brsa.py 文件源码

def __init__(
            self, n_iter=50, rank=None,
            auto_nuisance=True, n_nureg=None, nureg_zscore=True,
            nureg_method='PCA',
            baseline_single=False, logS_range=1.0, SNR_prior='exp',
            SNR_bins=21, rho_bins=20, tol=1e-4, optimizer='BFGS',
            minimize_options={'gtol': 1e-4, 'disp': False,
                              'maxiter': 20}, random_state=None,
            anneal_speed=10):

        self.n_iter = n_iter
        self.rank = rank
        self.auto_nuisance = auto_nuisance
        self.n_nureg = n_nureg
        self.nureg_zscore = nureg_zscore
        if auto_nuisance:
            assert (n_nureg is None) \
                or (isinstance(n_nureg, int) and n_nureg > 0), \
                'n_nureg should be a positive integer or None'\
                ' if auto_nuisance is True.'
        if self.nureg_zscore:
            self.preprocess_residual = lambda x: _zscore(x)
        else:
            self.preprocess_residual = lambda x: x
        if nureg_method == 'FA':
            self.nureg_method = lambda x: FactorAnalysis(n_components=x)
        elif nureg_method == 'PCA':
            self.nureg_method = lambda x: PCA(n_components=x, whiten=True)
        elif nureg_method == 'SPCA':
            self.nureg_method = lambda x: SparsePCA(n_components=x,
                                                    max_iter=20, tol=tol)
        elif nureg_method == 'ICA':
            self.nureg_method = lambda x: FastICA(n_components=x,
                                                  whiten=True)
        else:
            raise ValueError('nureg_method can only be FA, PCA, '
                             'SPCA(for sparse PCA) or ICA')
        self.baseline_single = baseline_single
        if type(logS_range) is int:
            logS_range = float(logS_range)
        self.logS_range = logS_range
        assert SNR_prior in ['unif', 'lognorm', 'exp'], \
            'SNR_prior can only be chosen from ''unif'', ''lognorm''' \
            ' and ''exp'''
        self.SNR_prior = SNR_prior
        self.SNR_bins = SNR_bins
        self.rho_bins = rho_bins
        self.tol = tol
        self.optimizer = optimizer
        self.minimize_options = minimize_options
        self.random_state = random_state
        self.anneal_speed = anneal_speed
        return