plan op• Data kinds: कोई नहीं → design (केवल arguments से तय होने वाला op — यह कोई image या data input नहीं लेता)
• Call: import fullseye as fs; fs.ledger.doe_central_composite(factors=3, alpha='rotatable', center=4, generators=None, inscribed=False) -> 'dict' (implementation को सीधे call करना हो तो import doe; doe.doe_central_composite(factors=3, alpha='rotatable', center=4, generators=None, inscribed=False) -> 'dict', registry से लेना हो तो opsmultivar.get("doe_central_composite"))
Central composite design (Box व Wilson 1951) -> design।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
Three parts: the two-level factorial (full 2^k, or a fraction from generators as in doe_fractional_factorial — it must be resolution V or better so that every two-factor interaction of the quadratic model stays estimable), 2k axial points at ±alpha on each axis, and center centre points.
alpha: "rotatable" — alpha = F^(1/4) with F the number of factorial points (the prediction variance then depends only on the distance from the centre; Box & Hunter 1957), "face" — alpha = 1 (face-centred, three levels only), "spherical" — alpha = sqrt(k), or a number > 0.
inscribed=True shrinks the whole design by 1/alpha so that the axial points sit at the ends of the range (lo/hi) and the factorial points inside it; otherwise the factorial points sit at lo/hi and the axial points go outside the range — meta['outside_range'] then names the factors whose natural values leave lo..hi (check that the bench can be set there).
• design_of_experiments family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• doe_plan_tour — py -3.11 examples/doe_plan_tour.py
• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py
design को input के रूप में लेते हैं)doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol
plan)doe_lhs · doe_orthogonal_array · doe_fractional_factorial · doe_box_behnken
*Provenance: doe.py — MULTIVAR operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.