hidimstat.reid#
- hidimstat.reid(X, y, epsilon=0.01, tolerance=0.0001, max_iterance=10000, n_splits=5, n_jobs=1, seed=0, group=False, stationary=True, method='median', order=1)[source]#
Residual sum of squares based estimators for noise standard deviation estimation.
This implementation follows the procedure described in [1] and [Reid et al., 2016]. It uses Lasso with cross-validation to estimate both the noise standard deviation and model coefficients.
For group, the implementation is based on the procedure from [2].
- Parameters:
- Xndarray, shape (n_samples, n_features)
Input data matrix.
- yndarray, shape (n_samples,)/(n_samples, n_times)
Target vector. The time means the presence of groups.
- epsilonfloat, optional (default=1e-2)
Length of the cross-validation path, where alpha_min / alpha_max = eps. Smaller values create a finer grid.
- tolerancefloat, optional (default=1e-4)
Tolerance for optimization convergence. The algorithm stops when updates are smaller than tol and dual gap is smaller than tol.
- max_iterationint, optional (default=10000)
Maximum number of iterations for the optimization algorithm.
- n_splitsint, optional (default=5)
Number of folds for cross-validation.
- n_jobsint, optional (default=1)
Number of parallel jobs for cross-validation. -1 means using all processors.
- seedint, optional (default=0)
Random seed for reproducible cross-validation splits.
- stationarybool, (default=True)
Whether noise has constant magnitude across time steps.
- method{‘median’, ‘AR’}, (default=’simple’)
Covariance estimation method: - ‘median’: Uses median correlation between consecutive time steps - ‘AR’: Uses Yule-Walker method with specified order
- orderint, default=1
Order of AR model when method=’AR’. Must be < n_times.
- Returns:
- sigma_hat/cov_hatfloat/ndarray, shape (n_times, n_times)
Estimated noise standard deviation based on residuals or estimated covariance matrix for group.
- beta_hatndarray, shape (n_features,)/(n_features, n_times)
Estimated sparse coefficient vector from Lasso regression.
References