0PhdZddlZddlmZddlmZdZdZdZ dZ d Z ee ee e d Z d Z d Zd ZdZe eeedZdZdZdZeeedZdS)z(Utilities for the neural network modulesN)expit)xlogycdS)zSimply leave the input array unchanged. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Data, where `n_samples` is the number of samples and `n_features` is the number of features. NXs \/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/neural_network/_base.pyinplace_identityr c(t||dS)zCompute the logistic function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. outN)logistic_sigmoidrs r inplace_logisticrsQAr c2tj||dS)zCompute the hyperbolic tan function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. rN)nptanhrs r inplace_tanhr"sGA1r c4tj|d|dS)zCompute the rectified linear unit function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. rrN)rmaximumrs r inplace_relur-s!Jq!r c||dddtjfz }tj||||dddtjfz}dS)zCompute the K-way softmax function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. axisNr)maxrnewaxisexpsum)rtmps r inplace_softmaxr"8sg aeeemmAAArzM* *CF3AAqqq"*} %%AAAr )identityrlogisticrelusoftmaxcdS)aApply the derivative of the identity function: do nothing. Parameters ---------- Z : {array-like, sparse matrix}, shape (n_samples, n_features) The data which was output from the identity activation function during the forward pass. delta : {array-like}, shape (n_samples, n_features) The backpropagated error signal to be modified inplace. NrZdeltas r inplace_identity_derivativer+Nr r c ||z}|d|z z}dS)aApply the derivative of the logistic sigmoid function. It exploits the fact that the derivative is a simple function of the output value from logistic function. Parameters ---------- Z : {array-like, sparse matrix}, shape (n_samples, n_features) The data which was output from the logistic activation function during the forward pass. delta : {array-like}, shape (n_samples, n_features) The backpropagated error signal to be modified inplace. rNrr(s r inplace_logistic_derivativer-]s QJE QUNEEEr c|d|dzz z}dS)aApply the derivative of the hyperbolic tanh function. It exploits the fact that the derivative is a simple function of the output value from hyperbolic tangent. Parameters ---------- Z : {array-like, sparse matrix}, shape (n_samples, n_features) The data which was output from the hyperbolic tangent activation function during the forward pass. delta : {array-like}, shape (n_samples, n_features) The backpropagated error signal to be modified inplace. rNrr(s r inplace_tanh_derivativer0ps QAXEEEr cd||dk<dS)aApply the derivative of the relu function. It exploits the fact that the derivative is a simple function of the output value from rectified linear units activation function. Parameters ---------- Z : {array-like, sparse matrix}, shape (n_samples, n_features) The data which was output from the rectified linear units activation function during the forward pass. delta : {array-like}, shape (n_samples, n_features) The backpropagated error signal to be modified inplace. rNrr(s r inplace_relu_derivativer2sE!q&MMMr )r#rr$r%c<||z dzdz S)aCompute the squared loss for regression. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) values. y_pred : array-like or label indicator matrix Predicted values, as returned by a regression estimator. Returns ------- loss : float The degree to which the samples are correctly predicted. r/)mean)y_truey_preds r squared_lossr7s$ f_ " ( ( * *Q ..r c~tj|jj}tj||d|z }|jddkrtjd|z |d}|jddkrtjd|z |d}t|| |jdz S)aCompute Logistic loss for classification. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels. y_prob : array-like of float, shape = (n_samples, n_classes) Predicted probabilities, as returned by a classifier's predict_proba method. Returns ------- loss : float The degree to which the samples are correctly predicted. rrr) rfinfodtypeepsclipshapeappendrr r5y_probr;s r log_lossrAs" (6< $C WVS!c' * *F |A!1v:vA666 |A!1v:vA666 && ! ! % % ' ' '&,q/ 99r c$tj|jj}tj||d|z }t ||t d|z d|z z |jdz S)a!Compute binary logistic loss for classification. This is identical to log_loss in binary classification case, but is kept for its use in multilabel case. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels. y_prob : array-like of float, shape = (n_samples, 1) Predicted probabilities, as returned by a classifier's predict_proba method. Returns ------- loss : float The degree to which the samples are correctly predicted. rr)rr9r:r;r<rr r=r?s r binary_log_lossrCs( (6< $C WVS!c' * *F    # # % %a&j!f*(E(E(I(I(K(K KL ,q/ r ) squared_errorrArC)__doc__numpyr scipy.specialrrrr rrrr" ACTIVATIONSr+r-r0r2 DERIVATIVESr7rArCLOSS_FUNCTIONSrr r rKsC.. 333333 & & &!       &$&, #+ #  ///&:::8:"&r