public int Compute(double[] inputs, MulticlassComputeMethod method)
{
lock (this.Models)
{
var prev = this.Method;
this.Method = method;
int decision;
this.Probabilities(inputs, out decision);
this.Method = prev;
return(decision);
}
}
public int Compute(double[] inputs, MulticlassComputeMethod method, out double[] responses, out double output)
{
lock (this.Models)
{
var prev = this.Method;
this.Method = method;
int decision;
responses = this.Probabilities(inputs, out decision);
this.Method = prev;
output = responses[decision];
return(decision);
}
}
/// <summary>
/// Computes the given input to produce the corresponding output.
/// </summary>
///
/// <param name="inputs">An input vector.</param>
/// <param name="method">The <see cref="MulticlassComputeMethod">
/// multi-class classification method</see> to use.</param>
/// <param name="output">The output of the machine. If this is a
/// <see cref="IsProbabilistic">probabilistic</see> machine, the
/// output is the probability of the positive class. If this is
/// a standard machine, the output is the distance to the decision
/// hyperplane in feature space.</param>
///
/// <returns>The class decision for the given input.</returns>
///
public int Compute(double[] inputs, MulticlassComputeMethod method, out double output)
{
if (method == MulticlassComputeMethod.Voting)
{
int[] votes;
return computeVoting(inputs, out votes, out output);
}
else
{
double[] responses;
return computeElimination(inputs, out responses, out output);
}
}
/// <summary>
/// Computes the given input to produce the corresponding output.
/// </summary>
///
/// <param name="inputs">An input vector.</param>
/// <param name="method">The <see cref="MulticlassComputeMethod">
/// multi-class classification method</see> to use.</param>
/// <param name="responses">The model response for each class.</param>
/// <param name="output">The output of the machine. If this is a
/// <see cref="IsProbabilistic">probabilistic</see> machine, the
/// output is the probability of the positive class. If this is
/// a standard machine, the output is the distance to the decision
/// hyperplane in feature space.</param>
///
/// <returns>The decision label for the given input.</returns>
///
public int Compute(double[] inputs, MulticlassComputeMethod method, out double[] responses, out double output)
{
if (method == MulticlassComputeMethod.Voting)
{
int[] votes;
int result = computeVoting(inputs, out votes, out output);
responses = new double[votes.Length];
for (int i = 0; i < responses.Length; i++)
responses[i] = votes[i] * (2.0 / (Classes * (Classes - 1)));
return result;
}
else
{
return computeElimination(inputs, out responses, out output);
}
}
/// <summary>
/// Computes the given input to produce the corresponding output.
/// </summary>
///
/// <param name="inputs">An input vector.</param>
/// <param name="method">The <see cref="MulticlassComputeMethod">
/// multi-class classification method</see> to use.</param>
///
/// <returns>The class decision for the given input.</returns>
///
public int Compute(double[] inputs, MulticlassComputeMethod method)
{
double output;
return Compute(inputs, method, out output);
}
/// <summary>
/// Computes the given input to produce the corresponding output.
/// </summary>
///
/// <param name="inputs">An input vector.</param>
/// <param name="method">The <see cref="MulticlassComputeMethod">
/// multi-class classification method</see> to use.</param>
/// <param name="responses">The model response for each class.</param>
///
/// <returns>The class decision for the given input.</returns>
///
public int Compute(double[] inputs, MulticlassComputeMethod method, out double[] responses)
{
double output;
return(Compute(inputs, method, out responses, out output));
}
