static void Main()
{
// Path to models extracted from `stanford-parser-3.6.0-models.jar`
var jarRoot = @"..\..\..\..\paket-files\nlp.stanford.edu\stanford-parser-full-2016-10-31\models\";
var modelsDirectory = jarRoot + @"\edu\stanford\nlp\models";
// Loading english PCFG parser from file
var lp = LexicalizedParser.loadModel(modelsDirectory + @"\lexparser\englishPCFG.ser.gz");
// This sample shows parsing a list of correctly tokenized words
var sent = new[] { "This", "is", "an", "easy", "sentence", "." };
var rawWords = SentenceUtils.toCoreLabelList(sent);
var tree = lp.apply(rawWords);
tree.pennPrint();
// This option shows loading and using an explicit tokenizer
var sent2 = "This is another sentence.";
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader = new StringReader(sent2);
var rawWords2 = tokenizerFactory.getTokenizer(sent2Reader).tokenize();
sent2Reader.close();
var tree2 = lp.apply(rawWords2);
// Extract dependencies from lexical tree
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(tree2);
var tdl = gs.typedDependenciesCCprocessed();
Console.WriteLine("\n{0}\n", tdl);
// Extract collapsed dependencies from parsed tree
var tp = new TreePrint("penn,typedDependenciesCollapsed");
tp.printTree(tree2);
}
public string parse(string sentence)
{
string parsedout = "";
// This option shows loading and using an explicit tokenizer
var sent2 = sentence;
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader = new StringReader(sent2);
var rawWords2 = tokenizerFactory.getTokenizer(sent2Reader).tokenize();
var parse = _sdpModel.apply(rawWords2);
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(parse);
var tdl = gs.typedDependenciesCCprocessed();
System.Console.WriteLine();
for (var it = tdl.iterator(); it.hasNext(); )
{
parsedout = parsedout + "\n" +it.next();
}
//System.Console.WriteLine("{0}", it.next());
//System.Console.WriteLine();
//var tp = new TreePrint("penn,typedDependenciesCollapsed");
return parsedout;
}
/// <summary>
/// Convert a Molfile into a CDK AtomContainer (e.g. Molecule)
/// </summary>
/// <param name="molfile"></param>
/// <returns></returns>
public static IAtomContainer MolfileToAtomContainer(string molfile)
{
// Do basic read step
if (Lex.Contains(molfile, "V2000"))
{
cdk.io.DefaultChemObjectReader cor;
java.io.StringReader sr = new java.io.StringReader(molfile);
cor = new MDLV2000Reader(sr);
cor.setReaderMode(IChemObjectReader.Mode.RELAXED);
IAtomContainer mol = (IAtomContainer)cor.read(new AtomContainer());
cor.close();
ConfigureAtomContainer(mol);
return(mol);
}
else if (Lex.Contains(molfile, "V3000"))
{
return(MolfileV3000ToAtomContainer(molfile));
}
else
{
throw new Exception("Unrecognized molfile format");
}
}
//use Stanford.NLP.Net to parse the sentence
static Tree Parse(string sent)
{
// Loading english PCFG parser from file
var lp = LexicalizedParser.loadModel(modelsDirectory + "\\lexparser\\englishPCFG.ser.gz");
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sentReader = new java.io.StringReader(sent);
var rawWords = tokenizerFactory.getTokenizer(sentReader).tokenize();
sentReader.close();
var tree = lp.apply(rawWords);
// Extract dependencies from lexical tree
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(tree);
var tdl = gs.typedDependenciesCCprocessed();
// Extract collapsed dependencies from parsed tree
//var tp = new TreePrint("penn,typedDependenciesCollapsed");
var tp = new TreePrint("penn");
tp.printTree(tree);
return(tree);
}
/// <summary>
/// MolfileV3000ToAtomContainer
/// </summary>
/// <param name="molfile"></param>
/// <returns></returns>
public static IAtomContainer MolfileV3000ToAtomContainer(string molfile)
{
// Extract any mass info before conversion to avoid losing our custom info in conversion
Dictionary <int, int> map = new Dictionary <int, int>();
if (molfile.Contains(" MASS="))
{
map = ExtractMassAttributes(ref molfile);
}
cdk.io.DefaultChemObjectReader cor;
java.io.StringReader sr = new java.io.StringReader(molfile);
cor = new MDLV3000Reader(sr);
cor.setReaderMode(IChemObjectReader.Mode.RELAXED);
IAtomContainer mol = (IAtomContainer)cor.read(new AtomContainer());
cor.close();
for (int ai = 0; ai < mol.getAtomCount(); ai++)
{
IAtom a = mol.getAtom(ai);
if (map.ContainsKey(ai + 1))
{
a.setMassNumber(new java.lang.Integer(map[ai + 1]));
}
else
{
a.setMassNumber(null);
}
}
ConfigureAtomContainer(mol);
return(mol);
}
Tree parseSentence(string sentence)
{
var sentenceReader = new StringReader(sentence);
var result = _tokenizerFactory.getTokenizer(sentenceReader).tokenize();
sentenceReader.close();
var tree = _parser.parse(result);
return tree;
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
String results = "";
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cl;
for (int i = 3; i < data.numInstances(); i++) {
cl = new weka.classifiers.bayes.NaiveBayes();
//cl = new weka.classifiers.trees.J48();
//cl = new weka.classifiers.lazy.IB1();
//cl = new weka.classifiers.functions.MultilayerPerceptron();
((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");
weka.core.Instances subset = new weka.core.Instances(data,0,i);
cl.buildClassifier(subset);
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
eval.crossValidateModel(cl, subset, 3, new java.util.Random(1));
results = results + eval.pctCorrect(); // For accuracy measurement
/* For Mathews Correlation Coefficient */
//double TP = eval.numTruePositives(1);
//double FP = eval.numFalsePositives(1);
//double TN = eval.numTrueNegatives(1);
//double FN = eval.numFalseNegatives(1);
//double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
//results = results + correlationCoeff;
if (i != data.numInstances()-1)
results = results + ", ";
if(i == data.numInstances()-1)
Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
}
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
// Write values to file for a matlab read
// For accuracy
StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldValidations_NeuralNet.txt");
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
writer.WriteLine(results);
writer.Close();
Debug.Log(playerID + " has been written to file");
}
/// <summary>
/// 進行斷詞，取回斷詞後的結果
/// </summary>
/// <param name="txt"></param>
/// <returns></returns>
public List<Pair<String, Int32>> SegWords(String txt)
{
Reader input = new StringReader(txt);
Seg seg = GetSeg();
MMSeg mmSeg = new MMSeg(input, seg);
Word word = null;
List<Pair<String, Int32>> result = new List<Pair<String, Int32>>();
while ((word = mmSeg.next()) != null)
{
// 兩種 Offset 方式
//word.getWordOffset();
//word.getStartOffset();
result.Add(new Pair<String, Int32>(word.getString(), word.getStartOffset()));
}
return result;
}
public static List <string> Tokenize(string s)
{
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
tokenizerFactory.setOptions("untokenizable=noneDelete");
var reader = new jio.StringReader(s);
var words = tokenizerFactory.getTokenizer(reader).tokenize();
var tokens = new List <string>();
for (int i = 0; i < words.size(); i++)
{
ling.CoreLabel word = (ling.CoreLabel)words.get(i);
string w = word.toString().ToUpper();
tokens.Add(w);
}
return(tokens);
}
static void Main(string[] args)
{
using (TextReader reader = System.IO.File.OpenText("C:\\Data\\msr_paraphrase_train.txt"))
{
TextWriter writer1 = System.IO.File.CreateText("C:\\Data\\msr_paraphrase_train_s1.token");
TextWriter writer2 = System.IO.File.CreateText("C:\\Data\\msr_paraphrase_train_s2.token");
string[] inputdata = reader.ReadToEnd().Split('\n');
foreach (string line in inputdata)
{
string[] sp = line.Split('\t');
//writer.Write(sp[0] + '\t');
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader1 = new java.io.StringReader(sp[3]);
java.util.List rawWords1 = tokenizerFactory.getTokenizer(sent2Reader1).tokenize();
sent2Reader1.close();
var sent2Reader2 = new java.io.StringReader(sp[4]);
java.util.List rawWords2 = tokenizerFactory.getTokenizer(sent2Reader2).tokenize();
sent2Reader2.close();
for (int i = 0; i < rawWords1.size(); ++i)
{
writer1.Write(rawWords1.get(i) + " ");
}
writer1.Write('\n');
for (int i = 0; i < rawWords2.size(); ++i)
{
writer2.Write(rawWords2.get(i) + " ");
}
writer2.Write('\n');
}
writer1.Close();
writer2.Close();
}
System.Console.ReadKey();
}
static void Main(string[] args)
{
using (TextReader reader = System.IO.File.OpenText("C:\\Data\\msr_paraphrase_train.txt"))
{
TextWriter writer1 = System.IO.File.CreateText("C:\\Data\\msr_paraphrase_train_s1.token");
TextWriter writer2 = System.IO.File.CreateText("C:\\Data\\msr_paraphrase_train_s2.token");
string[] inputdata = reader.ReadToEnd().Split('\n');
foreach (string line in inputdata)
{
string[] sp = line.Split('\t');
//writer.Write(sp[0] + '\t');
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader1 = new java.io.StringReader(sp[3]);
java.util.List rawWords1 = tokenizerFactory.getTokenizer(sent2Reader1).tokenize();
sent2Reader1.close();
var sent2Reader2 = new java.io.StringReader(sp[4]);
java.util.List rawWords2 = tokenizerFactory.getTokenizer(sent2Reader2).tokenize();
sent2Reader2.close();
for (int i = 0; i < rawWords1.size(); ++i)
{
writer1.Write(rawWords1.get(i) + " ");
}
writer1.Write('\n');
for (int i = 0; i < rawWords2.size(); ++i)
{
writer2.Write(rawWords2.get(i) + " ");
}
writer2.Write('\n');
}
writer1.Close();
writer2.Close();
}
System.Console.ReadKey();
}
/* Use when the player logs in to initially create the classifier with data from server */
public void InitializeClassifier(String dataString)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
playerData = new weka.core.Instances(buffReader);
/* State where in each Instance the class attribute is, if its not already specified by the file */
if (playerData.classIndex() == -1)
{
playerData.setClassIndex(playerData.numAttributes() - 1);
}
/* NAIVE BAYES */
//classifier = new weka.classifiers.bayes.NaiveBayes();
/* NEURAL NET */
//classifier = new weka.classifiers.functions.MultilayerPerceptron();
//((weka.classifiers.functions.MultilayerPerceptron)classifier).setHiddenLayers("12");
/* J48 TREE */
//classifier = new weka.classifiers.trees.J48();
/* IB1 NEAREST NEIGHBOUR */
//classifier = new weka.classifiers.lazy.IB1();
/* RANDOM FOREST */
classifier = new weka.classifiers.trees.RandomForest();
classifier.buildClassifier(playerData);
Debug.Log("Initialized Classifier");
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
//use Stanford.NLP.Net to parse the sentence
Tree Parse(string sent)
{
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sentReader = new java.io.StringReader(sent);
var rawWords = tokenizerFactory.getTokenizer(sentReader).tokenize();
sentReader.close();
var tree = lp.apply(rawWords);
// Extract dependencies from lexical tree
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(tree);
var tdl = gs.typedDependenciesCCprocessed();
// Extract collapsed dependencies from parsed tree
//var tp = new TreePrint("penn,typedDependenciesCollapsed");
var tp = new TreePrint("penn");
tp.printTree(tree);
return(tree);
}
public static void DemoAPI(LexicalizedParser lp)
{
// This option shows parsing a list of correctly tokenized words
var sent = new[] { "This", "is", "an", "easy", "sentence", "." };
var rawWords = Sentence.toCoreLabelList(sent);
var parse = lp.apply(rawWords);
parse.pennPrint();
// This option shows loading and using an explicit tokenizer
const string Sent2 = "This is another sentence.";
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader = new StringReader(Sent2);
var rawWords2 = tokenizerFactory.getTokenizer(sent2Reader).tokenize();
parse = lp.apply(rawWords2);
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(parse);
var tdl = gs.typedDependenciesCCprocessed();
System.Console.WriteLine("\n{0}\n", tdl);
var tp = new TreePrint("penn,typedDependenciesCollapsed");
tp.printTree(parse);
}
/* Use when the player logs in to initially create the classifier with data from server */
public void InitializeClassifier(String dataString)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
playerData = new weka.core.Instances(buffReader);
/* State where in each Instance the class attribute is, if its not already specified by the file */
if (playerData.classIndex() == -1)
playerData.setClassIndex(playerData.numAttributes() - 1);
/* NAIVE BAYES */
//classifier = new weka.classifiers.bayes.NaiveBayes();
/* NEURAL NET */
//classifier = new weka.classifiers.functions.MultilayerPerceptron();
//((weka.classifiers.functions.MultilayerPerceptron)classifier).setHiddenLayers("12");
/* J48 TREE */
//classifier = new weka.classifiers.trees.J48();
/* IB1 NEAREST NEIGHBOUR */
//classifier = new weka.classifiers.lazy.IB1();
/* RANDOM FOREST */
classifier = new weka.classifiers.trees.RandomForest();
classifier.buildClassifier(playerData);
Debug.Log("Initialized Classifier");
}
catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
public string Tags(string input)
{
// Path to models extracted from `stanford-parser-3.6.0-models.jar`
var jarRoot = @"";
var modelsDirectory = jarRoot;
var lp = LexicalizedParser.loadModel(modelsDirectory + @"\lexparser\englishPCFG.ser.gz");
// This option shows loading and using an explicit tokenizer
var sent2 = input;
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader = new java.io.StringReader(sent2);
var rawWords2 = tokenizerFactory.getTokenizer(sent2Reader).tokenize();
sent2Reader.close();
var tree2 = lp.apply(rawWords2);
// Extract dependencies from lexical tree
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(tree2);
var tdl = gs.typedDependenciesCCprocessed();
// Extract collapsed dependencies from parsed tree
var tp = new TreePrint("penn,typedDependenciesCollapsed");
UnityEngine.Debug.Log(tdl);
//tp.printTree(tree2);
for (int i = 0; i < tdl.size(); i++)
{
TypedDependency node = (TypedDependency)tdl.get(i);
string relation = node.reln().getShortName();
if (relation.Contains("nsubj"))
{
IndexedWord act = node.gov();
//node.dep().getword()
action = act.value();
UnityEngine.Debug.Log("This is the action " + action);
IndexedWord subject = node.dep();
subj = subject.value();
UnityEngine.Debug.Log("This is the subject " + subj);
}
if (relation.Contains("dobj"))
{
IndexedWord act = node.gov();
//node.dep().getword()
action = act.value();
UnityEngine.Debug.Log("This is the action " + action);
IndexedWord tar = node.dep();
target = tar.value();
UnityEngine.Debug.Log("This is the target " + target);
}
if (relation.Contains("nmod"))
{
IndexedWord tar_two = node.dep();
second_target = tar_two.value();
UnityEngine.Debug.Log("This is the target second " + second_target);
}
}
return(tdl.ToString());
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
String results = "";
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
weka.classifiers.Classifier cl;
for (int i = 3; i < data.numInstances(); i++)
{
cl = new weka.classifiers.bayes.NaiveBayes();
//cl = new weka.classifiers.trees.J48();
//cl = new weka.classifiers.lazy.IB1();
//cl = new weka.classifiers.functions.MultilayerPerceptron();
((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");
weka.core.Instances subset = new weka.core.Instances(data, 0, i);
cl.buildClassifier(subset);
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
eval.crossValidateModel(cl, subset, 3, new java.util.Random(1));
results = results + eval.pctCorrect(); // For accuracy measurement
/* For Mathews Correlation Coefficient */
//double TP = eval.numTruePositives(1);
//double FP = eval.numFalsePositives(1);
//double TN = eval.numTrueNegatives(1);
//double FN = eval.numFalseNegatives(1);
//double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
//results = results + correlationCoeff;
if (i != data.numInstances() - 1)
{
results = results + ", ";
}
if (i == data.numInstances() - 1)
{
Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
}
}
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
// Write values to file for a matlab read
// For accuracy
StreamWriter writer = new StreamWriter("DataForMatlab/" + playerID + "_CrossFoldValidations_NeuralNet.txt");
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
writer.WriteLine(results);
writer.Close();
Debug.Log(playerID + " has been written to file");
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances thisData = new weka.core.Instances(buffReader); //source.getDataSet();
if (thisData.classIndex() == -1)
thisData.setClassIndex(thisData.numAttributes() - 1);
weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
if (thisUniqueData.classIndex() == -1)
thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
thisUniqueData.delete();
if (allUniqueData == null) {
allUniqueData = new weka.core.Instances(thisData);
if (allUniqueData.classIndex() == -1)
allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
allUniqueData.delete();
}
weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < allUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i),allUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
allUniqueData.add(thisData.instance(i));
else
dupInstances++;
}
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < thisUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i),thisUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
thisUniqueData.add(thisData.instance(i));
else
dupInstancesSamePlayer++;
}
//Debug.Log("All Data Instance Count = " + thisData.numInstances());
//Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
//Debug.Log("Done!");
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
public void SentenceParser(string sent2)
{
var modelsDirectory = jarRoot + @"edu\stanford\nlp\models";
// Loading english PCFG parser from file
var lp = LexicalizedParser.loadModel(modelsDirectory + @"\lexparser\englishPCFG.ser.gz");
// This option shows loading and using an explicit tokenizer
sent2.ToLower();
var tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), "");
var sent2Reader = new java.io.StringReader(sent2);
var rawWords2 = tokenizerFactory.getTokenizer(sent2Reader).tokenize();
sent2Reader.close();
var tree2 = lp.apply(rawWords2);
// Extract dependencies from lexical tree
var tlp = new PennTreebankLanguagePack();
var gsf = tlp.grammaticalStructureFactory();
var gs = gsf.newGrammaticalStructure(tree2);
var tdl = gs.typedDependenciesCCprocessed();
//Console.WriteLine("\n{0}\n", tdl);
// Extract collapsed dependencies from parsed tree
var tp = new TreePrint("penn,typedDependenciesCollapsed");
tp.printTree(tree2);
ArrayList dep = gs.typedDependenciesCollapsed() as ArrayList;
foreach (TypedDependency td in dep)
{
for (int i = 0; i < keyword.Length; i++)
{
if (td.dep().originalText().Equals(keyword[i]))
{
keyFlag = true;
key = keyword[i];
break;
}
}
if (keyFlag)
{
break;
}
}
keyFlag = false;
switch (key)
{
case "circle":
Circle circle = new Circle();
shape = circle.GetProps();
propsUsed = Associator(shape, dep);
break;
case "rectangle":
Rectangle rect = new Rectangle();
shape = rect.GetProps();
propsUsed = Associator(shape, dep);
break;
case "triangle":
Triangle tri = new Triangle();
shape = tri.GetProps();
propsUsed = Associator(shape, dep);
break;
case "square":
Square square = new Square();
shape = square.GetProps();
propsUsed = Associator(shape, dep);
break;
default:
break;
} //End of Switch
dependency = tdl.ToString();
} //End of SentenceParser
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances thisData = new weka.core.Instances(buffReader); //source.getDataSet();
if (thisData.classIndex() == -1)
{
thisData.setClassIndex(thisData.numAttributes() - 1);
}
weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
if (thisUniqueData.classIndex() == -1)
{
thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
}
thisUniqueData.delete();
if (allUniqueData == null)
{
allUniqueData = new weka.core.Instances(thisData);
if (allUniqueData.classIndex() == -1)
{
allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
}
allUniqueData.delete();
}
weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < allUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), allUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
allUniqueData.add(thisData.instance(i));
}
else
{
dupInstances++;
}
}
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < thisUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i), thisUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
{
thisUniqueData.add(thisData.instance(i));
}
else
{
dupInstancesSamePlayer++;
}
}
//Debug.Log("All Data Instance Count = " + thisData.numInstances());
//Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
//Debug.Log("Done!");
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
