Hysteresis Compensation

Ramer-Douglas-Peucker Algorithm

2 min read

The Ramer-Douglas-Peucker algorithm simplifies curves by reducing the number of points while preserving the overall shape. It achieves this by creating “doors” that encompass data points falling within a certain deviation from the initial point. When a data point exceeds this threshold, the door closes, its endpoints are recorded, and a new door begins.

The algorithm’s parameters, deviation threshold (ε) and optional time interval (Δt), allow customization of the compression level and temporal constraints. The Swinging Door algorithm finds applications in diverse fields, such as sensor data management, financial analysis, and IoT device optimization, due to its ability to drastically reduce storage requirements while retaining crucial trend information. While it offers simplicity and significant data reduction, it’s important to note that this method involves lossy compression, potentially sacrificing some finer details in the data. Additionally, careful tuning of the parameters is essential to achieve optimal results for specific use cases.

Algorithm Steps

  1. Initialization: Begin with the full set of points representing the line.
  2. Furthest Point Identification: Determine the point on the line that is furthest (perpendicular distance) from the line segment connecting the first and last points.
  3. Distance Evaluation: Compare the distance of this furthest point to a predefined threshold (epsilon).
  4. Recursive Splitting: If the distance exceeds the threshold, split the line into two segments at the furthest point. Apply steps 2 and 3 recursively to each resulting segment.
  5. Simplification Complete: Terminate recursion when no points within a segment exceed the distance threshold. The remaining points constitute the simplified line.

Example

import pybind11_rdp
import numpy as np
 
rdp_mask = pybind11_rdp.rdp(np.column_stack((time, signal)), epsilon=5e-6, return_mask=True).astype(bool)

Applications

Used in Adaptive downsampling for B-Train data compression before neural network training.

Graph View

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