U f{j@sddlmZddlZddlmZmZddlmZddl m Z ddl m Z dd l mZmZdd lmZmZGd d d eeZedd dZddZddZdS))CounterN)TransformerMixinEstimatorMixin)_set_cv) _picks_to_idx) parallel_func)loggerverbose) pick_types pick_infoc@s(eZdZdZddZddZddZdS) EMSaTransformer to compute event-matched spatial filters. This version of EMS :footcite:`SchurgerEtAl2013` operates on the entire time course. No time window needs to be specified. The result is a spatial filter at each time point and a corresponding time course. Intuitively, the result gives the similarity between the filter at each time point and the data vector (sensors) at that time point. .. note:: EMS only works for binary classification. Attributes ---------- filters_ : ndarray, shape (n_channels, n_times) The set of spatial filters. classes_ : ndarray, shape (n_classes,) The target classes. References ---------- .. footbibliography:: cCs*t|dr"dt|jt|jfSdSdS)Nfilters_z,z)hasattrlenrclasses_)selfrO/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/decoding/ems.py__repr__+s  z EMS.__repr__cCszt|}t|dkrtd||_|||dkd|||dkd}|tjj|dddddf}||_|S)aFit the spatial filters. .. note : EMS is fitted on data normalized by channel type before the fitting of the spatial filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The training data. y : array of int, shape (n_epochs) The target classes. Returns ------- self : instance of EMS Returns self. rz)EMS only works for binary classification.rrZaxisN) npuniquer ValueErrorrmeanZlinalgZnormr)rXyclassesfiltersrrrfit2s  , zEMS.fitcCstj||jdd}|S)a<Transform the data by the spatial filters. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The input data. Returns ------- X : array, shape (n_epochs, n_times) The input data transformed by the spatial filters. rr)rsumr)rrZXtrrr transformMs z EMS.transformN)__name__ __module__ __qualname____doc__rr r"rrrrrsrc std|dkrdn|}tj|}tttjdddfdksTtd|dkrpj } n| t|dkrtdt|jdddft t|}fdd |Dtj|}j|d d D]^}|kr|d krt|d dd}nt||d d}dd|ftdd|f<qjdddf} t|d| | d\} } tt|d\} }| fdd| D} t| \}}t|}tj|dd}||jdddffS)a@Compute event-matched spatial filter on epochs. This version of EMS :footcite:`SchurgerEtAl2013` operates on the entire time course. No time window needs to be specified. The result is a spatial filter at each time point and a corresponding time course. Intuitively, the result gives the similarity between the filter at each time point and the data vector (sensors) at that time point. .. note : EMS only works for binary classification. .. note : The present function applies a leave-one-out cross-validation, following Schurger et al's paper. However, we recommend using a stratified k-fold cross-validation. Indeed, leave-one-out tends to overfit and cannot be used to estimate the variance of the prediction within a given fold. .. note : Because of the leave-one-out, this function needs an equal number of epochs in each of the two conditions. Parameters ---------- epochs : instance of mne.Epochs The epochs. conditions : list of str | None, default None If a list of strings, strings must match the epochs.event_id's key as well as the number of conditions supported by the objective_function. If None keys in epochs.event_id are used. %(picks_good_data)s %(n_jobs)s cv : cross-validation object | str | None, default LeaveOneOut The cross-validation scheme. %(verbose)s Returns ------- surrogate_trials : ndarray, shape (n_trials // 2, n_times) The trial surrogates. mean_spatial_filter : ndarray, shape (n_channels, n_times) The set of spatial filters. conditions : ndarray, shape (n_classes,) The conditions used. Values correspond to original event ids. References ---------- .. footbibliography:: z;...computing surrogate time series. This can take some timeNZ LeaveOneOutrrzfThe same number of epochs is required by this function. Please consider `epochs.equalize_event_counts`zGCurrently this function expects exactly 2 conditions but you gave me %ics$g|]}tj|kdqS)r)rwhereevent_id).0k)epochsevrr szcompute_ems..)picks)magZgradeegr0FT)Zmegr0 classifier)rr)n_jobsc3s"|]\}}t||VqdSN) _ems_diff)r)traintest)cond_idxdatap_funcrr szcompute_ems..rr)r inforrsetreventsvaluesrr(keyscopyZdrop_badlistsortedr get_datar rZstdrr _run_emsziparrayr)r+ conditionsr.r2Zcvr r;Zch_typeZ this_picksr_Z cv_splitsparalleloutZsurrogate_trialsZspatial_filterr)r7r8r+r,r9r compute_ems^sF2  &       ,  rKcCstj|ddtj|ddS)z,Compute the default diff objective function.rr)rr)Zdata0Zdata1rrrr4sr4cs^|fdd|D}|ttj|ddddddf}tj|d|dd|fS)zRun EMS.c3s|]}t|VqdSr3)rZ intersect1d)r)cr8r5rrr:sz_run_ems..rrrN)rsqrtr!)Zobjective_functionr8r7r5r6drrMrrDs(rD)NNNNN) collectionsrnumpyrZmixinrrbaserZio.pickrrIr utilsr r r r rrKr4rDrrrrs    K p