이동평균과 지연

창이 채워지기 전에도 현재까지 존재하는 값만 평균합니다.

import json,sys,math

def moving(values,window):
    out=[]
    for i in range(len(values)):
        chunk=values[max(0,i-window+1):i+1]
        out.append(sum(chunk)/len(chunk))
    return out

def evaluate(obj):
    values=obj['values'];windows=obj['windows'];dt=obj['dt_s']
    if not isinstance(values,list) or not values or any(type(v) not in (int,float) or not math.isfinite(v) for v in values): raise ValueError('VALUES')
    if not isinstance(windows,list) or not windows or any(type(w) is not int or w<1 or w>len(values) for w in windows): raise ValueError('WINDOW')
    if type(dt) not in (int,float) or not math.isfinite(dt) or dt<=0: raise ValueError('DT')
    mean=sum(values)/len(values);raw=sum((v-mean)**2 for v in values)/len(values)
    results=[]
    for window in windows:
        filtered=moving(values,window)[window-1:]
        avg=sum(filtered)/len(filtered);variance=sum((v-avg)**2 for v in filtered)/len(filtered)
        step=moving([1.0]*window+[0.0]*window,window)[window:]
        crossing=next(i for i,v in enumerate(step) if v<=.25)
        results.append([window,round(variance,6),round(crossing*dt,6)])
    return [round(raw,6),results]

print(moving([1.,3.,5.,7.],3))

각 행의 마지막 값은 초, 가운데 값은 m²입니다. 교대 기록의 창 2와 3을 비교합니다.

import json,sys,math

def moving(values,window):
    out=[]
    for i in range(len(values)):
        chunk=values[max(0,i-window+1):i+1]
        out.append(sum(chunk)/len(chunk))
    return out

def evaluate(obj):
    values=obj['values'];windows=obj['windows'];dt=obj['dt_s']
    if not isinstance(values,list) or not values or any(type(v) not in (int,float) or not math.isfinite(v) for v in values): raise ValueError('VALUES')
    if not isinstance(windows,list) or not windows or any(type(w) is not int or w<1 or w>len(values) for w in windows): raise ValueError('WINDOW')
    if type(dt) not in (int,float) or not math.isfinite(dt) or dt<=0: raise ValueError('DT')
    mean=sum(values)/len(values);raw=sum((v-mean)**2 for v in values)/len(values)
    results=[]
    for window in windows:
        filtered=moving(values,window)[window-1:]
        avg=sum(filtered)/len(filtered);variance=sum((v-avg)**2 for v in filtered)/len(filtered)
        step=moving([1.0]*window+[0.0]*window,window)[window:]
        crossing=next(i for i,v in enumerate(step) if v<=.25)
        results.append([window,round(variance,6),round(crossing*dt,6)])
    return [round(raw,6),results]

print(evaluate(dict(values=[1.1,.9,1.1,.9,1.1,.9],windows=[1,2,3,4],dt_s=.05)))

첫 가까운 입력을 0초로 놓습니다. 추가 이동량은 전체 제동 거리가 아닙니다.

import json,sys,math

def moving(values,window):
    out=[]
    for i in range(len(values)):
        chunk=values[max(0,i-window+1):i+1]
        out.append(sum(chunk)/len(chunk))
    return out

def evaluate(obj):
    values=obj['values'];windows=obj['windows'];dt=obj['dt_s']
    if not isinstance(values,list) or not values or any(type(v) not in (int,float) or not math.isfinite(v) for v in values): raise ValueError('VALUES')
    if not isinstance(windows,list) or not windows or any(type(w) is not int or w<1 or w>len(values) for w in windows): raise ValueError('WINDOW')
    if type(dt) not in (int,float) or not math.isfinite(dt) or dt<=0: raise ValueError('DT')
    mean=sum(values)/len(values);raw=sum((v-mean)**2 for v in values)/len(values)
    results=[]
    for window in windows:
        filtered=moving(values,window)[window-1:]
        avg=sum(filtered)/len(filtered);variance=sum((v-avg)**2 for v in filtered)/len(filtered)
        step=moving([1.0]*window+[0.0]*window,window)[window:]
        crossing=next(i for i,v in enumerate(step) if v<=.25)
        results.append([window,round(variance,6),round(crossing*dt,6)])
    return [round(raw,6),results]

s=moving([1.]*4+[0.]*4,4)[4:]
print(s)
t=next(i for i,v in enumerate(s) if v<=.25)*.05
print(f'crossing_s={t:.2f} extra_m={.2*t:.3f}')

valuesは固定入力のm値、windowsは1以上かつ入力長以下の整数の一覧、dt_sは正の周期です。movingのTODOを現在と過去の部分窓の平均にします。出力は[原分散,[[window,満窓出力の分散,crossing_s],...]]です。分散の分母は評価件数、単位はm²です。各windowについて1.0をwindow個・0.0をwindow個流し、最初の0.0入力を時刻0として出力が0.25以下になる最初の時刻を測ります。無効入力はERRORです。