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Soft Independent Modeling of Class Analogy (SIMCA) 1.0
File ID: 83925






Soft Independent Modeling of Class Analogy (SIMCA) 1.0
Download Soft Independent Modeling of Class Analogy (SIMCA) 1.0http://www.mathworks.comReport Error Link
License: Shareware
File Size: 10.0 KB
Downloads: 9
User Rating:1 Stars  (1 rating)
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Soft Independent Modeling of Class Analogy (SIMCA) 1.0 Description
Description: Model for Soft Independent Modeling of Class Analogy (SIMCA)

simca_model=simca(x,classes)

input:
x (samples x descriptors) for calibration
classes (samples x 1) classes numbers (must be >0)

output:
simca_model struct with:
models (1 x No. classes) struct with: (one for each class)
x (samples x descriptors) input for this class
clas (samples x 1) numbers for this class
S (samples x pc) scores for this model
L (descriptors x pc) loadings for this model
ev (pc x 1) eigenvalues for this model
T2 (samples x 1) Tdldc for samples of this model
Q2 (samples x 1) Qdldc for samples of this model
T2lim (1 x 1) Tdldc limit for this model
Q2lim (1 x 1) Qdldc limit for this model
stats (1 x 1) struct with:
classes (No. classes x 1) class types
T2 (samples x 1) Tdldc for calibration samples
Q2 (samples x 1) Qdldc for calibration samples
class_c (samples x 1) Predicted classes for calibration samples
RMSEP (1 x 1) Root Mean Square Error for Calibration
R2 (1 x 1) Correlation Coefficient for calibration
suc (1 x 1) Success (%) of classification for calibration samples

--------------------------------------------------------------------------

Classes prediction using SIMCA model

simca_pred = simcapred(x,model,classes)

input:
x (samples x descriptors) new samples for prediction
model (struct) with SIMCA calibration parameters
classes (samples x 1) classes numbers (must be >0) (optional for model test)

output:
simca_pred struct with:
class_p (samples x 1) predicted classes for new samples
T2 (samples x 1) Tdldc for new samples
Q2 (samples x 1) Qdldc for new samples
RMSEP (1 x 1) Root Mean Square Error for Prediction (only if 'classes' is supplied)
R2 (1 x 1) Correlation Coefficient for Prediction (only if 'classes' is supplied)
suc (1 x 1) Success (%) of classification for new samples (only if 'classes' is supplied)

License: Shareware

Related: Correlation, calibration, coefficient, success, classification, Error, Square, types, Class, predicted

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 10.0 KB

Downloads: 9



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