Examples with INDArray org.nd4j.linalg.api.ndarray.INDArray used on opensource projects

Example 31 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RegressionEvalTest method testRegressionEvaluationMerging.

@Test
public void testRegressionEvaluationMerging() {
    Nd4j.getRandom().setSeed(12345);
    int nRows = 20;
    int nCols = 3;
    int numMinibatches = 5;
    int nEvalInstances = 4;
    List<RegressionEvaluation> list = new ArrayList<>();
    RegressionEvaluation single = new RegressionEvaluation(nCols);
    for (int i = 0; i < nEvalInstances; i++) {
        list.add(new RegressionEvaluation(nCols));
        for (int j = 0; j < numMinibatches; j++) {
            INDArray p = Nd4j.rand(nRows, nCols);
            INDArray act = Nd4j.rand(nRows, nCols);
            single.eval(act, p);
            list.get(i).eval(act, p);
        }
    }
    RegressionEvaluation merged = list.get(0);
    for (int i = 1; i < nEvalInstances; i++) {
        merged.merge(list.get(i));
    }
    double prec = 1e-6;
    for (int i = 0; i < nCols; i++) {
        assertEquals(single.correlationR2(i), merged.correlationR2(i), prec);
        assertEquals(single.meanAbsoluteError(i), merged.meanAbsoluteError(i), prec);
        assertEquals(single.meanSquaredError(i), merged.meanSquaredError(i), prec);
        assertEquals(single.relativeSquaredError(i), merged.relativeSquaredError(i), prec);
        assertEquals(single.rootMeanSquaredError(i), merged.rootMeanSquaredError(i), prec);
    }
}

Example 32 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RegressionEvalTest method testPerfectPredictions.

@Test
public void testPerfectPredictions() {
    int nCols = 5;
    int nTestArrays = 100;
    int valuesPerTestArray = 3;
    RegressionEvaluation eval = new RegressionEvaluation(nCols);
    for (int i = 0; i < nTestArrays; i++) {
        INDArray rand = Nd4j.rand(valuesPerTestArray, nCols);
        eval.eval(rand, rand);
    }
    System.out.println(eval.stats());
    for (int i = 0; i < nCols; i++) {
        assertEquals(0.0, eval.meanSquaredError(i), 1e-6);
        assertEquals(0.0, eval.meanAbsoluteError(i), 1e-6);
        assertEquals(0.0, eval.rootMeanSquaredError(i), 1e-6);
        assertEquals(0.0, eval.relativeSquaredError(i), 1e-6);
        assertEquals(1.0, eval.correlationR2(i), 1e-6);
    }
}

Example 33 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class EvalTest method testEval2.

@Test
public void testEval2() {
    //Confusion matrix:
    //actual 0      20      3
    //actual 1      10      5
    Evaluation evaluation = new Evaluation(Arrays.asList("class0", "class1"));
    INDArray predicted0 = Nd4j.create(new double[] { 1, 0 });
    INDArray predicted1 = Nd4j.create(new double[] { 0, 1 });
    INDArray actual0 = Nd4j.create(new double[] { 1, 0 });
    INDArray actual1 = Nd4j.create(new double[] { 0, 1 });
    for (int i = 0; i < 20; i++) {
        evaluation.eval(actual0, predicted0);
    }
    for (int i = 0; i < 3; i++) {
        evaluation.eval(actual0, predicted1);
    }
    for (int i = 0; i < 10; i++) {
        evaluation.eval(actual1, predicted0);
    }
    for (int i = 0; i < 5; i++) {
        evaluation.eval(actual1, predicted1);
    }
    assertEquals(20, evaluation.truePositives().get(0), 0);
    assertEquals(3, evaluation.falseNegatives().get(0), 0);
    assertEquals(10, evaluation.falsePositives().get(0), 0);
    assertEquals(5, evaluation.trueNegatives().get(0), 0);
    assertEquals((20.0 + 5) / (20 + 3 + 10 + 5), evaluation.accuracy(), 1e-6);
    System.out.println(evaluation.confusionToString());
}

Example 34 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class EvalTest method testSingleClassBinaryClassification.

@Test
public void testSingleClassBinaryClassification() {
    Evaluation eval = new Evaluation(1);
    INDArray zero = Nd4j.create(1);
    INDArray one = Nd4j.ones(1);
    //One incorrect, three correct
    eval.eval(one, zero);
    eval.eval(one, one);
    eval.eval(one, one);
    eval.eval(zero, zero);
    System.out.println(eval.stats());
    assertEquals(0.75, eval.accuracy(), 1e-6);
    assertEquals(4, eval.getNumRowCounter());
    assertEquals(1, (int) eval.truePositives().get(0));
    assertEquals(2, (int) eval.truePositives().get(1));
    assertEquals(1, (int) eval.falseNegatives().get(1));
}

Example 35 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class EvalTest method testEvalMethods.

@Test
public void testEvalMethods() {
    //Check eval(int,int) vs. eval(INDArray,INDArray)
    Evaluation e1 = new Evaluation(4);
    Evaluation e2 = new Evaluation(4);
    INDArray i0 = Nd4j.create(new double[] { 1, 0, 0, 0 });
    INDArray i1 = Nd4j.create(new double[] { 0, 1, 0, 0 });
    INDArray i2 = Nd4j.create(new double[] { 0, 0, 1, 0 });
    INDArray i3 = Nd4j.create(new double[] { 0, 0, 0, 1 });
    //order: actual, predicted
    e1.eval(i0, i0);
    //order: predicted, actual
    e2.eval(0, 0);
    e1.eval(i0, i2);
    e2.eval(2, 0);
    e1.eval(i0, i2);
    e2.eval(2, 0);
    e1.eval(i1, i2);
    e2.eval(2, 1);
    e1.eval(i3, i3);
    e2.eval(3, 3);
    e1.eval(i3, i0);
    e2.eval(0, 3);
    e1.eval(i3, i0);
    e2.eval(0, 3);
    ConfusionMatrix<Integer> cm = e1.getConfusionMatrix();
    //Order: actual, predicted
    assertEquals(1, cm.getCount(0, 0));
    assertEquals(2, cm.getCount(0, 2));
    assertEquals(1, cm.getCount(1, 2));
    assertEquals(1, cm.getCount(3, 3));
    assertEquals(2, cm.getCount(3, 0));
    System.out.println(e1.stats());
    System.out.println(e2.stats());
    assertEquals(e1.stats(), e2.stats());
}