/// <summary>
/// Applies the transformation to a set of input vectors,
/// producing an associated set of output vectors.
/// </summary>
/// <param name="input">The input data to which
/// the transformation should be applied.</param>
/// <param name="result">The location to where to store the
/// result of this transformation.</param>
/// <returns>The output generated by applying this
/// transformation to the given input.</returns>
public int[] Transform(TInput[] input, int[] result)
{
// Detect all activation centroids
Parallel.For(0, input.Length, ParallelOptions, i =>
{
int j = classifier.Decide(input[i]);
Interlocked.Increment(ref result[j]);
});
return(result);
}
/// <summary>
/// Applies the transformation to a set of input vectors,
/// producing an associated set of output vectors.
/// </summary>
/// <param name="input">The input data to which
/// the transformation should be applied.</param>
/// <param name="result">The location to where to store the
/// result of this transformation.</param>
/// <returns>The output generated by applying this
/// transformation to the given input.</returns>
public int[] Transform(IList <TPoint> input, int[] result)
{
// Detect all activation centroids
Parallel.For(0, input.Count, ParallelOptions, i =>
{
TFeature x = input[i].Descriptor;
int j = classifier.Decide(x);
Interlocked.Increment(ref result[j]);
});
return(result);
}
protected override void OnHandle(object sender, FrameEventArgs eventArgs)
{
var frame = eventArgs.frame;
var rightHand = frame.Hands.FirstOrDefault(hand => hand.IsRight);
if (rightHand == null)
{
_eventAggregator
.GetEvent <AfterFrameHandleEvent>()
.Publish(
new AfterFrameHandleEventArgs()
{
ErrorMessage = "Right hand is required!"
});
return;
}
var controllerOutput = _controllerOutputService.GetControllerOutput(rightHand);
var decision = _classifier.Decide(controllerOutput);
_eventAggregator
.GetEvent <AfterFrameHandleEvent>()
.Publish(
new AfterFrameHandleEventArgs()
{
OutputClass = (OutputClass)decision
});
Debug.WriteLine((OutputClass)decision);
}
/// <summary>
/// Estimates a <see cref="ConfusionMatrix"/> directly from a classifier, a set of inputs and its expected outputs.
/// </summary>
///
/// <typeparam name="TInput">The type of the inputs accepted by the classifier.</typeparam>
///
/// <param name="classifier">The classifier.</param>
/// <param name="inputs">The input vectors.</param>
/// <param name="expected">The expected outputs associated with each input vector.</param>
///
/// <returns>A <see cref="ConfusionMatrix"/> capturing the performance of the classifier when
/// trying to predict the outputs <paramref name="expected"/> from the <paramref name="inputs"/>.</returns>
///
public static ConfusionMatrix Estimate <TInput>(IClassifier <TInput, bool> classifier, TInput[] inputs, bool[] expected)
{
return(new ConfusionMatrix(expected: expected, predicted: classifier.Decide(inputs)));
}
/// <summary>
/// Estimates a <see cref="GeneralConfusionMatrix"/> directly from a classifier, a set of inputs and its expected outputs.
/// </summary>
///
/// <typeparam name="TInput">The type of the inputs accepted by the classifier.</typeparam>
///
/// <param name="classifier">The classifier.</param>
/// <param name="inputs">The input vectors.</param>
/// <param name="expected">The expected outputs associated with each input vector.</param>
///
/// <returns>A <see cref="GeneralConfusionMatrix"/> capturing the performance of the classifier when
/// trying to predict the outputs <paramref name="expected"/> from the <paramref name="inputs"/>.</returns>
///
public static GeneralConfusionMatrix Estimate <TInput>(IClassifier <TInput, bool> classifier, TInput[] inputs, bool[] expected)
{
return(new GeneralConfusionMatrix(expected: Accord.Statistics.Classes.ToZeroOne(expected),
predicted: Accord.Statistics.Classes.ToZeroOne(classifier.Decide(inputs))));
}
/// <summary>
/// Estimates a <see cref="GeneralConfusionMatrix"/> directly from a classifier, a set of inputs and its expected outputs.
/// </summary>
///
/// <typeparam name="TInput">The type of the inputs accepted by the classifier.</typeparam>
///
/// <param name="classifier">The classifier.</param>
/// <param name="inputs">The input vectors.</param>
/// <param name="expected">The expected outputs associated with each input vector.</param>
///
/// <returns>A <see cref="GeneralConfusionMatrix"/> capturing the performance of the classifier when
/// trying to predict the outputs <paramref name="expected"/> from the <paramref name="inputs"/>.</returns>
///
public static GeneralConfusionMatrix Estimate <TInput>(IClassifier <TInput, int> classifier, TInput[] inputs, int[] expected)
{
return(new GeneralConfusionMatrix(expected: expected, predicted: classifier.Decide(inputs)));
}
