Accord.Neuro.Learning.ParallelResilientBackpropagationLearning.ParallelResilientBackpropagationLearning C# (CSharp) Method

        public ParallelResilientBackpropagationLearning(ActivationNetwork network)
        {
            this.network = network;

            networkOutputs = new ThreadLocal<double[][]>(() => new double[network.Layers.Length][]);

            networkErrors = new ThreadLocal<double[][]>(() =>
            {
                var e = new double[network.Layers.Length][];
                for (int i = 0; i < e.Length; i++)
                    e[i] = new double[network.Layers[i].Neurons.Length];
                return e;
            });


            weightsDerivatives = new double[network.Layers.Length][][];
            thresholdsDerivatives = new double[network.Layers.Length][];

            weightsPreviousDerivatives = new double[network.Layers.Length][][];
            thresholdsPreviousDerivatives = new double[network.Layers.Length][];

            weightsUpdates = new double[network.Layers.Length][][];
            thresholdsUpdates = new double[network.Layers.Length][];

            priorSumOfSquaredErrors = double.MaxValue;

            // Initialize layer derivatives and updates
            for (int i = 0; i < network.Layers.Length; i++)
            {
                Layer layer = network.Layers[i];

                weightsDerivatives[i] = new double[layer.Neurons.Length][];
                weightsPreviousDerivatives[i] = new double[layer.Neurons.Length][];
                weightsUpdates[i] = new double[layer.Neurons.Length][];

                // for each neuron
                for (int j = 0; j < layer.Neurons.Length; j++)
                {
                    weightsDerivatives[i][j] = new double[layer.InputsCount];
                    weightsPreviousDerivatives[i][j] = new double[layer.InputsCount];
                    weightsUpdates[i][j] = new double[layer.InputsCount];
                }

                thresholdsDerivatives[i] = new double[layer.Neurons.Length];
                thresholdsPreviousDerivatives[i] = new double[layer.Neurons.Length];
                thresholdsUpdates[i] = new double[layer.Neurons.Length];
            }

            // Initialize steps
            Reset(initialStep);
        }