | 5
2057 | 5
2058 | 5
2059 | 5
2060 | 5
2061 | 5
2062 | 5
2063 | 5
2064 | 5
2065 | 4
2066 | 5
2067 | 4
2068 | 5
2069 | 5
2070 | 6
2071 | 5
2072 | 6
2073 | 6
2074 | 6
2075 | 6
2076 | 5
2077 | 6
2078 | 6
2079 | 5
2080 | 6
2081 | 4
2082 | 5
2083 | 5
2084 | 5
2085 | 5
2086 | 4
2087 | 5
2088 | 5
2089 | 4
2090 | 4
2091 | 6
2092 | 4
2093 | 5
2094 | 5
2095 | 5
2096 | 5
2097 | 5
2098 | 5
2099 | 5
2100 | 5
2101 | 4
2102 | 5
2103 | 5
2104 | 4
2105 | 5
2106 | 5
2107 | 5
2108 | 4
2109 | 5
2110 | 5
2111 | 6
2112 | 6
2113 | 5
2114 | 6
2115 | 5
2116 | 5
2117 | 5
2118 | 5
2119 | 0
2120 | 5
2121 | 5
2122 | 5
2123 | 5
2124 | 0
2125 | 0
2126 | 0
2127 | 5
2128 | 5
2129 | 5
2130 | 5
2131 | 7
2132 | 7
2133 | 7
2134 | 7
2135 | 7
2136 | 5
2137 | 5
2138 | 5
2139 | 5
2140 | 5
2141 | 5
2142 | 5
2143 | 5
2144 | 5
2145 | 5
2146 | 5
2147 | 5
2148 | 5
2149 | 5
2150 | 5
2151 | 5
2152 | 5
2153 | 5
2154 | 5
2155 | 5
2156 | 5
2157 | 5
2158 | 5
2159 | 5
2160 | 5
2161 | 5
2162 | 5
2163 | 5
2164 | 5
2165 | 5
2166 | 5
2167 | 5
2168 | 5
2169 | 5
2170 | 5
2171 | 5
2172 | 5
2173 | 5
2174 | 6
2175 | 6
2176 | 6
2177 | 6
2178 | 6
2179 | 6
2180 | 5
2181 | 6
2182 | 5
2183 | 5
2184 | 6
2185 | 6
2186 | 5
2187 | 5
2188 | 4
2189 | 5
2190 | 6
2191 | 6
2192 | 4
2193 | 5
2194 | 6
2195 | 6
2196 | 5
2197 | 4
2198 | 5
2199 | 4
2200 | 5
2201 | 5
2202 | 5
2203 | 5
2204 | 5
2205 | 5
2206 | 5
2207 | 4
2208 | 5
2209 | 6
2210 | 5
2211 | 5
2212 | 5
2213 | 5
2214 | 5
2215 | 5
2216 | 5
2217 | 5
2218 | 5
2219 | 5
2220 | 5
2221 | 5
2222 | 5
2223 | 0
2224 | 0
2225 | 5
2226 | 5
2227 | 5
2228 | 5
2229 | 5
2230 | 5
2231 | 7
2232 | 5
2233 | 0
2234 | 0
2235 | 0
2236 | 5
2237 | 5
2238 | 5
2239 | 5
2240 | 7
2241 | 7
2242 | 7
2243 | 5
2244 | 5
2245 | 5
2246 | 5
2247 | 5
2248 | 5
2249 | 5
2250 | 5
2251 | 5
2252 | 5
2253 | 5
2254 | 5
2255 | 5
2256 | 5
2257 | 5
2258 | 5
2259 | 5
2260 | 5
2261 | 5
2262 | 5
2263 | 5
2264 | 5
2265 | 5
2266 | 5
2267 | 5
2268 | 5
2269 | 5
2270 | 5
2271 | 5
2272 | 5
2273 | 5
2274 | 5
2275 | 5
2276 | 5
2277 | 5
2278 | 5
2279 | 5
2280 | 5
2281 | 5
2282 | 5
2283 | 5
2284 | 5
2285 | 5
2286 | 5
2287 | 5
2288 | 5
2289 | 5
2290 | 5
2291 | 5
2292 | 5
2293 | 5
2294 | 5
2295 | 5
2296 | 5
2297 | 5
2298 | 5
2299 | 5
2300 | 5
2301 | 5
2302 | 5
2303 | 5
2304 | 5
2305 | 5
2306 | 5
2307 | 5
2308 | 5
2309 | 5
2310 | 5
2311 | 5
2312 | 5
2313 | 5
2314 | 5
2315 | 5
2316 | 5
2317 | 5
2318 | 5
2319 | 5
2320 | 5
2321 | 5
2322 | 5
2323 | 5
2324 | 5
2325 | 5
2326 | 5
2327 | 5
2328 | 5
2329 | 5
2330 | 5
2331 | 5
2332 | 5
2333 | 5
2334 | 5
2335 | 5
2336 | 5
2337 | 5
2338 | 5
2339 | 5
2340 | 5
2341 | 0
2342 | 0
2343 | 5
2344 | 5
2345 | 5
2346 | 5
2347 | 5
2348 | 5
2349 | 5
2350 | 5
2351 | 5
2352 | 5
2353 | 5
2354 | 5
2355 | 5
2356 | 5
2357 | 5
2358 | 5
2359 | 5
2360 | 5
2361 | 5
2362 | 5
2363 | 5
2364 | 5
2365 | 5
2366 | 5
2367 | 5
2368 | 5
2369 | 5
2370 | 5
2371 | 5
2372 | 5
2373 | 5
2374 | 5
2375 | 5
2376 | 4
2377 | 6
2378 | 6
2379 | 4
2380 | 6
2381 | 5
2382 | 6
2383 | 5
2384 | 5
2385 | 6
2386 | 4
2387 | 6
2388 | 6
2389 | 5
2390 | 4
2391 | 4
2392 | 5
2393 | 5
2394 | 5
2395 | 5
2396 | 5
2397 | 5
2398 | 5
2399 | 5
2400 | 5
2401 | 5
2402 | 5
2403 | 5
2404 | 5
2405 | 5
2406 | 5
2407 | 5
2408 | 5
2409 | 5
2410 | 4
2411 | 5
2412 | 5
2413 | 4
2414 | 4
2415 | 4
2416 | 5
2417 | 5
2418 | 5
2419 | 5
2420 | 5
2421 | 4
2422 | 5
2423 | 5
2424 | 5
2425 | 5
2426 | 5
2427 | 5
2428 | 5
2429 | 5
2430 | 5
2431 | 5
2432 | 5
2433 | 5
2434 | 5
2435 | 0
2436 | 6
2437 | 5
2438 | 5
2439 | 5
2440 | 6
2441 | 5
2442 | 5
2443 | 5
2444 | 5
2445 | 0
2446 | 5
2447 | 5
2448 | 5
2449 | 7
2450 | 7
2451 | 7
2452 | 5
2453 | 5
2454 | 5
2455 | 5
2456 | 5
2457 | 5
2458 | 5
2459 | 5
2460 | 5
2461 | 7
2462 | 7
2463 | 7
2464 | 7
2465 | 7
2466 | 7
2467 | 7
2468 | 7
2469 | 7
2470 | 7
2471 | 7
2472 | 7
2473 | 7
2474 | 7
2475 | 7
2476 | 7
2477 | 7
2478 | 7
2479 | 7
2480 | 7
2481 | 7
2482 | 7
2483 | 7
2484 | 7
2485 | 7
2486 | 7
2487 | 7
2488 | 7
2489 | 7
2490 | 7
2491 | 7
2492 | 7
2493 | 7
2494 | 7
2495 | 7
2496 | 7
2497 | 7
2498 | 7
2499 | 7
2500 | 7
2501 | 7
2502 | 7
2503 | 7
2504 | 7
2505 | 7
2506 | 5
2507 | 5
2508 | 5
2509 | 5
2510 | 5
2511 | 5
2512 | 5
2513 | 5
2514 | 7
2515 | 7
2516 | 7
2517 | 7
2518 | 7
2519 | 7
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5244 | 0
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5246 | 0
5247 | 0
5248 | 0
5249 | 0
5250 | 0
5251 | 0
5252 | 0
5253 | 2
5254 | 0
5255 | 0
5256 | 0
5257 | 2
5258 | 0
5259 | 0
5260 | 0
5261 | 0
5262 | 0
5263 | 0
5264 | 0
5265 | 0
5266 | 0
5267 | 0
5268 | 0
5269 | 0
5270 | 0
5271 | 0
5272 | 2
5273 | 0
5274 | 0
5275 | 0
5276 | 2
5277 | 0
5278 | 0
5279 | 0
5280 | 0
5281 | 0
5282 | 0
5283 | 0
5284 | 0
5285 | 0
5286 | 2
5287 | 2
5288 | 2
5289 | 0
5290 | 0
5291 | 0
5292 | 0
5293 | 0
5294 | 0
5295 | 0
5296 | 0
5297 | 0
5298 | 0
5299 | 0
5300 | 0
5301 | 0
5302 | 0
5303 | 0
5304 | 2
5305 | 0
5306 | 2
5307 | 2
5308 | 0
5309 | 0
5310 | 0
5311 | 0
5312 | 0
5313 | 0
5314 | 0
5315 | 0
5316 | 0
5317 | 0
5318 | 0
5319 | 0
5320 | 0
5321 | 2
5322 | 0
5323 | 0
5324 | 0
5325 | 2
5326 | 2
5327 | 0
5328 | 0
5329 | 0
5330 | 0
5331 | 0
5332 | 0
5333 | 0
5334 | 0
5335 | 0
5336 | 0
5337 | 0
5338 | 0
5339 | 0
5340 | 0
5341 | 0
5342 | 0
5343 | 0
5344 | 0
5345 | 0
5346 | 0
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5348 | 0
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5350 | 0
5351 | 0
5352 | 0
5353 | 0
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5355 | 0
5356 | 0
5357 | 0
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5360 | 0
5361 | 0
5362 | 0
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5364 | 0
5365 | 0
5366 | 0
5367 | 0
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5370 | 0
5371 | 0
5372 | 0
5373 | 0
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5375 | 0
5376 | 0
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5378 | 0
5379 | 0
5380 | 0
5381 | 0
5382 | 0
5383 | 0
5384 | 0
5385 | 0
5386 | 0
5387 | 0
5388 | 0
5389 | 0
5390 | 0
5391 | 0
5392 | 0
5393 | 0
5394 | 0
5395 | 0
5396 | 0
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5398 | 0
5399 | 0
5400 | 0
5401 | 0
5402 | 0
5403 | 0
5404 | 0
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5406 | 0
5407 | 0
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5410 | 0
5411 | 0
5412 | 0
5413 | 0
5414 | 0
5415 | 0
5416 | 0
5417 | 0
5418 | 0
5419 | 0
5420 | 0
5421 | 0
5422 | 0
5423 | 0
5424 | 0
5425 | 0
5426 | 0
5427 | 0
5428 | 0
5429 | 0
5430 | 0
5431 | 0
5432 | 0
5433 | 0
5434 | 0
5435 | 0
5436 | 0
5437 | 0
5438 | 0
5439 | 0
5440 | 0
5441 | 0
5442 | 0
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5445 | 0
5446 | 0
5447 | 0
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5450 | 0
5451 | 0
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5453 | 0
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5455 | 0
5456 | 0
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5458 | 0
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5460 | 0
5461 | 0
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5463 | 0
5464 | 0
5465 | 0
5466 | 0
5467 | 0
5468 | 0
5469 | 0
5470 | 0
5471 | 0
5472 | 0
5473 | 0
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5475 | 0
5476 | 0
5477 | 0
5478 | 0
5479 | 0
5480 | 0
5481 | 0
5482 | 0
5483 | 0
5484 | 0
5485 | 0
5486 | 0
5487 | 0
5488 | 0
5489 | 0
5490 | 0
5491 | 0
5492 | 0
5493 | 0
5494 | 0
5495 | 0
5496 | 0
5497 | 0
5498 | 0
5499 | 0
5500 | 0
5501 | 0
5502 | 0
5503 | 0
5504 | 0
5505 | 0
5506 | 0
5507 | 0
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5509 | 0
5510 | 0
5511 | 0
5512 | 0
5513 | 0
5514 | 0
5515 | 0
5516 | 0
5517 | 0
5518 | 0
5519 | 0
5520 | 0
5521 | 0
5522 | 0
5523 | 0
5524 | 0
5525 | 0
5526 | 0
5527 | 0
5528 | 0
5529 | 0
5530 | 0
5531 | 0
5532 | 0
5533 | 0
5534 | 0
5535 | 0
5536 | 0
5537 | 0
5538 | 0
5539 | 0
5540 | 0
5541 | 0
5542 | 0
5543 | 0
5544 | 0
5545 | 0
5546 | 0
5547 | 0
5548 | 0
5549 | 0
5550 | 0
5551 | 0
5552 | 0
5553 | 0
5554 | 0
5555 | 0
5556 | 0
5557 | 0
5558 | 0
5559 | 0
5560 | 0
5561 | 0
5562 | 0
5563 | 0
5564 | 0
5565 | 0
5566 | 0
5567 | 0
5568 | 0
5569 | 0
5570 | 0
5571 | 0
5572 | 0
5573 | 0
5574 | 0
5575 | 0
5576 | 0
5577 | 0
5578 | 0
5579 | 0
5580 | 0
5581 | 0
5582 | 0
5583 | 0
5584 | 0
5585 | 0
5586 | 0
5587 | 0
5588 | 0
5589 | 0
5590 | 0
5591 | 0
5592 | 0
5593 | 0
5594 | 0
5595 | 0
5596 | 0
5597 | 0
5598 | 0
5599 | 0
5600 | 0
5601 | 0
5602 | 0
5603 | 0
5604 | 0
5605 | 0
5606 | 0
5607 | 0
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5609 | 0
5610 | 0
5611 | 0
5612 | 0
5613 | 0
5614 | 0
5615 | 0
5616 | 0
5617 | 0
5618 | 0
5619 | 0
5620 | 0
5621 | 0
5622 | 0
5623 | 0
5624 | 0
5625 | 0
5626 | 0
5627 | 0
5628 | 0
5629 | 0
5630 | 0
5631 | 0
5632 | 0
5633 | 0
5634 | 0
5635 | 0
5636 | 0
5637 | 0
5638 | 0
5639 | 0
5640 | 0
5641 | 0
5642 | 0
5643 | 0
5644 | 0
5645 | 0
5646 | 0
5647 | 0
5648 | 0
5649 | 0
5650 | 0
5651 | 0
5652 | 0
5653 | 0
5654 | 0
5655 | 0
5656 | 0
5657 | 0
5658 | 0
5659 | 0
5660 | 0
5661 | 0
5662 | 0
5663 | 0
5664 | 0
5665 | 0
5666 | 0
5667 | 0
5668 | 0
5669 | 0
5670 | 0
5671 | 0
5672 | 0
5673 | 0
5674 | 0
5675 | 0
5676 | 0
5677 | 0
5678 | 0
5679 | 0
5680 | 0
5681 | 0
5682 | 0
5683 | 0
5684 | 0
5685 | 0
5686 | 0
5687 | 0
5688 | 0
5689 | 0
5690 | 0
5691 | 0
5692 | 0
5693 | 0
5694 | 0
5695 | 0
5696 | 0
5697 | 0
5698 | 0
5699 | 0
5700 | 0
5701 | 0
5702 | 0
5703 | 0
5704 | 0
5705 | 0
5706 | 0
5707 | 0
5708 | 0
5709 | 0
5710 | 0
5711 | 0
5712 | 0
5713 | 0
5714 | 0
5715 | 0
5716 | 0
5717 | 0
5718 | 0
5719 | 0
5720 | 0
5721 | 0
5722 | 0
5723 | 0
5724 | 0
5725 | 0
5726 | 0
5727 | 0
5728 | 0
5729 | 0
5730 | 0
5731 | 0
5732 | 0
5733 | 0
5734 | 0
5735 | 0
5736 | 0
5737 | 0
5738 | 0
5739 | 0
5740 | 0
5741 | 0
5742 | 0
5743 | 0
5744 | 0
5745 | 0
5746 | 0
5747 | 0
5748 | 0
5749 | 0
5750 | 0
5751 | 0
5752 | 0
5753 | 0
5754 | 0
5755 | 0
5756 | 0
5757 | 0
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5759 | 0
5760 | 0
5761 | 0
5762 | 0
5763 | 0
5764 | 0
5765 | 0
5766 | 0
5767 | 0
5768 | 0
5769 | 0
5770 | 0
5771 | 0
5772 | 0
5773 | 0
5774 | 0
5775 | 0
5776 | 0
5777 | 0
5778 | 0
5779 | 0
5780 | 0
5781 | 0
5782 | 0
5783 | 0
5784 | 0
5785 | 0
5786 | 0
5787 | 0
5788 | 0
5789 | 0
5790 | 0
5791 | 0
5792 | 0
5793 | 0
5794 | 0
5795 | 0
5796 | 0
5797 | 0
5798 | 0
5799 | 0
5800 | 0
5801 | 0
5802 | 0
5803 | 0
5804 | 0
5805 | 0
5806 | 0
5807 | 0
5808 | 0
5809 | 0
5810 | 0
5811 | 0
5812 | 0
5813 | 0
5814 | 0
5815 | 0
5816 | 0
5817 | 0
5818 | 0
5819 | 0
5820 | 0
5821 | 0
5822 | 0
5823 | 0
5824 | 0
5825 | 0
5826 | 0
5827 | 0
5828 | 0
5829 | 0
5830 | 0
5831 | 0
5832 | 0
5833 | 0
5834 | 0
5835 | 0
5836 | 0
5837 | 0
5838 | 0
5839 | 0
5840 | 0
5841 | 0
5842 | 0
5843 | 0
5844 | 0
5845 | 0
5846 | 0
5847 | 0
5848 | 0
5849 | 0
5850 | 0
5851 | 0
5852 | 0
5853 | 0
5854 | 0
5855 | 0
5856 | 0
5857 | 0
5858 | 0
5859 | 0
5860 | 0
5861 | 0
5862 | 0
5863 | 0
5864 | 0
5865 | 0
5866 | 0
5867 | 0
5868 | 0
5869 | 0
5870 | 0
5871 | 0
5872 | 0
5873 | 0
5874 | 0
5875 | 0
5876 | 0
5877 | 0
5878 | 0
5879 | 0
5880 | 0
5881 | 0
5882 | 0
5883 | 0
5884 | 0
5885 | 0
5886 | 0
5887 | 0
5888 | 0
5889 | 0
5890 | 0
5891 | 0
5892 | 0
5893 | 0
5894 | 0
5895 | 0
5896 | 0
5897 | 0
5898 | 0
5899 | 0
5900 | 0
5901 | 0
5902 | 8
5903 | 0
5904 | 0
5905 | 8
5906 | 0
5907 | 0
5908 | 0
5909 | 0
5910 | 0
5911 | 0
5912 | 0
5913 | 0
5914 | 0
5915 | 0
5916 | 0
5917 | 0
5918 | 0
5919 | 0
5920 | 0
5921 | 0
5922 | 0
5923 | 0
5924 | 0
5925 | 0
5926 | 0
5927 | 0
5928 | 0
5929 | 0
5930 | 0
5931 | 0
5932 | 0
5933 | 0
5934 | 0
5935 | 0
5936 | 0
5937 | 0
5938 | 0
5939 | 8
5940 | 0
5941 | 0
5942 | 0
5943 | 8
5944 | 0
5945 | 0
5946 | 0
5947 | 0
5948 | 0
5949 | 0
5950 | 0
5951 | 0
5952 | 0
5953 | 0
5954 | 0
5955 | 0
5956 | 0
5957 | 0
5958 | 0
5959 | 0
5960 | 0
5961 | 0
5962 | 0
5963 | 0
5964 | 0
5965 | 0
5966 | 0
5967 | 0
5968 | 0
5969 | 8
5970 | 0
5971 | 0
5972 | 0
5973 | 0
5974 | 0
5975 | 0
5976 | 0
5977 | 0
5978 | 0
5979 | 0
5980 | 0
5981 | 0
5982 | 0
5983 | 0
5984 | 0
5985 | 0
5986 | 0
5987 | 0
5988 | 0
5989 | 0
5990 | 0
5991 | 0
5992 | 8
5993 | 0
5994 | 0
5995 | 0
5996 | 8
5997 | 0
5998 | 0
5999 | 0
6000 | 0
6001 | 0
6002 | 0
6003 | 0
6004 | 0
6005 | 0
6006 | 0
6007 | 0
6008 | 0
6009 | 0
6010 | 0
6011 | 0
6012 | 0
6013 | 0
6014 | 0
6015 | 0
6016 | 0
6017 | 0
6018 | 0
6019 | 0
6020 | 0
6021 | 0
6022 | 0
6023 | 0
6024 | 0
6025 | 0
6026 | 8
6027 | 8
6028 | 0
6029 | 0
6030 | 8
6031 | 0
6032 | 0
6033 | 0
6034 | 0
6035 | 0
6036 | 0
6037 | 0
6038 | 0
6039 | 0
6040 | 0
6041 | 0
6042 | 0
6043 | 0
6044 | 0
6045 | 0
6046 | 0
6047 | 0
6048 | 0
6049 | 0
6050 | 0
6051 | 0
6052 | 0
6053 | 0
6054 | 8
6055 | 0
6056 | 8
6057 | 0
6058 | 0
6059 | 0
6060 | 0
6061 | 0
6062 | 0
6063 | 0
6064 | 0
6065 | 0
6066 | 0
6067 | 0
6068 | 0
6069 | 0
6070 | 0
6071 | 0
6072 | 0
6073 | 0
6074 | 0
6075 | 0
6076 | 0
6077 | 0
6078 | 0
6079 | 0
6080 | 0
6081 | 0
6082 | 0
6083 | 0
6084 | 0
6085 | 0
6086 | 0
6087 | 0
6088 | 0
6089 | 8
6090 | 0
6091 | 8
6092 | 0
6093 | 0
6094 | 0
6095 | 0
6096 | 0
6097 | 0
6098 | 0
6099 | 0
6100 | 0
6101 | 0
6102 | 0
6103 | 0
6104 | 0
6105 | 0
6106 | 0
6107 | 0
6108 | 0
6109 | 0
6110 | 0
6111 | 0
6112 | 0
6113 | 0
6114 | 0
6115 | 0
6116 | 0
6117 | 0
6118 | 0
6119 | 0
6120 | 0
6121 | 8
6122 | 8
6123 | 8
6124 | 0
6125 | 8
6126 | 0
6127 | 0
6128 | 0
6129 | 0
6130 | 0
6131 | 0
6132 | 0
6133 | 0
6134 | 0
6135 | 0
6136 | 0
6137 | 0
6138 | 0
6139 | 0
6140 | 0
6141 | 0
6142 | 0
6143 | 0
6144 | 0
6145 | 0
6146 | 0
6147 | 0
6148 | 0
6149 | 0
6150 | 8
6151 | 0
6152 | 0
6153 | 0
6154 | 0
6155 | 0
6156 | 0
6157 | 0
6158 | 0
6159 | 0
6160 | 0
6161 | 0
6162 | 0
6163 | 0
6164 | 0
6165 | 0
6166 | 0
6167 | 0
6168 | 0
6169 | 0
6170 | 0
6171 | 0
6172 | 0
6173 | 0
6174 | 8
6175 | 0
6176 | 8
6177 | 0
6178 | 0
6179 | 8
6180 | 0
6181 | 0
6182 | 0
6183 | 0
6184 | 0
6185 | 0
6186 | 0
6187 | 0
6188 | 0
6189 | 0
6190 | 0
6191 | 0
6192 | 0
6193 | 0
6194 | 0
6195 | 0
6196 | 0
6197 | 0
6198 | 0
6199 | 0
6200 | 0
6201 | 0
6202 | 0
6203 | 0
6204 | 0
6205 | 0
6206 | 0
6207 | 0
6208 | 0
6209 | 0
6210 | 0
6211 | 0
6212 | 0
6213 | 0
6214 | 0
6215 | 0
6216 | 0
6217 | 0
6218 | 0
6219 | 8
6220 | 0
6221 | 0
6222 | 0
6223 | 0
6224 | 0
6225 | 0
6226 | 0
6227 | 0
6228 | 0
6229 | 0
6230 | 0
6231 | 0
6232 | 0
6233 | 0
6234 | 0
6235 | 0
6236 | 0
6237 | 0
6238 | 0
6239 | 0
6240 | 0
6241 | 0
6242 | 0
6243 | 0
6244 | 0
6245 | 0
6246 | 0
6247 | 0
6248 | 0
6249 | 0
6250 | 0
6251 | 8
6252 | 0
6253 | 0
6254 | 0
6255 | 0
6256 | 0
6257 | 0
6258 | 0
6259 | 0
6260 | 0
6261 | 0
6262 | 0
6263 | 0
6264 | 0
6265 | 0
6266 | 0
6267 | 0
6268 | 0
6269 | 0
6270 | 0
6271 | 0
6272 | 0
6273 | 0
6274 | 0
6275 | 0
6276 | 0
6277 | 0
6278 | 0
6279 | 0
6280 | 0
6281 | 0
6282 | 0
6283 | 0
6284 | 0
6285 | 0
6286 | 0
6287 | 0
6288 | 0
6289 | 0
6290 | 0
6291 | 0
6292 | 0
6293 | 8
6294 | 0
6295 | 0
6296 | 0
6297 | 0
6298 | 0
6299 | 0
6300 | 0
6301 | 0
6302 | 0
6303 | 0
6304 | 0
6305 | 0
6306 | 0
6307 | 0
6308 | 0
6309 | 0
6310 | 0
6311 | 0
6312 | 0
6313 | 0
6314 | 0
6315 | 0
6316 | 0
6317 | 0
6318 | 0
6319 | 0
6320 | 0
6321 | 0
6322 | 0
6323 | 0
6324 | 0
6325 | 0
6326 | 8
6327 | 0
6328 | 0
6329 | 0
6330 | 0
6331 | 0
6332 | 0
6333 | 0
6334 | 0
6335 | 0
6336 | 0
6337 | 0
6338 | 0
6339 | 0
6340 | 0
6341 | 0
6342 | 0
6343 | 0
6344 | 0
6345 | 0
6346 | 0
6347 | 0
6348 | 0
6349 | 0
6350 | 0
6351 | 0
6352 | 0
6353 | 0
6354 | 8
6355 | 8
6356 | 0
6357 | 8
6358 | 0
6359 | 0
6360 | 0
6361 | 0
6362 | 0
6363 | 0
6364 | 0
6365 | 0
6366 | 0
6367 | 0
6368 | 0
6369 | 0
6370 | 0
6371 | 0
6372 | 0
6373 | 0
6374 | 8
6375 | 0
6376 | 0
6377 | 0
6378 | 0
6379 | 0
6380 | 0
6381 | 0
6382 | 0
6383 | 0
6384 | 8
6385 | 8
6386 | 0
6387 | 0
6388 | 0
6389 | 0
6390 | 0
6391 | 0
6392 | 0
6393 | 0
6394 | 0
6395 | 0
6396 | 0
6397 | 0
6398 | 0
6399 | 0
6400 | 0
6401 | 0
6402 | 0
6403 | 0
6404 | 0
6405 | 0
6406 | 1
6407 | 1
6408 | 0
6409 | 0
6410 | 0
6411 | 0
6412 | 0
6413 | 0
6414 | 0
6415 | 0
6416 | 0
6417 | 0
6418 | 1
6419 | 0
6420 | 0
6421 | 0
6422 | 0
6423 | 0
6424 | 1
6425 | 1
6426 | 0
6427 | 0
6428 | 0
6429 | 0
6430 | 0
6431 | 0
6432 | 0
6433 | 0
6434 | 0
6435 | 0
6436 | 0
6437 | 2
6438 | 0
6439 | 0
6440 | 0
6441 | 0
6442 | 0
6443 | 0
6444 | 0
6445 | 0
6446 | 2
6447 | 2
6448 | 2
6449 | 0
6450 | 0
6451 | 0
6452 | 0
6453 | 0
6454 | 0
6455 | 0
6456 | 2
6457 | 0
6458 | 0
6459 | 0
6460 | 0
6461 | 0
6462 | 0
6463 | 2
6464 | 0
6465 | 0
6466 | 0
6467 | 0
6468 | 0
6469 | 0
6470 | 2
6471 | 2
6472 | 0
6473 | 0
6474 | 0
6475 | 0
6476 | 0
6477 | 0
6478 | 0
6479 | 0
6480 | 0
6481 | 0
6482 | 0
6483 | 0
6484 | 0
6485 | 0
6486 | 2
6487 | 0
6488 | 0
6489 | 0
6490 | 0
6491 | 0
6492 | 0
6493 | 0
6494 | 2
6495 | 0
6496 | 0
6497 | 0
6498 | 0
6499 | 0
6500 | 0
6501 | 0
6502 | 0
6503 | 0
6504 | 0
6505 | 0
6506 | 0
6507 | 2
6508 | 2
6509 | 0
6510 | 0
6511 | 0
6512 | 0
6513 | 0
6514 | 2
6515 | 0
6516 | 0
6517 | 0
6518 | 2
6519 | 0
6520 | 2
6521 | 2
6522 | 0
6523 | 2
6524 | 0
6525 | 2
6526 | 0
6527 | 0
6528 | 2
6529 | 0
6530 | 0
6531 | 0
6532 | 0
6533 | 0
6534 | 2
6535 | 2
6536 | 2
6537 | 0
6538 | 0
6539 | 2
6540 | 2
6541 | 0
6542 | 0
6543 | 0
6544 | 0
6545 | 0
6546 | 0
6547 | 0
6548 | 0
6549 | 0
6550 | 0
6551 | 0
6552 | 0
6553 | 0
6554 | 0
6555 | 0
6556 | 0
6557 | 0
6558 | 0
6559 | 0
6560 | 0
6561 | 2
6562 | 0
6563 | 0
6564 | 0
6565 | 0
6566 | 2
6567 | 0
6568 | 0
6569 | 0
6570 | 0
6571 | 0
6572 | 2
6573 | 0
6574 | 0
6575 | 0
6576 | 0
6577 | 2
6578 | 0
6579 | 0
6580 | 0
6581 | 0
6582 | 0
6583 | 0
6584 | 0
6585 | 2
6586 | 0
6587 | 0
6588 | 2
6589 | 2
6590 | 2
6591 | 0
6592 | 0
6593 | 0
6594 | 0
6595 | 0
6596 | 0
6597 | 0
6598 | 0
6599 | 2
6600 | 0
6601 | 0
6602 | 0
6603 | 0
6604 | 0
6605 | 0
6606 | 0
6607 | 2
6608 | 0
6609 | 0
6610 | 0
6611 | 0
6612 | 0
6613 | 0
6614 | 0
6615 | 0
6616 | 0
6617 | 2
6618 | 0
6619 | 0
6620 | 0
6621 | 0
6622 | 0
6623 | 0
6624 | 0
6625 | 0
6626 | 0
6627 | 0
6628 | 0
6629 | 0
6630 | 0
6631 | 0
6632 | 0
6633 | 0
6634 | 0
6635 | 0
6636 | 0
6637 | 0
6638 | 0
6639 | 2
6640 | 0
6641 | 2
6642 | 0
6643 | 2
6644 | 0
6645 | 0
6646 | 0
6647 | 2
6648 | 0
6649 | 0
6650 | 2
6651 | 0
6652 | 0
6653 | 0
6654 | 0
6655 | 0
6656 | 0
6657 | 0
6658 | 0
6659 | 2
6660 | 2
6661 | 2
6662 | 2
6663 | 0
6664 | 0
6665 | 0
6666 | 0
6667 | 0
6668 | 0
6669 | 0
6670 | 0
6671 | 0
6672 | 0
6673 | 0
6674 | 0
6675 | 0
6676 | 0
6677 | 0
6678 | 0
6679 | 0
6680 | 0
6681 | 0
6682 | 0
6683 | 0
6684 | 0
6685 | 2
6686 | 2
6687 | 2
6688 | 0
6689 | 2
6690 | 0
6691 | 0
6692 | 0
6693 | 0
6694 | 0
6695 | 0
6696 | 0
6697 | 0
6698 | 0
6699 | 0
6700 | 2
6701 | 0
6702 | 0
6703 | 0
6704 | 0
6705 | 0
6706 | 0
6707 | 2
6708 | 2
6709 | 2
6710 | 0
6711 | 0
6712 | 0
6713 | 0
6714 | 2
6715 | 2
6716 | 0
6717 | 0
6718 | 0
6719 | 0
6720 | 0
6721 | 0
6722 | 0
6723 | 0
6724 | 0
6725 | 0
6726 | 0
6727 | 2
6728 | 0
6729 | 0
6730 | 2
6731 | 0
6732 | 0
6733 | 2
6734 | 0
6735 | 2
6736 | 0
6737 | 0
6738 | 0
6739 | 0
6740 | 0
6741 | 2
6742 | 0
6743 | 0
6744 | 2
6745 | 0
6746 | 0
6747 | 2
6748 | 0
6749 | 0
6750 | 0
6751 | 0
6752 | 0
6753 | 2
6754 | 0
6755 | 0
6756 | 0
6757 | 0
6758 | 0
6759 | 0
6760 | 0
6761 | 0
6762 | 0
6763 | 2
6764 | 0
6765 | 0
6766 | 0
6767 | 0
6768 | 0
6769 | 0
6770 | 0
6771 | 0
6772 | 0
6773 | 0
6774 | 0
6775 | 0
6776 | 2
6777 | 0
6778 | 0
6779 | 0
6780 | 2
6781 | 0
6782 | 0
6783 | 0
6784 | 0
6785 | 2
6786 | 0
6787 | 0
6788 | 2
6789 | 0
6790 | 0
6791 | 0
6792 | 0
6793 | 0
6794 | 0
6795 | 0
6796 | 2
6797 | 0
6798 | 0
6799 | 0
6800 | 0
6801 | 0
6802 | 0
6803 | 0
6804 | 0
6805 | 0
6806 | 0
6807 | 0
6808 | 0
6809 | 0
6810 | 0
6811 | 0
6812 | 0
6813 | 0
6814 | 0
6815 | 0
6816 | 0
6817 | 0
6818 | 0
6819 | 0
6820 | 0
6821 | 0
6822 | 0
6823 | 0
6824 | 0
6825 | 0
6826 | 0
6827 | 2
6828 | 0
6829 | 0
6830 | 0
6831 | 0
6832 | 0
6833 | 0
6834 | 0
6835 | 0
6836 | 0
6837 | 0
6838 | 0
6839 | 0
6840 | 2
6841 | 0
6842 | 2
6843 | 0
6844 | 2
6845 | 0
6846 | 0
6847 | 0
6848 | 2
6849 | 2
6850 | 2
6851 | 0
6852 | 2
6853 | 0
6854 | 0
6855 | 0
6856 | 0
6857 | 2
6858 | 2
6859 | 2
6860 | 0
6861 | 2
6862 | 0
6863 | 0
6864 | 2
6865 | 0
6866 | 0
6867 | 0
6868 | 0
6869 | 0
6870 | 0
6871 | 0
6872 | 0
6873 | 2
6874 | 2
6875 | 0
6876 | 0
6877 | 0
6878 | 2
6879 | 0
6880 | 0
6881 | 0
6882 | 0
6883 | 0
6884 | 0
6885 | 0
6886 | 0
6887 | 0
6888 | 0
6889 | 0
6890 | 0
6891 | 0
6892 | 0
6893 | 0
6894 | 2
6895 | 0
6896 | 0
6897 | 0
6898 | 0
6899 | 2
6900 | 2
6901 | 0
6902 | 0
6903 | 0
6904 | 0
6905 | 2
6906 | 0
6907 | 0
6908 | 0
6909 | 0
6910 | 0
6911 | 0
6912 | 0
6913 | 0
6914 | 0
6915 | 0
6916 | 0
6917 | 0
6918 | 0
6919 | 0
6920 | 0
6921 | 0
6922 | 2
6923 | 0
6924 | 0
6925 | 0
6926 | 0
6927 | 0
6928 | 0
6929 | 0
6930 | 0
6931 | 0
6932 | 0
6933 | 2
6934 | 0
6935 | 0
6936 | 0
6937 | 0
6938 | 2
6939 | 0
6940 | 0
6941 | 2
6942 | 2
6943 | 0
6944 | 2
6945 | 0
6946 | 0
6947 | 0
6948 | 0
6949 | 0
6950 | 0
6951 | 0
6952 | 2
6953 | 0
6954 | 0
6955 | 0
6956 | 0
6957 | 0
6958 | 0
6959 | 2
6960 | 0
6961 | 2
6962 | 0
6963 | 0
6964 | 0
6965 | 0
6966 | 0
6967 | 0
6968 | 0
6969 | 0
6970 | 0
6971 | 2
6972 | 0
6973 | 0
6974 | 0
6975 | 0
6976 | 0
6977 | 0
6978 | 2
6979 | 0
6980 | 0
6981 | 2
6982 | 0
6983 | 0
6984 | 0
6985 | 0
6986 | 0
6987 | 0
6988 | 0
6989 | 0
6990 | 0
6991 | 0
6992 | 0
6993 | 0
6994 | 2
6995 | 0
6996 | 0
6997 | 0
6998 | 0
6999 | 0
7000 | 0
7001 | 0
7002 | 0
7003 | 0
7004 | 0
7005 | 0
7006 | 0
7007 | 0
7008 | 0
7009 | 0
7010 | 0
7011 | 2
7012 | 0
7013 | 0
7014 | 0
7015 | 0
7016 | 0
7017 | 0
7018 | 0
7019 | 2
7020 | 0
7021 | 0
7022 | 9
7023 | 9
7024 | 0
7025 | 0
7026 | 0
7027 | 0
7028 | 0
7029 | 9
7030 | 0
7031 | 9
7032 | 9
7033 | 0
7034 | 0
7035 | 0
7036 | 0
7037 | 0
7038 | 0
7039 | 0
7040 | 0
7041 | 0
7042 | 0
7043 | 3
7044 | 3
7045 | 0
7046 | 0
7047 | 0
7048 | 0
7049 | 0
7050 | 0
7051 | 0
7052 | 0
7053 | 3
7054 | 0
7055 | 0
7056 | 0
7057 | 3
7058 | 0
7059 | 0
7060 | 0
7061 | 0
7062 | 0
7063 | 0
7064 | 3
7065 | 0
7066 | 0
7067 | 0
7068 | 3
7069 | 0
7070 | 0
7071 | 0
7072 | 0
7073 | 3
7074 | 0
7075 | 0
7076 | 0
7077 | 0
7078 | 0
7079 | 3
7080 | 3
7081 | 0
7082 | 0
7083 | 0
7084 | 0
7085 | 0
7086 | 0
7087 | 0
7088 | 0
7089 | 0
7090 | 0
7091 | 0
7092 | 0
7093 | 0
7094 | 0
7095 | 0
7096 | 0
7097 | 0
7098 | 3
7099 | 0
7100 | 0
7101 | 0
7102 | 0
7103 | 0
7104 | 0
7105 | 0
7106 | 0
7107 | 3
7108 | 0
7109 | 3
7110 | 0
7111 | 0
7112 | 0
7113 | 0
7114 | 0
7115 | 0
7116 | 0
7117 | 3
7118 | 0
7119 | 3
7120 | 3
7121 | 3
7122 | 0
7123 | 0
7124 | 3
7125 | 0
7126 | 0
7127 | 0
7128 | 0
7129 | 0
7130 | 0
7131 | 0
7132 | 0
7133 | 0
7134 | 0
7135 | 0
7136 | 0
7137 | 0
7138 | 0
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7140 | 0
7141 | 0
7142 | 0
7143 | 0
7144 | 0
7145 | 3
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7147 | 0
7148 | 0
7149 | 0
7150 | 0
7151 | 4
7152 | 0
7153 | 0
7154 | 0
7155 | 0
7156 | 0
7157 | 4
7158 | 0
7159 | 0
7160 | 0
7161 | 0
7162 | 0
7163 | 0
7164 | 0
7165 | 0
7166 | 0
7167 | 0
7168 | 0
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7170 | 4
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7172 | 0
7173 | 0
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7175 | 0
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7187 | 0
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7189 | 0
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7192 | 0
7193 | 0
7194 | 0
7195 | 0
7196 | 0
7197 | 0
7198 | 0
7199 | 4
7200 | 0
7201 | 0
7202 | 0
7203 | 0
7204 | 4
7205 | 0
7206 | 0
7207 | 0
7208 | 0
7209 | 0
7210 | 4
7211 | 0
7212 | 0
7213 | 0
7214 | 4
7215 | 4
7216 | 4
7217 | 0
7218 | 0
7219 | 0
7220 | 4
7221 | 0
7222 | 0
7223 | 0
7224 | 0
7225 | 0
7226 | 0
7227 | 0
7228 | 0
7229 | 0
7230 | 0
7231 | 0
7232 | 0
7233 | 0
7234 | 4
7235 | 0
7236 | 0
7237 | 0
7238 | 0
7239 | 0
7240 | 0
7241 | 4
7242 | 4
7243 | 0
7244 | 0
7245 | 0
7246 | 0
7247 | 0
7248 | 0
7249 | 0
7250 | 0
7251 | 0
7252 | 0
7253 | 0
7254 | 0
7255 | 0
7256 | 4
7257 | 0
7258 | 0
7259 | 0
7260 | 4
7261 | 0
7262 | 0
7263 | 0
7264 | 0
7265 | 0
7266 | 0
7267 | 0
7268 | 0
7269 | 0
7270 | 0
7271 | 4
7272 | 0
7273 | 0
7274 | 4
7275 | 0
7276 | 0
7277 | 4
7278 | 4
7279 | 0
7280 | 0
7281 | 0
7282 | 0
7283 | 0
7284 | 0
7285 | 0
7286 | 0
7287 | 0
7288 | 0
7289 | 0
7290 | 0
7291 | 4
7292 | 4
7293 | 0
7294 | 4
7295 | 0
7296 | 0
7297 | 0
7298 | 0
7299 | 0
7300 | 0
7301 | 0
7302 | 0
7303 | 4
7304 | 0
7305 | 0
7306 | 0
7307 | 0
7308 | 0
7309 | 0
7310 | 0
7311 | 0
7312 | 0
7313 | 0
7314 | 4
7315 | 0
7316 | 0
7317 | 0
7318 | 4
7319 | 0
7320 | 0
7321 | 4
7322 | 0
7323 | 0
7324 | 0
7325 | 0
7326 | 0
7327 | 0
7328 | 4
7329 | 0
7330 | 0
7331 | 0
7332 | 0
7333 | 0
7334 | 0
7335 | 0
7336 | 0
7337 | 0
7338 | 0
7339 | 0
7340 | 4
7341 | 0
7342 | 4
7343 | 4
7344 | 0
7345 | 0
7346 | 0
7347 | 0
7348 | 0
7349 | 0
7350 | 0
7351 | 0
7352 | 0
7353 | 0
7354 | 0
7355 | 0
7356 | 0
7357 | 0
7358 | 4
7359 | 0
7360 | 0
7361 | 0
7362 | 0
7363 | 0
7364 | 0
7365 | 0
7366 | 0
7367 | 0
7368 | 0
7369 | 0
7370 | 0
7371 | 0
7372 | 0
7373 | 0
7374 | 0
7375 | 0
7376 | 0
7377 | 0
7378 | 0
7379 | 0
7380 | 0
7381 | 5
7382 | 0
7383 | 0
7384 | 0
7385 | 0
7386 | 0
7387 | 0
7388 | 0
7389 | 0
7390 | 0
7391 | 0
7392 | 0
7393 | 0
7394 | 0
7395 | 0
7396 | 0
7397 | 0
7398 | 0
7399 | 5
7400 | 5
7401 | 0
7402 | 0
7403 | 0
7404 | 0
7405 | 0
7406 | 0
7407 | 0
7408 | 0
7409 | 0
7410 | 0
7411 | 5
7412 | 0
7413 | 0
7414 | 0
7415 | 0
7416 | 0
7417 | 0
7418 | 0
7419 | 0
7420 | 0
7421 | 0
7422 | 0
7423 | 0
7424 | 7
7425 | 0
7426 | 0
7427 | 0
7428 | 0
7429 | 0
7430 | 0
7431 | 0
7432 | 0
7433 | 0
7434 | 0
7435 | 0
7436 | 0
7437 | 0
7438 | 0
7439 | 0
7440 | 0
7441 | 0
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7445 | 0
7446 | 0
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7450 | 0
7451 | 0
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7453 | 0
7454 | 0
7455 | 0
7456 | 0
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7460 | 0
7461 | 0
7462 | 0
7463 | 0
7464 | 0
7465 | 0
7466 | 0
7467 | 0
7468 | 0
7469 | 0
7470 | 0
7471 | 0
7472 | 0
7473 | 0
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7475 | 0
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7477 | 0
7478 | 0
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7480 | 0
7481 | 0
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7485 | 0
7486 | 0
7487 | 0
7488 | 0
7489 | 0
7490 | 0
7491 | 0
7492 | 0
7493 | 0
7494 | 0
7495 | 0
7496 | 0
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7499 | 0
7500 | 0
7501 | 0
7502 | 0
7503 | 0
7504 | 0
7505 | 0
7506 | 0
7507 | 0
7508 | 0
7509 | 0
7510 | 0
7511 | 0
7512 | 0
7513 | 0
7514 | 0
7515 | 0
7516 | 0
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7518 | 0
7519 | 0
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7522 | 0
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7524 | 0
7525 | 0
7526 | 0
7527 | 0
7528 | 0
7529 | 0
7530 | 0
7531 | 0
7532 | 0
7533 | 0
7534 | 0
7535 | 0
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7554 | 0
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7600 | 0
7601 | 0
7602 | 0
7603 | 0
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7610 | 0
7611 | 0
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/result/tapcmNor.png:
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https://raw.githubusercontent.com/das-lab/TAP/5074840cc15a24fb53077e9886bd36a91c1fce0f/result/tapcmNor.png


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/sarderrors.png:
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https://raw.githubusercontent.com/das-lab/TAP/5074840cc15a24fb53077e9886bd36a91c1fce0f/sarderrors.png


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/tap.py:
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  1 | #coding=utf-8
  2 | 
  3 | import imp
  4 | import sys
  5 | 
  6 | imp.reload(sys)
  7 | import numpy as np
  8 | # import pandas as pd
  9 | import re
 10 | from gensim.models import Word2Vec
 11 | from keras.preprocessing import sequence
 12 | from gensim.corpora.dictionary import Dictionary
 13 | import multiprocessing
 14 | from sklearn.model_selection import train_test_split
 15 | import yaml
 16 | from keras.models import Sequential
 17 | from keras.layers.embeddings import Embedding
 18 | from keras.layers.recurrent import LSTM
 19 | from keras.layers.core import Dense, Dropout, Activation, Flatten, Masking
 20 | from keras.models import model_from_yaml
 21 | from keras.optimizers import Adam
 22 | from keras.layers import Conv1D,MaxPool1D
 23 | from gensim.models.word2vec import LineSentence
 24 | from keras import backend as K
 25 | from keras.layers import Bidirectional
 26 | 
 27 | K.clear_session()
 28 | 
 29 | np.random.seed(1337)  # For Reproducibility
 30 | # the dimension of word vector
 31 | vocab_dim = 256
 32 | # sentence length
 33 | maxlen = 350
 34 | # iter num
 35 | n_iterations = 1
 36 | # the number of words appearing
 37 | n_exposures = 1
 38 | # what is the maximum distance between the current word and the prediction word in a sentence, what is the maximum distance between the current and the prediction word in a sentence
 39 | window_size = 20
 40 | # batch size
 41 | batch_size = 512
 42 | # epoch num
 43 | n_epoch = 60
 44 | # input length
 45 | input_length = 350
 46 | # multi processing cpu number
 47 | cpu_count = multiprocessing.cpu_count()
 48 | 
 49 | labels = ["safe","CWE-78","CWE-79","CWE-89","CWE-90","CWE-91","CWE-95","CWE-98","CWE-601","CWE-862",]
 50 | 
 51 | 
 52 | def combine(safeFile,unsafeFile,unsafeYFile):
 53 | 	global labels
 54 | 	with open(safeFile,'r') as f:
 55 | 		safe_tokens = f.readlines()
 56 | 	with open(unsafeFile,'r') as f:
 57 | 		unsafe_tokens = f.readlines()       
 58 | 	combined = np.concatenate((safe_tokens,unsafe_tokens))
 59 | 	# generating label and meging label data
 60 | 	
 61 | 	with open(unsafeYFile, 'r') as f:
 62 | 		unsafe_labels = f.readlines()
 63 | 
 64 | 	def tran_label(label):
 65 | 		y_oh = np.zeros(10)
 66 | 		y_oh[labels.index(label)] = 1
 67 | 		return y_oh
 68 | 
 69 | 	y = np.concatenate((np.array([tran_label(i.strip()) for i in unsafe_labels]), np.array([tran_label("safe") for i in safe_tokens])))
 70 | 	return combined,y
 71 | 
 72 | 
 73 | # create a dictionary of words and phrases,return the index of each word,vector of words,and index of words corresponding to each sentence
 74 | def create_dictionaries(model=None,	combined=None):
 75 | 	''' Function does are number of Jobs:
 76 | 		1- Creates a word to index mapping
 77 | 		2- Creates a word to vector mapping
 78 | 		3- Transforms the Training and Testing Dictionaries
 79 | 	'''
 80 | 	if (combined is not None) and (model is not None):
 81 | 		gensim_dict = Dictionary()
 82 | 		gensim_dict.doc2bow(model.wv.vocab.keys(),
 83 | 							allow_update=True)		
 84 | 		# the index of a word which have word vector is not 0
 85 | 		w2indx = {v: k + 1 for k, v in gensim_dict.items()}
 86 | 		# integrate all the corresponding word vectors into the word vector matrix
 87 | 		w2vec = {word: model[word] for word in w2indx.keys()}
 88 | 
 89 | 		# a word without a word vector is indexed 0,return the index of word
 90 | 		def parse_dataset(combined):
 91 | 			''' Words become integers
 92 | 			'''
 93 | 			data = []
 94 | 			for sentence in combined:
 95 | 				new_txt = []
 96 | 				# words = sentence.split()
 97 | 				for word in sentence:
 98 | 					try:
 99 | 						new_txt.append(w2indx[word])
100 | 					except:
101 | 						new_txt.append(0)
102 | 				data.append(new_txt)
103 | 			return data
104 | 
105 | 		combined = parse_dataset(combined)
106 | 		# unify the length of the sentence with the pad_sequences function of keras
107 | 		combined = sequence.pad_sequences(combined, maxlen=maxlen)
108 | 		# return index, word vector matrix and the sentence with an unifying length and indexed
109 | 		return w2indx, w2vec, combined
110 | 	else:
111 | 		print('No data provided...')
112 | 
113 | 
114 | # the training of the word vector
115 | def word2vec_train(combined):
116 | 	model = Word2Vec(size=vocab_dim,
117 | 					 min_count=n_exposures,
118 | 					 window=window_size,
119 | 					 workers=cpu_count,
120 | 					 iter=n_iterations)
121 | 	# build the vocabulary dictionary
122 | 	sentences=LineSentence('./traindata_x.txt')
123 | 	model.build_vocab(sentences)
124 | 	# train the word vector model
125 | 	model.train(sentences, total_examples=model.corpus_count, epochs=50)
126 | 	# save the trained model
127 | 	model.save('./Word2vec_model.pkl')
128 | 	# index, word vector matrix and the sentence with an unifying length and indexed based on the trained model
129 | 	
130 | 	data = []
131 | 	for sentence in combined:
132 | 		words = sentence.split()
133 | 		data.append(words)
134 | 		
135 | 	index_dict, word_vectors, combined = create_dictionaries(model=model, combined=data)
136 | 
137 | 	return index_dict, word_vectors, combined
138 | 
139 | 
140 | def get_data(index_dict, word_vectors, combined, y):
141 | 	# total number of word including the word without word vector
142 | 	n_symbols = len(index_dict) + 1
143 | 	# build word vector matrix which corresponding to the word index one by one
144 | 	embedding_weights = np.zeros((n_symbols, vocab_dim))
145 | 	for word, index in index_dict.items():  
146 | 		embedding_weights[index, :] = word_vectors[word]
147 | 	# partition test set and training set
148 | 	x_train, x_validate, y_train, y_validate = train_test_split(combined, y, test_size=0.125)
149 | 	#print(x_train.shape, y_train.shape)
150 | 	# return the input parameters needed of the lstm model
151 | 	return n_symbols, embedding_weights, x_train, y_train, x_validate, y_validate
152 | 
153 | 
154 | ##定义网络结构
155 | def train_lstm(n_symbols, embedding_weights, x_train, y_train, x_validate, y_validate):
156 | 	print('Defining a Simple Keras Model...')
157 | 	model = Sequential()  # or Graph or whatever
158 | 
159 | 	model.add(Embedding(output_dim=vocab_dim,
160 | 						input_dim=n_symbols,
161 | 						mask_zero=True,
162 | 						weights=[embedding_weights],
163 | 						input_length=input_length))  # Adding Input Length
164 | 
165 | 
166 | 	model.add(LSTM(256))
167 | 	model.add(Dropout(0.5))
168 | 	model.add(Dense(10,activation='softmax'))
169 | 
170 | 
171 | 	print ('Compiling the Model...')
172 | 	adam = Adam(lr=0.0001)
173 | 	model.compile(loss='categorical_crossentropy',
174 | 				  optimizer=adam,
175 | 				  metrics=['accuracy'])
176 | 	model.summary()
177 | 	print ("Train...")
178 | 
179 | 	model.fit(x_train, y_train, batch_size=batch_size, epochs=n_epoch, verbose=2,shuffle=True,class_weight='balanced', validation_data=(x_validate, y_validate))
180 | 
181 | 	print ("Evaluate...")
182 | 	score = model.evaluate(x_validate, y_validate,
183 | 						   batch_size=batch_size)
184 | 	# save the trained lstm model
185 | 	yaml_string = model.to_yaml()
186 | 	with open('./lstm.yml', 'w') as outfile:
187 | 		outfile.write(yaml.dump(yaml_string, default_flow_style=True))
188 | 	model.save_weights('./lstm.h5')
189 | 	print ('Test score:', score)
190 | 
191 | 
192 | # 训练模型，并保存
193 | def train():
194 | 	print('Tokenising...')
195 | 	# load data
196 | 	safeFile = './safe_tokens.txt'
197 | 	unsafeFile = './unsafe_tokens.txt'
198 | 	unsafeYFile = './unsafe_y.txt'
199 | 	combined_x,combined_y = combine(safeFile,unsafeFile,unsafeYFile)	
200 | 	
201 | 	x_train_validate, x_test, y_train_validate, y_test = train_test_split(combined_x,combined_y,test_size=0.2)
202 | 	
203 | 	with open('./testdata_x.txt','w') as f:
204 | 		for i in x_test:
205 | 			f.write(i)
206 | 	with open('./testdata_y.txt','w') as f:
207 | 		for i in y_test:
208 | 			f.write(str(i))
209 | 			f.write('\n')
210 | 	with open('./traindata_x.txt','w') as f:
211 | 		for i in x_train_validate:
212 | 			f.write(i)
213 | 	with open('./traindata_y.txt','w') as f:
214 | 		for i in y_train_validate:
215 | 			f.write(str(i))
216 | 			f.write('\n')		
217 | 			
218 | 			
219 | 			
220 | 	print('Total: ',len(x_train_validate)+len(x_test),len(y_train_validate)+len(y_test))
221 | 	print('Train & Validate :',len(x_train_validate), len(y_train_validate))
222 | 	print('Test: ',len(x_test),len(y_test))
223 | 
224 | 	print('Training a Word2vec model...')
225 | 	index_dict, word_vectors, x_train_validate = word2vec_train(x_train_validate)
226 | 
227 | 
228 | 	print('Setting up Arrays for Keras Embedding Layer...')
229 | 	# n_symbols, embedding_weights, x_train, y_train, x_test, y_test = get_data(index_dict, word_vectors, combined, y)
230 | 	n_symbols, embedding_weights, x_train, y_train, x_validate, y_validate = get_data(index_dict, word_vectors, x_train_validate, y_train_validate)
231 | 	print(x_train.shape, y_train.shape)
232 | 	
233 | 		
234 | 	# echo results of each epoch
235 | 	data = []
236 | 	for sentence in x_test:
237 | 		words = sentence.split()
238 | 		data.append(words)
239 | 	model = Word2Vec.load('./Word2vec_model.pkl')
240 | 	_, _, x_test = create_dictionaries(model=model, combined=data)
241 | 	
242 | 	train_lstm(n_symbols, embedding_weights, x_train, y_train, x_validate, y_validate)
243 | 
244 | # building the input format data
245 | def input_transform(string):
246 | 	# reshape the list to bilayer list
247 | 	words = string.split()
248 | 	words = np.array(words).reshape(1, -1)
249 | 	model = Word2Vec.load('./Word2vec_model.pkl')
250 | 	# create a dictionary of words and phrases,return the index of each word,vector of words,and index of words corresponding to each senten
251 | 	_, _, combined = create_dictionaries(model, words)
252 | 	return combined
253 | 	
254 | def lstm_predict():
255 | 	global labels
256 | 	print('loading model......')
257 | 	with open('./lstm.yml', 'r') as f:
258 | 		yaml_string = yaml.load(f)
259 | 	model = model_from_yaml(yaml_string)
260 | 
261 | 	print('loading weights......')
262 | 	model.load_weights('./lstm.h5')
263 | 	with open('./testdata_x.txt','r') as f:
264 | 		strings = f.readlines()
265 | 
266 | 	
267 | 	with open('./testdata_y.txt','r') as f:
268 | 		y = f.readlines()
269 | 		
270 | 	i = 0
271 | 	right = 0
272 | 	false = 0
273 | 	prevalue = ''
274 | 	preresult = ''
275 | 	for string in strings:
276 | 		data = input_transform(string)
277 | 		result = model.predict_classes(data)[0]
278 | 		value = model.predict(data)[0]
279 | 		prevalue += ((','.join(str(i) for i in value))+'\n')
280 | 		# preresult += (labels[result]+'\n')
281 | 		preresult += (str(result)+'\n')
282 | 		
283 | 		t = (1+result*3)
284 | 		if 1 == int(y[i][t:t+1]):
285 | 			right += 1
286 | 		else:
287 | 			false += 1
288 | 			
289 | 		i += 1
290 | 		
291 | 	with open('./predict_value.txt','w') as f:
292 | 		f.write(prevalue)
293 | 	with open('./predict_result.txt','w') as f:
294 | 		f.write(preresult)
295 | 		
296 | 		
297 | 	print('right: ',right,' false: ',false)
298 | 	print('accuracy: ',right/(right+false))
299 | 		
300 | if __name__ == '__main__':
301 | 	train()
302 | 
303 | 	lstm_predict()
304 | 


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