File size: 2,354 Bytes
1de88a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | from __future__ import annotations
import math
import numpy as np
from typing import TypeVar, Generic
_T = TypeVar('_T')
class Window(Generic[_T]):
def __init__(self, window_length:int, window:list[_T]=None):
self.window_length = window_length
self.window:list[_T] = [] if window is None else window
def push(self, data:_T):
self.window.append(data)
if len(self.window) > self.window_length:
self.window.pop(0)
def clear(self):
self.window.clear()
def first(self) -> _T:
return self.window[0]
def last(self) -> _T:
return self.window[-1]
def get(self, index:int) -> _T:
return self.window[index]
def head(self, length:int) -> Window[_T]:
return Window[_T](self.window_length, self.window[:length])
def tail(self, length:int) -> Window[_T]:
return Window[_T](self.window_length, self.window[-length:])
def capacity(self):
return len(self.window)
def empty(self):
return len(self.window) == 0
def full(self):
return len(self.window) == self.window_length
def sum(self, func:function=lambda x:x):
return sum(map(func, self.window))
def count(self, func:function=lambda x:x):
return len(list(filter(lambda x:x == True, map(func, self.window))))
def map(self, func:function=lambda x:x) -> Window:
return Window(self.window_length, list(map(func, self.window)))
def argmax(self) -> tuple[int, _T]:
if self.capacity() == 0:
return 0
index = 0
value = self.window[0]
for i, v in enumerate(self.window):
if v > value:
value = v
index = i
return index, value
def to_numpy(self):
return np.concatenate(self.window, axis=0)
def to_numpy_inside(self):
return np.array([x.to_numpy() for x in self.window])
def feature(self) -> list[float]:
x = np.array(self.window)
std = np.std(x)
min = np.min(x)
max = np.max(x)
mean = np.mean(x)
sc = np.mean((x - mean) ** 3) / pow(std, 3)
ku = np.mean((x - mean) ** 4) / pow(std, 4)
if math.isnan(ku):
sc = 0
ku = 0
return [mean, min, max, sc, ku]
def set_to_last_value(self):
for i in range(self.window_length - 1):
if hasattr(self.window[i], "assigned_by"):
self.window[i].assigned_by(self.window[-1])
else:
self.window[i] = self.window[-1]
return self |