X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fnnet-0.1.13%2Fdoc-cache;fp=octave_packages%2Fnnet-0.1.13%2Fdoc-cache;h=5ccf12378069d5632ed009e37012225964136516;hp=0000000000000000000000000000000000000000;hb=c880e8788dfc484bf23ce13fa2787f2c6bca4863;hpb=1705066eceaaea976f010f669ce8e972f3734b05 diff --git a/octave_packages/nnet-0.1.13/doc-cache b/octave_packages/nnet-0.1.13/doc-cache new file mode 100644 index 0000000..5ccf123 --- /dev/null +++ b/octave_packages/nnet-0.1.13/doc-cache @@ -0,0 +1,905 @@ +# Created by Octave 3.6.1, Sun Apr 01 17:24:32 2012 UTC +# name: cache +# type: cell +# rows: 3 +# columns: 28 +# name: +# type: sq_string +# elements: 1 +# length: 8 +dhardlim + + +# name: +# type: sq_string +# elements: 1 +# length: 39 + -- Function File: [A = dhardlim (N) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 0 + + + + +# name: +# type: sq_string +# elements: 1 +# length: 10 +dividerand + + +# name: +# type: sq_string +# elements: 1 +# length: 556 + -- Function File: [ + TRAINVECTORS,VALIDATIONVECTORS,TESTVECTORS,INDEXOFTRAIN,INDEXOFVALIDATION,INDEXOFTEST] + = dividerand (ALLCASES,TRAINRATIO,VALRATIO,TESTRATIO) + Divide the vectors in training, validation and test group + according to the informed ratios + + + [trainVectors,validationVectors,testVectors,indexOfTrain,indexOfValidatio + n,indexOfTest] = dividerand(allCases,trainRatio,valRatio,testRatio) + + The ratios are normalized. This way: + + dividerand(xx,1,2,3) == dividerand(xx,10,20,30) + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 +Divide the vectors in training, validation and test group according to +the infor + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +dposlin + + +# name: +# type: sq_string +# elements: 1 +# length: 117 + -- Function File: A= poslin (N) + `poslin' is a positive linear transfer function used by neural + networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 72 +`poslin' is a positive linear transfer function used by neural networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +dsatlin + + +# name: +# type: sq_string +# elements: 1 +# length: 38 + -- Function File: [A = dsatlin (N) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 0 + + + + +# name: +# type: sq_string +# elements: 1 +# length: 8 +dsatlins + + +# name: +# type: sq_string +# elements: 1 +# length: 105 + -- Function File: [A = satlins (N) + A neural feed-forward network will be trained with `trainlm' + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 61 +A neural feed-forward network will be trained with `trainlm' + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +hardlim + + +# name: +# type: sq_string +# elements: 1 +# length: 38 + -- Function File: [A = hardlim (N) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 0 + + + + +# name: +# type: sq_string +# elements: 1 +# length: 8 +hardlims + + +# name: +# type: sq_string +# elements: 1 +# length: 39 + -- Function File: [A = hardlims (N) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 0 + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +ind2vec + + +# name: +# type: sq_string +# elements: 1 +# length: 256 + -- Function File: VEC = ind2vec (IND) + `vec2ind' convert indices to vector + + EXAMPLE 1 + vec = [1 2 3; 4 5 6; 7 8 9]; + + ind = vec2ind(vec) + The prompt output will be: + ans = + 1 2 3 1 2 3 1 2 3 + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 36 +`vec2ind' convert indices to vector + + + + +# name: +# type: sq_string +# elements: 1 +# length: 8 +isposint + + +# name: +# type: sq_string +# elements: 1 +# length: 293 + -- Function File: F = isposint(N) + `isposint' returns true for positive integer values. + + isposint(1) # this returns TRUE + isposint(0.5) # this returns FALSE + isposint(0) # this also return FALSE + isposint(-1) # this also returns FALSE + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 52 +`isposint' returns true for positive integer values. + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +logsig + + +# name: +# type: sq_string +# elements: 1 +# length: 224 + -- Function File: A = logsig (N) + `logsig' is a non-linear transfer function used to train neural + networks. This function can be used in newff(...) to create a new + feed forward multi-layer neural network. + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 73 +`logsig' is a non-linear transfer function used to train neural +networks. + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +mapstd + + +# name: +# type: sq_string +# elements: 1 +# length: 1273 + -- Function File: [ YY,PS] = mapstd (XX,YMEAN,YSTD) + Map values to mean 0 and standard derivation to 1. + + [YY,PS] = mapstd(XX,ymean,ystd) + + Apply the conversion and returns YY as (YY-ymean)/ystd. + + [YY,PS] = mapstd(XX,FP) + + Apply the conversion but using an struct to inform target mean/stddev. + This is the same of [YY,PS]=mapstd(XX,FP.ymean, FP.ystd). + + YY = mapstd('apply',XX,PS) + + Reapply the conversion based on a previous operation data. + PS stores the mean and stddev of the first XX used. + + XX = mapstd('reverse',YY,PS) + + Reverse a conversion of a previous applied operation. + + dx_dy = mapstd('dx',XX,YY,PS) + + Returns the derivative of Y with respect to X. + + dx_dy = mapstd('dx',XX,[],PS) + + Returns the derivative (less efficient). + + name = mapstd('name'); + + Returns the name of this convesion process. + + FP = mapstd('pdefaults'); + + Returns the default process parameters. + + names = mapstd('pnames'); + + Returns the description of the process parameters. + + mapstd('pcheck',FP); + + Raises an error if FP has some inconsistent. + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 50 +Map values to mean 0 and standard derivation to 1. + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +min_max + + +# name: +# type: sq_string +# elements: 1 +# length: 267 + -- Function File: PR = min_max (PP) + `min_max' returns variable Pr with range of matrix rows + + PR - R x 2 matrix of min and max values for R input elements + + Pp = [1 2 3; -1 -0.5 -3] + pr = min_max(Pp); + pr = [1 3; -0.5 -3]; + + + + +# name: +# type: sq_string +# elements: 1 +# length: 56 +`min_max' returns variable Pr with range of matrix rows + + + + +# name: +# type: sq_string +# elements: 1 +# length: 5 +newff + + +# name: +# type: sq_string +# elements: 1 +# length: 820 + -- Function File: NET = newff (PR,SS,TRF,BTF,BLF,PF) + `newff' create a feed-forward backpropagation network + + Pr - R x 2 matrix of min and max values for R input elements + Ss - 1 x Ni row vector with size of ith layer, for N layers + trf - 1 x Ni list with transfer function of ith layer, + default = "tansig" + btf - Batch network training function, + default = "trainlm" + blf - Batch weight/bias learning function, + default = "learngdm" + pf - Performance function, + default = "mse". + + EXAMPLE 1 + Pr = [0.1 0.8; 0.1 0.75; 0.01 0.8]; + it's a 3 x 2 matrix, this means 3 input neurons + + net = newff(Pr, [4 1], {"tansig","purelin"}, "trainlm", "learngdm", "mse"); + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 54 +`newff' create a feed-forward backpropagation network + + + + +# name: +# type: sq_string +# elements: 1 +# length: 4 +newp + + +# name: +# type: sq_string +# elements: 1 +# length: 545 + -- Function File: NET = newp (PR,SS,TRANSFUNC,LEARNFUNC) + `newp' create a perceptron + + PLEASE DON'T USE THIS FUNCTIONS, IT'S STILL NOT FINISHED! + ========================================================= + + Pr - R x 2 matrix of min and max values for R input elements + ss - a scalar value with the number of neurons + transFunc - a string with the transfer function + default = "hardlim" + learnFunc - a string with the learning function + default = "learnp" + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 27 +`newp' create a perceptron + + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +poslin + + +# name: +# type: sq_string +# elements: 1 +# length: 117 + -- Function File: A= poslin (N) + `poslin' is a positive linear transfer function used by neural + networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 72 +`poslin' is a positive linear transfer function used by neural networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +poststd + + +# name: +# type: sq_string +# elements: 1 +# length: 153 + -- Function File: [PP,TT] = poststd(PN,MEANP,,STDP,TN,MEANT,STDT) + `poststd' postprocesses the data which has been preprocessed by + `prestd'. + + + + +# name: +# type: sq_string +# elements: 1 +# length: 73 +`poststd' postprocesses the data which has been preprocessed by +`prestd'. + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +prestd + + +# name: +# type: sq_string +# elements: 1 +# length: 160 + -- Function File: [PN,MEANP,STDP,TN,MEANT,STDT] =prestd(P,T) + `prestd' preprocesses the data so that the mean is 0 and the + standard deviation is 1. + + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 +`prestd' preprocesses the data so that the mean is 0 and the standard +deviation + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +purelin + + +# name: +# type: sq_string +# elements: 1 +# length: 105 + -- Function File: A= purelin (N) + `purelin' is a linear transfer function used by neural networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 64 +`purelin' is a linear transfer function used by neural networks + + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +radbas + + +# name: +# type: sq_string +# elements: 1 +# length: 100 + -- Function File: radbas (N) + Radial basis transfer function. + + `radbas(n) = exp(-n^2)' + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 31 +Radial basis transfer function. + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +satlin + + +# name: +# type: sq_string +# elements: 1 +# length: 104 + -- Function File: [A = satlin (N) + A neural feed-forward network will be trained with `trainlm' + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 61 +A neural feed-forward network will be trained with `trainlm' + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +satlins + + +# name: +# type: sq_string +# elements: 1 +# length: 105 + -- Function File: [A = satlins (N) + A neural feed-forward network will be trained with `trainlm' + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 61 +A neural feed-forward network will be trained with `trainlm' + + + + +# name: +# type: sq_string +# elements: 1 +# length: 13 +saveMLPStruct + + +# name: +# type: sq_string +# elements: 1 +# length: 119 + -- Function File: saveMLPStruct (NET,STRFILENAME) + `saveStruct' saves a neural network structure to *.txt files + + + + +# name: +# type: sq_string +# elements: 1 +# length: 51 +`saveStruct' saves a neural network structure to *. + + + +# name: +# type: sq_string +# elements: 1 +# length: 3 +sim + + +# name: +# type: sq_string +# elements: 1 +# length: 215 + -- Function File: NETOUTPUT = sim (NET, MINPUT) + `sim' is usuable to simulate a before defined neural network. + `net' is created with newff(...) and MINPUT should be the + corresponding input data set! + + + + +# name: +# type: sq_string +# elements: 1 +# length: 61 +`sim' is usuable to simulate a before defined neural network. + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +subset + + +# name: +# type: sq_string +# elements: 1 +# length: 1632 + -- Function File: [MTRAIN, MTEST, MVALI] = subset + (MDATA,NTARGETS,IOPTI,FTEST,FVALI) + `subset' splits the main data matrix which contains inputs and + targets into 2 or 3 subsets depending on the parameters. + + The first parameter MDATA must be in row order. This means if the + network contains three inputs, the matrix must be have 3 rows and + x columns to define the data for the inputs. And some more rows + for the outputs (targets), e.g. a neural network with three inputs + and two outputs must have 5 rows with x columns! The second + parameter NTARGETS defines the number or rows which contains the + target values! The third argument `iOpti' is optional and can + have three status: 0: no optimization 1: will + randomise the column order and order the columns containing min + and max values to be in the train set 2: will NOT randomise + the column order, but order the columns containing min and max + values to be in the train set default value is `1' The + fourth argument `fTest' is also optional and defines how much data + sets will be in the test set. Default value is `1/3' The fifth + parameter `fTrain' is also optional and defines how much data sets + will be in the train set. Default value is `1/6' So we have 50% of + all data sets which are for training with the default values. + + [mTrain, mTest] = subset(mData,1) + returns three subsets of the complete matrix + with randomized and optimized columns! + + [mTrain, mTest] = subset(mData,1,) + returns two subsets + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 +`subset' splits the main data matrix which contains inputs and targets +into 2 or + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +tansig + + +# name: +# type: sq_string +# elements: 1 +# length: 224 + -- Function File: A = tansig (N) + `tansig' is a non-linear transfer function used to train neural + networks. This function can be used in newff(...) to create a new + feed forward multi-layer neural network. + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 73 +`tansig' is a non-linear transfer function used to train neural +networks. + + + +# name: +# type: sq_string +# elements: 1 +# length: 5 +train + + +# name: +# type: sq_string +# elements: 1 +# length: 614 + -- Function File: [NET] = train (MLPNET,MINPUTN,MOUTPUT,[],[],VV) + A neural feed-forward network will be trained with `train' + + [net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV); + + left side arguments: + net: the trained network of the net structure `MLPnet' + + right side arguments: + MLPnet : the untrained network, created with `newff' + mInputN: normalized input matrix + mOutput: output matrix (normalized or not) + [] : unused parameter + [] : unused parameter + VV : validize structure + + + + +# name: +# type: sq_string +# elements: 1 +# length: 59 +A neural feed-forward network will be trained with `train' + + + + +# name: +# type: sq_string +# elements: 1 +# length: 6 +trastd + + +# name: +# type: sq_string +# elements: 1 +# length: 447 + -- Function File: PN = trastd (P,MEANP,STDP) + `trastd' preprocess additional data for neural network simulation. + + `p' : test input data + `meanp': vector with standardization parameters of prestd(...) + `stdp' : vector with standardization parameters of prestd(...) + + meanp = [2.5; 6.5]; + stdp = [1.2910; 1.2910]; + p = [1 4; 2 5]; + + pn = trastd(p,meanp,stdp); + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 66 +`trastd' preprocess additional data for neural network simulation. + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +vec2ind + + +# name: +# type: sq_string +# elements: 1 +# length: 260 + -- Function File: IND = vec2ind (VECTOR) + `vec2ind' convert vectors to indices + + EXAMPLE 1 + vec = [1 2 3; 4 5 6; 7 8 9]; + + ind = vec2ind(vec) + The prompt output will be: + ans = + 1 2 3 1 2 3 1 2 3 + + + + + +# name: +# type: sq_string +# elements: 1 +# length: 37 +`vec2ind' convert vectors to indices + + + + + +