U f{j4@sdZddlZddlmZddlmZddlmZddl m Z dd l m Z m Z mZdd d Ze ddZe ddZdddZddZGdddeZdS)zTimeDelayingRidge class.N) BaseEstimator)!_setup_cuda_fft_multiply_repeated) next_fast_len)jit)warn ProgressBarloggerFTc% Cs8|rVtj|dd}tj|dd}|jdkrD|jdd}tj|dd}||}||}nd}}|jdkr|jdksvt|ddtjddf}|ddtjddf}|jdd|jddkst||} |j\} } } |j\} }}| | ksttd|jdd}t|dg|d \}}~t| | ft }t | D]N}t | ||||df<t |d|| |d }|||dd|f<q6t | | gd}tj | | |fd d }| | | dd| }t d | | ft|dd}d}||t | D]}|dd|ddf}|d||dd}|}|d|dd|ddf|dd}t | D]} tt | | D]\}!}"|d|dd| f|dd|"f|dd}#|#|}#|rt|#|||| |"|| | | d| |"| |"d| f|#7<| |"kr:||"| |"d| | | | d| f|#j7<|d7}||qp|d||ddt| | df|dd}$|dkr|dkr|d| | f|$|d7<|| |d| f|$d|7<n |dd| f|$||7<|d7}||q\qtj|| | |fd d }||| ||fS)z%Compute auto- and cross-correlations.r)axisrNrg?zcorrelation calculationsF)orderzFitting %d epochs, %d channelsZSample)ZmesgZrfft)nr Zirfft)npZmeanndimAssertionErrornewaxisshaperremptyintrangeZarangezerosr infor updateZconj enumerate _edge_correctTslicereshape)%Xysminsmaxn_jobs fit_interceptedge_correctionX_offsety_offsetZlen_trfZlen_xZn_epochsn_ch_xZlen_yZn_epcohsZn_ch_yZn_fft_Z cuda_dictZijiixx_xtx_yrZpbcounteithis_XZX_fftZ X_fft_conjZy_fftch0oich1 this_resultZcc_tempr8_/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/decoding/time_delaying_ridge.py_compute_corrss           $    "& r:cCs|dkrt|d| d|f|d| d|f}|dkrV||dd|ddf}|t| dddt| dddf|8<|dkr t|d| |f|d| |fddddddf}|dkr|d|d|f}|d| d| f|8<dS)Nrrr) _toeplitz_dotmax)r7r3r%r$r4r6tailheadr8r8r9rps$4 rcCs|j|jkr|jdkstt||}tdt|D]\}|||df||d|ddf7<||dd|f||d|df7<q4|S)zDCreate upper triangular Toeplitz matrices & compute the dot product.rNr)rrrrouterrlen)aboutr-r8r8r9r;s  ,.r;directcCsLddlm}ddlm}d}t|tr0|fd}t|dkrNtdt|f|D]}||krRtd||fqR|ddko|d k} |d dko|d k} | s| st ||S| r.t |} |d } | j d d | d 7<| j |d | d 7<| j |d | d | d 7<| g|} |j | } nt ||fd} | r||}||}||d } | j || | d 7<| j ||d | d 7<| j || | d 7<| j d || | d 7<t | d d d d d d f| st|d kr:|r6tt| }| |} | |d d tjf} | S|| |d } | S)z0Compute regularization parameter from neighbors.rlinalg) laplacian)ridgerGrz'reg_type must have two elements, got %sz*reg_type entries must be one of %s, got %srGrNrrD)normed)scipyrFZscipy.sparse.csgraphrG isinstancestrr@ ValueErrorreyeZflatZ block_diagrZ array_equalrsqrtZdiagr)r+n_delaysreg_typemethodrIrFrGZ known_typesrZreg_timeZreg_chsregZstrideargsblock row_offsetZnormr8r8r9_compute_reg_neighborssX      "   & rXc Csddlm}|jd}|jd|dks,t|jd|}t|||} ||| } z|j| |ddd} Wn4tjjk rtd|j | |dd d} YnX| j |||g} | S) z)Fit the model using correlation matrices.rrErFpos)Z overwrite_aZassume_azNSingular matrix in solving dual problem. Using least-squares solution instead.Zgelsy)Z lapack_driver) rJrFrrrXZsolverZ LinAlgErrorrZlstsqrr!) r/r0r+rQalphaZn_ch_inrFZn_ch_outrPrTZmatwr8r8r9 _fit_corrss    r\c@sFeZdZdZdZdddZed d Zed d Zd dZ ddZ dS)TimeDelayingRidgeaRidge regression of data with time delays. Parameters ---------- tmin : int | float The starting lag, in seconds (or samples if ``sfreq`` == 1). Negative values correspond to times in the past. tmax : int | float The ending lag, in seconds (or samples if ``sfreq`` == 1). Positive values correspond to times in the future. Must be >= tmin. sfreq : float The sampling frequency used to convert times into samples. alpha : float The ridge (or laplacian) regularization factor. reg_type : str | list Can be "ridge" (default) or "laplacian". Can also be a 2-element list specifying how to regularize in time and across adjacent features. fit_intercept : bool If True (default), the sample mean is removed before fitting. n_jobs : int | str The number of jobs to use. Can be an int (default 1) or ``'cuda'``. .. versionadded:: 0.18 edge_correction : bool If True (default), correct the autocorrelation coefficients for non-zero delays for the fact that fewer samples are available. Disabling this speeds up performance at the cost of accuracy depending on the relationship between epoch length and model duration. Only used if ``estimator`` is float or None. .. versionadded:: 0.18 See Also -------- mne.decoding.ReceptiveField Notes ----- This class is meant to be used with :class:`mne.decoding.ReceptiveField` by only implicitly doing the time delaying. For reasonable receptive field and input signal sizes, it should be more CPU and memory efficient by using frequency-domain methods (FFTs) to compute the auto- and cross-correlations. Z regressorr rHTNc Cs\||krtd||ft||_t||_t||_t||_||_||_||_||_ dS)Nz#tmin must be <= tmax, got %s and %s) rMfloattmintmaxsfreqrZrQr'r(r&) selfr_r`rarZrQr'r&r(r8r8r9__init__s    zTimeDelayingRidge.__init__cCstt|j|jS)N)rroundr_rarbr8r8r9_smin szTimeDelayingRidge._smincCstt|j|jdS)Nr)rrdr`rarer8r8r9_smax$szTimeDelayingRidge._smaxcCs|jdkr:|jdkst|jdd|jddksjtn0|jdkrN|jdksRt|jd|jdksjtt|||j|j|j|j|j\|_ }}}}t |j |||j |j ||_ |jr|t||j dj|_nd|_|S)aEstimate the coefficients of the linear model. Parameters ---------- X : array, shape (n_samples[, n_epochs], n_features) The training input samples to estimate the linear coefficients. y : array, shape (n_samples[, n_epochs], n_outputs) The target values. Returns ------- self : instance of TimeDelayingRidge Returns the modified instance. r Nrrrr )rrrr:rfrgr&r'r(Zcov_r\rQrZcoef_rdotsumr intercept_)rbr"r#Zx_y_r+r)r*r8r8r9fit(s. " zTimeDelayingRidge.fitc Cs<ddlm}|jdkr4|ddtjddf}d}nd}t|jdd|jjdf}|j}t |d}t |jdD]}t |jjdD]}t |jjdD]l} ||dd|| f|j|| f} | t | dddt ||} ||t | |||f| 7<qqqx||j 7}|r8|dddddf}|S)zPredict the output. Parameters ---------- X : array, shape (n_samples[, n_epochs], n_features) The data. Returns ------- X : ndarray The predicted response. r) fftconvolverNTFr) Z scipy.signalrmrrrrrrhrfr<rr@rk) rbr"rmZ singletonrCr$offsetr2r5fitempr8r8r9predictLs$  " "$( zTimeDelayingRidge.predict)r rHTNT) __name__ __module__ __qualname____doc__Z_estimator_typercpropertyrfrgrlrqr8r8r8r9r]s/   $r])NFT)rDF)runumpyrbasercudarfilterrfixesrutilsrr r r:rr;rXr\r]r8r8r8r9s$     _   8