public void createNetwork()
{
ActivationFunction threshold = new ActivationTANH();
this.network = new FeedforwardNetwork();
this.network.AddLayer(new FeedforwardLayer(threshold, PredictWeather.INPUT_SIZE * 4));
this.network.AddLayer(new FeedforwardLayer(threshold, PredictWeather.NEURONS_HIDDEN_1));
if (PredictWeather.NEURONS_HIDDEN_2 > 0)
{
this.network.AddLayer(new FeedforwardLayer(threshold, PredictWeather.NEURONS_HIDDEN_2));
}
this.network.AddLayer(new FeedforwardLayer(threshold, PredictWeather.OUTPUT_SIZE*3));
this.network.Reset();
}
/// <summary>
/// Convert from an array. Use an array to populate the memory of the neural network.
/// </summary>
/// <param name="array">An array that will hold the memory of the neural network.</param>
/// <param name="network">A neural network to convert to an array.</param>
public static void ArrayToNetwork(Double[] array,
FeedforwardNetwork network)
{
// copy data to array
int index = 0;
foreach (FeedforwardLayer layer in network.Layers)
{
// now the weight matrix(if it exists)
if (layer.Next != null)
{
index = layer.LayerMatrix.FromPackedArray(array, index);
}
}
}
/// <summary>
/// Convert to an array. This is used with some training algorithms that
/// require that the "memory" of the neuron(the weight and threshold values)
/// be expressed as a linear array.
/// </summary>
/// <param name="network">A neural network.</param>
/// <returns>The memory of the neural network as an array.</returns>
public static double[] NetworkToArray(FeedforwardNetwork network)
{
int size = 0;
// first determine size
foreach (FeedforwardLayer layer in network.Layers)
{
// count the size of the weight matrix
if (layer.HasMatrix())
{
size += layer.MatrixSize;
}
}
// allocate an array to hold
Double[] result = new Double[size];
// copy data to array
int index = 0;
foreach (FeedforwardLayer layer in network.Layers)
{
// now the weight matrix(if it exists)
if (layer.Next != null)
{
Double[] matrix = layer.LayerMatrix.ToPackedArray();
for (int i = 0; i < matrix.Length; i++)
{
result[index++] = matrix[i];
}
}
}
return result;
}
/// <summary>
/// Compare the two neural networks. For them to be equal they must be of the
/// same structure, and have the same matrix values.
/// </summary>
/// <param name="other">The other neural network.</param>
/// <returns>True if the two networks are equal.</returns>
public bool Equals(FeedforwardNetwork other)
{
int i = 0;
foreach (FeedforwardLayer layer in this.Layers)
{
FeedforwardLayer otherLayer = other.Layers[i++];
if (layer.NeuronCount != otherLayer.NeuronCount)
{
return false;
}
// make sure they either both have or do not have
// a weight matrix.
if ((layer.LayerMatrix == null) && (otherLayer.LayerMatrix != null))
{
return false;
}
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix == null))
{
return false;
}
// if they both have a matrix, then compare the matrices
if ((layer.LayerMatrix != null) && (otherLayer.LayerMatrix != null))
{
if (!layer.LayerMatrix.Equals(otherLayer.LayerMatrix))
{
return false;
}
}
}
return true;
}
/// <summary>
/// Return a clone of the structure of this neural network.
/// </summary>
/// <returns>A cloned copy of the structure of the neural network.</returns>
public FeedforwardNetwork CloneStructure()
{
FeedforwardNetwork result = new FeedforwardNetwork();
foreach (FeedforwardLayer layer in this.layers)
{
FeedforwardLayer clonedLayer = new FeedforwardLayer(layer.NeuronCount);
result.AddLayer(clonedLayer);
}
return result;
}
public void loadNeuralNetwork(string station)
{
this.network = (FeedforwardNetwork)SerializeObject.Load(station+"_weather.net");
}