專案描述

Complement document to generic-p0 and generic-p1 to predict serialized data.

This includes generic predictor on expectation value mediumly depends on data itself in fixed size. Also includes integrated predictor depends on p0, p1, p2, and the predictor uses implicitly them with copycat. Their predictor predict with the arithmetic average of 5 way prediction on the range and 1, infty range each 1 way prediction. Thus they includes p0, so we need large accuracy to continue prediction long range or we should restart them.

There also exists non Lebesgue measurable based method starting on the fixed data whether which matches or not, however, literally in bad luck, this can always fail because of multiple of the prediction makes nonsense prediction even they makes a sense after all data is received condition with the predictor learned in short range.

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source 原始碼 p2-master.zip (日期: 2022-09-25, 大小: 12.35 KB)
document Document p2-master.zip (日期: 2022-09-25, 大小: 12.35 KB)

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