-i/SrSSKrSSKJr SSKJr SrSrSr Sr S r S r \\\ \ \ \ S .r S rS rSrSr\\\\S.rSSjrSSjrSSjrSSjr\\\\S.rg)z(Utilities for the neural network modulesN)expit)xlogycg)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 O/var/www/html/venv/lib/python3.13/site-packages/sklearn/neural_network/_base.pyinplace_identityr c,[R"XS9 g)zCompute the exponential inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. outN)npexprs r inplace_exprsFF1r c[XS9 g)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"s Qr c,[R"XS9 g)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-sGGAr c0[R"USUS9 g)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_relur8sJJq!r cXRSS9SS2[R4- n[R"XS9 XR SS9SS2[R4-ng)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$CsN eeemArzzM* *CFF3Aq"**} %%Ar )identityrrlogisticrelusoftmaxcg)adApply 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 cX-nUSU- -ng)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 JE QUNEr cUSUS-- -ng)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 QAXEr cSXS:H'g)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_derivativer4sEq&Mr )r%rr&r'cZS[R"X- S-USS9R5-$)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>s.( bjj&/a/QOTTVVr cn[R"[XU- 5U- U-USS9R5$)aCompute (half of the) Poisson deviance loss for regression. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels. y_pred : array-like or label indicator matrix Predicted values, as returned by a regression estimator. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- loss : float The degree to which the samples are correctly predicted. rr6)rr8rr"r:s r poisson_lossr@s7, :: fvo&/&8-VW  ce r c[R"UR5Rn[R"XSU- 5nUR SS:Xa[R "SU- USS9nUR SS:Xa[R "SU- USS9n[R"[X5USS9R5*$)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. 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<< $ $C WWV!c' *F ||A!1v:vA6 ||A!1v:vA6 JJuV,m! L P P R RRr c [R"UR5Rn[R"XSU- 5n[R "[ X5[ SU- SU- 5-USS9R5*$)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_lossrLsq. ((6<< $ $C WWV!c' *F JJ fa&j!f* ==   ce   r ) squared_errorpoissonrJrL)N)__doc__numpyr scipy.specialrrrr rrrrr$ ACTIVATIONSr-r/r2r4 DERIVATIVESr>r@rJrLLOSS_FUNCTIONSrr r rUs. 3 &!      &$&, #+ #  0 6S> B"& r