/// <summary>
/// サンプル集合からガウス混合パラメータを推定する
/// </summary>
/// <param name="samples"></param>
/// <param name="means0"></param>
/// <param name="covs0"></param>
/// <param name="weights0"></param>
/// <param name="logLikelihoods"></param>
/// <param name="labels"></param>
/// <param name="probs"></param>
#else
/// <summary>
/// Estimates Gaussian mixture parameters from the sample set
/// </summary>
/// <param name="samples"></param>
/// <param name="means0"></param>
/// <param name="covs0"></param>
/// <param name="weights0"></param>
/// <param name="logLikelihoods"></param>
/// <param name="labels"></param>
/// <param name="probs"></param>
#endif
public virtual bool TrainE(
InputArray samples,
InputArray means0,
InputArray covs0 = null,
InputArray weights0 = null,
OutputArray logLikelihoods = null,
OutputArray labels = null,
OutputArray probs = null)
{
if (disposed)
{
throw new ObjectDisposedException(GetType().Name);
}
if (samples == null)
{
throw new ArgumentNullException("samples");
}
if (means0 == null)
{
throw new ArgumentNullException("means0");
}
samples.ThrowIfDisposed();
means0.ThrowIfDisposed();
if (logLikelihoods != null)
{
logLikelihoods.ThrowIfNotReady();
}
if (covs0 != null)
{
covs0.ThrowIfDisposed();
}
if (weights0 != null)
{
weights0.ThrowIfDisposed();
}
if (labels != null)
{
labels.ThrowIfNotReady();
}
if (probs != null)
{
probs.ThrowIfNotReady();
}
int ret = NativeMethods.ml_EM_trainE(
ptr,
samples.CvPtr,
means0.CvPtr,
Cv2.ToPtr(covs0),
Cv2.ToPtr(weights0),
Cv2.ToPtr(logLikelihoods),
Cv2.ToPtr(labels),
Cv2.ToPtr(probs));
if (logLikelihoods != null)
{
logLikelihoods.Fix();
}
if (labels != null)
{
labels.Fix();
}
if (probs != null)
{
probs.Fix();
}
GC.KeepAlive(samples);
GC.KeepAlive(means0);
GC.KeepAlive(covs0);
GC.KeepAlive(weights0);
return(ret != 0);
}