/// <summary>
/// Divide the example set into a number of smaller example sets, with
/// the sizes given in an array of values.
/// </summary>
/// <param name="sizes">The sizes to divide the result sets</param>
/// <returns>A shuffled subsets of the original example set</returns>
public ExampleSet[] DivideRandom(int[] sizes)
{
CheckSizeLimits(sizes);
ExampleSet[] result = new ExampleSet[sizes.Length];
ArrayList indexes = new ArrayList();
Random r = new Random();
for (int i = 0; i < Count; i++)
{
indexes.Add(i);
}
for (int sizeIdx = 0; sizeIdx < sizes.Length; sizeIdx++)
{
ExampleSet xset = new ExampleSet();
for (int i = 0; i < sizes[sizeIdx]; i++)
{
int randIdx = (int)indexes[r.Next(indexes.Count)];
Example ex = (Example)examples[randIdx];
indexes.Remove(randIdx);
xset.AddExample(ex);
}
result[sizeIdx] = xset;
}
return(result);
}
/// <summary>
/// Evaluates the network, returning the evaluation of the error.
/// </summary>
/// <param name="examples">The examples to evaluate</param>
/// <param name="network">The network to evalute</param>
/// <returns>An error report for the valuation.</returns>
public Dictionary <String, double> Evaluate(Network network, ExampleSet examples)
{
ZeroErrors();
for (int i = 0; i < examples.Count; i++)
{
Example example = examples[i];
Dictionary <String, double> results = network.Process(example.Inputs);
CalculateErrors(example.Expected, results);
}
return(errorValues);
}
/// <summary>
/// Divide an example set linearly (by simply iterating), with the given number of elements.
/// The total number of elements for each returned sub-example must be less than or
/// equal to the number of elements in the original example set.
/// </summary>
/// <param name="sizes">The sizes of the sub-sets</param>
/// <returns>A group of example subsets</returns>
public ExampleSet[] DivideLinear(int[] sizes)
{
CheckSizeLimits(sizes);
ExampleSet[] result = new ExampleSet[sizes.Length];
int startAt = 0;
for (int sizeIdx = 0; sizeIdx < sizes.Length; sizeIdx++)
{
ExampleSet xset = new ExampleSet();
for (int i = 0; i < sizes[sizeIdx]; i++)
{
xset.AddExample((Example)(examples[i + startAt]));
}
startAt += sizes[sizeIdx];
result[sizeIdx] = xset;
}
return(result);
}
/// <summary>
/// Constructs and example set from data read in as key, value pairs. takes an
/// array of input keys and ouptut keys to divide the raw data into a set of
/// inputs and expected values.
/// </summary>
/// <param name="data">A list of name value pairs of raw data</param>
/// <param name="inputs">A list of names of input values</param>
/// <param name="outputs">A list of names of output values</param>
/// <returns>A constructed example set</returns>
public static ExampleSet MakeExamples(List <Dictionary <String, double> > data, List <String> inputs, List <String> outputs)
{
ExampleSet result = new ExampleSet();
foreach (Dictionary <String, double> datum in data)
{
Example ex = new Example();
foreach (String name in inputs)
{
ex.Inputs[name] = datum[name];
}
foreach (String name in outputs)
{
ex.Expected[name] = datum[name];
}
result.AddExample(ex);
}
return(result);
}
/// <summary>
/// Evaluates the network, returning the evaluation of the error.
/// </summary>
/// <param name="examples">The examples to evaluate</param>
/// <param name="network">The network to evalute</param>
/// <returns>An error report for the valuation.</returns>
public Dictionary<String, double> Evaluate(Network network, ExampleSet examples)
{
ZeroErrors();
for(int i = 0; i < examples.Count; i++)
{
Example example = examples[i];
Dictionary<String, double> results = network.Process(example.Inputs);
CalculateErrors(example.Expected, results);
}
return errorValues;
}
/// <summary>
/// Divide an example set linearly (by simply iterating), with the given number of elements.
/// The total number of elements for each returned sub-example must be less than or
/// equal to the number of elements in the original example set.
/// </summary>
/// <param name="sizes">The sizes of the sub-sets</param>
/// <returns>A group of example subsets</returns>
public ExampleSet[] DivideLinear(int[] sizes)
{
CheckSizeLimits(sizes);
ExampleSet[] result = new ExampleSet[sizes.Length];
int startAt = 0;
for(int sizeIdx = 0; sizeIdx < sizes.Length; sizeIdx++)
{
ExampleSet xset = new ExampleSet();
for(int i = 0; i < sizes[sizeIdx]; i++)
{
xset.AddExample((Example)(examples[i + startAt]));
}
startAt += sizes[sizeIdx];
result[sizeIdx] = xset;
}
return result;
}
/// <summary>
/// Divide the example set into a number of smaller example sets, with
/// the sizes given in an array of values.
/// </summary>
/// <param name="sizes">The sizes to divide the result sets</param>
/// <returns>A shuffled subsets of the original example set</returns>
public ExampleSet[] DivideRandom(int[] sizes)
{
CheckSizeLimits(sizes);
ExampleSet[] result = new ExampleSet[sizes.Length];
ArrayList indexes = new ArrayList();
Random r = new Random();
for(int i = 0; i < Count; i++)
{
indexes.Add(i);
}
for(int sizeIdx = 0; sizeIdx < sizes.Length; sizeIdx++)
{
ExampleSet xset = new ExampleSet();
for(int i = 0; i < sizes[sizeIdx]; i++)
{
int randIdx = (int)indexes[r.Next(indexes.Count)];
Example ex = (Example)examples[randIdx];
indexes.Remove(randIdx);
xset.AddExample(ex);
}
result[sizeIdx] = xset;
}
return result;
}
/// <summary>
/// Constructs and example set from data read in as key, value pairs. takes an
/// array of input keys and ouptut keys to divide the raw data into a set of
/// inputs and expected values.
/// </summary>
/// <param name="data">A list of name value pairs of raw data</param>
/// <param name="inputs">A list of names of input values</param>
/// <param name="outputs">A list of names of output values</param>
/// <returns>A constructed example set</returns>
public static ExampleSet MakeExamples(List<Dictionary<String, double> > data, List<String> inputs, List<String> outputs)
{
ExampleSet result = new ExampleSet();
foreach(Dictionary<String, double> datum in data)
{
Example ex = new Example();
foreach(String name in inputs)
{
ex.Inputs[name] = datum[name];
}
foreach(String name in outputs)
{
ex.Expected[name] = datum[name];
}
result.AddExample(ex);
}
return result;
}
