AIMA.Test.Core.Unit.Learning.Neural.LayerTests.testWeightUpdateMatrixesFormedCorrectly C# (CSharp) Метод

        public void testWeightUpdateMatrixesFormedCorrectly()
        {
            Matrix weightMatrix1 = new Matrix(2, 1);
            weightMatrix1.set(0, 0, -0.27);
            weightMatrix1.set(1, 0, -0.41);

            Vector biasVector1 = new Vector(2);
            biasVector1.setValue(0, -0.48);
            biasVector1.setValue(1, -0.13);

            Layer layer1 = new Layer(weightMatrix1, biasVector1,
                    new LogSigActivationFunction());
            LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);

            Vector inputVector1 = new Vector(1);
            inputVector1.setValue(0, 1);

            layer1.feedForward(inputVector1);

            Matrix weightMatrix2 = new Matrix(1, 2);
            weightMatrix2.set(0, 0, 0.09);
            weightMatrix2.set(0, 1, -0.17);

            Vector biasVector2 = new Vector(1);
            biasVector2.setValue(0, 0.48);

            Layer layer2 = new Layer(weightMatrix2, biasVector2,
                    new PureLinearActivationFunction());
            Vector inputVector2 = layer1.getLastActivationValues();
            layer2.feedForward(inputVector2);

            Vector errorVector = new Vector(1);
            errorVector.setValue(0, 1.261);
            LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
            layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);

            layer1Sensitivity
                    .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);

            Matrix weightUpdateMatrix2 = BackPropLearning.calculateWeightUpdates(
                    layer2Sensitivity, layer1.getLastActivationValues(), 0.1);
            Assert.AreEqual(0.0809, weightUpdateMatrix2.get(0, 0), 0.001);
            Assert.AreEqual(0.0928, weightUpdateMatrix2.get(0, 1), 0.001);

            Matrix lastWeightUpdateMatrix2 = layer2.getLastWeightUpdateMatrix();
            Assert.AreEqual(0.0809, lastWeightUpdateMatrix2.get(0, 0), 0.001);
            Assert.AreEqual(0.0928, lastWeightUpdateMatrix2.get(0, 1), 0.001);

            Matrix penultimateWeightUpdatematrix2 = layer2
                    .getPenultimateWeightUpdateMatrix();
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.get(0, 0),
                    0.001);
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.get(0, 1),
                    0.001);

            Matrix weightUpdateMatrix1 = BackPropLearning.calculateWeightUpdates(
                    layer1Sensitivity, inputVector1, 0.1);
            Assert.AreEqual(0.0049, weightUpdateMatrix1.get(0, 0), 0.001);
            Assert.AreEqual(-0.00997, weightUpdateMatrix1.get(1, 0), 0.001);

            Matrix lastWeightUpdateMatrix1 = layer1.getLastWeightUpdateMatrix();
            Assert.AreEqual(0.0049, lastWeightUpdateMatrix1.get(0, 0), 0.001);
            Assert.AreEqual(-0.00997, lastWeightUpdateMatrix1.get(1, 0), 0.001);
            Matrix penultimateWeightUpdatematrix1 = layer1
                    .getPenultimateWeightUpdateMatrix();
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.get(0, 0),
                    0.001);
            Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.get(1, 0),
                    0.001);
        }