change op• 数据种类:signal → table
• 调用: import fullseye as fs; fs.ledger.spc_ewma(signal, target, lam=0.2, L=3.0, sigma=None)(要直接调用实现,import spc; spc.spc_ewma(signal, target, lam=0.2, L=3.0, sigma=None);从台账取用则 opsspc.get("spc_ewma"))
> 该算子的说明尚无译文,以下照原文给出。
EWMA control chart for individual measurements (Roberts 1959).
signal is a 1-D series of individual measurements. With a reference value target (the in-control mean), a smoothing constant lam in (0, 1] and a control-limit width L (in sigmas), the exponentially weighted moving average and its time-varying limits are::
z_i = lam * x_i + (1 - lam) * z_{i-1}, z_0 = target
var_i = sigma^2 * (lam / (2 - lam)) * (1 - (1 - lam) ** (2 (i + 1)))
UCL_i / LCL_i = target +/- L * sqrt(var_i)
sigma is the process standard deviation; if None it is estimated from the series as the sample std (ddof=1). The limits widen from the first sample to the asymptote target +/- L * sigma * sqrt(lam / (2 - lam)). EWMA, like CUSUM, catches small sustained shifts that a single-point Shewhart chart misses; lam trades memory (small = long memory, sensitive to small shifts) against speed.
Returns a dict with the z / ucl / lcl arrays, the integer alarms indices (z_i outside its limits), the first alarm index (or -1), the asymptotic ucl_inf / lcl_inf, and the echoed target / lam / L / sigma / in_control.
Ground truth (pinned in the tests): a series constant at target keeps z == target with no alarm; z is exactly the recursion above; ucl increases monotonically toward ucl_inf; with lam = 1 the chart reduces to a Shewhart individuals chart (z == x, limits constant at target +/- L sigma).
Raises ValueError: a non-1-D / empty *signal*, a non-finite *target* / *lam* / *L*, lam outside (0, 1], a non-positive *L*, a non-finite or non-positive *sigma*, or (when estimating) a constant series whose sample std is zero.
• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。
• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。
• 算法的正典(作者、年份)与用途见上面的族使用指南。
• poc_spc — py -3.11 examples/poc_spc.py
table 作为输入)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
change)*Provenance: spc.py — SPC 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.