refactoring the code

src/main/java/nikit/test/TimeLogger.java → src/main/java/org/aksw/word2vecrestful/utils/TimeLogger.java

@@ -1,4 +1,4 @@
-package nikit.test;
+package org.aksw.word2vecrestful.utils;
 
 import java.util.HashMap;
 import java.util.Map;

src/main/java/org/aksw/word2vecrestful/word2vec/W2VNrmlMemModelBinSrch.java

@@ -8,11 +8,17 @@
 import org.apache.log4j.Logger;
 import org.dice_research.topicmodeling.commons.sort.AssociativeSort;
 
-import nikit.test.TimeLogger;
-
 /**
  * Class to encapsulate word2vec in-memory model and expose methods to perform
- * search on the model
+ * search on the model.
+ * 
+ * This class selects {@link W2VNrmlMemModelBinSrch#compareVecCount} vectors (1
+ * mean vector and others on basis Map iterator) and then calculates the cosine
+ * similarity of all words in model to those vectors.
+ * 
+ * It uses the knowledge about pre-processed similarities with
+ * {@link W2VNrmlMemModelBinSrch#comparisonVecs} to narrow down the search of
+ * closest word for the user specified vector.
  * 
  * @author Nikit
  *
@@ -30,8 +36,6 @@ public class W2VNrmlMemModelBinSrch implements GenWord2VecModel {
 	private int compareVecCount = 150;
 	private int bucketCount = 10;
 	private BitSet[][] csBucketContainer;
-	// TODO : Remove this
-	private TimeLogger tl = new TimeLogger();
 
 	public W2VNrmlMemModelBinSrch(final Map<String, float[]> word2vec, final int vectorSize) {
 		this.word2vec = word2vec;
@@ -170,13 +174,12 @@ private String getClosestEntry(float[] vector, String subKey) {
 			// calculate cosine similarity of all distances
 			float[] curCompVec;
 			BitSet finBitSet = null;
-			tl.logTime(1);
 			for (int i = 0; i < compareVecCount; i++) {
 				curCompVec = comparisonVecs[i];
 				double cosSimVal = Word2VecMath.cosineSimilarityNormalizedVecs(curCompVec, vector);
 				int indx = getBucketIndex(cosSimVal);
 				BitSet curBs = new BitSet(word2vec.size());
-				if(csBucketContainer[i][indx]!=null) {
+				if (csBucketContainer[i][indx] != null) {
 					curBs.or(csBucketContainer[i][indx]);
 				}
 				int temIndx = indx + 1;
@@ -193,10 +196,8 @@ private String getClosestEntry(float[] vector, String subKey) {
 					finBitSet.and(curBs);
 				}
 			}
-			tl.printTime(1, "Setting Bits");
-			tl.logTime(1);
 			int nearbyWordsCount = finBitSet.cardinality();
-			LOG.info("Number of nearby words: "+nearbyWordsCount);
+			//LOG.info("Number of nearby words: " + nearbyWordsCount);
 			int[] nearbyIndexes = new int[nearbyWordsCount];
 			int j = 0;
 			for (int i = finBitSet.nextSetBit(0); i >= 0; i = finBitSet.nextSetBit(i + 1), j++) {
@@ -206,15 +207,12 @@ private String getClosestEntry(float[] vector, String subKey) {
 					break; // or (i+1) would overflow
 				}
 			}
-			tl.printTime(1, "Extracting words");
-			tl.logTime(1);
 			closestWord = findClosestWord(nearbyIndexes, vector);
-			tl.printTime(1, "finding closest word");
 		} catch (Exception e) {
 			LOG.error("Exception has occured while finding closest word.");
 			e.printStackTrace();
 		}
-		LOG.info("Closest word found is: "+closestWord);
+		//LOG.info("Closest word found is: " + closestWord);
 		return closestWord;
 	}
 

src/main/java/org/aksw/word2vecrestful/word2vec/W2VNrmlMemModelBruteForce.java

@@ -6,13 +6,12 @@
 import java.util.Set;
 
 import org.aksw.word2vecrestful.subset.DataSubsetProvider;
+import org.aksw.word2vecrestful.utils.TimeLogger;
 import org.aksw.word2vecrestful.utils.Word2VecMath;
 import org.apache.log4j.LogManager;
 import org.apache.log4j.Logger;
 import org.dice_research.topicmodeling.commons.sort.AssociativeSort;
 
-import nikit.test.TimeLogger;
-
 /**
  * Class to encapsulate word2vec in-memory model and expose methods to perform
  * search on the model

src/main/java/org/aksw/word2vecrestful/word2vec/W2VNrmlMemModelKMeans.java

@@ -20,11 +20,17 @@
 import com.opencsv.CSVReader;
 import com.opencsv.CSVWriter;
 
-import nikit.test.TimeLogger;
-
 /**
  * Class to encapsulate word2vec in-memory model and expose methods to perform
- * search on the model
+ * search on the model.
+ * 
+ * This class selects {@link W2VNrmlMemModelKMeans#compareVecCount} vectors
+ * (centroids of the KMeans result on the model vectors) and then calculates the
+ * cosine similarity of all words in model to those vectors.
+ * 
+ * It uses the knowledge about pre-processed similarities with
+ * {@link W2VNrmlMemModelKMeans#comparisonVecs} to narrow down the search of
+ * closest word for the user specified vector.
  * 
  * @author Nikit
  *
@@ -42,8 +48,6 @@ public class W2VNrmlMemModelKMeans implements GenWord2VecModel {
 	private int kMeansMaxItr = 5;
 	private BitSet[][] csBucketContainer;
 	private String vecFilePath = "data/kmeans/comparison-vecs.csv";
-	// TODO : Remove this
-	private TimeLogger tl = new TimeLogger();
 
 	public W2VNrmlMemModelKMeans(final Map<String, float[]> word2vec, final int vectorSize) throws IOException {
 		this.word2vec = word2vec;
@@ -175,14 +179,13 @@ private String getClosestEntry(float[] vector, String subKey) {
 			// calculate cosine similarity of all distances
 			float[] curCompVec;
 			BitSet finBitSet = null;
-			tl.logTime(1);
 			for (int i = 0; i < compareVecCount; i++) {
 				curCompVec = comparisonVecs[i];
 				double cosSimVal = Word2VecMath.cosineSimilarityNormalizedVecs(curCompVec, vector);
 				int indx = getBucketIndex(cosSimVal);
 				BitSet curBs = new BitSet(word2vec.size());
 				BitSet tempBs = csBucketContainer[i][indx];
-				if(tempBs!=null) {
+				if (tempBs != null) {
 					curBs.or(tempBs);
 				}
 				int temIndx = indx + 1;
@@ -199,10 +202,8 @@ private String getClosestEntry(float[] vector, String subKey) {
 					finBitSet.and(curBs);
 				}
 			}
-			tl.printTime(1, "Setting Bits");
-			tl.logTime(1);
 			int nearbyWordsCount = finBitSet.cardinality();
-			LOG.info("Number of nearby words: "+nearbyWordsCount);
+			//LOG.info("Number of nearby words: " + nearbyWordsCount);
 			int[] nearbyIndexes = new int[nearbyWordsCount];
 			int j = 0;
 			for (int i = finBitSet.nextSetBit(0); i >= 0; i = finBitSet.nextSetBit(i + 1), j++) {
@@ -212,15 +213,12 @@ private String getClosestEntry(float[] vector, String subKey) {
 					break; // or (i+1) would overflow
 				}
 			}
-			tl.printTime(1, "Extracting words");
-			tl.logTime(1);
 			closestWord = findClosestWord(nearbyIndexes, vector);
-			tl.printTime(1, "finding closest word");
 		} catch (Exception e) {
 			LOG.error("Exception has occured while finding closest word.");
 			e.printStackTrace();
 		}
-		LOG.info("Closest word found is: "+closestWord);
+		//LOG.info("Closest word found is: " + closestWord);
 		return closestWord;
 	}
 

src/test/java/org/aksw/word2vecrestful/NrmlzdThetaMdlPrfmncTester.java

@@ -14,8 +14,6 @@
 import org.apache.log4j.PropertyConfigurator;
 import org.junit.Test;
 
-import nikit.test.TestConst;
-
 public class NrmlzdThetaMdlPrfmncTester {
 	static {
 		PropertyConfigurator.configure(Cfg.LOG_FILE);

src/main/java/nikit/test/TestConst.java → src/test/java/org/aksw/word2vecrestful/TestConst.java

@@ -1,4 +1,4 @@
-package nikit.test;
+package org.aksw.word2vecrestful;
 
 import java.util.HashMap;
 import java.util.Map;