AForge.Neuro.Learning.PerceptronLearning.RunEpoch C# (CSharp) Method

        public double RunEpoch( double[][] input, double[][] output )
        {
            double error = 0.0;

            // run learning procedure for all samples
            for ( int i = 0, n = input.Length; i < n; i++ )
            {
                error += Run( input[i], output[i] );
            }

            // return summary error
            return error;
        }
    }

        public override ConfusionMatrix Execute()
        {
            //Create an network with one layer and one neuron in that layer
            var network = new ActivationNetwork(new ThresholdFunction(), 3, 1);

            //Bind the reference of the neuron
            var neuron = network.Layers[0].Neurons[0] as ActivationNeuron;

            //Create the Perceptron learning algorithm
            //Library perceptron implements a single layer linear classifier
            var teacher = new PerceptronLearning(network);

            teacher.LearningRate = 0.1;

            //Enrich the dimensions of the vectors, padding 1 to the end
            var richTraining = AlgorithmHelpers.PaddDimension(trainingSet);
            var richTesting = AlgorithmHelpers.PaddDimension(testSet);

            //Training the network until the error is small enough
            //or 500 hundred iterations have been computed
            int epochs = 0;
            while (true)
            {
                double error = teacher.RunEpoch(richTraining, trainingOutput);/// trainingSet.Length;
                ++epochs;
                if (error < 0.025 * trainingSet.Length || epochs == 500) break;
            }

            var predicted = richTesting
                   .Select(x => neuron.Compute(x))
                   .Select(x => Convert.ToInt32(x))
                   .ToArray();


            //Create a confusion matrix with the calculated parameters
            ConfusionMatrix cmatrix = new ConfusionMatrix(predicted, expected, POSITIVE, NEGATIVE);

            OnAlgorithmEnded(Enumerable.Repeat(neuron, 1), cmatrix);
            return cmatrix;
        }