query
stringlengths
7
33.1k
document
stringlengths
7
335k
metadata
dict
negatives
listlengths
3
101
negative_scores
listlengths
3
101
document_score
stringlengths
3
10
document_rank
stringclasses
102 values
Returns whether or not the limit has been hit.
public boolean limitHit() { return (count >= maxCount); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public boolean hasLimit() {\n return limit_ != null;\n }", "public boolean hasLimit() {\n return instance.hasLimit();\n }", "public boolean hasLimit() {\n return result.hasLimit();\n }", "private boolean requestLimitReached() {\n\t\treturn (requestCount >= maximumRequests);\n\t}",...
[ "0.82304066", "0.8052871", "0.7811996", "0.7659768", "0.7537405", "0.7204325", "0.7071204", "0.70081466", "0.6887347", "0.6815756", "0.6813124", "0.6773358", "0.675701", "0.6688428", "0.6661367", "0.6636524", "0.6627688", "0.6598571", "0.65768343", "0.65490675", "0.6481796", ...
0.8594084
0
Returns whether an exception has occured or not.
public boolean getError() { return error; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "boolean hasException();", "public boolean hasException() {\n return exception != null;\n }", "public boolean hasExceptions();", "public boolean hasException() {\n return (mException != null);\n }", "public boolean hasFailed ()\r\n {\r\n return exception_ != null;\r\n }", "boole...
[ "0.8512821", "0.82192135", "0.7974652", "0.78573424", "0.782752", "0.75380296", "0.7498919", "0.7435304", "0.74290234", "0.7272858", "0.72313386", "0.7195436", "0.7152073", "0.7152073", "0.7152073", "0.71410614", "0.7072854", "0.70546544", "0.6969092", "0.6894005", "0.6799321...
0.0
-1
If an exception occurs, prints its stack trace.
public void onException(Exception ex) { System.out.println("Exception on Listener."); ex.printStackTrace(); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public void printStackTrace(){\n\t\tprintStackTrace(System.err);\n\t}", "public void printStackTrace() {\n printStackTrace(System.err);\n }", "public final void printStackTrace()\r\n {\r\n printStackTrace( System.err );\r\n }", "private void printStackTraceHerder() {\n PrintStream printStr...
[ "0.785098", "0.7486177", "0.748343", "0.73420095", "0.72103626", "0.7058399", "0.704914", "0.70226914", "0.70128477", "0.7001085", "0.6997144", "0.69815886", "0.69815886", "0.6979562", "0.6930979", "0.68616873", "0.6843832", "0.68188506", "0.68143964", "0.681395", "0.67593205...
0.0
-1
When a new tweet is streamed, if less tweets than the limit have been streamed, saves the tweet.
public void onStatus(Status arg0) { count++; if (count <= maxCount) tweets.add(arg0); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "private void twitterStream(ServerAccess sa, String search, String geocode, \r\n int max) throws IOException {\r\n final TwitterStream twitterStream = \r\n new TwitterStreamFactory(getAuth()).getInstance();\r\n final Count count = new Count(max);\r\n \r\n final ...
[ "0.62221533", "0.58484054", "0.5712765", "0.5689838", "0.56237125", "0.5552245", "0.55308867", "0.5510924", "0.5481368", "0.5417942", "0.53390604", "0.532527", "0.5287386", "0.527013", "0.5245305", "0.52361244", "0.5230509", "0.5083348", "0.5080636", "0.50779915", "0.5064914"...
0.5419925
9
Gets the list of tweets that have been streamed so far.
public LinkedList<Status> getTweets() { return tweets; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public List<Status> getTweets() {\r\n\t\tincrementShutdownTimer();\r\n\t\treturn tweets;\r\n\t}", "@GetMapping(value = \"/stream/tweets\", produces = MediaType.TEXT_EVENT_STREAM_VALUE)\n\tpublic Flux<Tweet> streamAllTweets() {\n\t\treturn tweetRepository.findAll();\n\t}", "public List<Status> getTweetList(){\n...
[ "0.78514373", "0.7153167", "0.6722904", "0.66396105", "0.66146624", "0.64731926", "0.6470575", "0.6450624", "0.64276505", "0.63328743", "0.61496586", "0.614946", "0.6111993", "0.6108435", "0.60982347", "0.6092705", "0.6079835", "0.60522795", "0.6030936", "0.6008307", "0.60022...
0.6988457
2
Empty onDeletionNotice to fulfill the StatusListener interface.
public void onDeletionNotice(StatusDeletionNotice arg0) { }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\r\n public void onDeletionNotice(StatusDeletionNotice statusDeletionNotice) {\n }", "@Override\r\n public void onDeletionNotice(StatusDeletionNotice statusDeletionNotice) {\n }", "@Override\n public void onDeletionNotice(StatusDeletionNotice stat...
[ "0.8594669", "0.8594669", "0.85852206", "0.8514561", "0.84595126", "0.8451157", "0.8351647", "0.82817274", "0.8280431", "0.82681865", "0.82506716", "0.8237451", "0.7946378", "0.7946378", "0.7462609", "0.6812998", "0.66960925", "0.6573031", "0.65104395", "0.6394047", "0.625338...
0.82318354
12
Empty onScrubGeo to fulfill the StatusListener interface.
public void onScrubGeo(long arg0, long arg1) { }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\n\t\t\tpublic void onScrubGeo(long arg0, long arg1) {\n\n\t\t\t}", "@Override\n\tpublic void onScrubGeo(long arg0, long arg1) \n\t{\n\t}", "@Override\n\tpublic void onScrubGeo(long arg0, long arg1) {\n\t\t\n\t}", "@Override\n\t\tpublic void onScrubGeo(long arg0, long arg1) {\n\t\t\t\n\t\t}", "@Ov...
[ "0.77030903", "0.769833", "0.7665069", "0.7635848", "0.7635848", "0.76015013", "0.7506026", "0.74550575", "0.74550575", "0.73591805", "0.7186621", "0.7186621", "0.7121433", "0.69600576", "0.5634516", "0.55534625", "0.53253806", "0.528797", "0.52727664", "0.526014", "0.5254057...
0.719315
10
Empty onStallWarning to fulfill the StatusListener interface.
public void onStallWarning(StallWarning arg0) { }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\r\n public void onStallWarning(StallWarning warning) {\n }", "@Override\r\n public void onStallWarning(StallWarning warning) {\n }", "public void onStallWarning(StallWarning warning) {\n \t\t\t}", "@Override\n\t\t\tpublic void onStallWarning(StallWarning ...
[ "0.8212601", "0.8212601", "0.80872184", "0.8005256", "0.79748076", "0.79748076", "0.79697853", "0.78916264", "0.78916264", "0.7609376", "0.67217135", "0.6621437", "0.64912695", "0.63470596", "0.62776196", "0.6232455", "0.62131", "0.62073666", "0.61912024", "0.61495894", "0.61...
0.81281173
2
Empty onTrackLimitationNotice to fulfill the StatusListener interface.
public void onTrackLimitationNotice(int arg0) { }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\r\n public void onTrackLimitationNotice(int numberOfLimitedStatuses) {\n }", "@Override\r\n public void onTrackLimitationNotice(int numberOfLimitedStatuses) {\n }", "@Override\n public void onTrackLimitationNotice(int arg0) {\n }", "@Override\n\t\t\...
[ "0.8007345", "0.8007345", "0.77881575", "0.7770872", "0.7753901", "0.76893747", "0.7610541", "0.7541474", "0.63833684", "0.5967975", "0.5944894", "0.587813", "0.5876612", "0.58335465", "0.57863337", "0.57721317", "0.57721317", "0.57137537", "0.57137537", "0.5674862", "0.56730...
0.7468094
8
Generates a token like "TokenForprojects/P/instances/I/tables/T"
@Override public void generateConsistencyToken( GenerateConsistencyTokenRequest request, StreamObserver<GenerateConsistencyTokenResponse> responseObserver) { GenerateConsistencyTokenResponse response = GenerateConsistencyTokenResponse.newBuilder() .setConsistencyToken("TokenFor-" + request.getName()) .build(); responseObserver.onNext(response); responseObserver.onCompleted(); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "String getToken();", "String getToken();", "String getToken();", "String getToken();", "String getToken();", "java.lang.String getToken();", "java.lang.String getToken();", "java.lang.String getToken();", "java.lang.String getToken();", "java.lang.String getToken();", "java.lang.String getToken...
[ "0.70719194", "0.70719194", "0.70719194", "0.70719194", "0.70719194", "0.7065805", "0.7065805", "0.7065805", "0.7065805", "0.7065805", "0.7065805", "0.6833574", "0.6767911", "0.67373234", "0.6695935", "0.66403764", "0.65437144", "0.6523771", "0.64808494", "0.6442328", "0.6434...
0.0
-1
Consistent after a few calls.
@Test public void testConsistencySlow() throws Exception { service.setCallsToConsistency(10); backoff.setMaxTries(9); tableAdminClient.waitForReplication(TABLE_NAME, backoff); Assert.assertEquals(9, backoff.getNumberOfTries()); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\n\tpublic boolean isConsistent() {\n\t\treturn true;\n\t}", "protected void reInitialize() {\n resetCurrent();\n incrementIterCount();\n setFirst(true);\n recoverRunningVersion();\n }", "protected void aktualisieren() {\r\n\r\n\t}", "@Override\n public void sy...
[ "0.61774397", "0.6055388", "0.60484856", "0.60163677", "0.5839673", "0.58177674", "0.5797993", "0.5746546", "0.5735045", "0.5719134", "0.5712376", "0.5688001", "0.5683656", "0.56685925", "0.56657", "0.5660449", "0.5656846", "0.5656709", "0.5648004", "0.56224614", "0.56195813"...
0.0
-1
Time outs before consistency.
@Test(expected = TimeoutException.class) public void testConsistencyTimeOut() throws Exception { service.setCallsToConsistency(10); backoff.setMaxTries(8); tableAdminClient.waitForReplication(TABLE_NAME, backoff); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "void timedOut();", "protected void touchTimeoutCounter()\n\t{\n\t this.lastAccessTime = System.currentTimeMillis();\n\t thread.interrupt ();\n\t}", "private void checkTimeout() {\n/* 169 */ if (this.socket != null) {\n/* */ \n/* 171 */ long i = this.keepaliveTimeoutMs;\n/* */ \n...
[ "0.58891875", "0.57972383", "0.57652533", "0.5726435", "0.5671711", "0.56072915", "0.5587739", "0.55645", "0.5542834", "0.55272484", "0.549148", "0.5487368", "0.54860145", "0.5478986", "0.5474298", "0.5463728", "0.545705", "0.5439213", "0.5425081", "0.5419923", "0.54190165", ...
0.6036455
0
Inflate the layout for this fragment
@Override public View onCreateView(LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState) { View view = inflater.inflate(R.layout.fragment_gun_firing, container, false); clipSpinner = (Spinner)view.findViewById(R.id.spinner_clips); clipSpinner.setOnItemSelectedListener(this); modeSpinner = (Spinner)view.findViewById(R.id.spinner_mode); modeSpinner.setOnItemSelectedListener(this); Switch sw = (Switch)view.findViewById(R.id.switch_smart_gun); sw.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { otherClick(v); } }); sw = (Switch)view.findViewById(R.id.switch_wireless); sw.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { otherClick(v); } }); Button btn = (Button)view.findViewById(R.id.btn_reset_recoil); btn.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { otherClick(v); } }); TextView tv = (TextView)view.findViewById(R.id.text_wound_penalty); tv.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { woundPenaltyClick(v); } }); btn = (Button)view.findViewById(R.id.btn_fire); btn.setOnClickListener(new View.OnClickListener() { @Override public void onClick(View v) { fire(); } }); refresh = (SwipeRefreshLayout)view.findViewById(R.id.swipeRefresh); refresh.setOnRefreshListener(this); if(gunIndex >= 0) { updateView(view); } return view; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\n public View onCreateView(LayoutInflater inflater, ViewGroup container,\n Bundle savedInstanceState) {\n return inflater.inflate(R.layout.fragment_main_allinfo, container, false);\n }", "@Override\r\n\tpublic View onCreateView(LayoutInflater inflater, ViewGroup...
[ "0.6739604", "0.67235583", "0.6721706", "0.6698254", "0.6691869", "0.6687986", "0.66869223", "0.6684548", "0.66766286", "0.6674615", "0.66654444", "0.66654384", "0.6664403", "0.66596216", "0.6653321", "0.6647136", "0.66423255", "0.66388357", "0.6637491", "0.6634193", "0.66251...
0.0
-1
This interface must be implemented by activities that contain this fragment to allow an interaction in this fragment to be communicated to the activity and potentially other fragments contained in that activity. See the Android Training lesson Communicating with Other Fragments for more information.
public interface OnFragmentInteractionListener { void onFragmentInteraction(Uri uri); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public interface OnFragmentInteractionListener {\n void onFragmentMessage(String TAG, Object data);\n}", "public interface FragmentInteraction {\n void switchToBoardView();\n void switchToPinsView();\n void switchToPins(PDKBoard pdkBoard);\n void switchToDescription(PDKPin pin);\n}", "public int...
[ "0.7323901", "0.720826", "0.71346456", "0.712432", "0.71223646", "0.7014406", "0.6975986", "0.6975986", "0.6975986", "0.6973731", "0.69676983", "0.69656146", "0.6960717", "0.69542384", "0.6943149", "0.6933716", "0.69288176", "0.69264686", "0.6922715", "0.6909953", "0.6902666"...
0.0
-1
Created by kre5335 on 4/17/2017.
public interface IAuthenticationFacade { Authentication getAuthentication(); KeycloakUser getUser(); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\n public void perish() {\n \n }", "private stendhal() {\n\t}", "public final void mo51373a() {\n }", "@Override\n\tpublic void grabar() {\n\t\t\n\t}", "@Override\n public void func_104112_b() {\n \n }", "@Override\r\n\tpublic void bicar() {\n\t\t\r\n\t}", "@Ov...
[ "0.5937228", "0.58107895", "0.58004314", "0.5698632", "0.5676657", "0.56159717", "0.56159717", "0.5614266", "0.5557756", "0.5538066", "0.5533818", "0.55172014", "0.5512358", "0.55047154", "0.5487096", "0.54711694", "0.54711694", "0.54711694", "0.54711694", "0.54711694", "0.54...
0.0
-1
Read and return the model groups.
private MeshPolygons[] readMeshPolygons() throws IOException { final int groupCount = this.reader.readShort(); final MeshPolygons[] groups = new MeshPolygons[groupCount]; log.trace(" Number of groups: " + groupCount); for (int i = 0; i < groupCount; i++) { log.trace(" Loading group " + this.id); groups[i] = readMeshPolygon(); } return groups; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public Group[] getGroups(){\n\t\ttry{\n\t\t\tFile file = new File(ApplicationConstants.GROUPS_FILE);\n\t\t\tFileReader reader = new FileReader(file);\n\t\t\tBufferedReader buff = new BufferedReader(reader);\n\n\t\t\tboolean eof = false;\t\t//stores whether the end of the file has been reached\n\n\t\t\tint numberOf...
[ "0.7030068", "0.6965022", "0.6832358", "0.6817541", "0.6721832", "0.67114913", "0.6689423", "0.6591635", "0.65443134", "0.65420187", "0.6516164", "0.6493968", "0.647249", "0.64489627", "0.63942206", "0.63740784", "0.63275903", "0.63212687", "0.63207096", "0.6297289", "0.62822...
0.59587663
34
Reads a single group and return it.
private MeshPolygons readMeshPolygon() throws IOException { final int material = this.reader.readByte(); final byte flags = (byte) this.reader.readByte(); final boolean hasNormals = (flags & 1) != 0; final boolean hasTexCoords = (flags & 2) != 0; final byte mode = (byte) this.reader.readByte(); final int vertexCount = this.reader.readInt(); log.trace(" Loading vertices (" + vertexCount * 3 * 4 +" bytes)"); final FloatBuffer vertices = BufferUtils .convertToNativeEndian(this.reader .readFloatBuffer(vertexCount * 3)); FloatBuffer normals = null, texCoords = null; if (hasNormals) { log.trace(" Loading normals (" + vertexCount * 3 * 4+ " bytes)"); normals = BufferUtils.convertToNativeEndian(this.reader .readFloatBuffer(vertexCount * 3)); } if (hasTexCoords) { log.trace(" Loading tex coords (" + vertexCount * 2 * 4+ " bytes)"); texCoords = BufferUtils.convertToNativeEndian(this.reader .readFloatBuffer(vertexCount * 2)); } final int indexCount = this.reader.readInt(); log.trace(" Loading indices (" + indexCount * 2 + " bytes)"); final ShortBuffer indices = BufferUtils .convertToNativeEndian(this.reader .readShortBuffer(indexCount)); log.trace(" Creating mesh polygons object"); return new MeshPolygons(material, mode, indices, vertices, texCoords, normals); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__ReadElement__Group__1() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalBrowser.g:2513:1: ( rule__ReadElement__Group__1__Impl rule__ReadElement__Group__2 )\n // InternalBrowser.g:2514:2: rule__ReadEl...
[ "0.68039536", "0.67260456", "0.6693187", "0.6586022", "0.6579628", "0.6575492", "0.6572033", "0.642978", "0.6327991", "0.6318276", "0.62676156", "0.62365746", "0.62100726", "0.61845374", "0.6182971", "0.6182971", "0.6182971", "0.6182971", "0.6182971", "0.6182971", "0.6182971"...
0.0
-1
Reads and returns the materials.
private String[] readMaterials() throws IOException { final int materialCount = this.reader.readByte(); final String[] materials = new String[materialCount]; for (int i = 0; i < materialCount; i++) { materials[i] = this.reader.readString(this.reader.readByte()); } return materials; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public void read() {\n prepareAdditionalStates();\n materialParameters.clear();\n int level = 0;\n boolean params = false;\n boolean states = false;\n try {\n //scan material text\n for (String line : material.asLines()) {\n //trim line...
[ "0.7369688", "0.7351929", "0.72886074", "0.7186219", "0.71856964", "0.7066123", "0.6996026", "0.69858336", "0.69858336", "0.6966948", "0.6921153", "0.68787575", "0.68700105", "0.6841178", "0.6748096", "0.6715966", "0.6675202", "0.6656869", "0.65769863", "0.6544853", "0.645663...
0.82327276
0
assert sameExtremities(other) == false;
public boolean doesIntersectButNotSameExtremity(LineSegmentInt other) { if (doesIntersect(other) == false) { return false; } if (atLeastOneCommonExtremities(other)) { return false; } return true; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Test\n\tpublic void testEqualsFalso() {\n\t\t\n\t\tassertFalse(contato1.equals(contato2));\n\t}", "boolean hasIsEquivalent();", "@Test\n @DisplayName(\"Test should detect inequality between unequal states.\")\n public void testShouldResultInInequality() {\n ObjectBag os1 = new ObjectBag(null, \"H...
[ "0.6387555", "0.63342017", "0.6331206", "0.6302454", "0.6242964", "0.61941564", "0.6193765", "0.61915386", "0.61468947", "0.6091207", "0.6080056", "0.6061138", "0.5989564", "0.59865826", "0.59719515", "0.5940183", "0.59252924", "0.5904961", "0.5902776", "0.58840007", "0.58828...
0.0
-1
The main routine testing the stepped function generator SteppedDataGenerator. Sets up the logging facility and the data storage. It also assembles the result time series and writes them to a file.
public static void main(String[] args) { Logger root = (Logger)LoggerFactory.getLogger(Logger.ROOT_LOGGER_NAME); root.setLevel(Level.ERROR); // TODO create a program argument to switch to trace Logger logger = (Logger)LoggerFactory.getLogger("main"); System.out.println("\nTEST: " + TEST_ID); System.out.println("========================\n"); System.out.println("This is a simple test battery for this class.\n"); logger.trace("Setting up test for class '{}'\n", TEST_ID); /* * Read parameters from file * * To write a new parameter file template, uncomment the following two lines * TestBrownianProcessParams.writeParamDefinition("param_template.xml"); * System.exit(0); */ String fileName = CmdLineProcessor.process(args); // process command line arguments TestSteppedDataGeneratorParams params = null; try { params = TestSteppedDataGeneratorParams.readParameters(fileName); } catch (Throwable e) { logger.error(e.getMessage()); System.exit(0); } params.validate(); /** * Copy distribution parameters into a single array */ DoubleArrayList shifts = params.getValidatedDoubleSequence(TestSteppedDataGeneratorParams.Sequence.SHIFT); DoubleArrayList stepHeights = params.getValidatedDoubleSequence(TestSteppedDataGeneratorParams.Sequence.STEP_HEIGHT); DoubleArrayList stepWidths = params.getValidatedDoubleSequence(TestSteppedDataGeneratorParams.Sequence.STEP_WIDTH); DoubleArrayList valleyWidths = params.getValidatedDoubleSequence(TestSteppedDataGeneratorParams.Sequence.VALLEY_WIDTH); ArrayList<Double> distParams = new ArrayList<Double>(); for (int i = 0; i < shifts.size(); i++) { distParams.add(shifts.get(i)); distParams.add(stepHeights.get(i)); distParams.add(stepWidths.get(i)); distParams.add(valleyWidths.get(i)); } // Number formatter for the output DecimalFormat df = new DecimalFormat("0.00000", new DecimalFormatSymbols(Locale.UK)); DoubleTimeSeries.setFormatter(df); FormattedDoubleArrayList.setFormatter(df); /** * Data storage and formatting */ DoubleTimeSeries dts = new DoubleTimeSeries(); DoubleTimeSeriesList dtsl_1 = new DoubleTimeSeriesList(); DoubleTimeSeriesList dtsl_2 = new DoubleTimeSeriesList(); VersatileTimeSeriesCollection atc_1 = new VersatileTimeSeriesCollection("results"); VersatileTimeSeriesCollection atc_2 = new VersatileTimeSeriesCollection("results"); VersatileTimeSeries.StaticInternalParams.setTimePeriodFormat("tick"); VersatileTimeSeries.StaticInternalParams.setTimePeriod(VersatileTimeSeries.Period.DAY); VersatileTimeSeries.StaticInternalParams.setOutputHead(25); VersatileTimeSeries.StaticInternalParams.setOutputTail(10); int FIRST_COLUMN_WIDTH = 10; String baseName = "STEPPED"; /** * Setup */ SteppedDataGenerator gen = new SteppedDataGenerator(distParams.toArray(new Double[1])); int dim = (int) Math.floor(0.1 + distParams.size() / 4); // 4 is the number of parameters of the stepped function generator for (int i = 0; i < dim; i++) { String tsName = baseName; if (dim > 1) tsName += "_" + i; dtsl_1.add(new DoubleTimeSeries(tsName)); dtsl_2.add(new DoubleTimeSeries(tsName)); } /** * Run */ DoubleArrayList vec; // DoubleArrayList increments; for (int i = 0; i < params.nTicks; i++) { vec = gen.nextDoubleVector(); // increments = gen.nextDoubleVectorIncrements(); for (int j = 0; j < dim; j++) { dtsl_1.get(j).add(vec.get(j)); // dtsl_2.get(j).add(increments.get(j)); } } /** * Output - Text */ atc_1.populateSeries(1, "step", dtsl_1, 0); // atc_2.populateSeries(1, "step", dtsl_2, 0); System.out.println(atc_1.printDecoratedSeries(baseName, FIRST_COLUMN_WIDTH, true)); // System.out.println(atc_2.printDecoratedSeries(baseName, FIRST_COLUMN_WIDTH, true)); /** * Output - Graph */ VersatileChart charts_1 = new VersatileChart(); charts_1.getInternalParms().autoRange = true; charts_1.getInternalParms().autoRangePadding = 0; charts_1.getInternalParms().ticks = true; charts_1.draw(atc_1); // VersatileChart charts_2 = new VersatileChart(); // charts_2.getInternalParms().autoRange = true; // charts_2.getInternalParms().autoRangePadding = 0; // charts_2.getInternalParms().ticks = true; // // charts_2.draw(atc_2); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "static void initializeData() {\n\n if (timeLimitInHour != -1) {\n endTime = startTime + timeLimitInHour * 60 * 60 * 1000;\n }\n\n // Read the data file.\n data = new Data(dataFileName);\n\n // Build all variables.\n allVariable = data.buildAllVariable();\n\n ...
[ "0.6240432", "0.62305385", "0.60105145", "0.57615614", "0.5748995", "0.57488805", "0.5743571", "0.57301676", "0.5728712", "0.57257414", "0.571431", "0.5663429", "0.5618072", "0.56124604", "0.5595281", "0.55578756", "0.55309105", "0.5515488", "0.54923296", "0.546625", "0.54637...
0.62119555
2
The identifier of the job
@javax.annotation.Nullable @ApiModelProperty(value = "The identifier of the job") public String getJob() { return job; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public JobID getJobID() {\n \n \t\treturn this.environment.getJobID();\n \t}", "@DISPID(1)\r\n\t// = 0x1. The runtime will prefer the VTID if present\r\n\t@VTID(7)\r\n\tint jobID();", "public String getJobId();", "@Override\n\tpublic long getJobId() {\n\t\treturn model.getJobId();\n\t}", "java.lang.String ...
[ "0.7854194", "0.7839692", "0.7823392", "0.7748559", "0.77243125", "0.77127504", "0.7601393", "0.75898683", "0.75509936", "0.7504178", "0.75023705", "0.74815124", "0.7395411", "0.7361678", "0.7346626", "0.73361593", "0.72876894", "0.7276521", "0.72734606", "0.72734606", "0.727...
0.68661946
32
If the instantiation should be handled AUTOMATIC or MANUAL
@javax.annotation.Nullable @ApiModelProperty(value = "If the instantiation should be handled AUTOMATIC or MANUAL") public InstantiationEnum getInstantiation() { return instantiation; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "@Override\n public boolean getInstantiateForClass() throws G2AccessException {\n java.lang.Object retnValue = getStaticAttributeValue (SystemAttributeSymbols.INSTANTIATE_);\n return ((Boolean)retnValue).booleanValue ();\n }", "public void autonomousInit() {\n }", "public void autonomousInit() {\n ...
[ "0.6553813", "0.61124355", "0.61124355", "0.6097781", "0.60860443", "0.60508627", "0.5940807", "0.5934953", "0.59347504", "0.5866336", "0.58403224", "0.581128", "0.5799086", "0.57921547", "0.5789971", "0.5778708", "0.57690036", "0.5749738", "0.5749738", "0.5747737", "0.572708...
0.57793474
15
Convert the given object to string with each line indented by 4 spaces (except the first line).
private String toIndentedString(java.lang.Object o) { if (o == null) { return "null"; } return o.toString().replace("\n", "\n "); }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "private String toIndentedString(Object object) {\n if (object == null) {\n return EndpointCentralConstants.NULL_STRING;\n }\n return object.toString().replace(EndpointCentralConstants.LINE_BREAK,\n EndpointCentralConstants.LINE_BREAK + EndpointCentralConstants.TAB_SPA...
[ "0.78847593", "0.75493765", "0.74971926", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0.746168", "0...
0.0
-1
$ANTLR start "entryRuleModel" InternalMLRegression.g:53:1: entryRuleModel : ruleModel EOF ;
public final void entryRuleModel() throws RecognitionException { try { // InternalMLRegression.g:54:1: ( ruleModel EOF ) // InternalMLRegression.g:55:1: ruleModel EOF { before(grammarAccess.getModelRule()); pushFollow(FOLLOW_1); ruleModel(); state._fsp--; after(grammarAccess.getModelRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void entryRuleModel() throws RecognitionException {\n try {\n // ../eu.artist.migration.mdt.database.sql.editor.ui/src-gen/eu/artist/migration/mdt/database/sql/editor/ui/contentassist/antlr/internal/InternalSQLDSL.g:61:1: ( ruleModel EOF )\n // ../eu.artist.migration.mdt.d...
[ "0.7786461", "0.76685786", "0.7540406", "0.7488713", "0.7382038", "0.73170763", "0.7296038", "0.72930187", "0.72686577", "0.72285235", "0.71465206", "0.7120095", "0.65571505", "0.64450693", "0.623351", "0.612767", "0.60939234", "0.59402424", "0.58229876", "0.57940036", "0.556...
0.7554437
2
$ANTLR end "entryRuleModel" $ANTLR start "ruleModel" InternalMLRegression.g:62:1: ruleModel : ( ( rule__Model__Group__0 ) ) ;
public final void ruleModel() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:66:2: ( ( ( rule__Model__Group__0 ) ) ) // InternalMLRegression.g:67:2: ( ( rule__Model__Group__0 ) ) { // InternalMLRegression.g:67:2: ( ( rule__Model__Group__0 ) ) // InternalMLRegression.g:68:3: ( rule__Model__Group__0 ) { before(grammarAccess.getModelAccess().getGroup()); // InternalMLRegression.g:69:3: ( rule__Model__Group__0 ) // InternalMLRegression.g:69:4: rule__Model__Group__0 { pushFollow(FOLLOW_2); rule__Model__Group__0(); state._fsp--; } after(grammarAccess.getModelAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void ruleModel() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalCsv.g:67:2: ( ( ( rule__Model__Group__0 ) ) )\n // InternalCsv.g:68:2: ( ( rule__Model__Group__0 ) )\n {\n // InternalCsv.g...
[ "0.8056593", "0.7592338", "0.74871725", "0.73307174", "0.7312559", "0.72411984", "0.7229211", "0.7155707", "0.7123081", "0.70950705", "0.6937845", "0.6896051", "0.6850547", "0.68290246", "0.67257047", "0.67087966", "0.66847134", "0.6670967", "0.66463065", "0.6625784", "0.6526...
0.828282
0
$ANTLR end "ruleModel" $ANTLR start "entryRuleLanguageTarget" InternalMLRegression.g:78:1: entryRuleLanguageTarget : ruleLanguageTarget EOF ;
public final void entryRuleLanguageTarget() throws RecognitionException { try { // InternalMLRegression.g:79:1: ( ruleLanguageTarget EOF ) // InternalMLRegression.g:80:1: ruleLanguageTarget EOF { before(grammarAccess.getLanguageTargetRule()); pushFollow(FOLLOW_1); ruleLanguageTarget(); state._fsp--; after(grammarAccess.getLanguageTargetRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleLanguageTarget() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleLanguageTarget = null;\n\n\n try {\n // InternalMLRegression.g:121:55: (iv_ruleLanguageTarget= ruleLanguageTarget EOF )\n // InternalMLRegression.g:12...
[ "0.70509195", "0.67172045", "0.64014566", "0.6328601", "0.624791", "0.621322", "0.61720157", "0.6166928", "0.60575646", "0.6014957", "0.6006636", "0.59476435", "0.5941476", "0.5909423", "0.58738685", "0.5859087", "0.5803475", "0.5793136", "0.5773557", "0.571446", "0.56912297"...
0.71493745
0
$ANTLR end "entryRuleLanguageTarget" $ANTLR start "ruleLanguageTarget" InternalMLRegression.g:87:1: ruleLanguageTarget : ( ( rule__LanguageTarget__Group__0 ) ) ;
public final void ruleLanguageTarget() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:91:2: ( ( ( rule__LanguageTarget__Group__0 ) ) ) // InternalMLRegression.g:92:2: ( ( rule__LanguageTarget__Group__0 ) ) { // InternalMLRegression.g:92:2: ( ( rule__LanguageTarget__Group__0 ) ) // InternalMLRegression.g:93:3: ( rule__LanguageTarget__Group__0 ) { before(grammarAccess.getLanguageTargetAccess().getGroup()); // InternalMLRegression.g:94:3: ( rule__LanguageTarget__Group__0 ) // InternalMLRegression.g:94:4: rule__LanguageTarget__Group__0 { pushFollow(FOLLOW_2); rule__LanguageTarget__Group__0(); state._fsp--; } after(grammarAccess.getLanguageTargetAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:937:1: ( ( 'target_language' ) )\n // InternalMLRegression.g:938:1: ( 'target_language' )\n ...
[ "0.82883584", "0.7863562", "0.7689526", "0.76844144", "0.74705684", "0.7445329", "0.73803663", "0.7344517", "0.7173694", "0.7137104", "0.70725065", "0.7063945", "0.7039845", "0.6764889", "0.6750029", "0.6715564", "0.6682398", "0.65785646", "0.6563434", "0.6515295", "0.6460742...
0.84737754
0
$ANTLR end "ruleLanguageTarget" $ANTLR start "entryRulePython" InternalMLRegression.g:103:1: entryRulePython : rulePython EOF ;
public final void entryRulePython() throws RecognitionException { try { // InternalMLRegression.g:104:1: ( rulePython EOF ) // InternalMLRegression.g:105:1: rulePython EOF { before(grammarAccess.getPythonRule()); pushFollow(FOLLOW_1); rulePython(); state._fsp--; after(grammarAccess.getPythonRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRulePython() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_rulePython = null;\n\n\n try {\n // InternalMLRegression.g:189:46: (iv_rulePython= rulePython EOF )\n // InternalMLRegression.g:190:2: iv_rulePython=...
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0.7558843
1
$ANTLR end "entryRulePython" $ANTLR start "rulePython" InternalMLRegression.g:112:1: rulePython : ( ( rule__Python__Alternatives ) ) ;
public final void rulePython() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:116:2: ( ( ( rule__Python__Alternatives ) ) ) // InternalMLRegression.g:117:2: ( ( rule__Python__Alternatives ) ) { // InternalMLRegression.g:117:2: ( ( rule__Python__Alternatives ) ) // InternalMLRegression.g:118:3: ( rule__Python__Alternatives ) { before(grammarAccess.getPythonAccess().getAlternatives()); // InternalMLRegression.g:119:3: ( rule__Python__Alternatives ) // InternalMLRegression.g:119:4: rule__Python__Alternatives { pushFollow(FOLLOW_2); rule__Python__Alternatives(); state._fsp--; } after(grammarAccess.getPythonAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Python__Alternatives() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:727:1: ( ( 'python' ) | ( 'Python' ) | ( 'PYTHON' ) )\n int alt2=3;\n switch ( input.LA(1) ) {\n ...
[ "0.80470264", "0.70438063", "0.65226555", "0.6017118", "0.57412577", "0.56316495", "0.55946106", "0.5552968", "0.5524931", "0.55021447", "0.54837084", "0.54836273", "0.5439487", "0.5408195", "0.53858703", "0.5328762", "0.52635247", "0.525784", "0.52508354", "0.52219504", "0.5...
0.8523086
0
$ANTLR end "rulePython" $ANTLR start "entryRuleR" InternalMLRegression.g:128:1: entryRuleR : ruleR EOF ;
public final void entryRuleR() throws RecognitionException { try { // InternalMLRegression.g:129:1: ( ruleR EOF ) // InternalMLRegression.g:130:1: ruleR EOF { before(grammarAccess.getRRule()); pushFollow(FOLLOW_1); ruleR(); state._fsp--; after(grammarAccess.getRRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleR() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleR = null;\n\n\n try {\n // InternalMLRegression.g:225:41: (iv_ruleR= ruleR EOF )\n // InternalMLRegression.g:226:2: iv_ruleR= ruleR EOF\n {...
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0.75402504
0
$ANTLR end "entryRuleR" $ANTLR start "ruleR" InternalMLRegression.g:137:1: ruleR : ( ( rule__R__Alternatives ) ) ;
public final void ruleR() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:141:2: ( ( ( rule__R__Alternatives ) ) ) // InternalMLRegression.g:142:2: ( ( rule__R__Alternatives ) ) { // InternalMLRegression.g:142:2: ( ( rule__R__Alternatives ) ) // InternalMLRegression.g:143:3: ( rule__R__Alternatives ) { before(grammarAccess.getRAccess().getAlternatives()); // InternalMLRegression.g:144:3: ( rule__R__Alternatives ) // InternalMLRegression.g:144:4: rule__R__Alternatives { pushFollow(FOLLOW_2); rule__R__Alternatives(); state._fsp--; } after(grammarAccess.getRAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__R__Alternatives() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:754:1: ( ( 'r' ) | ( 'R' ) )\n int alt3=2;\n int LA3_0 = input.LA(1);\n\n if ( (LA3_0==20) ) {\n...
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0.8233876
0
$ANTLR end "ruleR" $ANTLR start "entryRuleMLRegression" InternalMLRegression.g:153:1: entryRuleMLRegression : ruleMLRegression EOF ;
public final void entryRuleMLRegression() throws RecognitionException { try { // InternalMLRegression.g:154:1: ( ruleMLRegression EOF ) // InternalMLRegression.g:155:1: ruleMLRegression EOF { before(grammarAccess.getMLRegressionRule()); pushFollow(FOLLOW_1); ruleMLRegression(); state._fsp--; after(grammarAccess.getMLRegressionRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleMLRegression() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleMLRegression = null;\n\n\n try {\n // InternalMLRegression.g:255:53: (iv_ruleMLRegression= ruleMLRegression EOF )\n // InternalMLRegression.g:256:2: iv_...
[ "0.7493214", "0.6538138", "0.6235277", "0.60497934", "0.59028697", "0.57362235", "0.5655362", "0.56277114", "0.5449462", "0.5400427", "0.53078765", "0.52727735", "0.52256435", "0.5169023", "0.51622057", "0.5152763", "0.5128117", "0.5111944", "0.50686073", "0.5019657", "0.5004...
0.752071
0
$ANTLR end "entryRuleMLRegression" $ANTLR start "ruleMLRegression" InternalMLRegression.g:162:1: ruleMLRegression : ( ( rule__MLRegression__Group__0 ) ) ;
public final void ruleMLRegression() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:166:2: ( ( ( rule__MLRegression__Group__0 ) ) ) // InternalMLRegression.g:167:2: ( ( rule__MLRegression__Group__0 ) ) { // InternalMLRegression.g:167:2: ( ( rule__MLRegression__Group__0 ) ) // InternalMLRegression.g:168:3: ( rule__MLRegression__Group__0 ) { before(grammarAccess.getMLRegressionAccess().getGroup()); // InternalMLRegression.g:169:3: ( rule__MLRegression__Group__0 ) // InternalMLRegression.g:169:4: rule__MLRegression__Group__0 { pushFollow(FOLLOW_2); rule__MLRegression__Group__0(); state._fsp--; } after(grammarAccess.getMLRegressionAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1033:1: ( rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 )\n // InternalMLRegression.g:1034:2:...
[ "0.7738021", "0.7418042", "0.721012", "0.71769387", "0.69023234", "0.6680932", "0.6590525", "0.6568444", "0.6466773", "0.6425741", "0.635946", "0.62322754", "0.61656886", "0.61307734", "0.61056226", "0.61023194", "0.6092846", "0.6025533", "0.59851724", "0.59814906", "0.598106...
0.8366602
0
$ANTLR end "ruleMLRegression" $ANTLR start "entryRuleDataset" InternalMLRegression.g:178:1: entryRuleDataset : ruleDataset EOF ;
public final void entryRuleDataset() throws RecognitionException { try { // InternalMLRegression.g:179:1: ( ruleDataset EOF ) // InternalMLRegression.g:180:1: ruleDataset EOF { before(grammarAccess.getDatasetRule()); pushFollow(FOLLOW_1); ruleDataset(); state._fsp--; after(grammarAccess.getDatasetRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleDataset() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleDataset = null;\n\n\n try {\n // InternalMLRegression.g:388:48: (iv_ruleDataset= ruleDataset EOF )\n // InternalMLRegression.g:389:2: iv_ruleDataset= ruleDat...
[ "0.6784154", "0.64480984", "0.6143085", "0.58359987", "0.56922835", "0.5356374", "0.52391046", "0.5188044", "0.5187707", "0.5136718", "0.5104688", "0.5102077", "0.5080719", "0.50470316", "0.5033991", "0.5007776", "0.49728343", "0.49708316", "0.49507645", "0.49339753", "0.4869...
0.71294826
0
$ANTLR end "entryRuleDataset" $ANTLR start "ruleDataset" InternalMLRegression.g:187:1: ruleDataset : ( ( rule__Dataset__Group__0 ) ) ;
public final void ruleDataset() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:191:2: ( ( ( rule__Dataset__Group__0 ) ) ) // InternalMLRegression.g:192:2: ( ( rule__Dataset__Group__0 ) ) { // InternalMLRegression.g:192:2: ( ( rule__Dataset__Group__0 ) ) // InternalMLRegression.g:193:3: ( rule__Dataset__Group__0 ) { before(grammarAccess.getDatasetAccess().getGroup()); // InternalMLRegression.g:194:3: ( rule__Dataset__Group__0 ) // InternalMLRegression.g:194:4: rule__Dataset__Group__0 { pushFollow(FOLLOW_2); rule__Dataset__Group__0(); state._fsp--; } after(grammarAccess.getDatasetAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Dataset__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1195:1: ( rule__Dataset__Group__0__Impl rule__Dataset__Group__1 )\n // InternalMLRegression.g:1196:2: rule__Dataset_...
[ "0.67529917", "0.6711923", "0.6644378", "0.66203535", "0.649752", "0.6271897", "0.6214454", "0.606972", "0.6067711", "0.6049623", "0.5953877", "0.57370484", "0.5388107", "0.5351356", "0.5312725", "0.52569234", "0.52516013", "0.5247711", "0.5212258", "0.5204243", "0.51839334",...
0.812245
0
$ANTLR end "ruleDataset" $ANTLR start "entryRuleVariables" InternalMLRegression.g:203:1: entryRuleVariables : ruleVariables EOF ;
public final void entryRuleVariables() throws RecognitionException { try { // InternalMLRegression.g:204:1: ( ruleVariables EOF ) // InternalMLRegression.g:205:1: ruleVariables EOF { before(grammarAccess.getVariablesRule()); pushFollow(FOLLOW_1); ruleVariables(); state._fsp--; after(grammarAccess.getVariablesRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleVariables() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleVariables = null;\n\n\n try {\n // InternalMLRegression.g:451:50: (iv_ruleVariables= ruleVariables EOF )\n // InternalMLRegression.g:452:2: iv_ruleVariable...
[ "0.7315895", "0.71600664", "0.65040755", "0.62119853", "0.6158096", "0.6010599", "0.5999955", "0.59997785", "0.5916859", "0.5844231", "0.5805593", "0.5801848", "0.576088", "0.57024217", "0.5639791", "0.5614435", "0.5486681", "0.54557395", "0.54201216", "0.539623", "0.53716654...
0.75635237
0
$ANTLR end "entryRuleVariables" $ANTLR start "ruleVariables" InternalMLRegression.g:212:1: ruleVariables : ( ( rule__Variables__Group__0 ) ) ;
public final void ruleVariables() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:216:2: ( ( ( rule__Variables__Group__0 ) ) ) // InternalMLRegression.g:217:2: ( ( rule__Variables__Group__0 ) ) { // InternalMLRegression.g:217:2: ( ( rule__Variables__Group__0 ) ) // InternalMLRegression.g:218:3: ( rule__Variables__Group__0 ) { before(grammarAccess.getVariablesAccess().getGroup()); // InternalMLRegression.g:219:3: ( rule__Variables__Group__0 ) // InternalMLRegression.g:219:4: rule__Variables__Group__0 { pushFollow(FOLLOW_2); rule__Variables__Group__0(); state._fsp--; } after(grammarAccess.getVariablesAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void entryRuleVariables() throws RecognitionException {\n try {\n // InternalMLRegression.g:204:1: ( ruleVariables EOF )\n // InternalMLRegression.g:205:1: ruleVariables EOF\n {\n before(grammarAccess.getVariablesRule()); \n pushFollow(FOL...
[ "0.7533121", "0.73415715", "0.7091388", "0.6980307", "0.63199943", "0.62777597", "0.6254423", "0.62468654", "0.6222569", "0.62080675", "0.61962295", "0.6164861", "0.6136414", "0.6108929", "0.6091955", "0.60846144", "0.6082942", "0.60601735", "0.6025149", "0.6004771", "0.59989...
0.8087322
0
$ANTLR end "ruleVariables" $ANTLR start "entryRulePredictiveVars" InternalMLRegression.g:228:1: entryRulePredictiveVars : rulePredictiveVars EOF ;
public final void entryRulePredictiveVars() throws RecognitionException { try { // InternalMLRegression.g:229:1: ( rulePredictiveVars EOF ) // InternalMLRegression.g:230:1: rulePredictiveVars EOF { before(grammarAccess.getPredictiveVarsRule()); pushFollow(FOLLOW_1); rulePredictiveVars(); state._fsp--; after(grammarAccess.getPredictiveVarsRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject ruleVariables() throws RecognitionException {\n EObject current = null;\n\n EObject lv_predictives_0_0 = null;\n\n EObject lv_targets_1_0 = null;\n\n\n\n \tenterRule();\n\n try {\n // InternalMLRegression.g:464:2: ( ( ( (lv_predictives_0_0= rulePre...
[ "0.76795286", "0.7492101", "0.72690725", "0.70985436", "0.6955379", "0.67222184", "0.6611806", "0.64473563", "0.632503", "0.63116574", "0.625095", "0.6250809", "0.5994823", "0.5911476", "0.58233625", "0.57914805", "0.57903904", "0.57729286", "0.5625789", "0.5615534", "0.56046...
0.78478736
0
$ANTLR end "entryRulePredictiveVars" $ANTLR start "rulePredictiveVars" InternalMLRegression.g:237:1: rulePredictiveVars : ( ( rule__PredictiveVars__Group__0 ) ) ;
public final void rulePredictiveVars() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:241:2: ( ( ( rule__PredictiveVars__Group__0 ) ) ) // InternalMLRegression.g:242:2: ( ( rule__PredictiveVars__Group__0 ) ) { // InternalMLRegression.g:242:2: ( ( rule__PredictiveVars__Group__0 ) ) // InternalMLRegression.g:243:3: ( rule__PredictiveVars__Group__0 ) { before(grammarAccess.getPredictiveVarsAccess().getGroup()); // InternalMLRegression.g:244:3: ( rule__PredictiveVars__Group__0 ) // InternalMLRegression.g:244:4: rule__PredictiveVars__Group__0 { pushFollow(FOLLOW_2); rule__PredictiveVars__Group__0(); state._fsp--; } after(grammarAccess.getPredictiveVarsAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__PredictiveVars__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1369:1: ( ( 'predictive_vars' ) )\n // InternalMLRegression.g:1370:1: ( 'predictive_vars' )\n ...
[ "0.8053743", "0.75869113", "0.7283372", "0.72654295", "0.7225559", "0.71596533", "0.70744", "0.7064841", "0.7057372", "0.70174736", "0.69267994", "0.6891757", "0.68481636", "0.67278963", "0.6718934", "0.6471797", "0.64163935", "0.63904625", "0.6249815", "0.6158678", "0.615470...
0.8464002
0
$ANTLR end "rulePredictiveVars" $ANTLR start "entryRuleTargetVars" InternalMLRegression.g:253:1: entryRuleTargetVars : ruleTargetVars EOF ;
public final void entryRuleTargetVars() throws RecognitionException { try { // InternalMLRegression.g:254:1: ( ruleTargetVars EOF ) // InternalMLRegression.g:255:1: ruleTargetVars EOF { before(grammarAccess.getTargetVarsRule()); pushFollow(FOLLOW_1); ruleTargetVars(); state._fsp--; after(grammarAccess.getTargetVarsRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleTargetVars() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleTargetVars = null;\n\n\n try {\n // InternalMLRegression.g:581:51: (iv_ruleTargetVars= ruleTargetVars EOF )\n // InternalMLRegression.g:582:2: iv_ruleTarg...
[ "0.7668108", "0.692469", "0.67506176", "0.66497135", "0.6610868", "0.6501131", "0.6430666", "0.61820227", "0.604241", "0.602079", "0.60013103", "0.59122807", "0.5722731", "0.56729126", "0.5664849", "0.5637243", "0.55836517", "0.55374444", "0.5436087", "0.54264486", "0.5399071...
0.79441404
0
$ANTLR end "entryRuleTargetVars" $ANTLR start "ruleTargetVars" InternalMLRegression.g:262:1: ruleTargetVars : ( ( rule__TargetVars__Group__0 ) ) ;
public final void ruleTargetVars() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:266:2: ( ( ( rule__TargetVars__Group__0 ) ) ) // InternalMLRegression.g:267:2: ( ( rule__TargetVars__Group__0 ) ) { // InternalMLRegression.g:267:2: ( ( rule__TargetVars__Group__0 ) ) // InternalMLRegression.g:268:3: ( rule__TargetVars__Group__0 ) { before(grammarAccess.getTargetVarsAccess().getGroup()); // InternalMLRegression.g:269:3: ( rule__TargetVars__Group__0 ) // InternalMLRegression.g:269:4: rule__TargetVars__Group__0 { pushFollow(FOLLOW_2); rule__TargetVars__Group__0(); state._fsp--; } after(grammarAccess.getTargetVarsAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__TargetVars__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1558:1: ( ( 'target_vars' ) )\n // InternalMLRegression.g:1559:1: ( 'target_vars' )\n {\n ...
[ "0.79509187", "0.7240503", "0.723015", "0.722664", "0.7200508", "0.71616465", "0.71475416", "0.7097462", "0.7049724", "0.69798625", "0.69423145", "0.67302793", "0.66209686", "0.65924025", "0.6501671", "0.6474313", "0.63267136", "0.6132994", "0.61204", "0.6120005", "0.6066058"...
0.8169772
0
$ANTLR end "ruleTargetVars" $ANTLR start "entryRuleEvaluationType" InternalMLRegression.g:278:1: entryRuleEvaluationType : ruleEvaluationType EOF ;
public final void entryRuleEvaluationType() throws RecognitionException { try { // InternalMLRegression.g:279:1: ( ruleEvaluationType EOF ) // InternalMLRegression.g:280:1: ruleEvaluationType EOF { before(grammarAccess.getEvaluationTypeRule()); pushFollow(FOLLOW_1); ruleEvaluationType(); state._fsp--; after(grammarAccess.getEvaluationTypeRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleEvaluationType() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleEvaluationType = null;\n\n\n try {\n // InternalMLRegression.g:654:55: (iv_ruleEvaluationType= ruleEvaluationType EOF )\n // InternalMLRegression.g:65...
[ "0.6625665", "0.5969083", "0.5953257", "0.5835401", "0.5811228", "0.5665571", "0.5562806", "0.55024856", "0.54650515", "0.54242307", "0.5390696", "0.53476316", "0.5330064", "0.5318333", "0.5255895", "0.52236205", "0.51675427", "0.5134406", "0.5129966", "0.5101722", "0.5038642...
0.69955015
0
$ANTLR end "entryRuleEvaluationType" $ANTLR start "ruleEvaluationType" InternalMLRegression.g:287:1: ruleEvaluationType : ( ( rule__EvaluationType__Alternatives ) ) ;
public final void ruleEvaluationType() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:291:2: ( ( ( rule__EvaluationType__Alternatives ) ) ) // InternalMLRegression.g:292:2: ( ( rule__EvaluationType__Alternatives ) ) { // InternalMLRegression.g:292:2: ( ( rule__EvaluationType__Alternatives ) ) // InternalMLRegression.g:293:3: ( rule__EvaluationType__Alternatives ) { before(grammarAccess.getEvaluationTypeAccess().getAlternatives()); // InternalMLRegression.g:294:3: ( rule__EvaluationType__Alternatives ) // InternalMLRegression.g:294:4: rule__EvaluationType__Alternatives { pushFollow(FOLLOW_2); rule__EvaluationType__Alternatives(); state._fsp--; } after(grammarAccess.getEvaluationTypeAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void entryRuleEvaluationType() throws RecognitionException {\n try {\n // InternalMLRegression.g:279:1: ( ruleEvaluationType EOF )\n // InternalMLRegression.g:280:1: ruleEvaluationType EOF\n {\n before(grammarAccess.getEvaluationTypeRule()); \n ...
[ "0.6822176", "0.67724186", "0.6273402", "0.62098765", "0.62031156", "0.6016017", "0.59476364", "0.585942", "0.5804181", "0.579961", "0.5738266", "0.57325584", "0.5669747", "0.5630158", "0.5434645", "0.5407315", "0.5351421", "0.53366446", "0.5244575", "0.522013", "0.52127653",...
0.83431524
0
$ANTLR end "ruleEvaluationType" $ANTLR start "entryRulePartition" InternalMLRegression.g:303:1: entryRulePartition : rulePartition EOF ;
public final void entryRulePartition() throws RecognitionException { try { // InternalMLRegression.g:304:1: ( rulePartition EOF ) // InternalMLRegression.g:305:1: rulePartition EOF { before(grammarAccess.getPartitionRule()); pushFollow(FOLLOW_1); rulePartition(); state._fsp--; after(grammarAccess.getPartitionRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRulePartition() throws RecognitionException {\n EObject current = null;\n\n EObject iv_rulePartition = null;\n\n\n try {\n // InternalMLRegression.g:690:50: (iv_rulePartition= rulePartition EOF )\n // InternalMLRegression.g:691:2: iv_rulePartitio...
[ "0.7413722", "0.6349081", "0.6231775", "0.6135619", "0.5806357", "0.5805788", "0.57498026", "0.5495496", "0.52538955", "0.5011783", "0.49855947", "0.4980955", "0.49666405", "0.49607742", "0.49448043", "0.49438572", "0.49437857", "0.4922767", "0.489395", "0.48879266", "0.48492...
0.75709933
0
$ANTLR end "entryRulePartition" $ANTLR start "rulePartition" InternalMLRegression.g:312:1: rulePartition : ( ( rule__Partition__Group__0 ) ) ;
public final void rulePartition() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:316:2: ( ( ( rule__Partition__Group__0 ) ) ) // InternalMLRegression.g:317:2: ( ( rule__Partition__Group__0 ) ) { // InternalMLRegression.g:317:2: ( ( rule__Partition__Group__0 ) ) // InternalMLRegression.g:318:3: ( rule__Partition__Group__0 ) { before(grammarAccess.getPartitionAccess().getGroup()); // InternalMLRegression.g:319:3: ( rule__Partition__Group__0 ) // InternalMLRegression.g:319:4: rule__Partition__Group__0 { pushFollow(FOLLOW_2); rule__Partition__Group__0(); state._fsp--; } after(grammarAccess.getPartitionAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Partition__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1747:1: ( ( 'partition' ) )\n // InternalMLRegression.g:1748:1: ( 'partition' )\n {\n ...
[ "0.7902956", "0.754186", "0.7401431", "0.682246", "0.67231023", "0.66708124", "0.6455272", "0.62839127", "0.62820333", "0.623313", "0.5973844", "0.58198726", "0.57409185", "0.56854004", "0.56627125", "0.56471", "0.5581849", "0.5480904", "0.54087883", "0.5380615", "0.5362322",...
0.8364447
0
$ANTLR end "rulePartition" $ANTLR start "entryRuleCrossValidation" InternalMLRegression.g:328:1: entryRuleCrossValidation : ruleCrossValidation EOF ;
public final void entryRuleCrossValidation() throws RecognitionException { try { // InternalMLRegression.g:329:1: ( ruleCrossValidation EOF ) // InternalMLRegression.g:330:1: ruleCrossValidation EOF { before(grammarAccess.getCrossValidationRule()); pushFollow(FOLLOW_1); ruleCrossValidation(); state._fsp--; after(grammarAccess.getCrossValidationRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleCrossValidation() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleCrossValidation = null;\n\n\n try {\n // InternalMLRegression.g:740:56: (iv_ruleCrossValidation= ruleCrossValidation EOF )\n // InternalMLRegression....
[ "0.70561916", "0.6869471", "0.6854924", "0.64964235", "0.60767597", "0.5998238", "0.59054214", "0.58768153", "0.5724189", "0.54258233", "0.529924", "0.5276916", "0.5036944", "0.49913517", "0.47635198", "0.47013056", "0.4692615", "0.46811232", "0.4680581", "0.46514943", "0.462...
0.70170957
1
$ANTLR end "entryRuleCrossValidation" $ANTLR start "ruleCrossValidation" InternalMLRegression.g:337:1: ruleCrossValidation : ( ( rule__CrossValidation__Group__0 ) ) ;
public final void ruleCrossValidation() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:341:2: ( ( ( rule__CrossValidation__Group__0 ) ) ) // InternalMLRegression.g:342:2: ( ( rule__CrossValidation__Group__0 ) ) { // InternalMLRegression.g:342:2: ( ( rule__CrossValidation__Group__0 ) ) // InternalMLRegression.g:343:3: ( rule__CrossValidation__Group__0 ) { before(grammarAccess.getCrossValidationAccess().getGroup()); // InternalMLRegression.g:344:3: ( rule__CrossValidation__Group__0 ) // InternalMLRegression.g:344:4: rule__CrossValidation__Group__0 { pushFollow(FOLLOW_2); rule__CrossValidation__Group__0(); state._fsp--; } after(grammarAccess.getCrossValidationAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__CrossValidation__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1855:1: ( ( 'cross_validation' ) )\n // InternalMLRegression.g:1856:1: ( 'cross_validation' )\n ...
[ "0.8298512", "0.7179475", "0.6838755", "0.68177384", "0.6703063", "0.66267675", "0.64632756", "0.6415243", "0.6413444", "0.63311625", "0.60977244", "0.57887554", "0.571342", "0.5388575", "0.5364205", "0.52780235", "0.52324396", "0.51766026", "0.51185066", "0.48187438", "0.479...
0.84594786
0
$ANTLR end "ruleCrossValidation" $ANTLR start "entryRuleAlgo" InternalMLRegression.g:353:1: entryRuleAlgo : ruleAlgo EOF ;
public final void entryRuleAlgo() throws RecognitionException { try { // InternalMLRegression.g:354:1: ( ruleAlgo EOF ) // InternalMLRegression.g:355:1: ruleAlgo EOF { before(grammarAccess.getAlgoRule()); pushFollow(FOLLOW_1); ruleAlgo(); state._fsp--; after(grammarAccess.getAlgoRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleAlgo() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleAlgo = null;\n\n\n try {\n // InternalMLRegression.g:789:45: (iv_ruleAlgo= ruleAlgo EOF )\n // InternalMLRegression.g:790:2: iv_ruleAlgo= ruleAlgo EOF\n ...
[ "0.68815917", "0.62172204", "0.57837737", "0.57403004", "0.56680906", "0.56253046", "0.5515127", "0.5508255", "0.5463105", "0.5388049", "0.53714633", "0.53392607", "0.5321764", "0.53038466", "0.52682096", "0.5200674", "0.519959", "0.5192437", "0.5177702", "0.5162446", "0.5151...
0.69802177
0
$ANTLR end "entryRuleAlgo" $ANTLR start "ruleAlgo" InternalMLRegression.g:362:1: ruleAlgo : ( ( rule__Algo__Group__0 ) ) ;
public final void ruleAlgo() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:366:2: ( ( ( rule__Algo__Group__0 ) ) ) // InternalMLRegression.g:367:2: ( ( rule__Algo__Group__0 ) ) { // InternalMLRegression.g:367:2: ( ( rule__Algo__Group__0 ) ) // InternalMLRegression.g:368:3: ( rule__Algo__Group__0 ) { before(grammarAccess.getAlgoAccess().getGroup()); // InternalMLRegression.g:369:3: ( rule__Algo__Group__0 ) // InternalMLRegression.g:369:4: rule__Algo__Group__0 { pushFollow(FOLLOW_2); rule__Algo__Group__0(); state._fsp--; } after(grammarAccess.getAlgoAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Algo__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1963:1: ( ( 'algorithm' ) )\n // InternalMLRegression.g:1964:1: ( 'algorithm' )\n {\n // In...
[ "0.7124245", "0.7107976", "0.6772767", "0.6727121", "0.6681439", "0.664913", "0.6413912", "0.6372714", "0.63000584", "0.62490916", "0.61990136", "0.5947407", "0.5818457", "0.5788493", "0.5706455", "0.57055616", "0.5678292", "0.5657835", "0.55971706", "0.5548743", "0.5516204",...
0.81922096
0
$ANTLR end "ruleAlgo" $ANTLR start "entryRuleAlgoType" InternalMLRegression.g:378:1: entryRuleAlgoType : ruleAlgoType EOF ;
public final void entryRuleAlgoType() throws RecognitionException { try { // InternalMLRegression.g:379:1: ( ruleAlgoType EOF ) // InternalMLRegression.g:380:1: ruleAlgoType EOF { before(grammarAccess.getAlgoTypeRule()); pushFollow(FOLLOW_1); ruleAlgoType(); state._fsp--; after(grammarAccess.getAlgoTypeRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleAlgoType() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleAlgoType = null;\n\n\n try {\n // InternalMLRegression.g:839:48: (iv_ruleAlgoType= ruleAlgoType EOF )\n // InternalMLRegression.g:840:2: iv_rul...
[ "0.73483455", "0.68876183", "0.6703655", "0.6612986", "0.6286057", "0.6126308", "0.60427403", "0.5991033", "0.5926531", "0.5906325", "0.58987314", "0.5886976", "0.57723594", "0.5640993", "0.56087255", "0.5606679", "0.5577799", "0.5570324", "0.5543356", "0.55311954", "0.536118...
0.78028023
0
$ANTLR end "entryRuleAlgoType" $ANTLR start "ruleAlgoType" InternalMLRegression.g:387:1: ruleAlgoType : ( ( rule__AlgoType__Alternatives ) ) ;
public final void ruleAlgoType() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:391:2: ( ( ( rule__AlgoType__Alternatives ) ) ) // InternalMLRegression.g:392:2: ( ( rule__AlgoType__Alternatives ) ) { // InternalMLRegression.g:392:2: ( ( rule__AlgoType__Alternatives ) ) // InternalMLRegression.g:393:3: ( rule__AlgoType__Alternatives ) { before(grammarAccess.getAlgoTypeAccess().getAlternatives()); // InternalMLRegression.g:394:3: ( rule__AlgoType__Alternatives ) // InternalMLRegression.g:394:4: rule__AlgoType__Alternatives { pushFollow(FOLLOW_2); rule__AlgoType__Alternatives(); state._fsp--; } after(grammarAccess.getAlgoTypeAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void entryRuleAlgoType() throws RecognitionException {\n try {\n // InternalMLRegression.g:379:1: ( ruleAlgoType EOF )\n // InternalMLRegression.g:380:1: ruleAlgoType EOF\n {\n before(grammarAccess.getAlgoTypeRule()); \n pushFollow(FOLLOW_...
[ "0.7060035", "0.70093536", "0.69996893", "0.6975625", "0.6969997", "0.67955995", "0.66181386", "0.66031563", "0.6510749", "0.6504501", "0.57622486", "0.5720246", "0.5685743", "0.5668664", "0.56383157", "0.55850303", "0.5562076", "0.5548115", "0.55368173", "0.55336434", "0.552...
0.83442
0
$ANTLR end "ruleAlgoType" $ANTLR start "entryRuleLineRegress" InternalMLRegression.g:403:1: entryRuleLineRegress : ruleLineRegress EOF ;
public final void entryRuleLineRegress() throws RecognitionException { try { // InternalMLRegression.g:404:1: ( ruleLineRegress EOF ) // InternalMLRegression.g:405:1: ruleLineRegress EOF { before(grammarAccess.getLineRegressRule()); pushFollow(FOLLOW_1); ruleLineRegress(); state._fsp--; after(grammarAccess.getLineRegressRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleLineRegress() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleLineRegress = null;\n\n\n try {\n // InternalMLRegression.g:890:51: (iv_ruleLineRegress= ruleLineRegress EOF )\n // InternalMLRegression.g:8...
[ "0.6481305", "0.6279911", "0.602157", "0.5882086", "0.58381784", "0.5482317", "0.5226623", "0.51974523", "0.50909203", "0.50746346", "0.506146", "0.50552183", "0.50406915", "0.50233907", "0.5023139", "0.49468854", "0.49448422", "0.4933638", "0.48563063", "0.47971213", "0.4791...
0.64738137
1
$ANTLR end "entryRuleLineRegress" $ANTLR start "ruleLineRegress" InternalMLRegression.g:412:1: ruleLineRegress : ( 'line_regress' ) ;
public final void ruleLineRegress() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:416:2: ( ( 'line_regress' ) ) // InternalMLRegression.g:417:2: ( 'line_regress' ) { // InternalMLRegression.g:417:2: ( 'line_regress' ) // InternalMLRegression.g:418:3: 'line_regress' { before(grammarAccess.getLineRegressAccess().getLine_regressKeyword()); match(input,11,FOLLOW_2); after(grammarAccess.getLineRegressAccess().getLine_regressKeyword()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final AntlrDatatypeRuleToken ruleLineRegress() throws RecognitionException {\n AntlrDatatypeRuleToken current = new AntlrDatatypeRuleToken();\n\n Token kw=null;\n\n\n \tenterRule();\n\n try {\n // InternalMLRegression.g:903:2: (kw= 'line_regress' )\n // Inte...
[ "0.8242222", "0.78830725", "0.7579758", "0.5737213", "0.56821823", "0.5490655", "0.5405261", "0.536997", "0.5269022", "0.52584064", "0.5240587", "0.52164894", "0.51837397", "0.5159728", "0.51424724", "0.5130868", "0.5080035", "0.5074068", "0.50528795", "0.5041795", "0.5037585...
0.8615656
0
$ANTLR end "ruleLineRegress" $ANTLR start "entryRuleDecisionTreeRegressor" InternalMLRegression.g:428:1: entryRuleDecisionTreeRegressor : ruleDecisionTreeRegressor EOF ;
public final void entryRuleDecisionTreeRegressor() throws RecognitionException { try { // InternalMLRegression.g:429:1: ( ruleDecisionTreeRegressor EOF ) // InternalMLRegression.g:430:1: ruleDecisionTreeRegressor EOF { before(grammarAccess.getDecisionTreeRegressorRule()); pushFollow(FOLLOW_1); ruleDecisionTreeRegressor(); state._fsp--; after(grammarAccess.getDecisionTreeRegressorRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleDecisionTreeRegressor() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleDecisionTreeRegressor = null;\n\n\n try {\n // InternalMLRegression.g:912:61: (iv_ruleDecisionTreeRegressor= ruleDecisionTreeRegressor EOF )\...
[ "0.68527675", "0.6591292", "0.57647175", "0.55403405", "0.5520893", "0.5146758", "0.51438063", "0.5136873", "0.5091237", "0.4913817", "0.49062142", "0.47884867", "0.46802577", "0.4636374", "0.46119383", "0.46008456", "0.45943305", "0.44677007", "0.44666493", "0.44473913", "0....
0.7263938
0
$ANTLR end "entryRuleDecisionTreeRegressor" $ANTLR start "ruleDecisionTreeRegressor" InternalMLRegression.g:437:1: ruleDecisionTreeRegressor : ( 'decision_tree_regressor' ) ;
public final void ruleDecisionTreeRegressor() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:441:2: ( ( 'decision_tree_regressor' ) ) // InternalMLRegression.g:442:2: ( 'decision_tree_regressor' ) { // InternalMLRegression.g:442:2: ( 'decision_tree_regressor' ) // InternalMLRegression.g:443:3: 'decision_tree_regressor' { before(grammarAccess.getDecisionTreeRegressorAccess().getDecision_tree_regressorKeyword()); match(input,12,FOLLOW_2); after(grammarAccess.getDecisionTreeRegressorAccess().getDecision_tree_regressorKeyword()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final AntlrDatatypeRuleToken ruleDecisionTreeRegressor() throws RecognitionException {\n AntlrDatatypeRuleToken current = new AntlrDatatypeRuleToken();\n\n Token kw=null;\n\n\n \tenterRule();\n\n try {\n // InternalMLRegression.g:925:2: (kw= 'decision_tree_regressor' )...
[ "0.76784205", "0.73522556", "0.72883934", "0.47932783", "0.47068965", "0.46616206", "0.4557975", "0.45532244", "0.438191", "0.4374963", "0.43466592", "0.42620236", "0.42495283", "0.42441866", "0.42405236", "0.42271918", "0.42160305", "0.41969785", "0.41772228", "0.41745928", ...
0.83046436
0
$ANTLR end "ruleDecisionTreeRegressor" $ANTLR start "entryRuleSVR" InternalMLRegression.g:453:1: entryRuleSVR : ruleSVR EOF ;
public final void entryRuleSVR() throws RecognitionException { try { // InternalMLRegression.g:454:1: ( ruleSVR EOF ) // InternalMLRegression.g:455:1: ruleSVR EOF { before(grammarAccess.getSVRRule()); pushFollow(FOLLOW_1); ruleSVR(); state._fsp--; after(grammarAccess.getSVRRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleSVR() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleSVR = null;\n\n\n try {\n // InternalMLRegression.g:934:43: (iv_ruleSVR= ruleSVR EOF )\n // InternalMLRegression.g:935:2: iv_ruleSVR= ruleSVR EOF\n ...
[ "0.7374006", "0.66045684", "0.60924786", "0.5920116", "0.5901736", "0.58842784", "0.56773984", "0.54535645", "0.5136863", "0.49675044", "0.47480392", "0.47000918", "0.46839613", "0.4652247", "0.45745137", "0.45396027", "0.45181572", "0.4496735", "0.4479353", "0.44651625", "0....
0.7349213
1
$ANTLR end "entryRuleSVR" $ANTLR start "ruleSVR" InternalMLRegression.g:462:1: ruleSVR : ( 'svr' ) ;
public final void ruleSVR() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:466:2: ( ( 'svr' ) ) // InternalMLRegression.g:467:2: ( 'svr' ) { // InternalMLRegression.g:467:2: ( 'svr' ) // InternalMLRegression.g:468:3: 'svr' { before(grammarAccess.getSVRAccess().getSvrKeyword()); match(input,13,FOLLOW_2); after(grammarAccess.getSVRAccess().getSvrKeyword()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final AntlrDatatypeRuleToken ruleSVR() throws RecognitionException {\n AntlrDatatypeRuleToken current = new AntlrDatatypeRuleToken();\n\n Token kw=null;\n\n\n \tenterRule();\n\n try {\n // InternalMLRegression.g:947:2: (kw= 'svr' )\n // InternalMLRegression....
[ "0.7248523", "0.7129581", "0.6707051", "0.52503705", "0.519012", "0.5009313", "0.5004937", "0.49705067", "0.49273306", "0.4840363", "0.48257354", "0.48029938", "0.47421762", "0.46277753", "0.45861644", "0.45215443", "0.45174116", "0.4512112", "0.45090142", "0.44907367", "0.44...
0.8441779
0
$ANTLR end "ruleSVR" $ANTLR start "entryRuleCalculate" InternalMLRegression.g:478:1: entryRuleCalculate : ruleCalculate EOF ;
public final void entryRuleCalculate() throws RecognitionException { try { // InternalMLRegression.g:479:1: ( ruleCalculate EOF ) // InternalMLRegression.g:480:1: ruleCalculate EOF { before(grammarAccess.getCalculateRule()); pushFollow(FOLLOW_1); ruleCalculate(); state._fsp--; after(grammarAccess.getCalculateRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleCalculate() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleCalculate = null;\n\n\n try {\n // InternalMLRegression.g:956:50: (iv_ruleCalculate= ruleCalculate EOF )\n // InternalMLRegression.g:957:2: iv_ruleCalculat...
[ "0.6479293", "0.599796", "0.595012", "0.5787971", "0.56070584", "0.5553204", "0.54211557", "0.53889334", "0.5343996", "0.5288166", "0.5283801", "0.52305275", "0.52093416", "0.5208122", "0.5167805", "0.51344246", "0.51163197", "0.5115273", "0.5053559", "0.4998459", "0.49793574...
0.61579424
1
$ANTLR end "entryRuleCalculate" $ANTLR start "ruleCalculate" InternalMLRegression.g:487:1: ruleCalculate : ( ( rule__Calculate__Group__0 ) ) ;
public final void ruleCalculate() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:491:2: ( ( ( rule__Calculate__Group__0 ) ) ) // InternalMLRegression.g:492:2: ( ( rule__Calculate__Group__0 ) ) { // InternalMLRegression.g:492:2: ( ( rule__Calculate__Group__0 ) ) // InternalMLRegression.g:493:3: ( rule__Calculate__Group__0 ) { before(grammarAccess.getCalculateAccess().getGroup()); // InternalMLRegression.g:494:3: ( rule__Calculate__Group__0 ) // InternalMLRegression.g:494:4: rule__Calculate__Group__0 { pushFollow(FOLLOW_2); rule__Calculate__Group__0(); state._fsp--; } after(grammarAccess.getCalculateAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Calculate__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:2071:1: ( ( 'calculate' ) )\n // InternalMLRegression.g:2072:1: ( 'calculate' )\n {\n ...
[ "0.7898905", "0.7197237", "0.6703953", "0.6509508", "0.63799715", "0.62972766", "0.61529094", "0.606953", "0.59505653", "0.5903239", "0.5859008", "0.5829676", "0.5508381", "0.54734915", "0.54091257", "0.5384055", "0.53618896", "0.53518134", "0.53384256", "0.5323548", "0.53133...
0.83648866
0
$ANTLR end "ruleCalculate" $ANTLR start "entryRuleCalculateType" InternalMLRegression.g:503:1: entryRuleCalculateType : ruleCalculateType EOF ;
public final void entryRuleCalculateType() throws RecognitionException { try { // InternalMLRegression.g:504:1: ( ruleCalculateType EOF ) // InternalMLRegression.g:505:1: ruleCalculateType EOF { before(grammarAccess.getCalculateTypeRule()); pushFollow(FOLLOW_1); ruleCalculateType(); state._fsp--; after(grammarAccess.getCalculateTypeRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final String entryRuleCalculateType() throws RecognitionException {\n String current = null;\n\n AntlrDatatypeRuleToken iv_ruleCalculateType = null;\n\n\n try {\n // InternalMLRegression.g:1006:53: (iv_ruleCalculateType= ruleCalculateType EOF )\n // InternalMLRegre...
[ "0.75183725", "0.6925556", "0.6422454", "0.6096929", "0.60601187", "0.5756052", "0.5712507", "0.56594193", "0.5638665", "0.5637758", "0.53746253", "0.5312434", "0.5277968", "0.51767534", "0.5130788", "0.51087826", "0.50562644", "0.5023974", "0.5013968", "0.5013703", "0.501061...
0.75438434
0
$ANTLR end "entryRuleCalculateType" $ANTLR start "ruleCalculateType" InternalMLRegression.g:512:1: ruleCalculateType : ( ( rule__CalculateType__Alternatives ) ) ;
public final void ruleCalculateType() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:516:2: ( ( ( rule__CalculateType__Alternatives ) ) ) // InternalMLRegression.g:517:2: ( ( rule__CalculateType__Alternatives ) ) { // InternalMLRegression.g:517:2: ( ( rule__CalculateType__Alternatives ) ) // InternalMLRegression.g:518:3: ( rule__CalculateType__Alternatives ) { before(grammarAccess.getCalculateTypeAccess().getAlternatives()); // InternalMLRegression.g:519:3: ( rule__CalculateType__Alternatives ) // InternalMLRegression.g:519:4: rule__CalculateType__Alternatives { pushFollow(FOLLOW_2); rule__CalculateType__Alternatives(); state._fsp--; } after(grammarAccess.getCalculateTypeAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject ruleCalculate() throws RecognitionException {\n EObject current = null;\n\n Token otherlv_0=null;\n Token otherlv_1=null;\n Token otherlv_3=null;\n AntlrDatatypeRuleToken lv_calculateType_2_0 = null;\n\n\n\n \tenterRule();\n\n try {\n ...
[ "0.65204245", "0.64004683", "0.6299789", "0.61529773", "0.60712415", "0.602425", "0.59144074", "0.58420235", "0.5697832", "0.5664529", "0.5365385", "0.53517205", "0.5065522", "0.5006457", "0.4939629", "0.49291024", "0.49036577", "0.49011743", "0.48878503", "0.4886026", "0.487...
0.8282066
0
$ANTLR end "entryRuleLoop" $ANTLR start "ruleLoop" InternalMLRegression.g:612:1: ruleLoop : ( ( rule__Loop__Group__0 ) ) ;
public final void ruleLoop() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:616:2: ( ( ( rule__Loop__Group__0 ) ) ) // InternalMLRegression.g:617:2: ( ( rule__Loop__Group__0 ) ) { // InternalMLRegression.g:617:2: ( ( rule__Loop__Group__0 ) ) // InternalMLRegression.g:618:3: ( rule__Loop__Group__0 ) { before(grammarAccess.getLoopAccess().getGroup()); // InternalMLRegression.g:619:3: ( rule__Loop__Group__0 ) // InternalMLRegression.g:619:4: rule__Loop__Group__0 { pushFollow(FOLLOW_2); rule__Loop__Group__0(); state._fsp--; } after(grammarAccess.getLoopAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Loop__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:2179:1: ( ( 'loop' ) )\n // InternalMLRegression.g:2180:1: ( 'loop' )\n {\n // InternalMLRe...
[ "0.77974576", "0.7461916", "0.72465086", "0.69855314", "0.6925043", "0.69005746", "0.6855882", "0.6589313", "0.64591527", "0.64252305", "0.6264844", "0.6252207", "0.6192814", "0.6181534", "0.61722744", "0.6168571", "0.6134339", "0.61330336", "0.6132686", "0.607596", "0.601804...
0.83775115
0
$ANTLR end "ruleLoop" $ANTLR start "entryRuleNumericValue" InternalMLRegression.g:628:1: entryRuleNumericValue : ruleNumericValue EOF ;
public final void entryRuleNumericValue() throws RecognitionException { try { // InternalMLRegression.g:629:1: ( ruleNumericValue EOF ) // InternalMLRegression.g:630:1: ruleNumericValue EOF { before(grammarAccess.getNumericValueRule()); pushFollow(FOLLOW_1); ruleNumericValue(); state._fsp--; after(grammarAccess.getNumericValueRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject entryRuleNumericValue() throws RecognitionException {\n EObject current = null;\n\n EObject iv_ruleNumericValue = null;\n\n\n try {\n // InternalMLRegression.g:1172:53: (iv_ruleNumericValue= ruleNumericValue EOF )\n // InternalMLRegression.g:1173:2: i...
[ "0.6812826", "0.6012342", "0.5658004", "0.5606461", "0.5606187", "0.5304891", "0.5269358", "0.522566", "0.5188507", "0.5145282", "0.51344156", "0.505918", "0.5042701", "0.5041811", "0.4952896", "0.4952273", "0.4904288", "0.489387", "0.48932028", "0.48907706", "0.48836806", ...
0.717073
0
$ANTLR end "entryRuleNumericValue" $ANTLR start "ruleNumericValue" InternalMLRegression.g:637:1: ruleNumericValue : ( ( rule__NumericValue__Alternatives ) ) ;
public final void ruleNumericValue() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:641:2: ( ( ( rule__NumericValue__Alternatives ) ) ) // InternalMLRegression.g:642:2: ( ( rule__NumericValue__Alternatives ) ) { // InternalMLRegression.g:642:2: ( ( rule__NumericValue__Alternatives ) ) // InternalMLRegression.g:643:3: ( rule__NumericValue__Alternatives ) { before(grammarAccess.getNumericValueAccess().getAlternatives()); // InternalMLRegression.g:644:3: ( rule__NumericValue__Alternatives ) // InternalMLRegression.g:644:4: rule__NumericValue__Alternatives { pushFollow(FOLLOW_2); rule__NumericValue__Alternatives(); state._fsp--; } after(grammarAccess.getNumericValueAccess().getAlternatives()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__NumericValue__Alternatives() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:850:1: ( ( ruleFLOAT ) | ( rulePERCENT ) )\n int alt7=2;\n int LA7_0 = input.LA(1);\n\n ...
[ "0.6444774", "0.6159567", "0.6072338", "0.5997395", "0.5966426", "0.59201133", "0.5812876", "0.57638264", "0.56609255", "0.56004083", "0.55317914", "0.5511758", "0.5448007", "0.53068846", "0.53031135", "0.52614546", "0.5257098", "0.51760167", "0.49947295", "0.49710086", "0.49...
0.8458363
0
$ANTLR end "ruleNumericValue" $ANTLR start "entryRuleFLOAT" InternalMLRegression.g:653:1: entryRuleFLOAT : ruleFLOAT EOF ;
public final void entryRuleFLOAT() throws RecognitionException { try { // InternalMLRegression.g:654:1: ( ruleFLOAT EOF ) // InternalMLRegression.g:655:1: ruleFLOAT EOF { before(grammarAccess.getFLOATRule()); pushFollow(FOLLOW_1); ruleFLOAT(); state._fsp--; after(grammarAccess.getFLOATRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void mFLOAT() throws RecognitionException {\r\n try {\r\n final int _type = SparqlMarcoLexer.FLOAT;\r\n final int _channel = BaseRecognizer.DEFAULT_TOKEN_CHANNEL;\r\n // /Users/Marco/Desktop/Tesi Davide/Antlr-doc/SparqlMarco.g:58:5: ( ( '0' .. '9' )+\r\n // '.' ( '...
[ "0.7131825", "0.6972036", "0.6871305", "0.6837618", "0.67219543", "0.67104787", "0.6630395", "0.65980965", "0.6557837", "0.6488259", "0.64520717", "0.634238", "0.63331145", "0.63267326", "0.6308031", "0.62541604", "0.61973786", "0.61023104", "0.6068311", "0.6064321", "0.60329...
0.73782253
0
$ANTLR end "entryRuleFLOAT" $ANTLR start "ruleFLOAT" InternalMLRegression.g:662:1: ruleFLOAT : ( ( rule__FLOAT__Group__0 ) ) ;
public final void ruleFLOAT() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:666:2: ( ( ( rule__FLOAT__Group__0 ) ) ) // InternalMLRegression.g:667:2: ( ( rule__FLOAT__Group__0 ) ) { // InternalMLRegression.g:667:2: ( ( rule__FLOAT__Group__0 ) ) // InternalMLRegression.g:668:3: ( rule__FLOAT__Group__0 ) { before(grammarAccess.getFLOATAccess().getGroup()); // InternalMLRegression.g:669:3: ( rule__FLOAT__Group__0 ) // InternalMLRegression.g:669:4: rule__FLOAT__Group__0 { pushFollow(FOLLOW_2); rule__FLOAT__Group__0(); state._fsp--; } after(grammarAccess.getFLOATAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void entryRuleFLOAT() throws RecognitionException {\n try {\n // InternalMLRegression.g:654:1: ( ruleFLOAT EOF )\n // InternalMLRegression.g:655:1: ruleFLOAT EOF\n {\n before(grammarAccess.getFLOATRule()); \n pushFollow(FOLLOW_1);\n ...
[ "0.7254078", "0.70942754", "0.6996342", "0.67510533", "0.6746282", "0.66829854", "0.6552675", "0.65184784", "0.6500281", "0.6481529", "0.6424635", "0.64243674", "0.6350376", "0.63493145", "0.6301378", "0.62908924", "0.62231165", "0.6193654", "0.61915183", "0.6151297", "0.6049...
0.7841792
0
$ANTLR end "ruleFLOAT" $ANTLR start "entryRulePERCENT" InternalMLRegression.g:678:1: entryRulePERCENT : rulePERCENT EOF ;
public final void entryRulePERCENT() throws RecognitionException { try { // InternalMLRegression.g:679:1: ( rulePERCENT EOF ) // InternalMLRegression.g:680:1: rulePERCENT EOF { before(grammarAccess.getPERCENTRule()); pushFollow(FOLLOW_1); rulePERCENT(); state._fsp--; after(grammarAccess.getPERCENTRule()); match(input,EOF,FOLLOW_2); } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final EObject rulePERCENT() throws RecognitionException {\n EObject current = null;\n\n Token otherlv_1=null;\n EObject lv_float_0_0 = null;\n\n\n\n \tenterRule();\n\n try {\n // InternalMLRegression.g:1282:2: ( ( ( (lv_float_0_0= ruleFLOAT ) ) otherlv_1= '%' ) ...
[ "0.75961375", "0.71488154", "0.68040484", "0.6408249", "0.6285421", "0.62127924", "0.6176568", "0.5845706", "0.58391535", "0.5529126", "0.5489262", "0.53295285", "0.53150463", "0.5163149", "0.51130444", "0.50750196", "0.50225234", "0.5017323", "0.5013745", "0.4978643", "0.496...
0.7160498
1
$ANTLR end "entryRulePERCENT" $ANTLR start "rulePERCENT" InternalMLRegression.g:687:1: rulePERCENT : ( ( rule__PERCENT__Group__0 ) ) ;
public final void rulePERCENT() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:691:2: ( ( ( rule__PERCENT__Group__0 ) ) ) // InternalMLRegression.g:692:2: ( ( rule__PERCENT__Group__0 ) ) { // InternalMLRegression.g:692:2: ( ( rule__PERCENT__Group__0 ) ) // InternalMLRegression.g:693:3: ( rule__PERCENT__Group__0 ) { before(grammarAccess.getPERCENTAccess().getGroup()); // InternalMLRegression.g:694:3: ( rule__PERCENT__Group__0 ) // InternalMLRegression.g:694:4: rule__PERCENT__Group__0 { pushFollow(FOLLOW_2); rule__PERCENT__Group__0(); state._fsp--; } after(grammarAccess.getPERCENTAccess().getGroup()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__PERCENT__Group__1__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:2421:1: ( ( '%' ) )\n // InternalMLRegression.g:2422:1: ( '%' )\n {\n // InternalMLRegre...
[ "0.7411869", "0.72915745", "0.7218772", "0.71128744", "0.7111978", "0.6807872", "0.65849423", "0.65593684", "0.6268669", "0.62513995", "0.55616736", "0.53887147", "0.5380067", "0.5371648", "0.5311043", "0.5293968", "0.52465063", "0.52348644", "0.523034", "0.5227165", "0.52184...
0.8342749
0
$ANTLR end "rulePERCENT" $ANTLR start "rule__LanguageTarget__LanguageAlternatives_2_0" InternalMLRegression.g:702:1: rule__LanguageTarget__LanguageAlternatives_2_0 : ( ( rulePython ) | ( ruleR ) );
public final void rule__LanguageTarget__LanguageAlternatives_2_0() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:706:1: ( ( rulePython ) | ( ruleR ) ) int alt1=2; int LA1_0 = input.LA(1); if ( ((LA1_0>=17 && LA1_0<=19)) ) { alt1=1; } else if ( ((LA1_0>=20 && LA1_0<=21)) ) { alt1=2; } else { NoViableAltException nvae = new NoViableAltException("", 1, 0, input); throw nvae; } switch (alt1) { case 1 : // InternalMLRegression.g:707:2: ( rulePython ) { // InternalMLRegression.g:707:2: ( rulePython ) // InternalMLRegression.g:708:3: rulePython { before(grammarAccess.getLanguageTargetAccess().getLanguagePythonParserRuleCall_2_0_0()); pushFollow(FOLLOW_2); rulePython(); state._fsp--; after(grammarAccess.getLanguageTargetAccess().getLanguagePythonParserRuleCall_2_0_0()); } } break; case 2 : // InternalMLRegression.g:713:2: ( ruleR ) { // InternalMLRegression.g:713:2: ( ruleR ) // InternalMLRegression.g:714:3: ruleR { before(grammarAccess.getLanguageTargetAccess().getLanguageRParserRuleCall_2_0_1()); pushFollow(FOLLOW_2); ruleR(); state._fsp--; after(grammarAccess.getLanguageTargetAccess().getLanguageRParserRuleCall_2_0_1()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rulePython() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:116:2: ( ( ( rule__Python__Alternatives ) ) )\n // InternalMLRegression.g:117:2: ( ( rule__Python__Alternatives ) )\n ...
[ "0.67811114", "0.6173717", "0.60696805", "0.58042115", "0.5776282", "0.5710256", "0.5587992", "0.5506801", "0.53585005", "0.5297899", "0.52380854", "0.52273595", "0.5088207", "0.502801", "0.50101686", "0.49957043", "0.49319735", "0.4924947", "0.49247476", "0.4924606", "0.4856...
0.7090102
0
$ANTLR end "rule__LanguageTarget__LanguageAlternatives_2_0" $ANTLR start "rule__Python__Alternatives" InternalMLRegression.g:723:1: rule__Python__Alternatives : ( ( 'python' ) | ( 'Python' ) | ( 'PYTHON' ) );
public final void rule__Python__Alternatives() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:727:1: ( ( 'python' ) | ( 'Python' ) | ( 'PYTHON' ) ) int alt2=3; switch ( input.LA(1) ) { case 17: { alt2=1; } break; case 18: { alt2=2; } break; case 19: { alt2=3; } break; default: NoViableAltException nvae = new NoViableAltException("", 2, 0, input); throw nvae; } switch (alt2) { case 1 : // InternalMLRegression.g:728:2: ( 'python' ) { // InternalMLRegression.g:728:2: ( 'python' ) // InternalMLRegression.g:729:3: 'python' { before(grammarAccess.getPythonAccess().getPythonKeyword_0()); match(input,17,FOLLOW_2); after(grammarAccess.getPythonAccess().getPythonKeyword_0()); } } break; case 2 : // InternalMLRegression.g:734:2: ( 'Python' ) { // InternalMLRegression.g:734:2: ( 'Python' ) // InternalMLRegression.g:735:3: 'Python' { before(grammarAccess.getPythonAccess().getPythonKeyword_1()); match(input,18,FOLLOW_2); after(grammarAccess.getPythonAccess().getPythonKeyword_1()); } } break; case 3 : // InternalMLRegression.g:740:2: ( 'PYTHON' ) { // InternalMLRegression.g:740:2: ( 'PYTHON' ) // InternalMLRegression.g:741:3: 'PYTHON' { before(grammarAccess.getPythonAccess().getPYTHONKeyword_2()); match(input,19,FOLLOW_2); after(grammarAccess.getPythonAccess().getPYTHONKeyword_2()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rulePython() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:116:2: ( ( ( rule__Python__Alternatives ) ) )\n // InternalMLRegression.g:117:2: ( ( rule__Python__Alternatives ) )\n ...
[ "0.8197919", "0.7602392", "0.658395", "0.58760047", "0.57973003", "0.5731793", "0.5731446", "0.57313263", "0.57284236", "0.5706378", "0.5705738", "0.5683585", "0.5674424", "0.560825", "0.55429685", "0.5533582", "0.55134904", "0.5511224", "0.5489172", "0.5485994", "0.5444349",...
0.79796606
1
$ANTLR end "rule__Python__Alternatives" $ANTLR start "rule__R__Alternatives" InternalMLRegression.g:750:1: rule__R__Alternatives : ( ( 'r' ) | ( 'R' ) );
public final void rule__R__Alternatives() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:754:1: ( ( 'r' ) | ( 'R' ) ) int alt3=2; int LA3_0 = input.LA(1); if ( (LA3_0==20) ) { alt3=1; } else if ( (LA3_0==21) ) { alt3=2; } else { NoViableAltException nvae = new NoViableAltException("", 3, 0, input); throw nvae; } switch (alt3) { case 1 : // InternalMLRegression.g:755:2: ( 'r' ) { // InternalMLRegression.g:755:2: ( 'r' ) // InternalMLRegression.g:756:3: 'r' { before(grammarAccess.getRAccess().getRKeyword_0()); match(input,20,FOLLOW_2); after(grammarAccess.getRAccess().getRKeyword_0()); } } break; case 2 : // InternalMLRegression.g:761:2: ( 'R' ) { // InternalMLRegression.g:761:2: ( 'R' ) // InternalMLRegression.g:762:3: 'R' { before(grammarAccess.getRAccess().getRKeyword_1()); match(input,21,FOLLOW_2); after(grammarAccess.getRAccess().getRKeyword_1()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void ruleR() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:141:2: ( ( ( rule__R__Alternatives ) ) )\n // InternalMLRegression.g:142:2: ( ( rule__R__Alternatives ) )\n {\n ...
[ "0.8101965", "0.74658984", "0.67663664", "0.655632", "0.6327933", "0.60413563", "0.60235995", "0.5971667", "0.5926642", "0.59184134", "0.5838315", "0.58172166", "0.5774965", "0.56941473", "0.56913674", "0.5606905", "0.55726075", "0.55680156", "0.5512719", "0.5473558", "0.5456...
0.8264683
0
$ANTLR end "rule__R__Alternatives" $ANTLR start "rule__EvaluationType__Alternatives" InternalMLRegression.g:771:1: rule__EvaluationType__Alternatives : ( ( rulePartition ) | ( ruleCrossValidation ) );
public final void rule__EvaluationType__Alternatives() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:775:1: ( ( rulePartition ) | ( ruleCrossValidation ) ) int alt4=2; int LA4_0 = input.LA(1); if ( (LA4_0==29) ) { alt4=1; } else if ( (LA4_0==30) ) { alt4=2; } else { NoViableAltException nvae = new NoViableAltException("", 4, 0, input); throw nvae; } switch (alt4) { case 1 : // InternalMLRegression.g:776:2: ( rulePartition ) { // InternalMLRegression.g:776:2: ( rulePartition ) // InternalMLRegression.g:777:3: rulePartition { before(grammarAccess.getEvaluationTypeAccess().getPartitionParserRuleCall_0()); pushFollow(FOLLOW_2); rulePartition(); state._fsp--; after(grammarAccess.getEvaluationTypeAccess().getPartitionParserRuleCall_0()); } } break; case 2 : // InternalMLRegression.g:782:2: ( ruleCrossValidation ) { // InternalMLRegression.g:782:2: ( ruleCrossValidation ) // InternalMLRegression.g:783:3: ruleCrossValidation { before(grammarAccess.getEvaluationTypeAccess().getCrossValidationParserRuleCall_1()); pushFollow(FOLLOW_2); ruleCrossValidation(); state._fsp--; after(grammarAccess.getEvaluationTypeAccess().getCrossValidationParserRuleCall_1()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void ruleEvaluationType() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:291:2: ( ( ( rule__EvaluationType__Alternatives ) ) )\n // InternalMLRegression.g:292:2: ( ( rule__EvaluationType__Altern...
[ "0.7663763", "0.65067995", "0.62122226", "0.6163375", "0.6138967", "0.6064752", "0.5978753", "0.5969928", "0.5925066", "0.58384806", "0.5726724", "0.56997645", "0.56035453", "0.5582727", "0.55428076", "0.5488263", "0.5453209", "0.54276514", "0.5425592", "0.53957134", "0.53511...
0.7724892
0
$ANTLR end "rule__EvaluationType__Alternatives" $ANTLR start "rule__AlgoType__Alternatives" InternalMLRegression.g:792:1: rule__AlgoType__Alternatives : ( ( ruleLineRegress ) | ( ruleSVR ) | ( ruleDecisionTreeRegressor ) );
public final void rule__AlgoType__Alternatives() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:796:1: ( ( ruleLineRegress ) | ( ruleSVR ) | ( ruleDecisionTreeRegressor ) ) int alt5=3; switch ( input.LA(1) ) { case 11: { alt5=1; } break; case 13: { alt5=2; } break; case 12: { alt5=3; } break; default: NoViableAltException nvae = new NoViableAltException("", 5, 0, input); throw nvae; } switch (alt5) { case 1 : // InternalMLRegression.g:797:2: ( ruleLineRegress ) { // InternalMLRegression.g:797:2: ( ruleLineRegress ) // InternalMLRegression.g:798:3: ruleLineRegress { before(grammarAccess.getAlgoTypeAccess().getLineRegressParserRuleCall_0()); pushFollow(FOLLOW_2); ruleLineRegress(); state._fsp--; after(grammarAccess.getAlgoTypeAccess().getLineRegressParserRuleCall_0()); } } break; case 2 : // InternalMLRegression.g:803:2: ( ruleSVR ) { // InternalMLRegression.g:803:2: ( ruleSVR ) // InternalMLRegression.g:804:3: ruleSVR { before(grammarAccess.getAlgoTypeAccess().getSVRParserRuleCall_1()); pushFollow(FOLLOW_2); ruleSVR(); state._fsp--; after(grammarAccess.getAlgoTypeAccess().getSVRParserRuleCall_1()); } } break; case 3 : // InternalMLRegression.g:809:2: ( ruleDecisionTreeRegressor ) { // InternalMLRegression.g:809:2: ( ruleDecisionTreeRegressor ) // InternalMLRegression.g:810:3: ruleDecisionTreeRegressor { before(grammarAccess.getAlgoTypeAccess().getDecisionTreeRegressorParserRuleCall_2()); pushFollow(FOLLOW_2); ruleDecisionTreeRegressor(); state._fsp--; after(grammarAccess.getAlgoTypeAccess().getDecisionTreeRegressorParserRuleCall_2()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void ruleAlgoType() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:391:2: ( ( ( rule__AlgoType__Alternatives ) ) )\n // InternalMLRegression.g:392:2: ( ( rule__AlgoType__Alternatives ) )\n ...
[ "0.75997615", "0.7184878", "0.66687226", "0.647346", "0.64422405", "0.643388", "0.63579065", "0.62799805", "0.6262656", "0.61859757", "0.61640495", "0.6155949", "0.61374867", "0.6090124", "0.6042062", "0.60004824", "0.59965646", "0.59158266", "0.59041846", "0.5695237", "0.563...
0.8496014
0
$ANTLR end "rule__CalculateType__Alternatives" $ANTLR start "rule__NumericValue__Alternatives" InternalMLRegression.g:846:1: rule__NumericValue__Alternatives : ( ( ruleFLOAT ) | ( rulePERCENT ) );
public final void rule__NumericValue__Alternatives() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:850:1: ( ( ruleFLOAT ) | ( rulePERCENT ) ) int alt7=2; int LA7_0 = input.LA(1); if ( (LA7_0==RULE_INT) ) { switch ( input.LA(2) ) { case 34: { int LA7_2 = input.LA(3); if ( (LA7_2==RULE_INT) ) { int LA7_5 = input.LA(4); if ( (LA7_5==EOF||LA7_5==24) ) { alt7=1; } else if ( (LA7_5==35) ) { alt7=2; } else { NoViableAltException nvae = new NoViableAltException("", 7, 5, input); throw nvae; } } else { NoViableAltException nvae = new NoViableAltException("", 7, 2, input); throw nvae; } } break; case EOF: case 24: { alt7=1; } break; case 35: { alt7=2; } break; default: NoViableAltException nvae = new NoViableAltException("", 7, 1, input); throw nvae; } } else { NoViableAltException nvae = new NoViableAltException("", 7, 0, input); throw nvae; } switch (alt7) { case 1 : // InternalMLRegression.g:851:2: ( ruleFLOAT ) { // InternalMLRegression.g:851:2: ( ruleFLOAT ) // InternalMLRegression.g:852:3: ruleFLOAT { before(grammarAccess.getNumericValueAccess().getFLOATParserRuleCall_0()); pushFollow(FOLLOW_2); ruleFLOAT(); state._fsp--; after(grammarAccess.getNumericValueAccess().getFLOATParserRuleCall_0()); } } break; case 2 : // InternalMLRegression.g:857:2: ( rulePERCENT ) { // InternalMLRegression.g:857:2: ( rulePERCENT ) // InternalMLRegression.g:858:3: rulePERCENT { before(grammarAccess.getNumericValueAccess().getPERCENTParserRuleCall_1()); pushFollow(FOLLOW_2); rulePERCENT(); state._fsp--; after(grammarAccess.getNumericValueAccess().getPERCENTParserRuleCall_1()); } } break; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void ruleNumericValue() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:641:2: ( ( ( rule__NumericValue__Alternatives ) ) )\n // InternalMLRegression.g:642:2: ( ( rule__NumericValue__Alternatives...
[ "0.6585567", "0.5822055", "0.5651128", "0.54235935", "0.53744054", "0.53635806", "0.5345786", "0.53194356", "0.5287636", "0.5233088", "0.51664835", "0.5092521", "0.50900924", "0.50779533", "0.506387", "0.50617325", "0.5060376", "0.50405884", "0.50085706", "0.5007996", "0.4963...
0.7710555
0
$ANTLR end "rule__NumericValue__Alternatives" $ANTLR start "rule__Model__Group__0" InternalMLRegression.g:867:1: rule__Model__Group__0 : rule__Model__Group__0__Impl rule__Model__Group__1 ;
public final void rule__Model__Group__0() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:871:1: ( rule__Model__Group__0__Impl rule__Model__Group__1 ) // InternalMLRegression.g:872:2: rule__Model__Group__0__Impl rule__Model__Group__1 { pushFollow(FOLLOW_3); rule__Model__Group__0__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__Model__Group__1(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Number__Group_1__0__Impl() throws RecognitionException {\r\n\r\n \t\tint stackSize = keepStackSize();\r\n \r\n try {\r\n // ../de.nie.fin.ui/src-gen/de/nie/fin/ui/contentassist/antlr/internal/InternalFin.g:15605:1: ( ( ( rule__Number__Alternatives_1_0 ) )...
[ "0.6717382", "0.6616991", "0.6530332", "0.6491891", "0.6481094", "0.63839", "0.63813454", "0.6345283", "0.63349885", "0.6329359", "0.62948805", "0.6245292", "0.6239784", "0.62221134", "0.62102354", "0.6163356", "0.6150719", "0.61502296", "0.6112165", "0.6109119", "0.61023414"...
0.68364125
0
$ANTLR end "rule__Model__Group__0" $ANTLR start "rule__Model__Group__0__Impl" InternalMLRegression.g:879:1: rule__Model__Group__0__Impl : ( ( rule__Model__LanguageTargetAssignment_0 )? ) ;
public final void rule__Model__Group__0__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:883:1: ( ( ( rule__Model__LanguageTargetAssignment_0 )? ) ) // InternalMLRegression.g:884:1: ( ( rule__Model__LanguageTargetAssignment_0 )? ) { // InternalMLRegression.g:884:1: ( ( rule__Model__LanguageTargetAssignment_0 )? ) // InternalMLRegression.g:885:2: ( rule__Model__LanguageTargetAssignment_0 )? { before(grammarAccess.getModelAccess().getLanguageTargetAssignment_0()); // InternalMLRegression.g:886:2: ( rule__Model__LanguageTargetAssignment_0 )? int alt8=2; int LA8_0 = input.LA(1); if ( (LA8_0==22) ) { alt8=1; } switch (alt8) { case 1 : // InternalMLRegression.g:886:3: rule__Model__LanguageTargetAssignment_0 { pushFollow(FOLLOW_2); rule__Model__LanguageTargetAssignment_0(); state._fsp--; } break; } after(grammarAccess.getModelAccess().getLanguageTargetAssignment_0()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:925:1: ( rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 )\n // InternalMLRegression.g:92...
[ "0.7413759", "0.7104205", "0.7103392", "0.7031564", "0.6930226", "0.6907487", "0.6868151", "0.6824536", "0.68062544", "0.6752296", "0.6715189", "0.6708896", "0.66976726", "0.66885495", "0.66732514", "0.66724116", "0.6628838", "0.66264176", "0.65970343", "0.6589739", "0.656704...
0.8394123
0
$ANTLR end "rule__Model__Group__0__Impl" $ANTLR start "rule__Model__Group__1" InternalMLRegression.g:894:1: rule__Model__Group__1 : rule__Model__Group__1__Impl ;
public final void rule__Model__Group__1() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:898:1: ( rule__Model__Group__1__Impl ) // InternalMLRegression.g:899:2: rule__Model__Group__1__Impl { pushFollow(FOLLOW_2); rule__Model__Group__1__Impl(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Model__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:871:1: ( rule__Model__Group__0__Impl rule__Model__Group__1 )\n // InternalMLRegression.g:872:2: rule__Model__Group__0_...
[ "0.7615373", "0.7613561", "0.7559885", "0.7441083", "0.72730654", "0.72407985", "0.72241104", "0.7178197", "0.7152186", "0.7115693", "0.70463514", "0.6996046", "0.69567364", "0.6801592", "0.67747855", "0.6733191", "0.672339", "0.6713637", "0.669871", "0.66666", "0.6664926", ...
0.79061145
0
$ANTLR end "rule__Model__Group__1" $ANTLR start "rule__Model__Group__1__Impl" InternalMLRegression.g:905:1: rule__Model__Group__1__Impl : ( ( rule__Model__MlAssignment_1 ) ) ;
public final void rule__Model__Group__1__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:909:1: ( ( ( rule__Model__MlAssignment_1 ) ) ) // InternalMLRegression.g:910:1: ( ( rule__Model__MlAssignment_1 ) ) { // InternalMLRegression.g:910:1: ( ( rule__Model__MlAssignment_1 ) ) // InternalMLRegression.g:911:2: ( rule__Model__MlAssignment_1 ) { before(grammarAccess.getModelAccess().getMlAssignment_1()); // InternalMLRegression.g:912:2: ( rule__Model__MlAssignment_1 ) // InternalMLRegression.g:912:3: rule__Model__MlAssignment_1 { pushFollow(FOLLOW_2); rule__Model__MlAssignment_1(); state._fsp--; } after(grammarAccess.getModelAccess().getMlAssignment_1()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Model__Group__1() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:898:1: ( rule__Model__Group__1__Impl )\n // InternalMLRegression.g:899:2: rule__Model__Group__1__Impl\n {\n...
[ "0.75843287", "0.72790754", "0.72453403", "0.7227382", "0.7219041", "0.71878046", "0.71782374", "0.71556526", "0.7146605", "0.71443534", "0.7129373", "0.7121188", "0.7114064", "0.7085664", "0.7080181", "0.70520246", "0.69730926", "0.69600654", "0.6946355", "0.6937436", "0.690...
0.83163166
0
$ANTLR end "rule__Model__Group__1__Impl" $ANTLR start "rule__LanguageTarget__Group__0" InternalMLRegression.g:921:1: rule__LanguageTarget__Group__0 : rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 ;
public final void rule__LanguageTarget__Group__0() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:925:1: ( rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 ) // InternalMLRegression.g:926:2: rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 { pushFollow(FOLLOW_4); rule__LanguageTarget__Group__0__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__LanguageTarget__Group__1(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Language__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \n try {\n // ../org.xtext.example.mydsl.extensions.ui/src-gen/org/xtext/example/mydsl/extensions/ui/contentassist/antlr/internal/InternalMyDsl.g:1370:1: ( rule_...
[ "0.7691212", "0.7351445", "0.7315445", "0.7289789", "0.72879225", "0.7184685", "0.71737385", "0.71233225", "0.71217865", "0.7065434", "0.7045309", "0.69798464", "0.69774663", "0.6954906", "0.6898987", "0.6871253", "0.6843063", "0.6842818", "0.6811051", "0.68017983", "0.678148...
0.794131
0
$ANTLR end "rule__LanguageTarget__Group__0" $ANTLR start "rule__LanguageTarget__Group__0__Impl" InternalMLRegression.g:933:1: rule__LanguageTarget__Group__0__Impl : ( 'target_language' ) ;
public final void rule__LanguageTarget__Group__0__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:937:1: ( ( 'target_language' ) ) // InternalMLRegression.g:938:1: ( 'target_language' ) { // InternalMLRegression.g:938:1: ( 'target_language' ) // InternalMLRegression.g:939:2: 'target_language' { before(grammarAccess.getLanguageTargetAccess().getTarget_languageKeyword_0()); match(input,22,FOLLOW_2); after(grammarAccess.getLanguageTargetAccess().getTarget_languageKeyword_0()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:925:1: ( rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 )\n // InternalMLRegression.g:92...
[ "0.8120805", "0.7879907", "0.7623422", "0.7594688", "0.7531214", "0.737006", "0.7329324", "0.7315194", "0.731163", "0.7300289", "0.7194975", "0.69311315", "0.6927609", "0.68636525", "0.67920226", "0.6762385", "0.6741317", "0.67181534", "0.6701089", "0.6700972", "0.6684692", ...
0.8260316
0
$ANTLR end "rule__LanguageTarget__Group__0__Impl" $ANTLR start "rule__LanguageTarget__Group__1" InternalMLRegression.g:948:1: rule__LanguageTarget__Group__1 : rule__LanguageTarget__Group__1__Impl rule__LanguageTarget__Group__2 ;
public final void rule__LanguageTarget__Group__1() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:952:1: ( rule__LanguageTarget__Group__1__Impl rule__LanguageTarget__Group__2 ) // InternalMLRegression.g:953:2: rule__LanguageTarget__Group__1__Impl rule__LanguageTarget__Group__2 { pushFollow(FOLLOW_5); rule__LanguageTarget__Group__1__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__LanguageTarget__Group__2(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:925:1: ( rule__LanguageTarget__Group__0__Impl rule__LanguageTarget__Group__1 )\n // InternalMLRegression.g:92...
[ "0.77455896", "0.7438177", "0.73946816", "0.73644227", "0.7269709", "0.72034466", "0.7068646", "0.70328635", "0.70294005", "0.70002586", "0.69594306", "0.68768454", "0.68656224", "0.68592393", "0.684428", "0.6841662", "0.6836444", "0.6754317", "0.67295617", "0.6728313", "0.67...
0.7806685
0
$ANTLR end "rule__LanguageTarget__Group__1" $ANTLR start "rule__LanguageTarget__Group__1__Impl" InternalMLRegression.g:960:1: rule__LanguageTarget__Group__1__Impl : ( ':' ) ;
public final void rule__LanguageTarget__Group__1__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:964:1: ( ( ':' ) ) // InternalMLRegression.g:965:1: ( ':' ) { // InternalMLRegression.g:965:1: ( ':' ) // InternalMLRegression.g:966:2: ':' { before(grammarAccess.getLanguageTargetAccess().getColonKeyword_1()); match(input,23,FOLLOW_2); after(grammarAccess.getLanguageTargetAccess().getColonKeyword_1()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Language__Group__1__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \n try {\n // ../org.xtext.example.mydsl.extensions.ui/src-gen/org/xtext/example/mydsl/extensions/ui/contentassist/antlr/internal/InternalMyDsl.g:1413:1: (...
[ "0.7317177", "0.72808844", "0.7223285", "0.71336716", "0.7131897", "0.7124381", "0.69815934", "0.6888761", "0.6833215", "0.67704266", "0.6729129", "0.6714177", "0.6667462", "0.6654804", "0.66468316", "0.662727", "0.6620998", "0.6609817", "0.66058934", "0.6601696", "0.65860623...
0.7504835
0
$ANTLR end "rule__LanguageTarget__Group__1__Impl" $ANTLR start "rule__LanguageTarget__Group__2" InternalMLRegression.g:975:1: rule__LanguageTarget__Group__2 : rule__LanguageTarget__Group__2__Impl rule__LanguageTarget__Group__3 ;
public final void rule__LanguageTarget__Group__2() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:979:1: ( rule__LanguageTarget__Group__2__Impl rule__LanguageTarget__Group__3 ) // InternalMLRegression.g:980:2: rule__LanguageTarget__Group__2__Impl rule__LanguageTarget__Group__3 { pushFollow(FOLLOW_6); rule__LanguageTarget__Group__2__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__LanguageTarget__Group__3(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__1() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:952:1: ( rule__LanguageTarget__Group__1__Impl rule__LanguageTarget__Group__2 )\n // InternalMLRegression.g:95...
[ "0.75614315", "0.73991686", "0.7332301", "0.72505856", "0.71390736", "0.70200413", "0.7004633", "0.6874356", "0.6873943", "0.6863033", "0.68395567", "0.67355263", "0.6702329", "0.6690814", "0.66711724", "0.6670377", "0.6624274", "0.6611725", "0.6547865", "0.6504337", "0.64965...
0.78164333
0
$ANTLR end "rule__LanguageTarget__Group__2" $ANTLR start "rule__LanguageTarget__Group__2__Impl" InternalMLRegression.g:987:1: rule__LanguageTarget__Group__2__Impl : ( ( rule__LanguageTarget__LanguageAssignment_2 ) ) ;
public final void rule__LanguageTarget__Group__2__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:991:1: ( ( ( rule__LanguageTarget__LanguageAssignment_2 ) ) ) // InternalMLRegression.g:992:1: ( ( rule__LanguageTarget__LanguageAssignment_2 ) ) { // InternalMLRegression.g:992:1: ( ( rule__LanguageTarget__LanguageAssignment_2 ) ) // InternalMLRegression.g:993:2: ( rule__LanguageTarget__LanguageAssignment_2 ) { before(grammarAccess.getLanguageTargetAccess().getLanguageAssignment_2()); // InternalMLRegression.g:994:2: ( rule__LanguageTarget__LanguageAssignment_2 ) // InternalMLRegression.g:994:3: rule__LanguageTarget__LanguageAssignment_2 { pushFollow(FOLLOW_2); rule__LanguageTarget__LanguageAssignment_2(); state._fsp--; } after(grammarAccess.getLanguageTargetAccess().getLanguageAssignment_2()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Language__Group__2__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalCsv.g:1180:1: ( ( ( rule__Language__TargetAssignment_2 ) ) )\n // InternalCsv.g:1181:1: ( ( rule__Language__TargetAssignment...
[ "0.8209069", "0.7886071", "0.7695925", "0.7617843", "0.74690604", "0.7268585", "0.72472143", "0.7193256", "0.7160364", "0.70781344", "0.70777446", "0.7034806", "0.6974788", "0.6936451", "0.6864861", "0.674098", "0.6700321", "0.6699964", "0.66891736", "0.66858935", "0.665565",...
0.8300397
0
$ANTLR end "rule__LanguageTarget__Group__2__Impl" $ANTLR start "rule__LanguageTarget__Group__3" InternalMLRegression.g:1002:1: rule__LanguageTarget__Group__3 : rule__LanguageTarget__Group__3__Impl ;
public final void rule__LanguageTarget__Group__3() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1006:1: ( rule__LanguageTarget__Group__3__Impl ) // InternalMLRegression.g:1007:2: rule__LanguageTarget__Group__3__Impl { pushFollow(FOLLOW_2); rule__LanguageTarget__Group__3__Impl(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Language__Group__3__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \n try {\n // ../org.xtext.example.mydsl.extensions.ui/src-gen/org/xtext/example/mydsl/extensions/ui/contentassist/antlr/internal/InternalMyDsl.g:1475:1: (...
[ "0.76974744", "0.75942034", "0.75098735", "0.7353613", "0.7304234", "0.724077", "0.7233596", "0.7197264", "0.704139", "0.701738", "0.7003602", "0.69517696", "0.69497925", "0.69415116", "0.6876948", "0.6859549", "0.6811369", "0.6776727", "0.6727117", "0.6709076", "0.669805", ...
0.8104335
0
$ANTLR end "rule__LanguageTarget__Group__3" $ANTLR start "rule__LanguageTarget__Group__3__Impl" InternalMLRegression.g:1013:1: rule__LanguageTarget__Group__3__Impl : ( ';' ) ;
public final void rule__LanguageTarget__Group__3__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1017:1: ( ( ';' ) ) // InternalMLRegression.g:1018:1: ( ';' ) { // InternalMLRegression.g:1018:1: ( ';' ) // InternalMLRegression.g:1019:2: ';' { before(grammarAccess.getLanguageTargetAccess().getSemicolonKeyword_3()); match(input,24,FOLLOW_2); after(grammarAccess.getLanguageTargetAccess().getSemicolonKeyword_3()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__LanguageTarget__Group__3() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1006:1: ( rule__LanguageTarget__Group__3__Impl )\n // InternalMLRegression.g:1007:2: rule__LanguageTarget__Gr...
[ "0.78706235", "0.73193556", "0.72613645", "0.7250216", "0.72430927", "0.7221126", "0.71272993", "0.7037389", "0.6940417", "0.69098", "0.6890507", "0.68740857", "0.68022364", "0.67087334", "0.670155", "0.66653955", "0.6640298", "0.66035974", "0.65888745", "0.65875703", "0.6587...
0.7489056
1
$ANTLR end "rule__LanguageTarget__Group__3__Impl" $ANTLR start "rule__MLRegression__Group__0" InternalMLRegression.g:1029:1: rule__MLRegression__Group__0 : rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 ;
public final void rule__MLRegression__Group__0() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1033:1: ( rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 ) // InternalMLRegression.g:1034:2: rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 { pushFollow(FOLLOW_7); rule__MLRegression__Group__0__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__MLRegression__Group__1(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Model__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:871:1: ( rule__Model__Group__0__Impl rule__Model__Group__1 )\n // InternalMLRegression.g:872:2: rule__Model__Group__0_...
[ "0.7396205", "0.7334891", "0.72692573", "0.7212898", "0.7177569", "0.7149625", "0.7132399", "0.70557714", "0.69590324", "0.6958398", "0.6937099", "0.6805539", "0.6728068", "0.6723262", "0.6699007", "0.6697091", "0.66970456", "0.66916156", "0.66782564", "0.66573393", "0.663893...
0.79278857
0
$ANTLR end "rule__MLRegression__Group__0" $ANTLR start "rule__MLRegression__Group__0__Impl" InternalMLRegression.g:1041:1: rule__MLRegression__Group__0__Impl : ( ( rule__MLRegression__DatasetAssignment_0 ) ) ;
public final void rule__MLRegression__Group__0__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1045:1: ( ( ( rule__MLRegression__DatasetAssignment_0 ) ) ) // InternalMLRegression.g:1046:1: ( ( rule__MLRegression__DatasetAssignment_0 ) ) { // InternalMLRegression.g:1046:1: ( ( rule__MLRegression__DatasetAssignment_0 ) ) // InternalMLRegression.g:1047:2: ( rule__MLRegression__DatasetAssignment_0 ) { before(grammarAccess.getMLRegressionAccess().getDatasetAssignment_0()); // InternalMLRegression.g:1048:2: ( rule__MLRegression__DatasetAssignment_0 ) // InternalMLRegression.g:1048:3: rule__MLRegression__DatasetAssignment_0 { pushFollow(FOLLOW_2); rule__MLRegression__DatasetAssignment_0(); state._fsp--; } after(grammarAccess.getMLRegressionAccess().getDatasetAssignment_0()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Dataset__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1195:1: ( rule__Dataset__Group__0__Impl rule__Dataset__Group__1 )\n // InternalMLRegression.g:1196:2: rule__Dataset_...
[ "0.7752706", "0.75140333", "0.7182322", "0.70619035", "0.703282", "0.7028664", "0.70019776", "0.6970915", "0.6967329", "0.6947809", "0.6916227", "0.6915115", "0.69116455", "0.68783844", "0.68680483", "0.6854291", "0.68542296", "0.68212783", "0.6804725", "0.6804033", "0.680112...
0.8367421
0
$ANTLR end "rule__MLRegression__Group__0__Impl" $ANTLR start "rule__MLRegression__Group__1" InternalMLRegression.g:1056:1: rule__MLRegression__Group__1 : rule__MLRegression__Group__1__Impl rule__MLRegression__Group__2 ;
public final void rule__MLRegression__Group__1() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1060:1: ( rule__MLRegression__Group__1__Impl rule__MLRegression__Group__2 ) // InternalMLRegression.g:1061:2: rule__MLRegression__Group__1__Impl rule__MLRegression__Group__2 { pushFollow(FOLLOW_7); rule__MLRegression__Group__1__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__MLRegression__Group__2(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1033:1: ( rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 )\n // InternalMLRegression.g:1034:2:...
[ "0.7595274", "0.74326086", "0.7136544", "0.7015204", "0.7006828", "0.69298476", "0.69154215", "0.6882086", "0.68314815", "0.6806793", "0.6786278", "0.67570734", "0.6753732", "0.6726431", "0.6717632", "0.67174846", "0.6715024", "0.6710197", "0.67063594", "0.67055035", "0.67054...
0.77214855
0
$ANTLR end "rule__MLRegression__Group__1" $ANTLR start "rule__MLRegression__Group__1__Impl" InternalMLRegression.g:1068:1: rule__MLRegression__Group__1__Impl : ( ( rule__MLRegression__VarsAssignment_1 )? ) ;
public final void rule__MLRegression__Group__1__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1072:1: ( ( ( rule__MLRegression__VarsAssignment_1 )? ) ) // InternalMLRegression.g:1073:1: ( ( rule__MLRegression__VarsAssignment_1 )? ) { // InternalMLRegression.g:1073:1: ( ( rule__MLRegression__VarsAssignment_1 )? ) // InternalMLRegression.g:1074:2: ( rule__MLRegression__VarsAssignment_1 )? { before(grammarAccess.getMLRegressionAccess().getVarsAssignment_1()); // InternalMLRegression.g:1075:2: ( rule__MLRegression__VarsAssignment_1 )? int alt9=2; int LA9_0 = input.LA(1); if ( (LA9_0==26) ) { alt9=1; } switch (alt9) { case 1 : // InternalMLRegression.g:1075:3: rule__MLRegression__VarsAssignment_1 { pushFollow(FOLLOW_2); rule__MLRegression__VarsAssignment_1(); state._fsp--; } break; } after(grammarAccess.getMLRegressionAccess().getVarsAssignment_1()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Variables__Group__1() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1330:1: ( rule__Variables__Group__1__Impl )\n // InternalMLRegression.g:1331:2: rule__Variables__Group__1__Impl\n ...
[ "0.78307533", "0.7332902", "0.73134935", "0.72506493", "0.7104511", "0.6933921", "0.69226235", "0.68876755", "0.68123096", "0.680883", "0.6797067", "0.6786434", "0.6784412", "0.6778698", "0.6777851", "0.6760073", "0.67553985", "0.6732988", "0.6721108", "0.66932297", "0.667549...
0.8382022
0
$ANTLR end "rule__MLRegression__Group__1__Impl" $ANTLR start "rule__MLRegression__Group__2" InternalMLRegression.g:1083:1: rule__MLRegression__Group__2 : rule__MLRegression__Group__2__Impl rule__MLRegression__Group__3 ;
public final void rule__MLRegression__Group__2() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1087:1: ( rule__MLRegression__Group__2__Impl rule__MLRegression__Group__3 ) // InternalMLRegression.g:1088:2: rule__MLRegression__Group__2__Impl rule__MLRegression__Group__3 { pushFollow(FOLLOW_8); rule__MLRegression__Group__2__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__MLRegression__Group__3(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__1() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1060:1: ( rule__MLRegression__Group__1__Impl rule__MLRegression__Group__2 )\n // InternalMLRegression.g:1061:2:...
[ "0.76584876", "0.7361686", "0.71954894", "0.71603894", "0.711963", "0.6966588", "0.6962202", "0.69375384", "0.69344836", "0.69051313", "0.6904899", "0.68959194", "0.6851873", "0.68281215", "0.68242043", "0.6810389", "0.6804661", "0.6795484", "0.67872757", "0.67864686", "0.675...
0.7998055
0
$ANTLR end "rule__MLRegression__Group__2" $ANTLR start "rule__MLRegression__Group__2__Impl" InternalMLRegression.g:1095:1: rule__MLRegression__Group__2__Impl : ( ( rule__MLRegression__EvaluationAssignment_2 ) ) ;
public final void rule__MLRegression__Group__2__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1099:1: ( ( ( rule__MLRegression__EvaluationAssignment_2 ) ) ) // InternalMLRegression.g:1100:1: ( ( rule__MLRegression__EvaluationAssignment_2 ) ) { // InternalMLRegression.g:1100:1: ( ( rule__MLRegression__EvaluationAssignment_2 ) ) // InternalMLRegression.g:1101:2: ( rule__MLRegression__EvaluationAssignment_2 ) { before(grammarAccess.getMLRegressionAccess().getEvaluationAssignment_2()); // InternalMLRegression.g:1102:2: ( rule__MLRegression__EvaluationAssignment_2 ) // InternalMLRegression.g:1102:3: rule__MLRegression__EvaluationAssignment_2 { pushFollow(FOLLOW_2); rule__MLRegression__EvaluationAssignment_2(); state._fsp--; } after(grammarAccess.getMLRegressionAccess().getEvaluationAssignment_2()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__2() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1087:1: ( rule__MLRegression__Group__2__Impl rule__MLRegression__Group__3 )\n // InternalMLRegression.g:1088:2:...
[ "0.7578675", "0.73670983", "0.72895926", "0.7114324", "0.7091495", "0.70877284", "0.7075217", "0.7037785", "0.70283294", "0.70133793", "0.7011248", "0.70095223", "0.6954362", "0.6944329", "0.6895392", "0.68651825", "0.6848218", "0.6847976", "0.68472207", "0.68456936", "0.6841...
0.8728404
0
$ANTLR end "rule__MLRegression__Group__2__Impl" $ANTLR start "rule__MLRegression__Group__3" InternalMLRegression.g:1110:1: rule__MLRegression__Group__3 : rule__MLRegression__Group__3__Impl rule__MLRegression__Group__4 ;
public final void rule__MLRegression__Group__3() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1114:1: ( rule__MLRegression__Group__3__Impl rule__MLRegression__Group__4 ) // InternalMLRegression.g:1115:2: rule__MLRegression__Group__3__Impl rule__MLRegression__Group__4 { pushFollow(FOLLOW_9); rule__MLRegression__Group__3__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__MLRegression__Group__4(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Calculate__Group__3() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:2140:1: ( rule__Calculate__Group__3__Impl )\n // InternalMLRegression.g:2141:2: rule__Calculate__Group__3__Impl\n ...
[ "0.72042435", "0.71246094", "0.7088214", "0.70545906", "0.70463455", "0.7036174", "0.6803273", "0.67585695", "0.6751849", "0.67439103", "0.6729785", "0.664139", "0.6620561", "0.6586095", "0.65818954", "0.65540045", "0.65398985", "0.6501266", "0.64960593", "0.6481215", "0.6462...
0.7910781
0
$ANTLR end "rule__MLRegression__Group__3" $ANTLR start "rule__MLRegression__Group__3__Impl" InternalMLRegression.g:1122:1: rule__MLRegression__Group__3__Impl : ( ( rule__MLRegression__AlgoAssignment_3 ) ) ;
public final void rule__MLRegression__Group__3__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1126:1: ( ( ( rule__MLRegression__AlgoAssignment_3 ) ) ) // InternalMLRegression.g:1127:1: ( ( rule__MLRegression__AlgoAssignment_3 ) ) { // InternalMLRegression.g:1127:1: ( ( rule__MLRegression__AlgoAssignment_3 ) ) // InternalMLRegression.g:1128:2: ( rule__MLRegression__AlgoAssignment_3 ) { before(grammarAccess.getMLRegressionAccess().getAlgoAssignment_3()); // InternalMLRegression.g:1129:2: ( rule__MLRegression__AlgoAssignment_3 ) // InternalMLRegression.g:1129:3: rule__MLRegression__AlgoAssignment_3 { pushFollow(FOLLOW_2); rule__MLRegression__AlgoAssignment_3(); state._fsp--; } after(grammarAccess.getMLRegressionAccess().getAlgoAssignment_3()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Algo__Group__3() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:2032:1: ( rule__Algo__Group__3__Impl )\n // InternalMLRegression.g:2033:2: rule__Algo__Group__3__Impl\n {\n ...
[ "0.74983245", "0.7407372", "0.73308104", "0.70313483", "0.70122486", "0.6865957", "0.6827369", "0.6808311", "0.68020576", "0.67839754", "0.6779167", "0.6758881", "0.674991", "0.6685721", "0.6680351", "0.6661014", "0.66572636", "0.66169274", "0.66098577", "0.659828", "0.656020...
0.8378787
0
$ANTLR end "rule__MLRegression__Group__3__Impl" $ANTLR start "rule__MLRegression__Group__4" InternalMLRegression.g:1137:1: rule__MLRegression__Group__4 : rule__MLRegression__Group__4__Impl rule__MLRegression__Group__5 ;
public final void rule__MLRegression__Group__4() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1141:1: ( rule__MLRegression__Group__4__Impl rule__MLRegression__Group__5 ) // InternalMLRegression.g:1142:2: rule__MLRegression__Group__4__Impl rule__MLRegression__Group__5 { pushFollow(FOLLOW_10); rule__MLRegression__Group__4__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__MLRegression__Group__5(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__3() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1114:1: ( rule__MLRegression__Group__3__Impl rule__MLRegression__Group__4 )\n // InternalMLRegression.g:1115:2:...
[ "0.7465063", "0.72502", "0.71540344", "0.69222784", "0.6916069", "0.6769315", "0.67319095", "0.66293085", "0.6604537", "0.65618914", "0.650256", "0.65009815", "0.64921945", "0.64890033", "0.64529455", "0.64435434", "0.6434031", "0.6430844", "0.642294", "0.6410112", "0.6409229...
0.7707035
0
$ANTLR end "rule__MLRegression__Group__4" $ANTLR start "rule__MLRegression__Group__4__Impl" InternalMLRegression.g:1149:1: rule__MLRegression__Group__4__Impl : ( ( rule__MLRegression__CalculateAssignment_4 ) ) ;
public final void rule__MLRegression__Group__4__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1153:1: ( ( ( rule__MLRegression__CalculateAssignment_4 ) ) ) // InternalMLRegression.g:1154:1: ( ( rule__MLRegression__CalculateAssignment_4 ) ) { // InternalMLRegression.g:1154:1: ( ( rule__MLRegression__CalculateAssignment_4 ) ) // InternalMLRegression.g:1155:2: ( rule__MLRegression__CalculateAssignment_4 ) { before(grammarAccess.getMLRegressionAccess().getCalculateAssignment_4()); // InternalMLRegression.g:1156:2: ( rule__MLRegression__CalculateAssignment_4 ) // InternalMLRegression.g:1156:3: rule__MLRegression__CalculateAssignment_4 { pushFollow(FOLLOW_2); rule__MLRegression__CalculateAssignment_4(); state._fsp--; } after(grammarAccess.getMLRegressionAccess().getCalculateAssignment_4()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__4() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1141:1: ( rule__MLRegression__Group__4__Impl rule__MLRegression__Group__5 )\n // InternalMLRegression.g:1142:2:...
[ "0.7073594", "0.70620066", "0.69441533", "0.6906415", "0.68942523", "0.68784195", "0.68658465", "0.68523866", "0.68051493", "0.67954", "0.67768586", "0.67734504", "0.67694163", "0.67681426", "0.6767648", "0.67434883", "0.6733898", "0.672342", "0.67073023", "0.6705466", "0.666...
0.8446291
0
$ANTLR end "rule__MLRegression__Group__4__Impl" $ANTLR start "rule__MLRegression__Group__5" InternalMLRegression.g:1164:1: rule__MLRegression__Group__5 : rule__MLRegression__Group__5__Impl ;
public final void rule__MLRegression__Group__5() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1168:1: ( rule__MLRegression__Group__5__Impl ) // InternalMLRegression.g:1169:2: rule__MLRegression__Group__5__Impl { pushFollow(FOLLOW_2); rule__MLRegression__Group__5__Impl(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__4() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1141:1: ( rule__MLRegression__Group__4__Impl rule__MLRegression__Group__5 )\n // InternalMLRegression.g:1142:2:...
[ "0.7467528", "0.6820136", "0.68020725", "0.6535648", "0.65023446", "0.6496376", "0.6438307", "0.64203805", "0.64040226", "0.6399376", "0.6379922", "0.63055044", "0.62548256", "0.6227423", "0.62206393", "0.62024826", "0.6198892", "0.61822367", "0.6173571", "0.61715436", "0.616...
0.782923
0
$ANTLR end "rule__MLRegression__Group__5" $ANTLR start "rule__MLRegression__Group__5__Impl" InternalMLRegression.g:1175:1: rule__MLRegression__Group__5__Impl : ( ( rule__MLRegression__LoopAssignment_5 )? ) ;
public final void rule__MLRegression__Group__5__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1179:1: ( ( ( rule__MLRegression__LoopAssignment_5 )? ) ) // InternalMLRegression.g:1180:1: ( ( rule__MLRegression__LoopAssignment_5 )? ) { // InternalMLRegression.g:1180:1: ( ( rule__MLRegression__LoopAssignment_5 )? ) // InternalMLRegression.g:1181:2: ( rule__MLRegression__LoopAssignment_5 )? { before(grammarAccess.getMLRegressionAccess().getLoopAssignment_5()); // InternalMLRegression.g:1182:2: ( rule__MLRegression__LoopAssignment_5 )? int alt10=2; int LA10_0 = input.LA(1); if ( (LA10_0==33) ) { alt10=1; } switch (alt10) { case 1 : // InternalMLRegression.g:1182:3: rule__MLRegression__LoopAssignment_5 { pushFollow(FOLLOW_2); rule__MLRegression__LoopAssignment_5(); state._fsp--; } break; } after(grammarAccess.getMLRegressionAccess().getLoopAssignment_5()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__5() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1168:1: ( rule__MLRegression__Group__5__Impl )\n // InternalMLRegression.g:1169:2: rule__MLRegression__Group__5...
[ "0.7770461", "0.7172384", "0.69940037", "0.6857335", "0.68317896", "0.6825164", "0.6753829", "0.66882545", "0.66793203", "0.664496", "0.6636157", "0.6632597", "0.6624976", "0.6585502", "0.6568484", "0.65181077", "0.65136665", "0.6448867", "0.64430135", "0.64189357", "0.639461...
0.78802955
0
$ANTLR end "rule__MLRegression__Group__5__Impl" $ANTLR start "rule__Dataset__Group__0" InternalMLRegression.g:1191:1: rule__Dataset__Group__0 : rule__Dataset__Group__0__Impl rule__Dataset__Group__1 ;
public final void rule__Dataset__Group__0() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1195:1: ( rule__Dataset__Group__0__Impl rule__Dataset__Group__1 ) // InternalMLRegression.g:1196:2: rule__Dataset__Group__0__Impl rule__Dataset__Group__1 { pushFollow(FOLLOW_11); rule__Dataset__Group__0__Impl(); state._fsp--; pushFollow(FOLLOW_2); rule__Dataset__Group__1(); state._fsp--; } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__MLRegression__Group__0() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \t\n try {\n // InternalMLRegression.g:1033:1: ( rule__MLRegression__Group__0__Impl rule__MLRegression__Group__1 )\n // InternalMLRegression.g:1034:2:...
[ "0.7241637", "0.71869963", "0.71477467", "0.70786417", "0.69054365", "0.68770796", "0.68628937", "0.68488246", "0.67703605", "0.672583", "0.6719776", "0.67004776", "0.669117", "0.6669452", "0.6639465", "0.66187525", "0.65893424", "0.65785295", "0.6574998", "0.65608376", "0.65...
0.78946227
0
$ANTLR end "rule__Dataset__Group__0" $ANTLR start "rule__Dataset__Group__0__Impl" InternalMLRegression.g:1203:1: rule__Dataset__Group__0__Impl : ( 'import' ) ;
public final void rule__Dataset__Group__0__Impl() throws RecognitionException { int stackSize = keepStackSize(); try { // InternalMLRegression.g:1207:1: ( ( 'import' ) ) // InternalMLRegression.g:1208:1: ( 'import' ) { // InternalMLRegression.g:1208:1: ( 'import' ) // InternalMLRegression.g:1209:2: 'import' { before(grammarAccess.getDatasetAccess().getImportKeyword_0()); match(input,25,FOLLOW_2); after(grammarAccess.getDatasetAccess().getImportKeyword_0()); } } } catch (RecognitionException re) { reportError(re); recover(input,re); } finally { restoreStackSize(stackSize); } return ; }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "public final void rule__Import__Group__0__Impl() throws RecognitionException {\n\n \t\tint stackSize = keepStackSize();\n \n try {\n // ../org.xtext.lwc.instances.ui/src-gen/org/xtext/lwc/instances/ui/contentassist/antlr/internal/InternalInstances.g:2575:1: ( ( 'import' ) )\n ...
[ "0.7514198", "0.7423914", "0.74194306", "0.72705346", "0.71300274", "0.70870155", "0.7040654", "0.7029105", "0.7003245", "0.69833606", "0.6910276", "0.6906974", "0.6903519", "0.68988097", "0.6865009", "0.6856855", "0.66760564", "0.6672137", "0.6667467", "0.66574556", "0.66238...
0.81717545
0