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core/window.py

@@ -0,0 +1,91 @@
+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