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Functions1,251 in github.com/IBM/Project_CodeNet

⨍ Functions 1,251◇ Types & classes 153
↓ 1 callersFunctionjsmn_parse_primitive
* Fills next available token with JSON primitive. */
tools/json-graph/src/jsmn.h:136
↓ 1 callersFunctionjsmn_parse_string
* Fills next token with JSON string. */
tools/json-graph/src/jsmn.h:194
↓ 1 callersFunctionjsonml_attributes
Helper for jsonml_parse. */
tools/json-graph/src/jgflib.c:616
↓ 1 callersFunctionjsonml_element
Helper for jsonml_parse. */
tools/json-graph/src/jgflib.c:632
↓ 1 callersFunctionjsonml_parse
Parse an explicit JSON tree structure consisting of nested arrays and scalars elements and optional objects for attributes, the result of conver
tools/json-graph/src/jgflib.c:680
↓ 1 callersMethodlabeling
(ReservedWordDeciderI rwdi)
tools/spt-generator/src/com/ibm/ai4code/parser/commons/SPT.java:62
↓ 1 callersFunctionlcs
Longest Common Subsequence (LCS) (not necessarily consecutive) Simplified. O(mn) time, O(n) space. */
tools/duplicates/duplicates.cpp:220
↓ 1 callersFunctionlcs_upperbound
An upperbound for the length of an LCS.
tools/duplicates/duplicates.cpp:249
↓ 1 callersFunctionloadLabels
Load ground truth lables if they exist Parameters: - filename -- Path to labels file Returns: - numpy array with labels to compa
Contest/ExampleSimAnalysis/TestSetEval.py:146
↓ 1 callersMethodloadProblem
Load tokenized code samples of one class i.e. source code files solving one programming problem Parameters: - fn
model-experiments/token-based-similarity-classification/src/Dataset/DataLoader.py:443
↓ 1 callersFunctionload_checkpoint
(checkpoint_fn, model, optimizer)
model-experiments/gnn-based-experiments/src/utils_file.py:29
↓ 1 callersFunctionload_checkpoint_results
(checkpoint_fn)
model-experiments/gnn-based-experiments/src/utils_file.py:37
↓ 1 callersFunctionlookfor_value_of_key
We have an object and are looking for a specific key, ignoring all others. If the key is not found, the object itself is considered the value.
tools/json-graph/src/jgflib.c:428
↓ 1 callersFunctionmain
Main function of program for predicting similarity testset samples Parameters: - args -- Parsed command line arguments a
Contest/ExampleSimAnalysis/TestSetEval.py:194
↓ 1 callersFunctionmain
()
model-experiments/gnn-based-experiments/src/main.py:88
↓ 1 callersFunctionmain
Main function of program for classifying source code Parameters: - args -- Parsed command line arguments as object retur
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SeqOfTokensClassifier.py:80
↓ 1 callersFunctionmain
Main function of program for classifying source code Parameters: - args -- Parsed command line arguments as object retur
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SeqClassParallel.py:80
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/SeqOfTokens/ExpSimSeqTok.py:78
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SimSeqTokParallel.py:76
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SimilarityBySeqTok.py:65
↓ 1 callersFunctionmain
Main function of program for analysing bags of tokens distribution of of sorce code solutions of several problems Parameters: - arg
model-experiments/token-based-similarity-classification/src/Clustering/DistAnnalysisDS.py:12
↓ 1 callersFunctionmain
Main function of program for bag of tokens cluster analysis of of one problem source code solutions Parameters: - args -- Parsed c
model-experiments/token-based-similarity-classification/src/Clustering/BagTokCluster.py:13
↓ 1 callersFunctionmain
Main function of program for classifying source code Parameters: - args -- Parsed command line arguments as object retur
model-experiments/token-based-similarity-classification/src/Clustering/ClusterClassifier.py:23
↓ 1 callersFunctionmain
Main function of program for verifying dataset Parameters: - args -- Parsed command line arguments as object returned by
model-experiments/token-based-similarity-classification/src/Verify/ClassDsVerify.py:128
↓ 1 callersFunctionmain
Main function of program for verifying dataset Parameters: - args -- Parsed command line arguments as object returned by
model-experiments/token-based-similarity-classification/src/Verify/SimDsVerify.py:381
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/PostProcessor/ExpSimFullTestEval.py:31
↓ 1 callersFunctionmain
Main function of program for classifying source code samples and evaluating its accuracy Parameters: - args -- Parsed command line
model-experiments/token-based-similarity-classification/src/PostProcessor/ClasSeqTokEvalParall.py:26
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/PostProcessor/ExpSimSeqTokEval.py:36
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/PostProcessor/SimSeqTokEvalParall.py:28
↓ 1 callersFunctionmain
Main function of program for computing MAP at R metric Arguments are descibed below
model-experiments/token-based-similarity-classification/src/PostProcessor/MapAtR.py:55
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/PostProcessor/SimSeqTokFullTest.py:24
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/BagOfTokens/SimBagTokEval.py:26
↓ 1 callersFunctionmain
Main function of program for classifying source code Parameters: - args -- Parsed command line arguments as object return
model-experiments/token-based-similarity-classification/src/BagOfTokens/BagOfTokensClassifier.py:34
↓ 1 callersFunctionmain
Main function of program for predicting similarity of two source code samples Parameters: - args -- Parsed command line arguments
model-experiments/token-based-similarity-classification/src/BagOfTokens/SimilarityByBoT.py:31
↓ 1 callersFunctionmain
Main function of program for classifying source code samples and evaluating its accuracy Parameters: - args -- Parsed command line
model-experiments/token-based-similarity-classification/src/BagOfTokens/ClasBagTokEval.py:25
↓ 1 callersFunctionmain
Main function of program for making dataset with tokenized solutions of given problem Parameters: - args -- Parsed command line arg
model-experiments/token-based-similarity-classification/src/DSMaker/MakeTokenizedDS.py:90
↓ 1 callersFunctionmain
Main function of program for making dataset with tokenized solutions of given problem Parameters: - args -- Parsed command line arg
model-experiments/token-based-similarity-classification/src/DSMaker/TokenizeImportDS.py:193
↓ 1 callersFunctionmakeDNN
Make classification DNN according to program arguments Parameters: - n_tokens -- number of token types in the sequences - n_labels
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SeqClassParallel.py:41
↓ 1 callersFunctionmakeDNN
Make classification DNN according to program arguments Parameters: - n_tokens -- number of token types in the sequences - args
model-experiments/token-based-similarity-classification/src/SeqOfTokens/ExpSimSeqTok.py:35
↓ 1 callersFunctionmakeDNN
Make classification DNN according to program arguments Parameters: - n_tokens -- number of token types in the sequences - args
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SimSeqTokParallel.py:34
↓ 1 callersFunctionmakeDataset
Make tensorflow dataset for predicting similarity of testset samples with Simaese DNN Parameters: - source -- path to directory w
Contest/ExampleSimAnalysis/TestSetEval.py:101
↓ 1 callersMethodmakeDoubleSeqDataset
Make TF dataset from two lists of sequences of tokens and array of labels The constructed dtaset of TensorFlow Dataset of fr
model-experiments/token-based-similarity-classification/src/Dataset/DoubleSeqTfDS.py:59
↓ 1 callersMethodmakeFilterCmd
Make command for tokenizing source code Returns: string as for command pipe with awk filter postprocessing output o
model-experiments/token-based-similarity-classification/src/DSMaker/DSTokenizer.py:113
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolLexer.java:42
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolParser.java:36
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolTokens.java:42
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolLexer.java:42
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolParser.java:36
↓ 1 callersMethodmakeLiteralNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/Cymbol_lexer_common.java:41
↓ 1 callersFunctionmakeProblemList
Make problem list if it i does not exist and write it down to directory for tokenized dataset Parameters: - out_ds_fn -- tokenized d
model-experiments/token-based-similarity-classification/src/DSMaker/MakeTokenizedDS.py:55
↓ 1 callersFunctionmakeProblemList
Analyze source code dataset and write down list of problems to tokenized dataset Parameters: - ds -- directory of tokenized dat
model-experiments/token-based-similarity-classification/src/DSMaker/TokenizeImportDS.py:32
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolLexer.java:32
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolParser.java:28
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolTokens.java:32
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolLexer.java:32
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolParser.java:28
↓ 1 callersMethodmakeRuleNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/Cymbol_lexer_common.java:32
↓ 1 callersMethodmakeSample
Abstract method for computing data sample from list of tokens Parameters: - tokens list of tokens as int values
model-experiments/token-based-similarity-classification/src/Dataset/DataLoader.py:493
↓ 1 callersMethodmakeSimDataset
Make similarity dataset in the form of numpy arrays of tensorflow datasets Parameters: - samples -- list
model-experiments/token-based-similarity-classification/src/Dataset/TokensSimilDS.py:604
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolLexer.java:50
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol/CymbolParser.java:44
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolTokens.java:50
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolLexer.java:50
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/CymbolParser.java:44
↓ 1 callersMethodmakeSymbolicNames
()
tools/spt-generator/src/com/ibm/ai4code/parser/cymbol_multi/Cymbol_lexer_common.java:49
↓ 1 callersMethodmakeTokenDict
Make dictionary for parsing tokenizer output Parameters: - token_set -- name of set of tokens to use Returns:
model-experiments/token-based-similarity-classification/src/DSMaker/TokenSets.py:182
↓ 1 callersFunctionmakeTokenSet
Make a token set The set of tokens must be the same as it used for trainning the DNN Here we make a CPP56X token set of C++ tokens R
Contest/ExampleSimAnalysis/TestSetEval.py:31
↓ 1 callersMethodmakeTokenizerCmd
Make command for tokenizing source code Parameters: - language -- programming language of code written in
model-experiments/token-based-similarity-classification/src/DSMaker/DSTokenizer.py:52
↓ 1 callersFunctionmap_at_r
Function for computing MAP at R metric Parameter: - sim -- 2D numpy array of predicted similarity measures for all pa
model-experiments/token-based-similarity-classification/src/PostProcessor/MapAtR.py:19
↓ 1 callersFunctionmk_adjacency_graph
Convert (nodelist,edgelist) graph into explicit adjacency structure. Assume graph is directed. */
tools/json-graph/src/jgflib.c:174
↓ 1 callersMethodmultiplyCNN
Siamese CNN with merging by multiplication
model-experiments/token-based-similarity-classification/src/ModelMaker/ExpSiamModel.py:354
↓ 1 callersFunctionnode_info
tools/json-graph/src/jgf2spt.c:33
↓ 1 callersMethodopenFile
(String fileName, String ... header)
tools/spt-generator/src/com/ibm/ai4code/parser/commons/CSVUtils.java:11
↓ 1 callersMethodplainCNN
Old plain siamese CNN Parameters: - loss_function -- loss function
model-experiments/token-based-similarity-classification/src/ModelMaker/ExpSiamModel.py:366
↓ 1 callersFunctionprecompile
Pre-compile the token_RE regular expression.
tools/tokenizer/antlr4tojson.c:143
↓ 1 callersFunctionprecompile
Pre-compile the token_RE regular expression.
tools/tokenizer/ntokenize.c:181
↓ 1 callersFunctionpredict
(text)
model-experiments/masked-language-model/test.py:100
↓ 1 callersFunctionpredict
(text)
model-experiments/masked-language-model/infer.py:74
↓ 1 callersFunctionpredict
Predict if dataset samples represent similar or dissimilar source code solutions of problems Parameters: - dnn -- neural network c
model-experiments/token-based-similarity-classification/src/SeqOfTokens/SimilarityBySeqTok.py:30
↓ 1 callersMethodprintClassAccuracy
Report accuracy of classifications and their misclassified samples Parameters: -f -- file to write report to
model-experiments/token-based-similarity-classification/src/PostProcessor/ClassConfusion.py:306
↓ 1 callersMethodprintClustersStat
model-experiments/token-based-similarity-classification/src/Clustering/VectorKMeans.py:260
↓ 1 callersMethodprintConfusionReport
Print confusion report
model-experiments/token-based-similarity-classification/src/PostProcessor/SimilConfusion.py:88
↓ 1 callersMethodprintNonexistentTokens
Print tokens not found in any of the processed files
model-experiments/token-based-similarity-classification/src/DSMaker/DSTokenizer.py:233
↓ 1 callersMethodprintTokenDict
Print tokens used for tokenization
model-experiments/token-based-similarity-classification/src/DSMaker/DSTokenizer.py:211
↓ 1 callersMethodprintUnknownTokens
Print tokens that were not recognized
model-experiments/token-based-similarity-classification/src/DSMaker/DSTokenizer.py:223
↓ 1 callersFunctionprocess_sample
Split tokens and determine number of occurrences and store as vectors under id in global samples. */
tools/duplicates/duplicates.cpp:114
↓ 1 callersFunctionprocess_ws
cc in [ \t\f]
tools/tokenizer/pytokenize.c:125
↓ 1 callersMethodrandUnordered
Randomize list of unordered elements by shuffling it in place It is assumed that the elements are in some random order, which
model-experiments/token-based-similarity-classification/src/Dataset/DsUtilities.py:149
↓ 1 callersMethodreadGraph
(String fileName)
tools/analysis-graph-generator/src/main/java/com/ibm/wala/codeNet/WalaToGNNFiles.java:226
↓ 1 callersFunctionread_input
Get input file as one long string in global input. Returns number of bytes read. */
tools/json-graph/src/jgflib.c:190
↓ 1 callersFunctionremove_checkpoint
(checkpoint_fn)
model-experiments/gnn-based-experiments/src/utils_file.py:25
↓ 1 callersMethodreportClassProblems
Writes absolute and relative numbers of correct and incorrect classifications of problems Parameters: - tot_samples
model-experiments/token-based-similarity-classification/src/PostProcessor/ClassConfusion.py:109
↓ 1 callersMethodreportConfusedClasses
Report worst classification confusions, i.e. confused pairs of confused problems
model-experiments/token-based-similarity-classification/src/PostProcessor/ClassConfusion.py:248
↓ 1 callersMethodreportDissimilarity
Analyze and report information on detection of dissimilarity of problem solutions Parameters: - dissim_acc -- list
model-experiments/token-based-similarity-classification/src/PostProcessor/SimilConfusion.py:447
↓ 1 callersMethodreportRightClassifications
Report correct classifications of given classes Parameters: - classes -- indices of classes to report - f
model-experiments/token-based-similarity-classification/src/PostProcessor/ClassConfusion.py:153
↓ 1 callersMethodreportSimilarity
Analyze and report information on detection of similarity of problem solutions Parameters: - sim_accuracy -- list o
model-experiments/token-based-similarity-classification/src/PostProcessor/SimilConfusion.py:319
↓ 1 callersMethodreportWrongLengthCode
Report too long and too short source code files
model-experiments/token-based-similarity-classification/src/Dataset/DataLoader.py:226
↓ 1 callersMethodresetStaticFields
reset static fields (e.g., tokenIdxMax) before building SPT tree
tools/spt-generator/src/com/ibm/ai4code/parser/commons/SPTNode.java:332
↓ 1 callersMethodsamplesDsFromGenerator
Make batched TF dataset of samples froms samples generator Samples are defined with list of sequences Parameters: - s
model-experiments/token-based-similarity-classification/src/Dataset/TokensClassifDS.py:105
↓ 1 callersFunctionsave_args
(args, fn)
model-experiments/gnn-based-experiments/src/utils_file.py:6
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