train_class_gmm — 训练高斯混合模型。
train_class_gmm( : : GMMHandle, MaxIter, Threshold, ClassPriors, Regularize : Centers, Iter)
train_class_gmm trains the Gaussian Mixture Model (GMM)
referenced by GMMHandle. Before the GMM can be trained, all
training samples to be used for the training must be stored in the
GMM using add_sample_class_gmm,
add_samples_image_class_gmm, or
read_samples_class_gmm。After the training, new training
samples can be added to the GMM and the GMM can be trained
again.
During the training, the error that results from the GMM applied to the training vectors will be minimized with the expectation maximization (EM) algorithm.
MaxIter specifies the maximum number of iterations per
class for the EM algorithm. In practice, values between 20
and 200 should be sufficient for most problems.
Threshold specifies a threshold for the relative changes of
the error. If the relative change in error exceeds the threshold
after MaxIter iterations, the algorithm will be canceled for
this class. Because the algorithm starts with the maximum specified
number of centers (parameter NumCenters in
create_class_gmm), in case of a premature termination the
number of centers and the error for this class will not be
optimal. In this case, a new training with different parameters
(e.g., another value for RandSeed in
create_class_gmm) can be tried.
ClassPriors specifies the method of calculation of the
class priors in GMM. If 'training' is specified, the priors
of the classes are taken from the proportion of the corresponding
sample data during training. If 'uniform' is specified,
the priors are set equal to
1/NumClasses for all classes.
Regularize is used to regularize (nearly) singular
covariance matrices during the training. A covariance matrix might
collapse to singularity if it is trained with linearly
dependent data. To avoid this, a small value specified by
Regularize is added to each main diagonal element of the
covariance matrix, which prevents this element from becoming smaller
than Regularize. A recommended value for
Regularize is 0.0001. If Regularize is set
to 0.0, no regularization is performed.
The centers are initially randomly distributed. In individual cases,
relatively high errors will result from the algorithm because the
initial random values determined by RandSeed in
create_class_gmm lead to local minima. In this case, a new
GMM with a different value for RandSeed should be generated
to test whether a significantly smaller error can be obtained.
It should be noted that, depending on the number of centers, the type of covariance matrix, and the number of training samples, the training can take from a few seconds to several hours.
On output, train_class_gmm returns in Centers the
number of centers per class that have been found to be optimal by the
EM algorithm. These values can be used as a reference in
NumCenters (in create_class_gmm) for future GMMs.
If the number of centers found by training a new GMM on integer
training data is unexpectedly high, this might be corrected by
adding a Randomize noise to the training data in
add_sample_class_gmm。Iter contains the number of
performed iterations per class. If a value in Iter equals
MaxIter, the training algorithm has been terminated
prematurely (see above).
此算子修改后续输入参数的状态：
在执行此算子时，若该参数值需在多个线程间使用，则必须对其访问进行同步。
GMMHandle (输入控制，状态被修改) class_gmm → (handle)
GMM 句柄。
MaxIter (输入控制) integer → (integer)
Maximum number of iterations of the expectation maximization algorithm
默认值： 100
建议值： 10, 20, 30, 50, 100, 200
Threshold (输入控制) real → (real)
Threshold for relative change of the error for the expectation maximization algorithm to terminate.
默认值： 0.001
建议值： 0.001, 0.0001
限制：
Threshold >= 0.0 && Threshold <= 1.0
ClassPriors (输入控制) string → (string)
Mode to determine the a-priori probabilities of the classes
默认值： 'training'
值列表： 'training', 'uniform'
Regularize (输入控制) real → (real)
Regularization value for preventing covariance matrix singularity.
默认值： 0.0001
限制：
Regularize >= 0.0 && Regularize < 1.0
Centers (输出控制) integer-array → (integer)
Number of found centers per class
Iter (输出控制) integer-array → (integer)
Number of executed iterations per class
create_class_gmm (NumDim, NumClasses, [1,5], 'full', 'none', 0, 42,\
GMMHandle)
* Add the training data
read_samples_class_gmm (GMMHandle, 'samples.gsf')
* Train the GMM
train_class_gmm (GMMHandle, 100, 1e-4, 'training', 1e-4, Centers, Iter)
* Write the Gaussian Mixture Model to file
write_class_gmm (GMMHandle, 'gmmclassifier.gmm')
如果参数有效，算子 train_class_gmm 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。
add_sample_class_gmm,
read_samples_class_gmm
evaluate_class_gmm,
classify_class_gmm,
write_class_gmm,
create_class_lut_gmm
Christopher M. Bishop: “Neural Networks for Pattern Recognition”;
Oxford University Press, Oxford; 1995.
Mario A.T. Figueiredo: “Unsupervised Learning of Finite Mixture
Models”; IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 3; March 2002.
基础