o tBh@sdZddlZddlmZddlmZddZddZd d Z d d Z d dZ ee ee e dZ ddZ ddZddZddZe eeedZddZddZddZeeedZdS) z)Utilities for the neural network modules N)expit)xlogycCdS)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. NXrrs/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/neural_network/_base.pyinplace_identity r cCst||ddS)zCompute the logistic function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. outN)logistic_sigmoidrrrrinplace_logisticsrcCstj||ddS)zCompute the hyperbolic tan function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. r N)nptanhrrrr inplace_tanh$srcCstj|d|ddS)zCompute the rectified linear unit function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. rr N)rmaximumrrrr inplace_relu/srcCsN||jddddtjf}tj||d||jddddtjf}dS)zCompute the K-way softmax function inplace. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) The input data. axisNr )maxrnewaxisexpsum)rtmprrrinplace_softmax:s"r)identityrlogisticrelusoftmaxcCr)aApply the derivative of the identity function: do nothing. Parameters ---------- Z : {array-like, sparse matrix}, shape (n_samples, n_features) The data which was output from the identity activation function during the forward pass. delta : {array-like}, shape (n_samples, n_features) The backpropagated error signal to be modified inplace. NrZdeltarrrinplace_identity_derivativePr r$cCs||9}|d|9}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!rrrinplace_logistic_derivative_sr%cCs|d|d9}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!rrrinplace_tanh_derivativersr'cCsd||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!rrrinplace_relu_derivativesr()rrrrcCs||ddS)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_predrrr squared_losssr,cCs~t|jj}t||d|}|jddkr!tjd||dd}|jddkr2tjd||dd}t|| |jdS)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) rfinfodtypeepsclipshapeappendrrr*y_probr/rrrlog_losssr5cCsPt|jj}t||d|}t||td|d| |jdS)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)rr-r.r/r0rrr1r3rrrbinary_log_losss $r6) squared_errorr5r6)__doc__numpyr scipy.specialrr rr rrrr ACTIVATIONSr$r%r'r( DERIVATIVESr,r5r6LOSS_FUNCTIONSrrrrs>