get_prep_info_class_mlp🔗
Short description🔗
get_prep_info_class_mlp — Compute the information content of the preprocessed feature vectors
of a multilayer perceptron.
Signature🔗
get_prep_info_class_mlp( class_mlp MLPHandle, string Preprocessing, out real InformationCont, out real CumInformationCont )
Description🔗
get_prep_info_class_mlp computes the information content of
the training vectors that have been transformed with the
preprocessing given by Preprocessing.
Preprocessing can be set to 'principal_components'
or 'canonical_variates'. The preprocessing methods are
described with create_class_mlp. The information content is
derived from the variations of the transformed components of the
feature vector, i.e., it is computed solely based on the training
data, independent of any error rate on the training data. The
information content is computed for all relevant components of the
transformed feature vectors (NumInput for
'principal_components' and min(NumOutput - 1,
NumInput) for 'canonical_variates', see
create_class_mlp), and is returned in
InformationCont as a number between 0 and 1. To convert
the information content into a percentage, it simply needs to be
multiplied by 100. The cumulative information content of the first
\(n\) components is returned in the \(n\)-th component of
CumInformationCont, i.e., CumInformationCont
contains the sums of the first \(n\) elements of
InformationCont. To use get_prep_info_class_mlp, a
sufficient number of samples must be added to the multilayer
perceptron (MLP) given by MLPHandle by using
add_sample_class_mlp or read_samples_class_mlp.
InformationCont and CumInformationCont can be used
to decide how many components of the transformed feature vectors
contain relevant information. An often used criterion is to require
that the transformed data must represent x% (e.g., 90%) of the
data. This can be decided easily from the first value of
CumInformationCont that lies above x%. The number thus
obtained can be used as the value for NumComponents in a
new call to create_class_mlp. The call to
get_prep_info_class_mlp already requires the creation of an
MLP, and hence the setting of NumComponents in
create_class_mlp to an initial value. However, if
get_prep_info_class_mlp is called it is typically not known
how many components are relevant, and hence how to set
NumComponents in this call. Therefore, the following
two-step approach should typically be used to select
NumComponents: In a first step, an MLP with the maximum
number for NumComponents is created (NumInput for
'principal_components' and
min(NumOutput - 1, NumInput)
for 'canonical_variates'). Then, the
training samples are added to the MLP and are saved in a file using
write_samples_class_mlp. Subsequently,
get_prep_info_class_mlp is used to determine the information
content of the components, and with this NumComponents.
After this, a new MLP with the desired number of components is
created, and the training samples are read with
read_samples_class_mlp. Finally, the MLP is trained with
train_class_mlp.
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
Processed without parallelization.
Parameters🔗
MLPHandle (input_control) class_mlp → (handle)
MLP handle.
Preprocessing (input_control) string → (string)
Type of preprocessing used to transform the feature vectors.
Default: 'principal_components'
List of values: 'canonical_variates', 'principal_components'
InformationCont (output_control) real-array → (real)
Relative information content of the transformed feature vectors.
CumInformationCont (output_control) real-array → (real)
Cumulative information content of the transformed feature vectors.
Example🔗
(HDevelop)
* Create the initial MLP
create_class_mlp (NumIn, NumHidden, NumOut, 'softmax', \
'principal_components', NumIn, 42, MLPHandle)
* Generate and add the training data
for J := 0 to NumData-1 by 1
* Generate training features and classes
* Data = [...]
* Class = [...]
add_sample_class_mlp (MLPHandle, Data, Class)
endfor
write_samples_class_mlp (MLPHandle, 'samples.mtf')
* Compute the information content of the transformed features
get_prep_info_class_mlp (MLPHandle, 'principal_components',\
InformationCont, CumInformationCont)
* Determine NumComp by inspecting InformationCont and CumInformationCont
* NumComp = [...]
* Create the actual MLP
create_class_mlp (NumIn, NumHidden, NumOut, 'softmax', \
'principal_components', NumComp, 42, MLPHandle)
* Train the MLP
read_samples_class_mlp (MLPHandle, 'samples.mtf')
train_class_mlp (MLPHandle, 100, 1, 0.01, Error, ErrorLog)
write_class_mlp (MLPHandle, 'classifier.mlp')
Result🔗
If the parameters are valid, the operator
get_prep_info_class_mlp returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
get_prep_info_class_mlp may return the error 9211 (Matrix is
not positive definite) if Preprocessing \(=\)
'canonical_variates' is used. This typically indicates
that not enough training samples have been stored for each class.
Combinations with other operators🔗
References🔗
Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Andrew Webb: “Statistical Pattern Recognition”; Arnold, London; 1999.
Module🔗
Foundation