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Aluode/PerceptionLabPortable

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�ZS)�_GEDTransformeraM/EEG signal decomposition using the generalized eigenvalue decomposition (GED).11 12    Given two channel covariance matrices S and R, the goal is to find spatial filters13    that maximise contrast between S and R.14 15    Parameters16    ----------17    n_components : int | None18        The number of spatial filters to decompose M/EEG signals.19        If None, all of the components will be used for transformation.20        Defaults to None.21    cov_callable : callable22        Function used to estimate covariances and reference matrix (C_ref) from the23        data. The only required arguments should be 'X' and optionally 'y'. The function24        should return covs, C_ref, info, rank and additional kwargs passed further25        to mod_ged_callable. C_ref, info, rank can be None and kwargs can be empty dict.26    mod_ged_callable : callable | None27        Function used to modify (e.g. sort or normalize) generalized28        eigenvalues and eigenvectors. It should accept as arguments evals, evecs29        and also covs and optional kwargs returned by cov_callable. It should return30        sorted and/or modified evals and evecs and the list of indices according31        to which the first two were sorted. If None, evals and evecs will be32        ordered according to :func:`~scipy.linalg.eigh` default. Defaults to None.33    dec_type : "single" | "multi"34        When "single" and cov_callable returns > 2 covariances,35        approximate joint diagonalization based on Pham's algorithm36        will be used instead of GED.37        When 'multi', GED is performed separately for each class, i.e. each covariance38        (except the last) returned by cov_callable is decomposed with the last39        covariance. In this case, number of covariances should be number of classes + 1.40        Defaults to "single".41    restr_type : "restricting" | "whitening" | None42        Restricting transformation for covariance matrices before performing GED.43        If "restricting" only restriction to the principal subspace of the C_ref44        will be performed.45        If "whitening", covariance matrices will be additionally rescaled according46        to the whitening for the C_ref.47        If None, no restriction will be applied. Defaults to None.48    R_func : callable | None49        If provided, GED will be performed on (S, R_func([S,R])). When dec_type is50        "single", R_func applicable only if two covariances returned by cov_callable.51        If None, GED is performed on (S, R). Defaults to None.52 53    Attributes54    ----------55    evals_ : ndarray, shape (n_channels)56        If fit, generalized eigenvalues used to decompose S and R, else None.57    filters_ :  ndarray, shape (n_channels or less, n_channels)58        If fit, spatial filters (unmixing matrix) used to decompose the data,59        else None.60    patterns_ : ndarray, shape (n_channels or less, n_channels)61        If fit, spatial patterns (mixing matrix) used to restore M/EEG signals,62        else None.63 64    See Also65    --------66    CSP67    SPoC68    SSD69 70    Notes71    -----72    .. versionadded:: 1.1173    N�singlecCs(||_||_||_||_||_||_dS�N)�n_components�cov_callable�mod_ged_callable�dec_type�74restr_type�R_func)�selfr*r)r+r,r-r.�r0�{E:\DocsHouse\542 percep lab latest\PerceptionLab\PerceptionLab_Portable\python_embed\Lib\site-packages\mne/decoding/base.py�__init__ts	75z_GEDTransformer.__init__Tcst�jdi|��d|_dS)NFr0)�super�__init_subclass__�_is_base_ged)�cls�kwargs��	__class__r0r1r4�s76z!_GEDTransformer.__init_subclass__cCs|jr|j||ddd�\}}|��|�||�\}}}}}t�|�}|�|�|dur1|�|g�|jdur9|jnt}t	||j77||�}	|jdkr�t|�dkr^|�
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285s�t��|�\��}t|�t��d�}t|���|��}286t�|d��tt�|	d�\}��t�d��|���������f	dd�t	|287�D��}t288�|�dd�dd	fS)289a�290Evaluate a score by cross-validation.291 292    Parameters293    ----------294    estimator : instance of sklearn.base.BaseEstimator295        The object to use to fit the data.296        Must implement the 'fit' method.297    X : array-like, shape (n_samples, n_dimensional_features,)298        The data to fit. Can be, for example a list, or an array at least 2d.299    y : array-like, shape (n_samples, n_targets,)300        The target variable to try to predict in the case of301        supervised learning.302    groups : array-like, with shape (n_samples,)303        Group labels for the samples used while splitting the dataset into304        train/test set.305    scoring : str, callable | None306        A string (see model evaluation documentation) or307        a scorer callable object / function with signature308        ``scorer(estimator, X, y)``.309        Note that when using an estimator which inherently returns310        multidimensional output - in particular, SlidingEstimator311        or GeneralizingEstimator - you should set the scorer312        there, not here.313    cv : int, cross-validation generator | iterable314        Determines the cross-validation splitting strategy.315        Possible inputs for cv are:316 317        - None, to use the default 5-fold cross validation,318        - integer, to specify the number of folds in a ``(Stratified)KFold``,319        - An object to be used as a cross-validation generator.320        - An iterable yielding train, test splits.321 322        For integer/None inputs, if the estimator is a classifier and ``y`` is323        either binary or multiclass,324        :class:`sklearn.model_selection.StratifiedKFold` is used. In all325        other cases, :class:`sklearn.model_selection.KFold` is used.326    %(n_jobs)s327    %(verbose)s328    fit_params : dict, optional329        Parameters to pass to the fit method of the estimator.330    pre_dispatch : int, or str, optional331        Controls the number of jobs that get dispatched during parallel332        execution. Reducing this number can be useful to avoid an333        explosion of memory consumption when more jobs get dispatched334        than CPUs can process. This parameter can be:335 336        - None, in which case all the jobs are immediately337          created and spawned. Use this for lightweight and338          fast-running jobs, to avoid delays due to on-demand339          spawning of the jobs340        - An int, giving the exact number of total jobs that are341          spawned342        - A string, giving an expression as a function of n_jobs,343          as in '2*n_jobs'344 345    Returns346    -------347    scores : array of float, shape (n_splits,) | shape (n_splits, n_scores)348        Array of scores of the estimator for each run of the cross validation.349    )r�)�scoring)�pre_dispatch�positionc
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