Introduces Sigmoid Lag Estimator

Add SigmoidLagEstimator for Time Lag Estimation

Description

This merge request introduces the SigmoidLagEstimator class, which is designed to estimate the time lag between two signals using a layer-wise cross-correlation approach. The estimator utilizes a Bayesian aggregation method to combine the estimations from different layers of the sigmoid decomposition, providing a probabilistic distribution of the time lags.

Key Features

• Layer-wise Cross-Correlation: Estimates time lag between signals using sigmoid decomposition layers.
• Bayesian Aggregation: Combines layer estimations probabilistically to account for uncertainty.
• Flexibility: Allows for customization of layers, scaler, and prior distribution.

Usage

from sigmoid_lag_estimator import SigmoidLagEstimator

# Initialize the estimator with desired parameters
estimator = SigmoidLagEstimator(layers=[5, 10, 15], scaler=2)

# Estimate the time lag between two signals
mean_lag = estimator.predict(signal1, signal2)

# Get the probability distribution of time lags
posterior, lag_space = estimator.predict_proba(signal1, signal2)