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Builtin Reference
    • adamupdate
    • analyzeNetwork
    • bilstmLayer
    • classificationLayer
    • combvec
    • convolution1dLayer
    • crossentropy
    • dlarray
    • dlfeval
    • dlgradient
    • dlnetwork
    • dlupdate
    • eluLayer
    • exportONNXNetwork
    • featureInputLayer
    • forward
    • fullyConnectedLayer
    • globalAveragePooling1dLayer
    • layerGraph
    • layerNormalizationLayer
    • lstmLayer
    • padsequences
    • regressionLayer
    • reluLayer
    • sequenceInputLayer
    • softmaxLayer
    • trainingOptions
    • trainnet
    • trainNetwork

combvec — Generate all column combinations from numeric matrices.

combvec(A,B,...) returns a numeric matrix whose columns are all combinations of the input matrix columns, with the first input varying fastest.

Syntax

M = combvec(A, B, ...)

How combvec works

  • Inputs must be finite numeric scalars or 2-D matrices.
  • The output row count is the sum of the input row counts, and the output column count is the product of the input column counts.
  • Very large outputs are rejected before allocation.

GPU memory and residency

combvec materializes a host numeric matrix in this slice; provider-resident Cartesian generation remains future GPU work.

Examples

Combine Vectors

M = combvec([1 2], [10 20 30])

Expected output:

`M` is 2-by-6.

Combine Matrix Columns

M = combvec([1 3; 2 4], [10 20])

Expected output:

`M` is 3-by-4, stacking one column from each input per output column.

Using combvec with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how combvec changes the result.

Run a small combvec example, explain the result, then change one input and compare the output.

Related Deep Learning functions

adamupdate · analyzeNetwork · bilstmLayer · classificationLayer · convolution1dLayer · crossentropy · dlarray · dlfeval · dlgradient · dlnetwork · dlupdate · eluLayer · exportONNXNetwork · featureInputLayer · forward · fullyConnectedLayer · globalAveragePooling1dLayer · layerGraph · layerNormalizationLayer · lstmLayer · padsequences · regressionLayer · reluLayer · sequenceInputLayer · softmaxLayer · trainingOptions · trainnet · trainNetwork

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.

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On this page
  • Syntax
  • How combvec works
  • GPU memory and residency
  • Examples
  • Combine Vectors
  • Combine Matrix Columns
  • Using combvec with coding agents
  • Related Deep Learning functions
  • About RunMat