Examples of tv.floe.metronome.eval.Evaluation.eval()

tv.floe.metronome.eval.Evaluation.eval()
Collects statistics on the real outcomes vs the guesses. This is for logistic outcome matrices such that the Note that an IllegalArgumentException is thrown if the two passed in matrices aren't the same length. @param realOutcomes the real outcomes (usually binary) @param guesses the guesses (usually a probability vector)

    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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    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() );

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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 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() );

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    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() );

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    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() );

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      DataSet inputs = iterator.next();


      Matrix in = inputs.getFirst();
      Matrix outcomes = inputs.getSecond();
      Matrix predicted = model.predict(in);
      eval.eval( outcomes, predicted );
      
    }

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