rh/dZddlZddlmZddlmZdZdZdZ dZ d Z d Z eee e e e d Z d Zd ZdZdZeeeedZddZddZddZddZeeeedZy)z(Utilities for the neural network modulesN)expit)xlogycy)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/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/sklearn/neural_network/_base.pyinplace_identityr c2tj||y)zCompute the exponential inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. outN)npexprs r inplace_exprsFF1!r ct||y)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||y)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-sGGA1r c4tj|d|y)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 c||jdddtjfz }tj||||j dddtjfz}y)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$CsT aeeemArzzM* *CFF3AAq"**} %%Ar )identityrrlogisticrelusoftmaxcy)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}y)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 QUNEr c|d|dzz z}y)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 cd||dk(<y)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&Mr )r%rr&r'c`dtj||z dz|djzS)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>s0( bjj&6/a/QOTTVVr cttjt|||z |z |z|djS)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@s9, :: ffvo&/&8-VW  ce r ctj|jj}tj||d|z }|j ddk(rtj d|z |d}|j ddk(rtj d|z |d}tjt|||dj 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 WWVS!c' *F ||A!1v:vA6 ||A!1v:vA6 JJuVV,m! L P P R RRr ctj|jj}tj||d|z }tj t ||t d|z d|z z|dj 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_lossrLsu. ((6<< $ $C WWVS!c' *F JJ ffa&j!f* ==   ce   r ) squared_errorpoissonrJrL)N)__doc__numpyr scipy.specialrrrr rrrrr$ ACTIVATIONSr-r/r2r4 DERIVATIVESr>r@rJrLLOSS_FUNCTIONSrr r rUs. 3 &!      &$&, #+ #  0 6S> B"& r