padsequences — Pad or truncate numeric sequence cell arrays.

padsequences(sequences, paddingDim) pads or truncates numeric sequences stored in a cell array along the requested dimension.

Syntax

Y = padsequences(sequences, paddingDim)
Y = padsequences(sequences, paddingDim, Name, Value)

How padsequences works

• sequences must be a cell array of numeric scalar/vector/tensor sequences.
• paddingDim is a required positive integer dimension.
• PaddingDirection accepts left, right, or both; the default is right.
• Length accepts longest, shortest, or a positive integer.
• PaddingValue accepts a finite numeric scalar and defaults to 0.
• UniformOutput defaults to true and stacks padded sequences into a numeric tensor; false preserves the input cell shape.

GPU memory and residency

padsequences materializes host sequence tensors in this slice; provider-resident sequence padding remains future GPU work.

Examples

Right Pad Sequences

Y = padsequences({[1 2], 3}, 2, 'PaddingValue', -1)

Expected output:

A numeric tensor with right-padded row sequences.

Preserve Cell Shape

C = padsequences({[1 2 3], [4 5]}, 2, 'Length', 2, 'PaddingDirection', 'left', 'UniformOutput', false)

Expected output:

A cell array whose first sequence keeps the tail `[2 3]`.

Using padsequences with coding agents

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

Related Deep Learning functions

About RunMat

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