/*
 * File:                DifferentiableGeneralizedLinearModelTest.java
 * Authors:             Kevin R. Dixon
 * Company:             Sandia National Laboratories
 * Project:             Cognitive Foundry
 *
 * Copyright February 28, 2007, Sandia Corporation.  Under the terms of Contract
 * DE-AC04-94AL85000, there is a non-exclusive license for use of this work by
 * or on behalf of the U.S. Government. Export of this program may require a
 * license from the United States Government. See CopyrightHistory.txt for
 * complete details.
 *
 */
package gov.sandia.cognition.learning.function.vector;

import gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableTestHarness;
import gov.sandia.cognition.learning.function.scalar.IdentityScalarFunction;
import gov.sandia.cognition.learning.function.scalar.SigmoidFunction;
import gov.sandia.cognition.math.matrix.DifferentiableVectorFunction;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFunction;

/**
 * Unit tests for DifferentiableGeneralizedLinearModelTest
 *
 * @author Kevin R. Dixon
 * @since  1.0
 */
public class DifferentiableGeneralizedLinearModelTest
    extends GeneralizedLinearModelTest
{
    
    public DifferentiableGeneralizedLinearModelTest(
        String testName)
    {
        super(testName);
    }

    public void testConstructors()
    {
        System.out.println( "Constructors" );

        DifferentiableGeneralizedLinearModel f =
            new DifferentiableGeneralizedLinearModel();
        assertEquals( 1, f.getInputDimensionality() );
        assertEquals( 1, f.getOutputDimensionality() );
        assertNotNull( f.getSquashingFunction() );
        assertTrue(f.getSquashingFunction() instanceof ElementWiseVectorFunction);
        assertTrue(((ElementWiseVectorFunction) f.getSquashingFunction()).getScalarFunction() instanceof IdentityScalarFunction);

        SigmoidFunction s = new SigmoidFunction();
        f = new DifferentiableGeneralizedLinearModel(5, 2, s);
        assertEquals(5, f.getInputDimensionality());
        assertEquals(2, f.getOutputDimensionality());
        assertTrue(f.getSquashingFunction() instanceof ElementWiseVectorFunction);
        assertSame(s, ((ElementWiseVectorFunction) f.getSquashingFunction()).getScalarFunction());

        MultivariateDiscriminant d = new MultivariateDiscriminant(3, 4);
        f = new DifferentiableGeneralizedLinearModel(d, s);
        assertEquals(3, f.getInputDimensionality());
        assertEquals(4, f.getOutputDimensionality());
        assertSame(d, f.getDiscriminant());
        assertTrue(f.getSquashingFunction() instanceof ElementWiseVectorFunction);
        assertSame(s, ((ElementWiseVectorFunction) f.getSquashingFunction()).getScalarFunction());

        DifferentiableVectorFunction v = new LinearVectorFunction();
        f = new DifferentiableGeneralizedLinearModel(d, v);
        assertEquals(3, f.getInputDimensionality());
        assertEquals(4, f.getOutputDimensionality());
        assertSame(d, f.getDiscriminant());
        assertSame(v, f.getSquashingFunction());

        f = new DifferentiableGeneralizedLinearModel(f);
        assertEquals(3, f.getInputDimensionality());
        assertEquals(4, f.getOutputDimensionality());
        assertNotSame(d, f.getDiscriminant());
        assertEquals(d.convertToVector(), f.getDiscriminant().convertToVector());
        assertSame(v, f.getSquashingFunction());
    }

    /**
     * Test of getSquashingFunction method, of class gov.sandia.isrc.learning.util.function.DifferentiableGeneralizedLinearModelTest.
     */
    public void testDiffyGetSquashingFunction()
    {
        System.out.println("getSquashingFunction");
        
        DifferentiableGeneralizedLinearModel instance = this.createRandom();
        assertNotNull( instance.getSquashingFunction() );
    }

    /**
     * Test of computeParameterGradient method, of class gov.sandia.isrc.learning.util.function.DifferentiableGeneralizedLinearModelTest.
     */
    public void testComputeParameterGradient()
    {
        System.out.println("computeParameterGradient");
        double A = 1.0;
        for( int i = 0; i < 10; i++ )
        {
            DifferentiableGeneralizedLinearModel instance = this.createRandom();
            int N = instance.getDiscriminant().getDiscriminant().getNumColumns();
            Vector input = VectorFactory.getDefault().createUniformRandom( N, -A, A, random );
            GradientDescendableTestHarness.testGradient( instance, input );
        }
        

    }
    
}