type hints

Author: open-source-modelling

StationaryBootstrap.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-def StationaryBootstrap(data: np.ndarray, m, sampleLength)-> np.ndarray:
+def StationaryBootstrap(data: np.ndarray, m: float, sampleLength: int)-> np.ndarray:
  """
  Returns a bootstraped sample of the time-series "data" of length "sampleLength. 
  The algorithm used is stationary bootstrap from 1994 Politis & Romano.
@@ -37,12 +37,12 @@ def StationaryBootstrap(data: np.ndarray, m, sampleLength)-> np.ndarray:
  Association, 89:428, 1303-1313, DOI: 10.1080/01621459.1994.10476870 
 
  Implemented by Gregor Fabjan from Qnity Consultants on 12/11/2021.
+
  """
- 
  accept = 1/m 
  lenData = data.shape[0]
 
- sampleIndex = np.random.randint(0,high =lenData,size=1);
+ sampleIndex = np.random.randint(0,high =lenData,size=1)
  sample = np.zeros((sampleLength,1))
  for iSample in range(sampleLength):
  if np.random.uniform(0,1,1)>=accept:
@@ -53,4 +53,4 @@ def StationaryBootstrap(data: np.ndarray, m, sampleLength)-> np.ndarray:
  sampleIndex = np.random.randint(0,high = lenData,size=1)
 
  sample[iSample,0] = data[sampleIndex]
- return sample
+ return sample