Examples of MetronomeRecordFactory

tv.floe.metronome.io.records.MetronomeRecordFactory
Built specifically for the Neural Network case Parses schema similar to libsvm's format but takes a schema schema: i:N | o:M Example: i:10 | o:3 would parse 0:0.3 4:0.21 | 0:1.0 1:0.5 2:0.1 Parses sparse representations, but allows for definition of multiple output values - pipe delimiter is left in to make it easier to work w the sparse representation @author josh

Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

  
  
  public static Pair<Matrix, Matrix> getIrisAsDataset() throws IOException {
    


    MetronomeRecordFactory rec_factory = new MetronomeRecordFactory("i:4 | o:3");
  
      
      String recs = "src/test/resources/data/uci/iris/iris_data_normalised.mne";


      Vector v_in_0 = new RandomAccessSparseVector(rec_factory.getInputVectorSize());
      Vector v_out_0 = new RandomAccessSparseVector(rec_factory.getOutputVectorSize());
      
      
//      ArrayList<Double[]> input_recs = new ArrayList<Double[]>(); 


      // not very well built but in a hurry
      BufferedReader br = new BufferedReader(new FileReader(recs));
      
      
      int row_count = 0;
      String line;
      while ((line = br.readLine()) != null) {
        row_count++;
      }


      Matrix input_matrix = new DenseMatrix( row_count, rec_factory.getInputVectorSize() );
      Matrix output_matrix = new DenseMatrix( row_count, rec_factory.getOutputVectorSize() );
      
      int row_num = 0;
      //String line;
      br.close();
      br = new BufferedReader(new FileReader(recs));
      while ((line = br.readLine()) != null) {
        


        rec_factory.vectorizeLine( line, v_in_0, v_out_0 );
        
        //System.out.println("line: " + line);
        //System.out.println("v in: " + v_in_0.toString());
        
        input_matrix.viewRow( row_num ).assign( v_in_0 );

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

    
    //Utils.PrintNeuralNetwork(  mlp );
    
    //System.out.println("Layers: " + mlp.getLayersCount());
    
    MetronomeRecordFactory rec_factory = new MetronomeRecordFactory("i:4 | o:3");
  
      
      String recs = "src/test/resources/data/uci/iris/iris_data_normalised.mne";
      Vector v_in_0 = new RandomAccessSparseVector(rec_factory.getInputVectorSize());
      Vector v_out_0 = new RandomAccessSparseVector(rec_factory.getOutputVectorSize());
      
      
      
      int total_recs = 0;
      int correct = 0;
      
      BufferedReader br = new BufferedReader(new FileReader(recs));
      String line;
      while ((line = br.readLine()) != null) {
        
        


        rec_factory.vectorizeLine( line, v_in_0, v_out_0 );
        
        if (isCorrect(mlp, v_in_0, v_out_0) ) {
          correct++;
        }
        total_recs++;

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

    
    //Utils.PrintNeuralNetwork(  mlp );
    
    //System.out.println("Layers: " + mlp.getLayersCount());
    
    MetronomeRecordFactory rec_factory = new MetronomeRecordFactory("i:8 | o:29");
  
      
      String recs = "src/test/resources/data/uci/abalone/oneworker/abalone.mne";
      Vector v_in_0 = new RandomAccessSparseVector(rec_factory.getInputVectorSize());
      Vector v_out_0 = new RandomAccessSparseVector(rec_factory.getOutputVectorSize());
      
      
      
      int total_recs = 0;
      int correct = 0;
      
      BufferedReader br = new BufferedReader(new FileReader(recs));
      String line;
      while ((line = br.readLine()) != null) {
        
        


        rec_factory.vectorizeLine( line, v_in_0, v_out_0 );
        
        if (isCorrect(mlp, v_in_0, v_out_0) ) {
          correct++;
        }
        total_recs++;

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

    
    String line = "0:1.0 1:0.7 2:0.7 3:0.6 4:0.4 5:1.0 6:0.4 7:0.1 8:0.2 | 0:1.0";
    
    String schema = "i:9 | o:1";
    
    MetronomeRecordFactory rec_factory = new MetronomeRecordFactory(schema);
    
    assertEquals(rec_factory.getInputVectorSize(), 9);
    assertEquals(rec_factory.getOutputVectorSize(), 1);
    
    Vector v_in_0 = new RandomAccessSparseVector(rec_factory.getInputVectorSize());
    Vector v_out_0 = new RandomAccessSparseVector(rec_factory.getOutputVectorSize());


    rec_factory.vectorizeLine( line, v_in_0, v_out_0 );
    
    assertEquals(0.7, v_in_0.get(1), 0.0);
    assertEquals(0.7, v_in_0.get(2), 0.0);
    
    // test output

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

   * @throws IOException
   */
  public MnistHDFSDataFetcher( TextRecordParser hdfsLineParser ) throws IOException {


    this.record_reader = hdfsLineParser;
    this.vector_factory = new MetronomeRecordFactory( this.vectorSchema );
    numOutcomes = 10;
    cursor = 1;
    inputColumns = 784; //ArrayUtils.flatten(image).length;


  }

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

    
    //Utils.PrintNeuralNetwork(  mlp );
    
    //System.out.println("Layers: " + mlp.getLayersCount());
    
    MetronomeRecordFactory rec_factory = new MetronomeRecordFactory(schema);
  
      
      String recs = records_file;
      Vector v_in_0 = new RandomAccessSparseVector(rec_factory.getInputVectorSize());
      Vector v_out_0 = new RandomAccessSparseVector(rec_factory.getOutputVectorSize());
      
      
      
      int total_recs = 0;
      int correct = 0;
      
      BufferedReader br = new BufferedReader(new FileReader(recs));
      String line;
      while ((line = br.readLine()) != null) {
        
        


        rec_factory.vectorizeLine( line, v_in_0, v_out_0 );
        
        if (isCorrect(mlp, v_in_0, v_out_0) ) {
          correct++;
        }
        total_recs++;

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Examples of tv.floe.metronome.io.records.MetronomeRecordFactory

          this.vectorSchema = LoadStringConfVarOrException(
              "tv.floe.metronome.neuralnetwork.conf.InputRecordSchema",
              "Error Loading Config: Need a vector schema!" );
          
          
          this.rec_factory = new MetronomeRecordFactory( this.vectorSchema );
          
          
        } else {
          // default to libsvm format

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