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removed lib files, updated to intellij from netbeans, changed to kotlin coroutines

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This commit is contained in:
christian 2021-07-07 12:45:49 +02:00
parent ce4933d124
commit 00699e7b96
175 changed files with 2011 additions and 1171 deletions
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8
ArtificialAutism/.idea/.gitignore generated vendored Normal file
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# Default ignored files
/shelf/
/workspace.xml
# Datasource local storage ignored files
/../../../../../:\stationær backup filer\Projects\ArtificialAutism_intellij\.idea/dataSources/
/dataSources.local.xml
# Editor-based HTTP Client requests
/httpRequests/

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ArtificialAutism/.idea/.name generated Normal file
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ArtificialAutism

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ArtificialAutism/.idea/checkstyle-idea.xml generated Normal file
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ArtificialAutism/.idea/compiler.xml generated Normal file
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ArtificialAutism/.idea/encodings.xml generated Normal file
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ArtificialAutism/.idea/jarRepositories.xml generated Normal file
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ArtificialAutism/.idea/kotlinc.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__StanfordParserModel_StanfordParserModel_1_0.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__StanfordParser_StanfordParser_1_0.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_austinv11_servicer_Servicer_1_0_3.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord4j_common_3_1_1.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord4j_core_3_1_1.xml generated Normal file
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13
ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord4j_gateway_3_1_1.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord4j_rest_3_1_1.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord4j_voice_3_1_1.xml generated Normal file
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13
ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord_json_1_5_4.xml generated Normal file
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ArtificialAutism/.idea/libraries/Maven__com_discord4j_discord_json_api_1_5_4.xml generated Normal file
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ArtificialAutism/.idea/modules.xml generated Normal file
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ArtificialAutism/nbactions.xml
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314
ArtificialAutism/pom.xml
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<dependency>
<groupId>jmweAnno</groupId>
<artifactId>jmweAnno</artifactId>
<version>1.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>edu.mit.jmwe</groupId>
<artifactId>edu.mit.jmwe</artifactId>
<version>1.0.2</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>CoreNLP</groupId>
<artifactId>CoreNLP</artifactId>
<version>1.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-analyzers-common</artifactId>
<version>7.2.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>CorenlpModels</groupId>
<artifactId>CorenlpModels</artifactId>
<version>1.0</version>
</dependency>
<dependency>
<groupId>com.discord4j</groupId>
<artifactId>discord4j-core</artifactId>
<version>3.1.1</version>
</dependency>
</dependencies>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<mainClass>PresentationLayer.DiscordHandler</mainClass>
</properties>
<build>
<plugins>
<plugin>
<artifactId>maven-dependency-plugin</artifactId>
<executions>
<execution>
<phase>install</phase>
<goals>
<goal>copy-dependencies</goal>
</goals>
<configuration>
<outputDirectory>${project.build.directory}/lib</outputDirectory>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-jar-plugin</artifactId>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<classpathPrefix>lib/</classpathPrefix>
<mainClass>${mainClass}</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
</plugins>
</build>
</project>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.mycompany</groupId>
<artifactId>ArtificialAutism</artifactId>
<version>1.0</version>
<packaging>jar</packaging>
<dependencies>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-dbcp2</artifactId>
<version>2.5.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.13</version>
</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>26.0-jre</version>
</dependency>
<dependency>
<groupId>com.github.mpkorstanje</groupId>
<artifactId>simmetrics-core</artifactId>
<version>4.1.1</version>
</dependency>
<dependency>
<groupId>ws4j</groupId>
<artifactId>ws4j</artifactId>
<version>1.0.1</version>
</dependency>
<dependency>
<groupId>StanfordParser</groupId>
<artifactId>StanfordParser</artifactId>
<version>1.0</version>
</dependency>
<dependency>
<groupId>StanfordParserModel</groupId>
<artifactId>StanfordParserModel</artifactId>
<version>1.0</version>
</dependency>
<dependency>
<groupId>srParser</groupId>
<artifactId>srParser</artifactId>
<version>1</version>
</dependency>
<dependency>
<groupId>ejml</groupId>
<artifactId>ejml</artifactId>
<version>0.2.3</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>jmweAnno</groupId>
<artifactId>jmweAnno</artifactId>
<version>1.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>edu.mit.jmwe</groupId>
<artifactId>edu.mit.jmwe</artifactId>
<version>1.0.2</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>CoreNLP</groupId>
<artifactId>CoreNLP</artifactId>
<version>1.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-analyzers-common</artifactId>
<version>7.2.0</version>
<type>jar</type>
</dependency>
<dependency>
<groupId>com.discord4j</groupId>
<artifactId>discord4j-core</artifactId>
<version>3.1.1</version>
</dependency>
<dependency>
<groupId>org.jetbrains.kotlin</groupId>
<artifactId>kotlin-stdlib-jdk8</artifactId>
<version>${kotlin.version}</version>
</dependency>
<dependency>
<groupId>org.jetbrains.kotlin</groupId>
<artifactId>kotlin-test</artifactId>
<version>${kotlin.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.jetbrains.kotlinx</groupId>
<artifactId>kotlinx-coroutines-core-jvm</artifactId>
<version>1.5.0</version>
</dependency>
</dependencies>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<mainClass>PresentationLayer.DiscordHandler</mainClass>
<kotlin.version>1.5.20-M1</kotlin.version>
</properties>
<build>
<plugins>
<plugin>
<artifactId>maven-dependency-plugin</artifactId>
<executions>
<execution>
<phase>install</phase>
<goals>
<goal>copy-dependencies</goal>
</goals>
<configuration>
<outputDirectory>${project.build.directory}/lib</outputDirectory>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-jar-plugin</artifactId>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<classpathPrefix>lib/</classpathPrefix>
<mainClass>${mainClass}</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
<plugin>
<groupId>org.jetbrains.kotlin</groupId>
<artifactId>kotlin-maven-plugin</artifactId>
<version>${kotlin.version}</version>
<executions>
<execution>
<id>compile</id>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>test-compile</id>
<phase>test-compile</phase>
<goals>
<goal>test-compile</goal>
</goals>
</execution>
</executions>
<configuration>
<jvmTarget>1.8</jvmTarget>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<executions>
<execution>
<id>compile</id>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>testCompile</id>
<phase>test-compile</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>

42
ArtificialAutism/src/main/java/DataLayer/DBCPDataSourceAutismo.java
View File

@ -1,42 +0,0 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package DataLayer;
import java.sql.Connection;
import java.sql.SQLException;
import java.util.logging.Level;
import java.util.logging.Logger;
import org.apache.commons.dbcp2.BasicDataSource;
import DataLayer.settings;
/**
*
* @author install1
*/
public class DBCPDataSourceAutismo {
private static BasicDataSource ds = new BasicDataSource();
static {
try {
ds.setDriver(new com.mysql.cj.jdbc.Driver());
ds.setUrl(settings.autismo_url);
ds.setUsername(settings.autismo_username);
ds.setPassword(settings.autismo_password);
ds.setMaxTotal(-1);
ds.setMinIdle(5);
ds.setMaxIdle(-1);
ds.setMaxOpenPreparedStatements(100);
} catch (SQLException ex) {
Logger.getLogger(DBCPDataSourceAutismo.class.getName()).log(Level.SEVERE, null, ex);
}
}
public static Connection getConnection() throws SQLException {
return ds.getConnection();
}
private DBCPDataSourceAutismo() {
}
}

16
ArtificialAutism/src/main/java/DataLayer/DataMapper.java
View File

@ -88,22 +88,6 @@ public class DataMapper {
}
}
public static ConcurrentMap<Integer, String> getHLstatsMessages() {
ConcurrentMap<Integer, String> hlStatsMessages = new MapMaker().concurrencyLevel(2).makeMap();
try (Connection l_cCon = DBCPDataSourceHLstats.getConnection()) {
String l_sSQL = "SELECT message FROM `hlstats_Events_Chat`";
try (PreparedStatement l_pStatement = l_cCon.prepareStatement(l_sSQL)) {
try (ResultSet l_rsSearch = l_pStatement.executeQuery()) {
while (l_rsSearch.next()) {
hlStatsMessages.put(hlStatsMessages.size() + 1, l_rsSearch.getString(1));
}
}
}
} catch (SQLException ex) {
Logger.getLogger(DataMapper.class.getName()).log(Level.SEVERE, null, ex);
}
return hlStatsMessages;
}
public static void CloseConnections(PreparedStatement ps, ResultSet rs, Connection con) {
if (rs != null) {

823
ArtificialAutism/src/main/java/FunctionLayer/Datahandler.java
View File

@ -1,823 +0,0 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package FunctionLayer;
import DataLayer.DataMapper;
import FunctionLayer.StanfordParser.SentimentAnalyzerTest;
import FunctionLayer.StanfordParser.SentimentValueCache;
import com.google.common.base.Stopwatch;
import com.google.common.collect.MapMaker;
import edu.stanford.nlp.ie.AbstractSequenceClassifier;
import edu.stanford.nlp.ie.crf.CRFClassifier;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.parser.lexparser.LexicalizedParser;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.CoreDocument;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.tagger.maxent.MaxentTagger;
import edu.stanford.nlp.trees.GrammaticalStructureFactory;
import edu.stanford.nlp.trees.TreebankLanguagePack;
import java.io.IOException;
import java.io.UnsupportedEncodingException;
import static java.lang.Math.random;
import java.net.DatagramPacket;
import java.net.DatagramSocket;
import java.net.InetAddress;
import java.net.SocketException;
import java.sql.SQLException;
import java.util.AbstractMap;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.ListIterator;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Properties;
import java.util.Set;
import java.util.concurrent.Callable;
import java.util.concurrent.CompletionService;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorCompletionService;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.ForkJoinTask;
import java.util.concurrent.Future;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.function.Consumer;
import java.util.logging.Level;
import java.util.logging.Logger;
import java.util.stream.Collectors;
import java.util.stream.Stream;
/**
*
* @author install1
*/
public class Datahandler {
public static final long EXPIRE_TIME_IN_SECONDS = TimeUnit.SECONDS.convert(10, TimeUnit.MINUTES);
public static final long EXPIRE_TIME_IN_SECONDS1 = TimeUnit.SECONDS.convert(10, TimeUnit.HOURS);
public static Datahandler instance = new Datahandler();
private static Annotation strAnno;
private static Annotation strAnnoSentiment;
private static Annotation strAnnoJMWE;
private static CoreDocument coreDoc;
private static final ConcurrentMap<Integer, String> stringCache = new MapMaker().concurrencyLevel(6).makeMap();
private static ConcurrentMap<String, Annotation> pipelineAnnotationCache;
private static ConcurrentMap<String, Annotation> pipelineSentimentAnnotationCache;
private static ConcurrentMap<String, Annotation> jmweAnnotationCache;
private static ConcurrentMap<String, CoreDocument> coreDocumentAnnotationCache;
private static ConcurrentMap<String, SentimentValueCache> sentimentCachingMap = new MapMaker().concurrencyLevel(6).makeMap();
private LinkedHashMap<String, LinkedHashMap<String, Double>> lHMSMX = new LinkedHashMap();
private final Stopwatch stopwatch;
private static String similar = "";
private static String shiftReduceParserPath = "edu/stanford/nlp/models/srparser/englishSR.ser.gz";
private static String sentimentModel = "edu/stanford/nlp/models/sentiment/sentiment.ser.gz";
private static String lexParserEnglishRNN = "edu/stanford/nlp/models/lexparser/englishRNN.ser.gz";
private static String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger";
private static String nerModel = "edu/stanford/nlp/models/ner/english.all.3class.caseless.distsim.crf.ser.gz";
private static String nerModel2 = "edu/stanford/nlp/models/ner/english.conll.4class.caseless.distsim.crf.ser.gz";
private static String nerModel3 = "edu/stanford/nlp/models/ner/english.muc.7class.caseless.distsim.crf.ser.gz";
private static final String customStopWordList = "start,starts,period,periods,a,an,and,are,as,at,be,but,by,for,if,in,into,is,it,no,not,of,on,or,such,that,the,their,then,there,these,they,this,to,was,will,with";
private static MaxentTagger tagger;
private static String[] options = {"-maxLength", "100"};
private static Properties props = new Properties();
private static Properties propsSentiment = new Properties();
private static GrammaticalStructureFactory gsf;
private static LexicalizedParser lp;
private static TreebankLanguagePack tlp;
private static AbstractSequenceClassifier<CoreLabel> classifier;
// set up Stanford CoreNLP pipeline
private static final StanfordCoreNLP pipeline = getPipeLineSetUp();
private static StanfordCoreNLP pipelineSentiment;
public Datahandler() {
this.stopwatch = Stopwatch.createUnstarted();
this.jmweAnnotationCache = new MapMaker().concurrencyLevel(3).makeMap();
this.pipelineAnnotationCache = new MapMaker().concurrencyLevel(4).makeMap();
this.pipelineSentimentAnnotationCache = new MapMaker().concurrencyLevel(4).makeMap();
this.coreDocumentAnnotationCache = new MapMaker().concurrencyLevel(5).makeMap();
}
public static StanfordCoreNLP getPipeline() {
return pipeline;
}
private static StanfordCoreNLP getPipeLineSetUp() {
props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse");
props.setProperty("parse.model", shiftReduceParserPath);
props.setProperty("parse.maxlen", "90");
props.setProperty("parse.binaryTrees", "true");
props.setProperty("threads", "8");
props.setProperty("pos.maxlen", "90");
props.setProperty("tokenize.maxlen", "90");
props.setProperty("ssplit.maxlen", "90");
props.setProperty("lemma.maxlen", "90");
props.setProperty("ner.model", nerModel + "," + nerModel2 + "," + nerModel3);
props.setProperty("ner.combinationMode", "HIGH_RECALL");
props.setProperty("regexner.ignorecase", "true");
props.setProperty("ner.fine.regexner.ignorecase", "true");
props.setProperty("tokenize.options", "untokenizable=firstDelete");
return new StanfordCoreNLP(props);
}
public void shiftReduceParserInitiate() {
//got 8 cores
CountDownLatch cdl = new CountDownLatch(2);
new Thread(() -> {
try {
classifier = CRFClassifier.getClassifierNoExceptions(nerModel);
} catch (ClassCastException ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
cdl.countDown();
}).start();
new Thread(() -> {
propsSentiment.setProperty("parse.model", lexParserEnglishRNN);
propsSentiment.setProperty("sentiment.model", sentimentModel);
propsSentiment.setProperty("parse.maxlen", "90");
propsSentiment.setProperty("threads", "8");
propsSentiment.setProperty("pos.maxlen", "90");
propsSentiment.setProperty("tokenize.maxlen", "90");
propsSentiment.setProperty("ssplit.maxlen", "90");
propsSentiment.setProperty("annotators", "tokenize,ssplit,pos,parse,sentiment,lemma,stopword"); //coref too expensive memorywise
propsSentiment.setProperty("customAnnotatorClass.stopword", "FunctionLayer.StopwordAnnotator");
propsSentiment.setProperty(StopwordAnnotator.STOPWORDS_LIST, customStopWordList);
propsSentiment.setProperty("tokenize.options", "untokenizable=firstDelete");
pipelineSentiment = new StanfordCoreNLP(propsSentiment);
tagger = new MaxentTagger(taggerPath);
cdl.countDown();
}).start();
lp = LexicalizedParser.loadModel(lexParserEnglishRNN, options);
tlp = lp.getOp().langpack();
gsf = tlp.grammaticalStructureFactory();
try {
cdl.await();
} catch (InterruptedException ex) {
//System.out.println("cdl await interrupted: " + ex.getLocalizedMessage() + "\n");
}
System.out.println("finished shiftReduceParserInitiate\n");
}
public static AbstractSequenceClassifier<CoreLabel> getClassifier() {
return classifier;
}
public static void setClassifier(AbstractSequenceClassifier<CoreLabel> classifier) {
Datahandler.classifier = classifier;
}
public void updateStringCache() {
try {
checkIfUpdateStrings();
} catch (CustomError ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
}
public static GrammaticalStructureFactory getGsf() {
return gsf;
}
public static MaxentTagger getTagger() {
return tagger;
}
private Map<Integer, String> getCache() throws SQLException, IOException, CustomError {
return DataMapper.getAllStrings();
}
public int getlHMSMXSize() {
return lHMSMX.size();
}
public int getstringCacheSize() {
return stringCache.size();
}
public void initiateMYSQL() throws SQLException, IOException {
try {
DataMapper.createTables();
stringCache.putAll(getCache());
// lHMSMX = DataMapper.getAllRelationScores();
} catch (CustomError ex) {
Logger.getLogger(Datahandler.class
.getName()).log(Level.SEVERE, null, ex);
}
}
public void addHLstatsMessages() {
ConcurrentMap<String, Integer> hlStatsMessages = new MapMaker().concurrencyLevel(2).makeMap();
ConcurrentMap<Integer, String> strCacheLocal = stringCache;
Collection<String> strs = DataMapper.getHLstatsMessages().values();
for (String str : strs) {
if (hlStatsMessages.get(str) == null) {
hlStatsMessages.put(str, hlStatsMessages.size());
}
}
int capacity = 150;
hlStatsMessages.keySet().forEach(str -> {
if (!str.startsWith("!") && MessageResponseHandler.getStr().values().size() < capacity) {
String orElse = strCacheLocal.values().parallelStream().filter(e -> e.equals(str)).findAny().orElse(null);
if (orElse == null) {
MessageResponseHandler.getMessage(str);
}
}
});
}
public void instantiateAnnotationMapJMWE() {
if (!stringCache.isEmpty()) {
ConcurrentMap<String, Annotation> jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(stringCache.values());
for (Entry<String, Annotation> entries : jmweAnnotation.entrySet()) {
jmweAnnotationCache.put(entries.getKey(), entries.getValue());
}
}
}
public void instantiateAnnotationMap() {
if (!stringCache.isEmpty()) {
ConcurrentMap<String, Annotation> Annotationspipeline = new MapMaker().concurrencyLevel(2).makeMap();
ConcurrentMap<String, Annotation> AnnotationspipelineSentiment = new MapMaker().concurrencyLevel(2).makeMap();
stringCache.values().parallelStream().forEach(str -> {
Annotation strAnno = new Annotation(str);
strAnno.compact();
Annotationspipeline.put(str, strAnno);
Annotation strAnno2 = new Annotation(str);
strAnno2.compact();
AnnotationspipelineSentiment.put(str, strAnno2);
});
ConcurrentMap<String, CoreDocument> coreDocumentpipelineMap = getMultipleCoreDocumentsWaySuggestion(stringCache.values(), pipeline);
pipeline.annotate(Annotationspipeline.values());
pipelineSentiment.annotate(AnnotationspipelineSentiment.values());
Annotationspipeline.entrySet().forEach(pipelineEntry -> {
//relatively experimental change
pipelineEntry.getValue().compact();
pipelineAnnotationCache.put(pipelineEntry.getKey(), pipelineEntry.getValue());
});
AnnotationspipelineSentiment.entrySet().forEach(pipelineEntry -> {
pipelineEntry.getValue().compact();
pipelineSentimentAnnotationCache.put(pipelineEntry.getKey(), pipelineEntry.getValue());
});
coreDocumentpipelineMap.entrySet().stream().forEach(CD -> {
coreDocumentAnnotationCache.put(CD.getKey(), CD.getValue());
});
}
}
private ConcurrentMap<Integer, String> futuresReturnOverallEvaluation(List<SimilarityMatrix> similarityMatrixes) {
ConcurrentMap<Integer, String> strmapreturn = new MapMaker().concurrencyLevel(6).makeMap();
if (!similarityMatrixes.isEmpty()) {
int iterator = 0;
for (SimilarityMatrix SMX : similarityMatrixes) {
final Double scoreRelationNewMsgToRecentMsg = SMX.getDistance();
if (scoreRelationNewMsgToRecentMsg > 0.0) {
strmapreturn = addSMXToMapReturn(strmapreturn, SMX);
}
//System.out.println("scoreRelationNewMsgToRecentMsg: " + scoreRelationNewMsgToRecentMsg + "\niterator: " + iterator);
iterator++;
}
}
return strmapreturn;
}
private ConcurrentMap<Integer, String> addSMXToMapReturn(ConcurrentMap<Integer, String> strmapreturn, SimilarityMatrix SMX) {
if (!strmapreturn.containsValue(SMX.getPrimaryString())) {
strmapreturn.put(strmapreturn.size(), SMX.getPrimaryString());
String transmittedStr = SMX.getSecondaryString();
SentimentValueCache cacheValue1 = SMX.getCacheValue1();
SentimentValueCache cacheValue2 = SMX.getCacheValue2();
if (cacheValue1 != null && !sentimentCachingMap.keySet().contains(SMX.getPrimaryString())) {
sentimentCachingMap.put(SMX.getSecondaryString(), SMX.getCacheValue1());
}
if (cacheValue2 != null && !sentimentCachingMap.keySet().contains(transmittedStr)) {
sentimentCachingMap.put(transmittedStr, SMX.getCacheValue2());
}
}
return strmapreturn;
}
private List<SimilarityMatrix> StrComparringNoSentenceRelationMap(
ConcurrentMap<Integer, String> strCacheLocal, Collection<String> strCollection, ConcurrentMap<String, Annotation> localJMWEMap,
ConcurrentMap<String, Annotation> localPipelineAnnotation, ConcurrentMap<String, Annotation> localPipelineSentimentAnnotation,
ConcurrentMap<String, CoreDocument> localCoreDocumentMap) {
ExecutorService threadPool = Executors.newCachedThreadPool();
CompletionService<SimilarityMatrix> ecs = new ExecutorCompletionService<>(threadPool);
int index = 0;
int prefix_size = 150;
SentimentValueCache sentimentCacheStr = sentimentCachingMap.getOrDefault(strCollection, null);
List<SimilarityMatrix> smxReturnList = new ArrayList();
if (strCacheLocal.size() < prefix_size)
{
for (String colStr : strCollection)
{
strCacheLocal.put(strCacheLocal.size(), colStr);
}
}
for (String str1 : strCollection) {
for (String str : strCollection) {
if (!str.equals(str1)) {
SimilarityMatrix SMXInit = new SimilarityMatrix(str, str1);
SentimentValueCache sentimentCacheStr1 = sentimentCachingMap.getOrDefault(str1, null);
Callable<SimilarityMatrix> worker;
if (stringCache.size() < prefix_size) {
worker = new SentimentAnalyzerTest(str, str1, SMXInit,
localJMWEMap.get(str), localJMWEMap.get(str1), localPipelineAnnotation.get(str),
localPipelineAnnotation.get(str1), localPipelineSentimentAnnotation.get(str),
localPipelineSentimentAnnotation.get(str1), localCoreDocumentMap.get(str), localCoreDocumentMap.get(str1), sentimentCacheStr, sentimentCacheStr1);
} else {
worker = new SentimentAnalyzerTest(str, str1, SMXInit,
localJMWEMap.get(str), jmweAnnotationCache.get(str1), localPipelineAnnotation.get(str),
pipelineAnnotationCache.get(str1), localPipelineSentimentAnnotation.get(str),
pipelineSentimentAnnotationCache.get(str1), localCoreDocumentMap.get(str), coreDocumentAnnotationCache.get(str1), sentimentCacheStr, sentimentCacheStr1);
}
ecs.submit(worker);
index++;
if (index % 1000 == 0 && index > 0) {
for (int i = 0; i < index; i++) {
try {
Future<SimilarityMatrix> take = ecs.take();
SimilarityMatrix smx = take.get();
if (smx != null) {
smxReturnList.add(smx);
}
} catch (InterruptedException | ExecutionException ex) {
//
}
}
index = 0;
//System.out.println("smxReturnList size iterating ECS.take(): " + smxReturnList.size());
}
}
}
}
for (int i = 0; i < index; i++) {
try {
Future<SimilarityMatrix> take = ecs.take();
SimilarityMatrix smx = take.get();
double distance_requirement = 1500.0;
if (smx != null && smx.getDistance() > distance_requirement) {
smxReturnList.add(smx);
}
} catch (InterruptedException | ExecutionException ex) {
//
}
}
//System.out.println("smxReturnList size: " + smxReturnList.size());
threadPool.shutdown();
return smxReturnList;
}
private ConcurrentMap<Integer, String> stringIteratorComparator(ConcurrentMap<Integer, String> strmap,
ConcurrentMap<Integer, String> strCacheLocal, ConcurrentMap<String, Annotation> localJMWEMap,
ConcurrentMap<String, Annotation> localPipelineAnnotation, ConcurrentMap<String, Annotation> localPipelineSentimentAnnotation,
ConcurrentMap<String, CoreDocument> localCoreDocumentMap) {
//System.out.println("strmap siuze: " + strmap.size());
List<SimilarityMatrix> StrComparringNoSentenceRelationMap = StrComparringNoSentenceRelationMap(strCacheLocal, strmap.values(),
localJMWEMap, localPipelineAnnotation, localPipelineSentimentAnnotation, localCoreDocumentMap);
Collections.sort(StrComparringNoSentenceRelationMap, (e1, e2) -> e1.getPrimaryString().compareTo(e2.getPrimaryString()));
ConcurrentMap<Integer, String> strmapreturn = futuresReturnOverallEvaluation(StrComparringNoSentenceRelationMap);
//System.out.println("strmapreturn size: " + strmapreturn.size());
return strmapreturn;
}
private ConcurrentMap<Integer, String> removeNonSensicalStrings(ConcurrentMap<Integer, String> strmap) {
final ConcurrentMap<Integer, String> strCacheLocal = stringCache;
final ConcurrentMap<String, Annotation> localJMWEMap = getMultipleJMWEAnnotation(strmap.values());
final ConcurrentMap<String, Annotation> localPipelineAnnotation = getMultiplePipelineAnnotation(strmap.values());
final ConcurrentMap<String, Annotation> localPipelineSentimentAnnotation = getMultiplePipelineSentimentAnnotation(strmap.values());
final ConcurrentMap<String, CoreDocument> localCoreDocumentMap = getMultipleCoreDocumentsWaySuggestion(strmap.values(), pipeline);
return stringIteratorComparator(strmap, strCacheLocal, localJMWEMap, localPipelineAnnotation, localPipelineSentimentAnnotation, localCoreDocumentMap);
}
public synchronized void checkIfUpdateStrings() throws CustomError {
if (stopwatch.elapsed(TimeUnit.SECONDS) >= EXPIRE_TIME_IN_SECONDS || !stopwatch.isRunning()) {
ConcurrentMap<Integer, String> str = MessageResponseHandler.getStr();
System.out.println("str size: " + str.size());
str = filterContent(str);
str = removeNonSensicalStrings(str);
//System.out.println("removeNonSensicalStrings str size POST: " + str.size() + "\n");
str = annotationCacheUpdate(str);
System.out.println("annotationCacheUpdate str size POST: " + str.size() + "\n");
ConcurrentMap<Integer, String> strf = str;
if (!stringCache.isEmpty()) {
new Thread(() -> {
try {
DataMapper.InsertMYSQLStrings(strf);
} catch (CustomError ex) {
Logger.getLogger(Datahandler.class
.getName()).log(Level.SEVERE, null, ex);
}
MessageResponseHandler.setStr(new MapMaker().concurrencyLevel(6).makeMap());
}).start();
} else {
try {
DataMapper.InsertMYSQLStrings(strf);
} catch (CustomError ex) {
Logger.getLogger(Datahandler.class
.getName()).log(Level.SEVERE, null, ex);
}
MessageResponseHandler.setStr(new MapMaker().concurrencyLevel(2).makeMap());
}
if (!stopwatch.isRunning()) {
stopwatch.start();
} else {
stopwatch.reset();
}
}
}
private String trimString(String str) {
str = str.trim();
if (str.startsWith("<@")) {
str = str.substring(str.indexOf("> ") + 2);
}
return str;
}
private String getResponseFutures(String strF) {
List<String> values_copy = new ArrayList<String>(stringCache.values());
Collections.shuffle(values_copy);
double preRelationUserCounters = -155000.0;
List<String> concurrentRelations = new ArrayList();
for (String str1 : values_copy) {
if (!strF.equals(str1)) {
SentimentValueCache sentimentCacheStr1 = sentimentCachingMap.getOrDefault(str1, null);
Callable<SimilarityMatrix> worker = new SentimentAnalyzerTest(strF, str1, new SimilarityMatrix(strF, str1),
strAnnoJMWE, jmweAnnotationCache.get(str1), strAnno,
pipelineAnnotationCache.get(str1), strAnnoSentiment,
pipelineSentimentAnnotationCache.get(str1), coreDoc, coreDocumentAnnotationCache.get(str1), null, sentimentCacheStr1);
try {
SimilarityMatrix getSMX = worker.call();
if (getSMX != null) {
Double scoreRelationLastUserMsg = getSMX.getDistance();
if (scoreRelationLastUserMsg > preRelationUserCounters) {
preRelationUserCounters = scoreRelationLastUserMsg;
concurrentRelations.add(getSMX.getSecondaryString());
//System.out.println("secondary: " + getSMX.getSecondaryString() + "\nDistance: " + getSMX.getDistance() + "\n");
//System.out.println("SUCESS concurrentRelationsMap size: " + concurrentRelations.size() + "\n");
}
}
} catch (Exception ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
}
}
StringBuilder SB = new StringBuilder();
double randomLenghtPermit = strF.length() * ((Math.random() * Math.random() * Math.random()) * 5);
Collections.reverse(concurrentRelations);
if (concurrentRelations.isEmpty()) {
return "failure, preventing stuckness";
}
String firstRelation = concurrentRelations.get(0);
for (String secondaryRelation : concurrentRelations) {
if (SB.toString().length() > randomLenghtPermit && !SB.toString().isEmpty()) {
break;
}
boolean append = appendToString(firstRelation, secondaryRelation);
if (append) {
SB.append(secondaryRelation).append(" ");
}
}
return SB.toString();
}
private boolean appendToString(String firstRelation, String secondaryRelation) {
if (firstRelation.equals(secondaryRelation)) {
return true;
}
Double scoreRelationStrF = getScoreRelationStrF(firstRelation, secondaryRelation);
if (scoreRelationStrF > 1900) {
return true;
}
return false;
}
public String getResponseMsg(String str) throws CustomError {
String strF = trimString(str);
getSingularAnnotation(strF);
return getResponseFutures(strF);
}
public void getSingularAnnotation(String str) {
strAnno = new Annotation(str);
strAnno.compact();
pipeline.annotate(strAnno);
strAnnoSentiment = new Annotation(str);
strAnnoSentiment.compact();
pipelineSentiment.annotate(strAnnoSentiment);
List<String> notactualList = new ArrayList();
notactualList.add(str);
ConcurrentMap<String, Annotation> jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(notactualList);
strAnnoJMWE = jmweAnnotation.values().iterator().next();
strAnnoJMWE.compact();
CoreDocument coreDocument = new CoreDocument(str);
pipeline.annotate(coreDocument);
coreDoc = coreDocument;
}
private static ConcurrentMap<String, Annotation> getMultipleJMWEAnnotation(Collection<String> str) {
ConcurrentMap<String, Annotation> jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(str);
return jmweAnnotation;
}
private static ConcurrentMap<String, Annotation> getMultiplePipelineAnnotation(Collection<String> str) {
ConcurrentMap<String, Annotation> pipelineAnnotationMap = new MapMaker().concurrencyLevel(2).makeMap();
for (String str1 : str) {
Annotation strAnno1 = new Annotation(str1);
pipelineAnnotationMap.put(str1, strAnno1);
}
pipeline.annotate(pipelineAnnotationMap.values());
return pipelineAnnotationMap;
}
private static ConcurrentMap<String, Annotation> getMultiplePipelineSentimentAnnotation(Collection<String> str) {
ConcurrentMap<String, Annotation> pipelineAnnotationMap = new MapMaker().concurrencyLevel(2).makeMap();
for (String str1 : str) {
Annotation strAnno1 = new Annotation(str1);
pipelineAnnotationMap.put(str1, strAnno1);
}
pipelineSentiment.annotate(pipelineAnnotationMap.values());
return pipelineAnnotationMap;
}
private Double getScoreRelationNewMsgToRecentMsg(String str, String mostRecentMsg) {
SimilarityMatrix SMX = new SimilarityMatrix(str, mostRecentMsg);
SentimentValueCache cacheSentiment1 = sentimentCachingMap.getOrDefault(str, null);
SentimentValueCache cacheSentiment2 = sentimentCachingMap.getOrDefault(mostRecentMsg, null);
Callable<SimilarityMatrix> worker = new SentimentAnalyzerTest(str, mostRecentMsg, SMX,
jmweAnnotationCache.get(str), jmweAnnotationCache.get(mostRecentMsg), pipelineAnnotationCache.get(str),
pipelineAnnotationCache.get(mostRecentMsg), pipelineSentimentAnnotationCache.get(str),
pipelineSentimentAnnotationCache.get(mostRecentMsg), coreDocumentAnnotationCache.get(str),
coreDocumentAnnotationCache.get(mostRecentMsg), cacheSentiment1, cacheSentiment2);
SimilarityMatrix callSMX = null;
try {
callSMX = worker.call();
} catch (Exception ex) {
Logger.getLogger(Datahandler.class
.getName()).log(Level.SEVERE, null, ex);
}
if (callSMX != null) {
double smxDistance = callSMX.getDistance();
return smxDistance;
}
return 0.0;
}
private Double getScoreRelationStrF(String str, String mostRecentMsg) {
SimilarityMatrix SMX = new SimilarityMatrix(str, mostRecentMsg);
SentimentValueCache cacheSentiment1 = sentimentCachingMap.getOrDefault(str, null);
SentimentValueCache cacheSentiment2 = sentimentCachingMap.getOrDefault(mostRecentMsg, null);
Callable<SimilarityMatrix> worker = new SentimentAnalyzerTest(str, mostRecentMsg, SMX,
strAnnoJMWE, jmweAnnotationCache.get(mostRecentMsg), strAnno,
pipelineAnnotationCache.get(mostRecentMsg), strAnnoSentiment,
pipelineSentimentAnnotationCache.get(mostRecentMsg), coreDoc, coreDocumentAnnotationCache.get(mostRecentMsg), cacheSentiment1, cacheSentiment2);
SimilarityMatrix callSMX = null;
try {
callSMX = worker.call();
} catch (Exception ex) {
Logger.getLogger(Datahandler.class
.getName()).log(Level.SEVERE, null, ex);
}
if (callSMX != null) {
double smxDistance = callSMX.getDistance();
return smxDistance;
}
return 0.0;
}
public static ConcurrentMap<Integer, String> filterContent(ConcurrentMap<Integer, String> str) {
ConcurrentMap<Integer, String> strlistreturn = new MapMaker().concurrencyLevel(2).makeMap();
str.values().forEach(str1 -> {
if (!str1.isEmpty() && str1.length() > 3) {
str1 = str1.trim();
if (str1.contains("PM*")) {
str1 = str1.substring(str1.indexOf("PM*") + 3);
}
if (str1.contains("AM*")) {
str1 = str1.substring(str1.indexOf("AM*") + 3);
}
if (str1.contains("?") || str1.contains("°"))
{
if (!str1.contains("http"))
{
str1 = str1.replace("?", " <:wlenny:514861023002624001> ");
str1 = str1.replace("°", " <:wlenny:514861023002624001> ");
}
}
if (str1.contains("(Counter-Terrorist)")) {
str1 = str1.replace("(Counter-Terrorist)", " ");
}
if (str1.contains("(Terrorist)")) {
str1 = str1.replace("(Terrorist)", " ");
}
if (str1.contains("(Spectator)")) {
str1 = str1.replace("(Spectator)", " ");
}
if (str1.contains("*DEAD*")) {
str1 = str1.replace("*DEAD*", " ");
}
if (str1.contains("{red}")) {
str1 = str1.replace("{red}", " ");
}
if (str1.contains("{orange}")) {
str1 = str1.replace("{orange}", " ");
}
if (str1.contains("{yellow}")) {
str1 = str1.replace("{yellow}", " ");
}
if (str1.contains("{green}")) {
str1 = str1.replace("{green}", " ");
}
if (str1.contains("{lightblue}")) {
str1 = str1.replace("{lightblue}", " ");
}
if (str1.contains("{blue}")) {
str1 = str1.replace("{blue}", " ");
}
if (str1.contains("{purple}")) {
str1 = str1.replace("{purple}", " ");
}
if (str1.contains("{white}")) {
str1 = str1.replace("{white}", " ");
}
if (str1.contains("{fullblue}")) {
str1 = str1.replace("{fullblue}", " ");
}
if (str1.contains("{cyan}")) {
str1 = str1.replace("{cyan}", " ");
}
if (str1.contains("{lime}")) {
str1 = str1.replace("{lime}", " ");
}
if (str1.contains("{deeppink}")) {
str1 = str1.replace("{deeppink}", " ");
}
if (str1.contains("{slategray}")) {
str1 = str1.replace("{slategray}", " ");
}
if (str1.contains("{dodgerblue}")) {
str1 = str1.replace("{dodgerblue}", " ");
}
if (str1.contains("{black}")) {
str1 = str1.replace("{black}", " ");
}
if (str1.contains("{orangered}")) {
str1 = str1.replace("{orangered}", " ");
}
if (str1.contains("{darkorchid}")) {
str1 = str1.replace("{darkorchid}", " ");
}
if (str1.contains("{pink}")) {
str1 = str1.replace("{pink}", " ");
}
if (str1.contains("{lightyellow}")) {
str1 = str1.replace("{lightyellow}", " ");
}
if (str1.contains("{chocolate}")) {
str1 = str1.replace("{chocolate}", " ");
}
if (str1.contains("{beige}")) {
str1 = str1.replace("{beige}", " ");
}
if (str1.contains("{azure}")) {
str1 = str1.replace("{azure}", " ");
}
if (str1.contains("{yellowgreen}")) {
str1 = str1.replace("{yellowgreen}", " ");
}
str1 = str1.trim();
if (str1.length() > 2 && (!str1.startsWith("!"))) {
strlistreturn.put(strlistreturn.size(), str1);
}
}
});
return strlistreturn;
}
private ConcurrentMap<Integer, String> annotationCacheUpdate(ConcurrentMap<Integer, String> strmap) {
ConcurrentMap<String, Annotation> jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(strmap.values());
for (Entry<String, Annotation> jmweitr : jmweAnnotation.entrySet()) {
jmweAnnotationCache.put(jmweitr.getKey(), jmweitr.getValue());
}
ConcurrentMap<String, Annotation> Annotationspipeline = new MapMaker().concurrencyLevel(4).makeMap();
ConcurrentMap<String, Annotation> AnnotationspipelineSentiment = new MapMaker().concurrencyLevel(4).makeMap();
ConcurrentMap<String, CoreDocument> coreDocumentpipelineMap = getMultipleCoreDocumentsWaySuggestion(strmap.values(), pipeline);
strmap.values().forEach(str -> {
Annotation strAnno1 = new Annotation(str);
Annotationspipeline.put(str, strAnno1);
Annotation strAnno2 = new Annotation(str);
AnnotationspipelineSentiment.put(str, strAnno2);
stringCache.put(stringCache.size() + 1, str);
});
pipeline.annotate(Annotationspipeline.values());
pipelineSentiment.annotate(AnnotationspipelineSentiment.values());
Annotationspipeline.entrySet().forEach(pipelineEntry -> {
if (pipelineEntry != null) {
pipelineAnnotationCache.put(pipelineEntry.getKey(), pipelineEntry.getValue());
}
});
AnnotationspipelineSentiment.entrySet().forEach(pipelineEntry -> {
if (pipelineEntry != null) {
pipelineSentimentAnnotationCache.put(pipelineEntry.getKey(), pipelineEntry.getValue());
}
});
coreDocumentpipelineMap.entrySet().forEach(coreDocumentEntry -> {
coreDocumentAnnotationCache.put(coreDocumentEntry.getKey(), coreDocumentEntry.getValue());
});
return strmap;
}
public int getMessageOverHead() {
return stringCache.values().size() - (stringCache.values().size() / 10);
}
public void update_autismo_socket_msg() {
try {
try (DatagramSocket serverSocket = new DatagramSocket(48477)) {
try (DatagramSocket serverSocket1 = new DatagramSocket(48478)) {
byte[] receiveData = new byte[4096];
InetAddress IPAddress = InetAddress.getByName("144.76.218.19");
DatagramPacket receivePacket = new DatagramPacket(receiveData, receiveData.length);
while (true) {
serverSocket.receive(receivePacket);
String sentence = new String(receivePacket.getData(), 0, receivePacket.getLength());
sentence = sentence.replace("clientmessage:", "");
String getResponseMsg = getResponseMsg(sentence);
byte[] sendData = getResponseMsg.getBytes("UTF-8");
DatagramPacket sendPacket = new DatagramPacket(sendData, sendData.length, IPAddress, 48477);
serverSocket.send(sendPacket);
receivePacket = new DatagramPacket(receiveData, receiveData.length);
serverSocket1.receive(receivePacket);
sentence = new String(receivePacket.getData(), 0, receivePacket.getLength());
sentence = sentence.replace("clientmessage:", "");
getResponseMsg = getResponseMsg(sentence);
sendData = getResponseMsg.getBytes("UTF-8");
sendPacket = new DatagramPacket(sendData, sendData.length, IPAddress, 48478);
serverSocket1.send(sendPacket);
}
}
} catch (CustomError ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
} catch (SocketException ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
} catch (UnsupportedEncodingException ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
} catch (IOException ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
}
private static class AnnotationCollector<T> implements Consumer<T> {
private static int i = 0;
private List<T> annotationsT = new ArrayList();
@Override
public void accept(T ann) {
//System.out.println("adding ann: " + ann.toString());
annotationsT.add(ann);
}
}
public static ConcurrentMap<String, CoreDocument> getMultipleCoreDocumentsWaySuggestion(Collection<String> str, StanfordCoreNLP localNLP) {
AnnotationCollector<Annotation> annCollector = new AnnotationCollector();
for (String exampleString : str) {
localNLP.annotate(new Annotation(exampleString), annCollector);
annCollector.i++;
//System.out.println("iterator: " + annCollector.i + "\nstr size: " + str.size() + "\n");
}
try {
Thread.sleep(8000);
} catch (InterruptedException ex) {
Logger.getLogger(Datahandler.class.getName()).log(Level.SEVERE, null, ex);
}
ConcurrentMap<String, CoreDocument> annotationreturnMap = new MapMaker().concurrencyLevel(6).makeMap();
for (Annotation ann : annCollector.annotationsT) {
if (ann != null) {
ann.compact();
CoreDocument CD = new CoreDocument(ann);
annotationreturnMap.put(CD.text(), CD);
//System.out.println("CD text:" + CD.text() + "\niterator: " + iterator + "\nsize: " + annCollector.annotationsT.size());
}
}
return annotationreturnMap;
}
}

804
ArtificialAutism/src/main/java/FunctionLayer/Datahandler.kt Normal file
View File

@ -0,0 +1,804 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
package FunctionLayer
import DataLayer.DataMapper
import FunctionLayer.StanfordParser.SentimentAnalyzerTest
import FunctionLayer.StanfordParser.SentimentValueCache
import com.google.common.base.Stopwatch
import com.google.common.collect.MapMaker
import edu.stanford.nlp.ie.AbstractSequenceClassifier
import edu.stanford.nlp.ie.crf.CRFClassifier
import edu.stanford.nlp.ling.CoreLabel
import edu.stanford.nlp.parser.lexparser.LexicalizedParser
import edu.stanford.nlp.pipeline.Annotation
import edu.stanford.nlp.pipeline.CoreDocument
import edu.stanford.nlp.pipeline.StanfordCoreNLP
import edu.stanford.nlp.tagger.maxent.MaxentTagger
import edu.stanford.nlp.trees.GrammaticalStructureFactory
import edu.stanford.nlp.trees.TreebankLanguagePack
import kotlinx.coroutines.*
import java.io.IOException
import java.io.UnsupportedEncodingException
import java.lang.Runnable
import java.net.DatagramPacket
import java.net.DatagramSocket
import java.net.InetAddress
import java.net.SocketException
import java.sql.SQLException
import java.util.*
import java.util.concurrent.*
import java.util.function.Consumer
import java.util.logging.Level
import java.util.logging.Logger
import kotlin.collections.ArrayList
/**
*
* @author install1
*/
class Datahandler {
private val lHMSMX: LinkedHashMap<Any?, Any?> = LinkedHashMap<Any?, Any?>()
private val stopwatch: Stopwatch
fun shiftReduceParserInitiate() {
val cdl = CountDownLatch(2)
Thread(Runnable {
try {
classifier = CRFClassifier.getClassifierNoExceptions(nerModel)
} catch (ex: ClassCastException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
cdl.countDown()
}).start()
Thread(Runnable {
propsSentiment.setProperty("parse.model", lexParserEnglishRNN)
propsSentiment.setProperty("sentiment.model", sentimentModel)
propsSentiment.setProperty("parse.maxlen", "90")
propsSentiment.setProperty("threads", "5")
propsSentiment.setProperty("pos.maxlen", "90")
propsSentiment.setProperty("tokenize.maxlen", "90")
propsSentiment.setProperty("ssplit.maxlen", "90")
propsSentiment.setProperty("annotators", "tokenize,ssplit,pos,parse,sentiment,lemma,stopword") //coref too expensive memorywise
propsSentiment.setProperty("customAnnotatorClass.stopword", "FunctionLayer.StopwordAnnotator")
propsSentiment.setProperty(StopwordAnnotator.STOPWORDS_LIST, customStopWordList)
propsSentiment.setProperty("tokenize.options", "untokenizable=firstKeep")
pipelineSentiment = StanfordCoreNLP(propsSentiment)
tagger = MaxentTagger(taggerPath)
cdl.countDown()
}).start()
lp = LexicalizedParser.loadModel(lexParserEnglishRNN, *options)
tlp = lp.getOp().langpack()
gsf = tlp.grammaticalStructureFactory()
try {
cdl.await()
} catch (ex: InterruptedException) {
//System.out.println("cdl await interrupted: " + ex.getLocalizedMessage() + "\n");
}
println("finished shiftReduceParserInitiate\n")
}
fun updateStringCache() {
try {
checkIfUpdateStrings()
} catch (ex: CustomError) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
}
@get:Throws(SQLException::class, IOException::class, CustomError::class)
private val cache: Map<Int, String?>
private get() = DataMapper.getAllStrings()
@Throws(SQLException::class, IOException::class)
fun initiateMYSQL() {
try {
DataMapper.createTables()
stringCache.putAll(cache)
// lHMSMX = DataMapper.getAllRelationScores();
} catch (ex: CustomError) {
Logger.getLogger(Datahandler::class.java
.name).log(Level.SEVERE, null, ex)
}
}
fun instantiateAnnotationMapJMWE() {
if (!stringCache.isEmpty()) {
val jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(stringCache.values)
for ((key, value) in jmweAnnotation) {
jmweAnnotationCache[key] = value
}
}
}
fun instantiateAnnotationMap() {
if (!stringCache.isEmpty()) {
val Annotationspipeline = MapMaker().concurrencyLevel(4).makeMap<String?, Annotation>()
val AnnotationspipelineSentiment = MapMaker().concurrencyLevel(4).makeMap<String?, Annotation>()
runBlocking {
val job = launch(Dispatchers.Default) {
for (str in stringCache.values) {
val strAnno = Annotation(str)
strAnno.compact()
Annotationspipeline[str] = strAnno
val strAnno2 = Annotation(str)
strAnno2.compact()
AnnotationspipelineSentiment[str] = strAnno2
}
yield()
}
job.join();
}
val coreDocumentpipelineMap = getMultipleCoreDocumentsWaySuggestion(stringCache.values, pipeline)
pipeline.annotate(Annotationspipeline.values)
pipelineSentiment!!.annotate(AnnotationspipelineSentiment.values)
runBlocking {
val job = launch(Dispatchers.Default) {
for (i in Annotationspipeline.entries) {
i.value.compact()
pipelineAnnotationCache[i.key] = i.value
}
yield()
}
job.join()
}
runBlocking {
val job = launch(Dispatchers.Default) {
for (i in AnnotationspipelineSentiment.entries) {
i.value.compact()
pipelineSentimentAnnotationCache[i.key] = i.value
}
yield()
}
job.join()
}
runBlocking {
val job = launch(Dispatchers.Default) {
for (i in coreDocumentpipelineMap.entries) {
coreDocumentAnnotationCache[i.key] = i.value
}
yield()
}
job.join()
}
}
}
private fun futuresReturnOverallEvaluation(similarityMatrixes: List<SimilarityMatrix?>): ConcurrentMap<Int?, String?> {
var strmapreturn = MapMaker().concurrencyLevel(6).makeMap<Int?, String?>()
if (!similarityMatrixes.isEmpty()) {
var iterator = 0
for (SMX in similarityMatrixes) {
val scoreRelationNewMsgToRecentMsg = SMX!!.distance
if (scoreRelationNewMsgToRecentMsg > 0.0) {
strmapreturn = addSMXToMapReturn(strmapreturn, SMX)
}
//System.out.println("scoreRelationNewMsgToRecentMsg: " + scoreRelationNewMsgToRecentMsg + "\niterator: " + iterator);
iterator++
}
}
return strmapreturn
}
private fun addSMXToMapReturn(strmapreturn: ConcurrentMap<Int?, String?>, SMX: SimilarityMatrix?): ConcurrentMap<Int?, String?> {
if (!strmapreturn.containsValue(SMX!!.primaryString)) {
strmapreturn[strmapreturn.size] = SMX.primaryString
val transmittedStr = SMX.secondaryString
val cacheValue1 = SMX.cacheValue1
val cacheValue2 = SMX.cacheValue2
if (cacheValue1 != null && !sentimentCachingMap.keys.contains(SMX.primaryString)) {
sentimentCachingMap[SMX.secondaryString] = SMX.cacheValue1
}
if (cacheValue2 != null && !sentimentCachingMap.keys.contains(transmittedStr)) {
sentimentCachingMap[transmittedStr] = SMX.cacheValue2
}
}
return strmapreturn
}
private fun StrComparringNoSentenceRelationMap(strCacheLocal: ConcurrentMap<Int, String?>, strCollection: Collection<String?>, localJMWEMap: ConcurrentMap<String, Annotation>,
localPipelineAnnotation: ConcurrentMap<String?, Annotation>, localPipelineSentimentAnnotation: ConcurrentMap<String?, Annotation>,
localCoreDocumentMap: ConcurrentMap<String, CoreDocument>): List<SimilarityMatrix?> {
//TODO here
val distance_requirement = 15500.0
val prefix_size = 150
val smxReturnList: ArrayList<SimilarityMatrix> = ArrayList<SimilarityMatrix>()
runBlocking {
val job = launch(Dispatchers.Default) {
for (j in strCollection) {
for (i in strCollection) {
if (j != i) {
val SMXInit = SimilarityMatrix(j, i)
val sentimentCacheStr1 = sentimentCachingMap.getOrDefault(i, null)
val sentimentCacheStr = sentimentCachingMap.getOrDefault(j, null)
var sentimentAnalyzerTest: SentimentAnalyzerTest? = null
if (stringCache.size < prefix_size) {
sentimentAnalyzerTest = SentimentAnalyzerTest(j, i, SMXInit,
localJMWEMap[j], localJMWEMap[i], localPipelineAnnotation[j],
localPipelineAnnotation[i], localPipelineSentimentAnnotation[j],
localPipelineSentimentAnnotation[i], localCoreDocumentMap[j], localCoreDocumentMap[i],
sentimentCacheStr, sentimentCacheStr1)
} else {
sentimentAnalyzerTest = SentimentAnalyzerTest(j, i, SMXInit,
localJMWEMap[j], jmweAnnotationCache[i], localPipelineAnnotation[j],
pipelineAnnotationCache[i], localPipelineSentimentAnnotation[j],
pipelineSentimentAnnotationCache[i], localCoreDocumentMap[j],
coreDocumentAnnotationCache[i], sentimentCacheStr, sentimentCacheStr1)
}
val call = sentimentAnalyzerTest.call();
if (call != null && call.distance > distance_requirement) {
smxReturnList.add(call)
}
}
}
}
yield()
}
job.join()
}
return smxReturnList
}
private fun stringIteratorComparator(strmap: ConcurrentMap<Int?, String?>,
strCacheLocal: ConcurrentMap<Int, String?>, localJMWEMap: ConcurrentMap<String, Annotation>,
localPipelineAnnotation: ConcurrentMap<String?, Annotation>, localPipelineSentimentAnnotation: ConcurrentMap<String?, Annotation>,
localCoreDocumentMap: ConcurrentMap<String, CoreDocument>): ConcurrentMap<Int?, String?> {
//System.out.println("strmap siuze: " + strmap.size());
val ComparringNoSentenceRelationMap: List<SimilarityMatrix> = StrComparringNoSentenceRelationMap(strCacheLocal, strmap.values,
localJMWEMap, localPipelineAnnotation, localPipelineSentimentAnnotation, localCoreDocumentMap) as List<SimilarityMatrix>
Collections.sort(ComparringNoSentenceRelationMap, Comparator<SimilarityMatrix> { e1: SimilarityMatrix, e2: SimilarityMatrix -> e1.primaryString.compareTo(e2.primaryString) })
//System.out.println("strmapreturn size: " + strmapreturn.size());
return futuresReturnOverallEvaluation(ComparringNoSentenceRelationMap)
}
private fun removeNonSensicalStrings(strmap: ConcurrentMap<Int?, String?>): ConcurrentMap<Int?, String?> {
val strCacheLocal = stringCache
val localJMWEMap = getMultipleJMWEAnnotation(strmap.values)
val localPipelineAnnotation = getMultiplePipelineAnnotation(strmap.values)
val localPipelineSentimentAnnotation = getMultiplePipelineSentimentAnnotation(strmap.values)
val localCoreDocumentMap = getMultipleCoreDocumentsWaySuggestion(strmap.values, pipeline)
return stringIteratorComparator(strmap, strCacheLocal, localJMWEMap, localPipelineAnnotation, localPipelineSentimentAnnotation, localCoreDocumentMap)
}
@Synchronized
@Throws(CustomError::class)
fun checkIfUpdateStrings() {
if (stopwatch.elapsed(TimeUnit.SECONDS) >= EXPIRE_TIME_IN_SECONDS || !stopwatch.isRunning) {
var str = MessageResponseHandler.getStr()
println("str size: " + str.size)
str = filterContent(str)
str = removeNonSensicalStrings(str)
//System.out.println("removeNonSensicalStrings str size POST: " + str.size() + "\n");
str = annotationCacheUpdate(str)
println("""
annotationCacheUpdate str size POST: ${str.size}
""".trimIndent())
val strf = str
if (!stringCache.isEmpty()) {
Thread(Runnable {
try {
DataMapper.InsertMYSQLStrings(strf)
} catch (ex: CustomError) {
Logger.getLogger(Datahandler::class.java
.name).log(Level.SEVERE, null, ex)
}
MessageResponseHandler.setStr(MapMaker().concurrencyLevel(6).makeMap())
}).start()
} else {
try {
DataMapper.InsertMYSQLStrings(strf)
} catch (ex: CustomError) {
Logger.getLogger(Datahandler::class.java
.name).log(Level.SEVERE, null, ex)
}
MessageResponseHandler.setStr(MapMaker().concurrencyLevel(6).makeMap())
}
if (!stopwatch.isRunning) {
stopwatch.start()
} else {
stopwatch.reset()
}
}
}
private fun trimString(str: String): String {
var str = str
str = str.trim { it <= ' ' }
if (str.startsWith("<@")) {
str = str.substring(str.indexOf("> ") + 2)
}
return str
}
private fun getResponseFutures(strF: String): String {
val values_copy: List<String?> = ArrayList(stringCache.values)
Collections.shuffle(values_copy)
var preRelationUserCounters = -155000.0
val concurrentRelations: MutableList<String?> = arrayListOf()
runBlocking {
val job = launch(Dispatchers.Default) {
for (str1 in values_copy) {
if (strF != str1) {
val sentimentCacheStr1 = sentimentCachingMap.getOrDefault(str1, null)
val worker: Callable<SimilarityMatrix> = SentimentAnalyzerTest(strF, str1, SimilarityMatrix(strF, str1),
strAnnoJMWE, jmweAnnotationCache[str1], strAnno,
pipelineAnnotationCache[str1], strAnnoSentiment,
pipelineSentimentAnnotationCache[str1], coreDoc, coreDocumentAnnotationCache[str1],
null, sentimentCacheStr1)
try {
val getSMX = worker.call()
if (getSMX != null) {
val scoreRelationLastUserMsg = getSMX.distance
if (scoreRelationLastUserMsg > preRelationUserCounters) {
preRelationUserCounters = scoreRelationLastUserMsg
concurrentRelations.add(getSMX.secondaryString)
}
}
} catch (ex: Exception) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
}
}
yield()
}
job.join()
}
val SB = StringBuilder()
val randomLenghtPermit = strF.length * (Math.random() * Math.random() * Math.random() * 5)
Collections.reverse(concurrentRelations)
if (concurrentRelations.isEmpty()) {
return "failure, preventing stuckness"
}
val firstRelation = concurrentRelations[0]
runBlocking {
val job = launch(Dispatchers.Default) {
for (secondaryRelation in concurrentRelations) {
if (SB.toString().length > randomLenghtPermit && !SB.toString().isEmpty()) {
break
}
val append = appendToString(firstRelation, secondaryRelation)
if (append) {
SB.append(secondaryRelation).append(" ")
}
}
yield()
}
job.join()
}
return SB.toString()
}
private fun appendToString(firstRelation: String?, secondaryRelation: String?): Boolean {
if (firstRelation == secondaryRelation) {
return true
}
val scoreRelationStrF = getScoreRelationStrF(firstRelation, secondaryRelation)
return if (scoreRelationStrF > 1900) {
true
} else false
}
@Throws(CustomError::class)
fun getResponseMsg(str: String): String {
val strF = trimString(str)
getSingularAnnotation(strF)
return getResponseFutures(strF)
}
fun getSingularAnnotation(str: String?) {
strAnno = Annotation(str)
strAnno!!.compact()
pipeline.annotate(strAnno)
strAnnoSentiment = Annotation(str)
strAnnoSentiment!!.compact()
pipelineSentiment!!.annotate(strAnnoSentiment)
val notactualList: MutableList<String?> = arrayListOf()
notactualList.add(str)
val jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(notactualList)
strAnnoJMWE = jmweAnnotation.values.iterator().next()
strAnnoJMWE.compact()
val coreDocument = CoreDocument(str)
pipeline.annotate(coreDocument)
coreDoc = coreDocument
}
private fun getScoreRelationStrF(str: String?, mostRecentMsg: String?): Double {
val SMX = SimilarityMatrix(str, mostRecentMsg)
val cacheSentiment1 = sentimentCachingMap.getOrDefault(str, null)
val cacheSentiment2 = sentimentCachingMap.getOrDefault(mostRecentMsg, null)
val worker: Callable<SimilarityMatrix> = SentimentAnalyzerTest(str, mostRecentMsg, SMX,
strAnnoJMWE, jmweAnnotationCache[mostRecentMsg], strAnno,
pipelineAnnotationCache[mostRecentMsg], strAnnoSentiment,
pipelineSentimentAnnotationCache[mostRecentMsg], coreDoc, coreDocumentAnnotationCache[mostRecentMsg], cacheSentiment1, cacheSentiment2)
var callSMX: SimilarityMatrix? = null
try {
callSMX = worker.call()
} catch (ex: Exception) {
Logger.getLogger(Datahandler::class.java
.name).log(Level.SEVERE, null, ex)
}
return callSMX?.distance ?: 0.0
}
private fun annotationCacheUpdate(strmap: ConcurrentMap<Int?, String?>): ConcurrentMap<Int?, String?> {
val jmweAnnotation = PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(strmap.values)
for ((key, value) in jmweAnnotation) {
jmweAnnotationCache[key] = value
}
val Annotationspipeline = MapMaker().concurrencyLevel(6).makeMap<String?, Annotation>()
val AnnotationspipelineSentiment = MapMaker().concurrencyLevel(6).makeMap<String?, Annotation>()
val coreDocumentpipelineMap = getMultipleCoreDocumentsWaySuggestion(strmap.values, pipeline)
runBlocking {
val job = launch(Dispatchers.Default) {
for (str in strmap.values) {
val strAnno1 = Annotation(str)
Annotationspipeline[str] = strAnno1
val strAnno2 = Annotation(str)
AnnotationspipelineSentiment[str] = strAnno2
stringCache[stringCache.size + 1] = str
}
yield()
}
job.join()
}
pipeline.annotate(Annotationspipeline.values)
pipelineSentiment!!.annotate(AnnotationspipelineSentiment.values)
runBlocking {
val job = launch(Dispatchers.Default) {
for (pipelineEntry in Annotationspipeline.entries) {
if (pipelineEntry != null) {
pipelineAnnotationCache[pipelineEntry.key] = pipelineEntry.value
}
}
yield()
}
job.join()
}
runBlocking {
val job = launch(Dispatchers.Default) {
for (pipelineEntry in AnnotationspipelineSentiment.entries) {
if (pipelineEntry != null) {
pipelineSentimentAnnotationCache[pipelineEntry.key] = pipelineEntry.value
}
}
yield()
}
job.join()
}
runBlocking {
val job = launch(Dispatchers.Default) {
for (coreDocumentEntry in coreDocumentpipelineMap.entries) {
coreDocumentAnnotationCache[coreDocumentEntry.key] = coreDocumentEntry.value
}
yield()
}
job.join()
}
return strmap
}
val messageOverHead: Int
get() = stringCache.values.size - stringCache.values.size / 10
fun update_autismo_socket_msg() {
try {
try {
DatagramSocket(48480).use { serverSocket ->
DatagramSocket(48471).use { serverSocket1 ->
val receiveData = ByteArray(4096)
val IPAddress = InetAddress.getByName("135.125.188.157") //later moving autism bot 1 and 3 to OVH from SYS
var receivePacket = DatagramPacket(receiveData, receiveData.size)
while (true) {
serverSocket.receive(receivePacket)
var sentence = String(receivePacket.data, 0, receivePacket.length)
sentence = sentence.replace("clientmessage:", "")
var getResponseMsg = getResponseMsg(sentence)
var sendData = getResponseMsg.toByteArray(charset("UTF-8"))
var sendPacket = DatagramPacket(sendData, sendData.size, IPAddress, 48479)
serverSocket.send(sendPacket)
receivePacket = DatagramPacket(receiveData, receiveData.size)
serverSocket1.receive(receivePacket)
sentence = String(receivePacket.data, 0, receivePacket.length)
sentence = sentence.replace("clientmessage:", "")
getResponseMsg = getResponseMsg(sentence)
sendData = getResponseMsg.toByteArray(charset("UTF-8"))
sendPacket = DatagramPacket(sendData, sendData.size, IPAddress, 48476)
serverSocket1.send(sendPacket)
}
}
}
} catch (ex: CustomError) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
} catch (ex: SocketException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
} catch (ex: UnsupportedEncodingException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
} catch (ex: IOException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
}
private class AnnotationCollector<T> : Consumer<T> {
val annotationsT: MutableList<T?> = arrayListOf()
override fun accept(ann: T) {
//System.out.println("adding ann: " + ann.toString());
annotationsT.add(ann)
}
companion object {
var i = 0
}
}
companion object {
val EXPIRE_TIME_IN_SECONDS = TimeUnit.SECONDS.convert(10, TimeUnit.MINUTES)
val EXPIRE_TIME_IN_SECONDS1 = TimeUnit.SECONDS.convert(10, TimeUnit.HOURS)
@JvmField
var instance = Datahandler()
private var strAnno: Annotation? = null
private var strAnnoSentiment: Annotation? = null
private lateinit var strAnnoJMWE: Annotation
private var coreDoc: CoreDocument? = null
private val stringCache = MapMaker().concurrencyLevel(6).makeMap<Int, String?>()
private lateinit var pipelineAnnotationCache: ConcurrentMap<String?, Annotation>
private lateinit var pipelineSentimentAnnotationCache: ConcurrentMap<String?, Annotation>
private lateinit var jmweAnnotationCache: ConcurrentMap<String, Annotation>
private lateinit var coreDocumentAnnotationCache: ConcurrentMap<String, CoreDocument>
private val sentimentCachingMap = MapMaker().concurrencyLevel(6).makeMap<String, SentimentValueCache>()
private const val similar = ""
private const val shiftReduceParserPath = "edu/stanford/nlp/models/srparser/englishSR.ser.gz"
private const val sentimentModel = "edu/stanford/nlp/models/sentiment/sentiment.ser.gz"
private const val lexParserEnglishRNN = "edu/stanford/nlp/models/lexparser/englishRNN.ser.gz"
private const val taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger"
private const val nerModel = "edu/stanford/nlp/models/ner/english.all.3class.caseless.distsim.crf.ser.gz"
private const val nerModel2 = "edu/stanford/nlp/models/ner/english.conll.4class.caseless.distsim.crf.ser.gz"
private const val nerModel3 = "edu/stanford/nlp/models/ner/english.muc.7class.caseless.distsim.crf.ser.gz"
private const val customStopWordList = "start,starts,period,periods,a,an,and,are,as,at,be,but,by,for,if,in,into,is,it,no,not,of,on,or,such,that,the,their,then,there,these,they,this,to,was,will,with"
@JvmStatic
var tagger: MaxentTagger? = null
private set
private val options = arrayOf("-maxLength", "100")
private val props = Properties()
private val propsSentiment = Properties()
@JvmStatic
var gsf: GrammaticalStructureFactory? = null
private set
private lateinit var lp: LexicalizedParser
private lateinit var tlp: TreebankLanguagePack
private lateinit var classifier: AbstractSequenceClassifier<CoreLabel>
public fun getPipeLine(): StanfordCoreNLP {
return pipeline
}
// set up Stanford CoreNLP pipeline
@JvmStatic
val pipeline = pipeLineSetUp
private var pipelineSentiment: StanfordCoreNLP? = null
private val pipeLineSetUp: StanfordCoreNLP
private get() {
props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse")
props.setProperty("parse.model", shiftReduceParserPath)
props.setProperty("parse.maxlen", "90")
props.setProperty("parse.binaryTrees", "true")
props.setProperty("threads", "5")
props.setProperty("pos.maxlen", "90")
props.setProperty("tokenize.maxlen", "90")
props.setProperty("ssplit.maxlen", "90")
props.setProperty("lemma.maxlen", "90")
props.setProperty("ner.model", "$nerModel,$nerModel2,$nerModel3")
props.setProperty("ner.combinationMode", "HIGH_RECALL")
props.setProperty("regexner.ignorecase", "true")
props.setProperty("ner.fine.regexner.ignorecase", "true")
props.setProperty("tokenize.options", "untokenizable=firstDelete")
return StanfordCoreNLP(props)
}
@JvmStatic
fun getClassifier(): AbstractSequenceClassifier<CoreLabel>? {
return classifier
}
fun setClassifier(classifier: AbstractSequenceClassifier<CoreLabel>?) {
if (classifier != null) {
Companion.classifier = classifier
}
}
private fun getMultipleJMWEAnnotation(str: Collection<String?>): ConcurrentMap<String, Annotation> {
return PipelineJMWESingleton.INSTANCE.getJMWEAnnotation(str)
}
private fun getMultiplePipelineAnnotation(str: Collection<String?>): ConcurrentMap<String?, Annotation> {
val pipelineAnnotationMap = MapMaker().concurrencyLevel(2).makeMap<String?, Annotation>()
for (str1 in str) {
val strAnno1 = Annotation(str1)
pipelineAnnotationMap[str1] = strAnno1
}
pipeline.annotate(pipelineAnnotationMap.values)
return pipelineAnnotationMap
}
private fun getMultiplePipelineSentimentAnnotation(str: Collection<String?>): ConcurrentMap<String?, Annotation> {
val pipelineAnnotationMap = MapMaker().concurrencyLevel(2).makeMap<String?, Annotation>()
for (str1 in str) {
val strAnno1 = Annotation(str1)
pipelineAnnotationMap[str1] = strAnno1
}
pipelineSentiment!!.annotate(pipelineAnnotationMap.values)
return pipelineAnnotationMap
}
fun filterContent(str: ConcurrentMap<Int?, String?>): ConcurrentMap<Int?, String?> {
val strlistreturn = MapMaker().concurrencyLevel(2).makeMap<Int?, String?>()
str.values.forEach(Consumer {
var str1: String = ""
if (!str1.isEmpty() && str1.length > 3) {
str1 = str1.trim { it <= ' ' }
if (str1.contains("PM*")) {
str1 = str1.substring(str1.indexOf("PM*") + 3)
}
if (str1.contains("AM*")) {
str1 = str1.substring(str1.indexOf("AM*") + 3)
}
/*
if (str1.contains("?") || str1.contains("°"))
{
if (!str1.contains("http"))
{
str1 = str1.replace("?", " <:wlenny:514861023002624001> ");
str1 = str1.replace("°", " <:wlenny:514861023002624001> ");
}
}
*/if (str1.contains("(Counter-Terrorist)")) {
str1 = str1.replace("(Counter-Terrorist)", " ")
}
if (str1.contains("(Terrorist)")) {
str1 = str1.replace("(Terrorist)", " ")
}
if (str1.contains("(Spectator)")) {
str1 = str1.replace("(Spectator)", " ")
}
if (str1.contains("*DEAD*")) {
str1 = str1.replace("*DEAD*", " ")
}
if (str1.contains("{red}")) {
str1 = str1.replace("{red}", " ")
}
if (str1.contains("{orange}")) {
str1 = str1.replace("{orange}", " ")
}
if (str1.contains("{yellow}")) {
str1 = str1.replace("{yellow}", " ")
}
if (str1.contains("{green}")) {
str1 = str1.replace("{green}", " ")
}
if (str1.contains("{lightblue}")) {
str1 = str1.replace("{lightblue}", " ")
}
if (str1.contains("{blue}")) {
str1 = str1.replace("{blue}", " ")
}
if (str1.contains("{purple}")) {
str1 = str1.replace("{purple}", " ")
}
if (str1.contains("{white}")) {
str1 = str1.replace("{white}", " ")
}
if (str1.contains("{fullblue}")) {
str1 = str1.replace("{fullblue}", " ")
}
if (str1.contains("{cyan}")) {
str1 = str1.replace("{cyan}", " ")
}
if (str1.contains("{lime}")) {
str1 = str1.replace("{lime}", " ")
}
if (str1.contains("{deeppink}")) {
str1 = str1.replace("{deeppink}", " ")
}
if (str1.contains("{slategray}")) {
str1 = str1.replace("{slategray}", " ")
}
if (str1.contains("{dodgerblue}")) {
str1 = str1.replace("{dodgerblue}", " ")
}
if (str1.contains("{black}")) {
str1 = str1.replace("{black}", " ")
}
if (str1.contains("{orangered}")) {
str1 = str1.replace("{orangered}", " ")
}
if (str1.contains("{darkorchid}")) {
str1 = str1.replace("{darkorchid}", " ")
}
if (str1.contains("{pink}")) {
str1 = str1.replace("{pink}", " ")
}
if (str1.contains("{lightyellow}")) {
str1 = str1.replace("{lightyellow}", " ")
}
if (str1.contains("{chocolate}")) {
str1 = str1.replace("{chocolate}", " ")
}
if (str1.contains("{beige}")) {
str1 = str1.replace("{beige}", " ")
}
if (str1.contains("{azure}")) {
str1 = str1.replace("{azure}", " ")
}
if (str1.contains("{yellowgreen}")) {
str1 = str1.replace("{yellowgreen}", " ")
}
str1 = str1.trim { it <= ' ' }
if (str1.length > 2 && !str1.startsWith("!")) {
strlistreturn[strlistreturn.size] = str1
}
}
})
return strlistreturn
}
fun getMultipleCoreDocumentsWaySuggestion(str: Collection<String?>, localNLP: StanfordCoreNLP): ConcurrentMap<String, CoreDocument> {
val annCollector: AnnotationCollector<Annotation?> = AnnotationCollector<Annotation?>()
val annotationreturnMap = MapMaker().concurrencyLevel(6).makeMap<String, CoreDocument>()
runBlocking {
val job = launch(Dispatchers.Default) {
for (exampleString in str) {
localNLP.annotate(Annotation(exampleString), annCollector)
AnnotationCollector.i++
}
yield()
}
job.join()
}
try {
Thread.sleep(1500)
} catch (ex: InterruptedException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
runBlocking {
val job1 = launch(Dispatchers.Default) {
for (ann in annCollector.annotationsT) {
if (ann != null) {
ann.compact()
val CD = CoreDocument(ann)
annotationreturnMap[CD.text()] = CD
}
}
yield()
}
job1.join()
}
try {
Thread.sleep(1500)
} catch (ex: InterruptedException) {
Logger.getLogger(Datahandler::class.java.name).log(Level.SEVERE, null, ex)
}
return annotationreturnMap
}
}
init {
stopwatch = Stopwatch.createUnstarted()
jmweAnnotationCache = MapMaker().concurrencyLevel(4).makeMap<String, Annotation>()
pipelineAnnotationCache = MapMaker().concurrencyLevel(4).makeMap<String, Annotation>()
pipelineSentimentAnnotationCache = MapMaker().concurrencyLevel(4).makeMap<String, Annotation>()
coreDocumentAnnotationCache = MapMaker().concurrencyLevel(5).makeMap<String, CoreDocument>()
}
}

14
ArtificialAutism/src/main/java/FunctionLayer/MessageResponseHandler.java
View File

@ -44,16 +44,8 @@ public class MessageResponseHandler {
}
public static String selectReponseMessage(String toString, String personName) throws CustomError {
ConcurrentMap<Integer, String> str1 = new MapMaker().concurrencyLevel(6).makeMap();
str1.put(str1.size() + 1, toString);
String strreturn = "";
for (String str : str1.values()) {
if (!str.isEmpty()) {
strreturn = str;
}
}
String getResponseMsg = Datahandler.instance.getResponseMsg(strreturn);
getResponseMsg = checkPersonPresentInSentence(personName, getResponseMsg, strreturn);
String getResponseMsg = Datahandler.instance.getResponseMsg(toString);
getResponseMsg = checkPersonPresentInSentence(personName, getResponseMsg, toString);
return getResponseMsg;
}
@ -66,6 +58,8 @@ public class MessageResponseHandler {
CoreDocument pipelineCoreDcoumentLastMsg = new CoreDocument(userLastMessage);
Datahandler.getPipeline().annotate(pipelineCoreDcoument);
Datahandler.getPipeline().annotate(pipelineCoreDcoumentLastMsg);
//Datahandler.pipeline.annotate(pipelineCoreDcoument);
//Datahandler.pipeline.annotate(pipelineCoreDcoumentLastMsg);
String regex = "(.*?\\d){10,}";
for (CoreEntityMention em : pipelineCoreDcoument.entityMentions()) {
String entityType = em.entityType();

2
ArtificialAutism/src/main/java/FunctionLayer/PipelineJMWESingleton.java
View File

@ -54,7 +54,7 @@ public class PipelineJMWESingleton {
public final ConcurrentMap<String, Annotation> getJMWEAnnotation(Collection<String> strvalues) {
boolean verbose = false;
IMWEIndex index;
String jmweIndexData = "/home/debian/autism_bot/lib/mweindex_wordnet3.0_semcor1.6.data"; // ./lib/mweindex_wordnet3.0_semcor1.6.data
String jmweIndexData = "/home/gameservers/autism_bot/lib/mweindex_wordnet3.0_semcor1.6.data"; // ./lib/mweindex_wordnet3.0_semcor1.6.data
String jmweIndexDataLocalTest = "E:/java8/Projects/mweindex_wordnet3.0_semcor1.6.data";
File indexFile = new File((String) jmweIndexData);
index = new MWEIndex(indexFile);

3
ArtificialAutism/src/main/java/META-INF/MANIFEST.MF Normal file
View File

@ -0,0 +1,3 @@
Manifest-Version: 1.0
Main-Class: PresentationLayer.DiscordHandler

2
ArtificialAutism/src/main/java/PresentationLayer/DiscordHandler.java
View File

@ -37,7 +37,6 @@ import java.math.BigInteger;
*/
public class DiscordHandler {
public static void main(String[] args) {
System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", "15");
try {
Datahandler.instance.initiateMYSQL();
//nohup screen -d -m -S nonroot java -Xmx6900M -jar /home/javatests/ArtificialAutism-1.0.jar
@ -51,7 +50,6 @@ public class DiscordHandler {
Datahandler.instance.shiftReduceParserInitiate();
Datahandler.instance.instantiateAnnotationMap();
System.out.println("FINISHED ALL ANNOTATIONS");
Datahandler.instance.addHLstatsMessages();
Datahandler.instance.updateStringCache();
//String token = "NTI5NzAxNTk5NjAyMjc4NDAx.Dw0vDg.7-aMjVWdQMYPl8qVNyvTCPS5F_A";
String token = new settings().getDiscordToken();

BIN
ArtificialAutism/target/ArtificialAutism-1.0.jar
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0
ArtificialAutism/target/classes/.netbeans_automatic_build
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