org.deeplearning4j.arbiter.optimize.parameter.TestParameterSpaces.java Source code

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Here is the source code for org.deeplearning4j.arbiter.optimize.parameter.TestParameterSpaces.java

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/*
 *  * Copyright 2016 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 */

package org.deeplearning4j.arbiter.optimize.parameter;

import org.apache.commons.math3.distribution.NormalDistribution;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.continuous.ContinuousParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.discrete.DiscreteParameterSpace;
import org.deeplearning4j.arbiter.optimize.parameter.integer.IntegerParameterSpace;
import org.junit.Test;

import static org.junit.Assert.assertEquals;

public class TestParameterSpaces {

    @Test
    public void testContinuousParameterSpace() {

        ContinuousParameterSpace cps = new ContinuousParameterSpace(0, 1);
        cps.setIndices(0);

        for (int i = 0; i < 10; i++) {
            double d = i / 10.0;
            assertEquals(d, cps.getValue(new double[] { d }), 0.0);
        }

        cps = new ContinuousParameterSpace(10, 20);
        cps.setIndices(0);

        for (int i = 0; i < 10; i++) {
            double d = i / 10.0;
            double exp = d * 10 + 10;
            assertEquals(exp, cps.getValue(new double[] { d }), 0.0);
        }

        cps = new ContinuousParameterSpace(new NormalDistribution(0, 1));
        NormalDistribution nd = new NormalDistribution(0, 1);
        cps.setIndices(0);
        for (int i = 0; i < 11; i++) {
            double d = i / 10.0;
            assertEquals(nd.inverseCumulativeProbability(d), cps.getValue(new double[] { d }), 1e-4);
        }
    }

    @Test
    public void testDiscreteParameterSpace() {
        ParameterSpace<Integer> dps = new DiscreteParameterSpace<>(0, 1, 2, 3, 4);
        dps.setIndices(0);

        for (int i = 0; i < 5; i++) {
            double d = i / 5.0 + 0.1; //Center
            double dEdgeLower = i / 5.0 + 1e-8; //Edge case: just above split threshold
            double dEdgeUpper = (i + 1) / 5.0 - 1e-8; //Edge case: just below split threshold
            assertEquals(i, (int) dps.getValue(new double[] { d }));
            assertEquals(i, (int) dps.getValue(new double[] { dEdgeLower }));
            assertEquals(i, (int) dps.getValue(new double[] { dEdgeUpper }));
        }
    }

    @Test
    public void testIntegerParameterSpace() {
        ParameterSpace<Integer> ips = new IntegerParameterSpace(0, 4);
        ips.setIndices(0);

        for (int i = 0; i < 5; i++) {
            double d = i / 5.0 + 0.1; //Center
            double dEdgeLower = i / 5.0 + 1e-8; //Edge case: just above split threshold
            double dEdgeUpper = (i + 1) / 5.0 - 1e-8; //Edge case: just below split threshold
            assertEquals(i, (int) ips.getValue(new double[] { d }));
            assertEquals(i, (int) ips.getValue(new double[] { dEdgeLower }));
            assertEquals(i, (int) ips.getValue(new double[] { dEdgeUpper }));
        }
    }

}