Package tv.floe.metronome.io.records

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

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  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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    //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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    //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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    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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   * @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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    //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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          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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Related Classes of tv.floe.metronome.io.records.MetronomeRecordFactory

  • org.apache.mahout.math.Vector.Element
  • tv.floe.metronome.classification.neuralnetworks.eval.ModelEvaluator
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.abalone.AbaloneModelTestingTools
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.bcw.Test_BreastCancerWisconsin_IR_NN
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.iris.IrisDatasetUtils
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.WorkerNode
  • tv.floe.metronome.deeplearning.datasets.fetchers.MnistHDFSDataFetcher
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