1. import numpy as np
  3. def flag_anomalies(data, threshold=3, retries=3):
  4. """
  5. Flags anomalies in a collection of numerical data with retry logic.
  7. Args:
  8. data (list or numpy array): The collection of numerical data.
  9. threshold (float): The number of standard deviations from the mean to consider an anomaly.
  10. retries (int): Number of retry attempts.
  12. Returns:
  13. list: A list of indices where anomalies are detected. Returns an empty list if no anomalies are found after retries.
  14. """
  15. data = np.array(data) # Convert to numpy array for easier calculations
  16. anomalies = []
  18. for attempt in range(retries):
  19. try:
  20. mean = np.mean(data)
  21. std = np.std(data)
  23. #Identify anomalies
  24. anomalies = np.where(np.abs(data - mean) > threshold * std)[0].tolist()
  26. if anomalies:
  27. break # Exit retry loop if anomalies are found
  28. except Exception as e:
  29. print(f"Attempt {attempt + 1} failed: {e}")
  30. if attempt < retries - 1:
  31. # Retry the process
  32. pass # continue to the next attempt
  33. else:
  34. #If all retries failed, return empty list
  35. return []
  37. return anomalies
  39. if __name__ == '__main__':
  40. # Example Usage
  41. data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100]
  42. anomalies = flag_anomalies(data)
  43. print(f"Anomalies found at indices: {anomalies}")
  45. data2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  46. anomalies2 = flag_anomalies(data2)
  47. print(f"Anomalies found at indices: {anomalies2}")
  49. data3 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 1000]
  50. anomalies3 = flag_anomalies(data3, threshold=2, retries=5)
  51. print(f"Anomalies found at indices: {anomalies3}")

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