File size: 1,201 Bytes
987ed1b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | import numpy as np
import pdb
class NoiseGenerator:
def __init__(self, noise_strength, correlation_factor=0.9):
self.noise_strength = noise_strength
self.correlation_factor = correlation_factor
self.previous_noise = None
def step(self, pred):
# Generate random noise
# noise_seed = np.random.randn(*pred) * self.noise_strength
noise_seed = (np.random.rand(pred.shape[0], 1, pred.shape[2]) + 0.5) * np.random.choice([-1, 1], size=(pred.shape[0], 1, pred.shape[2]))
action_step = (pred[:, 1:] - pred[:, :-1])
noise_step = noise_seed.repeat(action_step.shape[1], axis=1) * action_step * self.noise_strength
# If it's the first time step, there's no previous noise, so use the seed directly
if self.previous_noise is None:
self.previous_noise = noise_step
else:
# Combine the previous noise with new noise to create temporally correlated noise
noise_step = self.correlation_factor * self.previous_noise + (1 - self.correlation_factor) * noise_step
self.previous_noise = noise_step
noise_cum = np.cumsum(noise_step, axis=1)
return noise_cum
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