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Code Generation Support, Usage Notes, and Limitations

Generating C/C++ code requires MATLAB® Coder™ . MATLAB Coder generates C/C++ code for the Statistics and Machine Learning Toolbox™ functions that support code generation, given these conditions:

  • You cannot call any function at the top level when generating code by using codegen. Instead, call the function within an entry-point function, and then generate code from the entry-point function. The entry-point function, also known as the top-level or primary function, is a function you define for code generation. All functions within the entry-point function must support code generation.

  • The MATLAB Coder limitations also apply to Statistics and Machine Learning Toolbox for code generation. In particular, code generation does not support categorical arrays and tables. For details, see MATLAB Language Features Supported for C/C++ Code Generation (MATLAB Coder).

  • Code generation in Statistics and Machine Learning Toolbox does not support sparse matrices.

To learn about code generation, see Introduction to Code Generation.

This table lists the Statistics and Machine Learning Toolbox functions that support code generation.

An asterisk (*) indicates that the reference page has usage notes and limitations for C/C++ code generation.

Descriptive Statistics and Visualization
geomean*
grp2idx*
harmmean*
iqr*
kurtosis*

mad*

moment*

nancov*

nanmax*

nanmean*

nanmedian*

nanmin*

nanstd*

nansum*

nanvar*

prctile*

quantile*

skewness*

zscore*

Probability Distributions

betacdf

betafit

betainv

betalike

betapdf

betarnd*

betastat

binocdf

binoinv

binopdf

binornd*

binostat

cdf*

chi2cdf

chi2inv

chi2pdf

chi2rnd*

chi2stat

evcdf

evinv

evpdf

evrnd*

evstat

expcdf

expinv

exppdf

exprnd*

expstat

fcdf

finv

fpdf

frnd*

fstat

gamcdf

gaminv

gampdf

gamrnd*

gamstat

geocdf

geoinv

geopdf

geornd*

geostat

gevcdf

gevinv

gevpdf

gevrnd*

gevstat

gpcdf

gpinv

gppdf

gprnd*

gpstat

hygecdf

hygeinv

hygepdf

hygernd*

hygestat

icdf*

logncdf

logninv

lognpdf

lognrnd*

lognstat

mnpdf

nbincdf

nbininv

nbinpdf

nbinrnd*

nbinstat

ncfcdf

ncfinv

ncfpdf

ncfrnd*

ncfstat

nctcdf

nctinv

nctpdf

nctrnd*

nctstat

ncx2cdf

ncx2rnd*

ncx2stat

normcdf

norminv

normpdf

normrnd*

normstat

pdf*

pearsrnd*

poisscdf

poissinv

poisspdf

poissrnd*

poisstat

randg

random*

randsample*

raylcdf

raylinv

raylpdf

raylrnd*

raylstat

tcdf

tinv

tpdf

trnd*

tstat

unidcdf

unidinv

unidpdf

unidrnd*

unidstat

unifcdf

unifinv

unifpdf

unifrnd*

unifstat

wblcdf

wblinv

wblpdf

wblrnd*

wblstat

Cluster Analysis

kmeans*

knnsearch* and knnsearch* of ExhaustiveSearcher and KDTreeSearcher

pdist*

pdist2*

rangesearch* and rangesearch* of ExhaustiveSearcher and KDTreeSearcher

squareform*

ExhaustiveSearcher*

KDTreeSearcher*

Regression
glmval*
loadCompactModel
predict* of GeneralizedLinearModel and CompactGeneralizedLinearModel
predict* of LinearModel and CompactLinearModel
predict* of RegressionEnsemble, RegressionBaggedEnsemble, and CompactRegressionEnsemble
predict* of RegressionGP and CompactRegressionGP
predict* of RegressionLinear
predict* of RegressionSVM and CompactRegressionSVM
predict* of RegressionTree and CompactRegressionTree
random* of GeneralizedLinearModel and CompactGeneralizedLinearModel
random* of LinearModel and CompactLinearModel
update* of CompactRegressionSVM
GeneralizedLinearModel* and CompactGeneralizedLinearModel*
LinearModel* and CompactLinearModel*
RegressionEnsemble*, RegressionBaggedEnsemble*, and CompactRegressionEnsemble*
RegressionGP* and CompactRegressionGP*
RegressionLinear*
RegressionSVM* and CompactRegressionSVM*
RegressionTree* and CompactRegressionTree*
Classification
loadCompactModel
predict* of ClassificationECOC and CompactClassificationECOC
predict* of ClassificationEnsemble, ClassificationBaggedEnsemble, and CompactClassificationEnsemble
predict* of ClassificationDiscriminant and CompactClassificationDiscriminant
predict* of ClassificationKNN
predict* of ClassificationLinear
predict* of ClassificationSVM and CompactClassificationSVM
predict* of ClassificationTree and CompactClassificationTree
update* of CompactClassificationSVM
ClassificationECOC* and CompactClassificationECOC*
ClassificationEnsemble*, ClassificationBaggedEnsemble*, and CompactClassificationEnsemble*
ClassificationDiscriminant* and CompactClassificationDiscriminant*
ClassificationKNN*
ClassificationLinear*
ClassificationSVM* and CompactClassificationSVM*
ClassificationTree* and CompactClassificationTree*
Dimensionality Reduction

pca*

Industrial Statistics
coxphfit*

Consulte también

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