tb_stat_correlation — 2D `typed` op

• Data kinds: matrix → matrix

• Call: fullseye.apply(img, "tb_stat_correlation", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Pearson correlation matrix of `(N, D) observations → (D, D)`.

Same orientation as :func:stat_covariance (rows = observations).

Entries are clipped to `[-1, 1]` (floating-point can overshoot by an

ulp), the diagonal is exactly `1` and the matrix exactly symmetric by

construction.

**A constant column raises `ValueError`** (naming the column) instead of

yielding NaN: correlation with a zero-variance variable is mathematically

undefined (0/0), and a NaN that surfaces three ops downstream is the

classic zero-division bug family this module fails closed against. Drop or

perturb the constant column deliberately if that is what you mean.

HALCON: no public tuple operator (see :func:stat_covariance).

Typed bridge of the math op `stat_correlation into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b` are unused.

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept `matrix` as input)

identity · tb_mat_pinv · tb_mat_cond · tb_stat_covariance

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

tb_stat_correlation — 2D typed op