training — Return training-set masks from a cvpartition object.
training(c) returns a logical training mask for single-test-set partitions. training(c,i) returns masks for selected K-fold, leaveout, or custom test sets.
Syntax
mask = training(c)
mask = training(c, i)Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
c | Any | Yes | — | cvpartition object. |
i | Any | No | — | One-based test-set index, vector of indices, or 'all'. |
Returns
| Name | Type | Description |
|---|---|---|
mask | LogicalArray | Logical observation mask for one or more test sets. |
Errors
| Identifier | When | Message |
|---|---|---|
RunMat:cvpartition:InvalidArgument | Inputs, partition kinds, dimensions, indices, or name-value options are malformed. | cvpartition: invalid argument |
RunMat:cvpartition:Internal | RunMat cannot allocate or construct a partition output. | cvpartition: internal error |
How training works
training(c)works for holdout, resubstitution, and custom partitions with one test-set column.training(c,i)accepts a one-based numeric scalar or vector of test-set indices.training(c,'all')returns one logical mask column per test set.- For every returned column, training observations are the complement of that column's test mask.
Example
Get training masks for every fold
c = cvpartition(6, 'KFold', 3);
idx = training(c, 'all')Expected output:
`idx` is a 6-by-3 logical matrix.Using training with coding agents
Open a RunMat example with live inputs, then ask the agent to explain how training changes the result.
Run a small training example, explain the result, then change one input and compare the output.
FAQ
Why does training(c) error for K-fold partitions?⌄
K-fold partitions have multiple test sets, so specify a fold index or use 'all'.
Related Stats functions
Ml
bayesopt · classify · confusionmat · crossvalind · cvpartition · fitclinear · fitctree · fitlm · kmeans · knnsearch · lasso · lassoglm · linkage · lscov · mnrfit · optimizableVariable · pdist · pdist2 · perfcurve · predict · regress · ridge · squareform · test · tsne
Summary
binocdf · boxplot · cdf · cdfplot · chi2cdf · corr · corrcoef · corrcov · cov · cov2corr · dummyvar · ecdf · filloutliers · fitdist · geomean · grpstats · harmmean · icdf · isoutlier · kstest · kurtosis · lsline · mad · mode · nanmax · normalize · normcdf · norminv · normpdf · onehotdecode · onehotencode · pdf · prctile · quantile · refline · rmse · skewness · tabulate · tcdf · tiedrank · tinv · tpdf · ttest2 · wblinv
Random
binornd · bootstrp · datasample · dividerand · exprnd · gamrnd · lhsdesign · mvnrnd · normrnd · random · randsample · rng · trnd · unidrnd · unifrnd · wblrnd
Hist
Open-source implementation
Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how training is executed, line by line, in Rust.
- View the source for training in Rust on GitHub
- Learn how the RunMat runtime works
- Found a bug? Open an issue with a minimal reproduction.
About RunMat
RunMat is an open-source runtime that executes MATLAB-syntax code blazing on any GPU. It is licensed under the Apache 2.0 license.
- RunMat automatically optimizes your math for GPU execution on Apple, Nvidia, and AMD hardware. No code changes needed. Simulations that took hours now take minutes.
- Start running code in seconds. RunMat runs in the browser, on the desktop, or from the CLI. No license server, no IT ticket.