Package tv.floe.metronome.eval

Examples of tv.floe.metronome.eval.Evaluation

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    Matrix predict = dbn.predict( x_xor_Matrix );
    System.out.println("--- Predictions XOR ----");
    MatrixUtils.debug_print(predict);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval( y_xor_Matrix, predict );
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + x_xor_Matrix.numRows() );
 
 
  }
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    Matrix predict = dbn.predict( x_toy_Matrix );
    System.out.println("--- Predictions Toy Matrix ----");
    MatrixUtils.debug_print(predict);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval( y_toy_Matrix, predict );
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + x_toy_Matrix.numRows() );
 
 
  } 
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    Matrix predict = logreg.predict(x);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval(y, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + n );

   
   
   
   
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        Matrix predict = logreg.predict(xMatrix);
        //log.info(predict.toString());

        Evaluation eval = new Evaluation();
        eval.eval(yMatrix, predict);
        //log.info(eval.stats());
        System.out.println( eval.stats() );

        System.out.println( "Total Correct: " + eval.correctScores() + " out of " + yMatrix.numRows() );
       
       
   
   
  }
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    Matrix predict = logRegression.predict(input);

    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + labels.numRows() );
   
    assertEquals( 0.95, eval.f1(), 0.1 );
   
    //MatrixUtils.debug_print(predict);   
   
  }   
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    //Matrix predict = logRegression.predict(x);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval(y_xor_Matrix, predictions);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + y_xor_Matrix.numRows() );
   
   
  }
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//    Matrix predictions = logRegression.predict(xTestMatrix);
   
    Matrix predict = logRegression.predict(xMatrix);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval(yMatrix, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + yMatrix.numRows() );
   
   
    MatrixUtils.debug_print(predict);
   
  }
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//    Matrix predictions = logRegression.predict(xTestMatrix);
   
    Matrix predict = logRegression.predict(input);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + labels.numRows() );
   
    assertEquals( 0.90, eval.f1(), 0.02 );
   
    //MatrixUtils.debug_print(predict);   
   
  }
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//    Matrix predictions = logRegression.predict(xTestMatrix);
   
    Matrix predict = logRegression.predict(input);
    //log.info(predict.toString());

    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

    System.out.println( "Total Correct: " + eval.correctScores() + " out of " + labels.numRows() );
   
    assertEquals( 0.95, eval.f1(), 0.1 );
   
    //MatrixUtils.debug_print(predict);   
   
  } 
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  public static void evaluateModel( BaseDatasetIterator iterator, BaseMultiLayerNeuralNetworkVectorized model ) throws IOException {
   

    Evaluation eval = new Evaluation();
    //BaseMultiLayerNeuralNetworkVectorized load = BaseMultiLayerNeuralNetworkVectorized.loadFromFile(new FileInputStream(new File(modelLocation)));
   
    while (iterator.hasNext()) {
     
      DataSet inputs = iterator.next();

      Matrix in = inputs.getFirst();
      Matrix outcomes = inputs.getSecond();
      Matrix predicted = model.predict(in);
      eval.eval( outcomes, predicted );
     
    }
   
   
   
    log.warn( "evaluateModel" );
    log.info( eval.stats() );   
   
    //writeReportToDisk( eval, pathForReport );
   
  } 
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Related Classes of tv.floe.metronome.eval.Evaluation

  • org.apache.mahout.math.Vector
  • tv.floe.metronome.deeplearning.dbn.model.evaluation.ModelTester
  • tv.floe.metronome.deeplearning.neuralnetwork.dbn.TestDeepBeliefNetwork
  • tv.floe.metronome.deeplearning.neuralnetwork.layer.TestLogisticRegressionLayer
  • tv.floe.metronome.deeplearning.neuralnetwork.optimize.TestLogisticRegressionOptimizer
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