Low-level Classes

C++17 classes exposed via pybind11. Use them for streaming (one value at a time) or to read multiple statistics from a single pass.

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class robustrolling.MonotonicMax(window_size)

Rolling maximum — monotonic deque, O(1) amortised.

Parameters:

window_size – int

update(value: float)

get_max() → float

process_batch(x: numpy.ndarray) → numpy.ndarray

mm = MonotonicMax(3)
mm.update(1.0); mm.update(3.0); mm.update(2.0)
mm.get_max()  # 3.0

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class robustrolling.MonotonicMin(window_size)

Rolling minimum — monotonic deque, O(1) amortised.

Parameters:

window_size – int

update(value: float)

get_min() → float

process_batch(x: numpy.ndarray) → numpy.ndarray

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class robustrolling.MultisetMedian(window_size)

Rolling median — std::multiset with tracked iterator, O(log n). Even-size windows return the average of the two middle elements.

Parameters:

window_size – int

update(value: float)

get_median() → float

process_batch(x: numpy.ndarray) → numpy.ndarray

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class robustrolling.SlidingWelford(window_size)

Rolling sample variance (ddof=1) — Welford algorithm with ring buffer, O(1).

Parameters:

window_size – int

update(value: float)

get_variance() → float

process_batch(x: numpy.ndarray) → numpy.ndarray

sw = SlidingWelford(3)
for v in [1., 2., 3., 4.]:
    sw.update(v)
sw.get_variance()  # 1.0

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class robustrolling.SlidingMoments(window_size)

Rolling mean, skewness, and excess kurtosis — Terriberry’s 4th-moment algorithm, O(1). Requires ≥ 3 observations for skewness, ≥ 4 for kurtosis.

Parameters:

window_size – int

update(x: float)

reset()

current_size() → int

get_mean() → float

get_skewness() → float

get_kurtosis() → float

process_mean_batch(x: numpy.ndarray) → numpy.ndarray

process_skewness_batch(x: numpy.ndarray) → numpy.ndarray

process_kurtosis_batch(x: numpy.ndarray) → numpy.ndarray

sm = SlidingMoments(4)
for v in [1., 2., 3., 4.]:
    sm.update(v)
sm.get_mean(), sm.get_skewness(), sm.get_kurtosis()
# (2.5, 0.0, -1.2)

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class robustrolling.SlidingCovariance(window_size)

Rolling sample covariance and Pearson correlation — 2-D Welford algorithm, O(1).

Parameters:

window_size – int

update(x: float, y: float)

get_covariance() → float

get_correlation() → float

get_mean_x() → float

get_mean_y() → float

process_covariance_batch(x: numpy.ndarray, y: numpy.ndarray) → numpy.ndarray

process_correlation_batch(x: numpy.ndarray, y: numpy.ndarray) → numpy.ndarray

sc = SlidingCovariance(3)
for x, y in [(1,2),(2,4),(3,6)]:
    sc.update(x, y)
sc.get_covariance(), sc.get_correlation()
# (2.0, 1.0)