// Path: src/main/scala/com/stefansavev/similaritysearch/implementation/VectorTypes.java
// public class VectorTypes {
//     public static VectorType uncorrelatedFeatures(int dimension, VectorType.StorageSize storageSize){
//         return new VectorType.UncorrelatedFeatures(dimension, storageSize);
//     }
// }


import com.stefansavev.similaritysearch.*;
import com.stefansavev.randomprojections.actors.Application;
import com.stefansavev.similaritysearch.implementation.VectorTypes;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
package com.stefansavev.fuzzysearchtest;



public class Cifar100Parts {
    static SimilaritySearchItem parseItem(int lineNumber, String line, int numDimensions) {
        String[] parts = line.split("\\s+");
        //System.out.println("Parts len: " + parts.length);
        if (parts.length != 67) {
            return null;
        }

        if (parts.length != (3 + numDimensions)) {
            throw new IllegalStateException("Invalid data format. Expecting data of dimension " + numDimensions + line);
        }
        String fileIndex = parts[0];
        String partId = parts[1];
        int labelId = Integer.parseInt(parts[2]);

        double[] vec = new double[numDimensions];
        for (int i = 0; i < numDimensions; i++) {
            vec[i] = Double.parseDouble(parts[i + 1]);
        }
        return new SimilaritySearchItem(fileIndex + "_" + partId, labelId, vec);

    }

    static void buildIndex(String inputFile, String outputIndexFile) throws IOException, InvalidDataPointException {
        int dataDimension = 64;
        int numTrees = 50; //150;
        //create an indexer
        SimilaritySearchIndexBuilder indexBuilder = new SimilaritySearchIndexBuilder(outputIndexFile,
// Path: src/main/scala/com/stefansavev/similaritysearch/implementation/VectorTypes.java
// public class VectorTypes {
//     public static VectorType uncorrelatedFeatures(int dimension, VectorType.StorageSize storageSize){
//         return new VectorType.UncorrelatedFeatures(dimension, storageSize);
//     }
// }


// Path: src/test/scala/com/stefansavev/fuzzysearchtest/Cifar100Parts.java
import com.stefansavev.similaritysearch.*;
import com.stefansavev.randomprojections.actors.Application;
import com.stefansavev.similaritysearch.implementation.VectorTypes;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.Iterator;
import java.util.List;
import java.util.Random;

package com.stefansavev.fuzzysearchtest;



public class Cifar100Parts {
    static SimilaritySearchItem parseItem(int lineNumber, String line, int numDimensions) {
        String[] parts = line.split("\\s+");
        //System.out.println("Parts len: " + parts.length);
        if (parts.length != 67) {
            return null;
        }

        if (parts.length != (3 + numDimensions)) {
            throw new IllegalStateException("Invalid data format. Expecting data of dimension " + numDimensions + line);
        }
        String fileIndex = parts[0];
        String partId = parts[1];
        int labelId = Integer.parseInt(parts[2]);

        double[] vec = new double[numDimensions];
        for (int i = 0; i < numDimensions; i++) {
            vec[i] = Double.parseDouble(parts[i + 1]);
        }
        return new SimilaritySearchItem(fileIndex + "_" + partId, labelId, vec);

    }

    static void buildIndex(String inputFile, String outputIndexFile) throws IOException, InvalidDataPointException {
        int dataDimension = 64;
        int numTrees = 50; //150;
        //create an indexer
        SimilaritySearchIndexBuilder indexBuilder = new SimilaritySearchIndexBuilder(outputIndexFile,
                VectorTypes.uncorrelatedFeatures(dataDimension, VectorType.StorageSize.TwoBytes),