A simple implementation of the LOESS algorithm using numpy based on NIST.
The purpose of this code is to illustrate a possible implementation of the LOESS smoothing algorithm. The code has been fully implemented in Python, heavily using NumPy and vectorization.
To use the code simply instantiate an object of the Loess class like so:
xx = np.array([0.5578196, 2.0217271, 2.5773252, 3.4140288, 4.3014084,
4.7448394, 5.1073781, 6.5411662, 6.7216176, 7.2600583,
8.1335874, 9.1224379, 11.9296663, 12.3797674, 13.2728619,
4.2767453, 15.3731026, 15.6476637, 18.5605355, 18.5866354,
18.7572812])
yy = np.array([18.63654, 103.49646, 150.35391, 190.51031, 208.70115,
213.71135, 228.49353, 233.55387, 234.55054, 223.89225,
227.68339, 223.91982, 168.01999, 164.95750, 152.61107,
160.78742, 168.55567, 152.42658, 221.70702, 222.69040,
243.18828])
loess = Loess(xx, yy)
for x in xx:
y = loess.estimate(x, window=7, use_matrix=False, degree=1)
print(x, y)
The included TorchLoess.py file is experimental. If you can find a better
and faster implementation using PyTorch, please let me know.