rtj/dZddlZddlmZddlmZdZdZdZ dZ d Z d Z eee e e e d Z d Zd ZdZdZeeeedZddZddZddZddZeeeedZdS)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 Z/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/neural_network/_base.pyinplace_identityr c2tj||dS)zCompute the exponential inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. outN)npexprs r inplace_exprsF1!r c(t||dS)zCompute the logistic function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. rN)logistic_sigmoidrs r inplace_logisticr"sQAr c2tj||dS)zCompute the hyperbolic tan function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. rN)rtanhrs 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_relur8s!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)maxrnewaxisrsum)rtmps r inplace_softmaxr$Csg aeeemmAAArzM* *CF3AAqqq"*} %%AAAr )identityrrlogisticrelusoftmaxcdS)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-Zr 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/is 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_derivativer2|s 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_derivativer4sE!q&MMMr )r%rr&r'cfdtj||z dz|dzS)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. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- loss : float The degree to which the samples are correctly predicted. g?r1rweightsr)raveragemeany_truey_pred sample_weights r squared_lossr>s7( bj&6/a/QOOOTTVVVr ctjt|||z |z |z|dS)aCompute (half of the) Poisson deviance loss for regression. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels. y_pred : array-like or label indicator matrix Predicted values, as returned by a regression estimator. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- loss : float The degree to which the samples are correctly predicted. rr6)rr8rr"r:s r poisson_lossr@sF, : ffvo&&/&8-VW    cee r ctj|jj}tj||d|z }|jddkrtjd|z |d}|jddkrtjd|z |d}tjt|||d S)a Compute 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. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- loss : float The degree to which the samples are correctly predicted. rrrr6) rfinfodtypeepsclipshapeappendr8rr"r;y_probr=rDs r log_lossrJs( (6< $C WVS!c' * *F |A!1v:vA666 |A!1v:vA666 JuVV,,m! L L L P P R R RRr ctj|jj}tj||d|z }tjt ||t d|z d|z z|d 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. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- loss : float The degree to which the samples are correctly predicted. rrr6)rrBrCrDrEr8rr"rHs r binary_log_lossrLs. (6< $C WVS!c' * *F J ffa&j!f* = ==     cee   r ) squared_errorpoissonrJrL)N)__doc__numpyr scipy.specialrrrr rrrrr$ ACTIVATIONSr-r/r2r4 DERIVATIVESr>r@rJrLLOSS_FUNCTIONSrr r rUs.. 333333 & & &!        &$&, #+ #  0    6SSSS>    B"& r