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% Neural Network Toolbox Data Functions. % % Neural network data % nndata - Create neural network data. % catelements - Concatenate neural network data elements. % catsamples - Concatenate neural network data samples. % catsignals - Concatenate neural network data signals. % cattimesteps - Concatenate neural network data timesteps. % gadd - Generalized addition. % gdivide - Generalized right division. % getelements - Get neural network data elements. % getsamples - Get samples from neural network data. % getsignals - Get signals from neural network data. % gettimesteps - Get neural network data timesteps. % gmultiply - Generalized multiplication. % gnegate - Generalized negation. % gsqrt - Generalized square root. % gsubtract - Generalized subtraction. % meanabs - Mean of absolute elements of a matrix or matrices. % meansqr - Mean of squared elements of a matrix or matrices. % minmax - Ranges of matrix rows. % nncell2mat - Combines NN cell data into a matrix. % nnsize - Number of neural data elements, samples, time steps and signals. % numelements - Number of elements in neural network data. % numfinite - Number of finite values in neural network data. % numnan - Number of finite values in neural network data. % numsamples - Number of samples in neural network data. % numsignals - Number of signals in neural network data. % numtimesteps - Number of samples in neural network data. % setelements - Set neural network data elements. % setsamples - Set neural network data samples. % setsignals - Set neural network data signals. % settimesteps - Set neural network data timesteps. % sumabs - Sum of absolute elements of a matrix or matrices. % sumsqr - Sum of squared elements of a matrix or matrices. % % Time series % preparets - Prepare time series data for network simulation or training. % extendts - Extends time series data to a given number of timesteps. % tapdelay - Shift neural network time series data for a tap delay. % con2seq - Convert concurrent vectors to sequential vectors. % seq2con - Convert sequential vectors to concurrent vectors. % nncorr - Cross-correlation between neural time series. % % Vector/index conversion % ind2vec - Convert indices to vectors. % vec2ind - Transform vectors to indices. % % Analyisis % confusion - Classification confusion matrix. % regression - Linear regression. % roc - Receiver operating characteristic. % % Plotting % plotep - Plot a weight-bias position on an error surface. % plotes - Plot the error surface of a single input neuron. % plotpc - Plot a classification line on a perceptron vector plot. % plotpv - Plot perceptron input/target vectors. % plotv - Plot vectors as lines from the origin. % plotvec - Plot vectors with different colors. % errsurf - Error surface of single input neuron. % % Simulink % nndata2sim - Convert neural network data to Simulink time-series. % sim2nndata - Convert Simulink time-series to neural network data. % prunedata - Prune data for a pruned network % % GPU % nndata2gpu - Formats neural data for efficient GPU training or simulation. % gpu2nndata - Reformats neural data back from GPU. % % Alternate row/col representations of samples/timesteps % fromnndata - Convert data from standard neural network cell array form. % tonndata - Convert data to standard neural network cell array form. % % Other functions % cellmat - Create a cell array of matrices. % combvec - Create all combinations of vectors. % concur - Create concurrent bias vectors. % maxlinlr - Maximum learning rate for a linear layer. % normc - Normalize columns of matrices. % normr - Normalize rows of matrices. % pnormc - Pseudo-normalize columns of a matrix. % quant - Discretize NN data as multiples of a quantity. % % <a href="matlab:help nnet/Contents.m">Main nnet function list</a>. % Copyright 1992-2010 The MathWorks, Inc.