/// <summary>
/// Passes a collection of <see cref="Node"/> objects to a given <see cref="NeuralNetwork"/> and adapts the parameters based on the <see cref="Node.ExpectedOutput"/>. Returns a <see cref="double"/> value indicating the average value of the <see cref="Node.GetCost(double[])"/> function of the network over each iteration.
/// </summary>
/// <param name="algorithm">The given <see cref="INodeLearningAlgorithm"/> used to teach the <see cref="NeuralNetwork"/>.</param>
/// <param name="network">The <see cref="NeuralNetwork"/> to test and teach.</param>
/// <param name="data">The <see cref="Node"/> inputs and expected outputs to use for learning.</param>
public static double Teach(this INodeLearningAlgorithm algorithm, NeuralNetwork network, IEnumerable <Node> data)
{
List <double> costs = new List <double>();
foreach (var d in data)
{
costs.Add(
algorithm.Teach(network, d));
}
return(costs.Sum() / costs.Count);
}
/// <summary>
/// Passes a collection of <see cref="Node"/> objects to a given <see cref="NeuralNetwork"/> and adapts the parameters based on the <see cref="Node.ExpectedOutput"/>. Returns a <see cref="double"/> value indicating the average value of the <see cref="Node.GetCost(double[])"/> function of the network over each iteration.
/// </summary>
/// <param name="algorithm">The given <see cref="INodeLearningAlgorithm"/> used to teach the <see cref="NeuralNetwork"/>.</param>
/// <param name="network">The <see cref="NeuralNetwork"/> to test and teach.</param>
/// <param name="data">The <see cref="NodeSet"/> containing inputs and expected outputs (as <see cref="Node"/>) to use for learning.</param>
/// <param name="sampleSize">The number of <see cref="Node"/> objects to retrive from the <see cref="NodeSet"/> during this stage of learning.</param>
public static double Teach(this INodeLearningAlgorithm algorithm, NeuralNetwork network, NodeSet data, int sampleSize)
{
return(Teach(algorithm, network, data.GetData().Take(sampleSize)));
}
