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Java example source code file (RegressionEvalTest.java)
The RegressionEvalTest.java Java example source codepackage org.deeplearning4j.eval; import org.junit.Test; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.factory.Nd4j; import static org.junit.Assert.assertEquals; /** * @author Alex Black */ public class RegressionEvalTest { @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); } } @Test public void testKnownValues(){ double[][] labelsD = new double[][]{ {1,2,3}, {0.1,0.2,0.3}, {6,5,4} }; double[][] predictedD = new double[][]{ {2.5,3.2,3.8}, {2.15,1.3,-1.2}, {7,4.5,3} }; double[] expMSE = {2.484166667,0.966666667,1.296666667}; double[] expMAE = {1.516666667,0.933333333,1.1}; double[] expRSE = {0.368813923,0.246598639,0.530937216}; double[] expCorrs = {0.997013483, 0.968619605, 0.915603032}; INDArray labels = Nd4j.create(labelsD); INDArray predicted = Nd4j.create(predictedD); RegressionEvaluation eval = new RegressionEvaluation(3); eval.eval(labels,predicted); for( int i=0; i<3; i++ ){ assertEquals(expMSE[i],eval.meanSquaredError(i),1e-5); assertEquals(expMAE[i],eval.meanAbsoluteError(i),1e-5); assertEquals(Math.sqrt(expMSE[i]),eval.rootMeanSquaredError(i),1e-5); assertEquals(expRSE[i],eval.relativeSquaredError(i),1e-5); assertEquals(expCorrs[i],eval.correlationR2(i),1e-5); } } } Other Java examples (source code examples)Here is a short list of links related to this Java RegressionEvalTest.java source code file: |
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