AForge.Neuro.Learning.ResilientBackpropagationLearning.CalculateError C# (CSharp) Method

        private double CalculateError( double[] desiredOutput )
        {
            double error = 0;
            int layersCount = network.Layers.Length;

            // assume, that all neurons of the network have the same activation function
            IActivationFunction function = ( network.Layers[0].Neurons[0] as ActivationNeuron ).ActivationFunction;

            // calculate error values for the last layer first
            ActivationLayer layer = network.Layers[layersCount - 1] as ActivationLayer;
            double[] layerDerivatives = neuronErrors[layersCount - 1];

            for ( int i = 0; i < layer.Neurons.Length; i++ )
            {
                double output = layer.Neurons[i].Output;

                double e = output - desiredOutput[i];
                layerDerivatives[i] = e * function.Derivative2( output );
                error += ( e * e );
            }

            // calculate error values for other layers
            for ( int j = layersCount - 2; j >= 0; j-- )
            {
                layer = network.Layers[j] as ActivationLayer;
                layerDerivatives = neuronErrors[j];

                ActivationLayer layerNext = network.Layers[j + 1] as ActivationLayer;
                double[] nextDerivatives = neuronErrors[j + 1];

                // for all neurons of the layer
                for ( int i = 0, n = layer.Neurons.Length; i < n; i++ )
                {
                    double sum = 0.0;

                    for ( int k = 0; k < layerNext.Neurons.Length; k++ )
                    {
                        sum += nextDerivatives[k] * layerNext.Neurons[k].Weights[i];
                    }

                    layerDerivatives[i] = sum * function.Derivative2( layer.Neurons[i].Output );
                }
            }

            // return squared error of the last layer divided by 2
            return error / 2.0;
        }