/// <summary> /// Craete a freeform network from a basic network. /// </summary> /// <param name="network">The basic network to use.</param> public FreeformNetwork(BasicNetwork network) { if (network.LayerCount < 2) { throw new FreeformNetworkError( "The BasicNetwork must have at least two layers to be converted."); } // handle each layer IFreeformLayer previousLayer = null; for (int currentLayerIndex = 0; currentLayerIndex < network .LayerCount; currentLayerIndex++) { // create the layer IFreeformLayer currentLayer = _layerFactory.Factor(); // Is this the input layer? if (_inputLayer == null) { _inputLayer = currentLayer; } // Add the neurons for this layer for (int i = 0; i < network.GetLayerNeuronCount(currentLayerIndex); i++) { // obtain the summation object. IInputSummation summation = null; if (previousLayer != null) { summation = _summationFactory.Factor(network .GetActivation(currentLayerIndex)); } // add the new neuron currentLayer.Add(_neuronFactory.FactorRegular(summation)); } // Fully connect this layer to previous if (previousLayer != null) { ConnectLayersFromBasic(network, currentLayerIndex - 1, previousLayer, currentLayer); } // Add the bias neuron // The bias is added after connections so it has no inputs if (network.IsLayerBiased(currentLayerIndex)) { IFreeformNeuron biasNeuron = _neuronFactory .FactorRegular(null); biasNeuron.IsBias = true; biasNeuron.Activation = network .GetLayerBiasActivation(currentLayerIndex); currentLayer.Add(biasNeuron); } // update previous layer previousLayer = currentLayer; } // finally, set the output layer. _outputLayer = previousLayer; }