--- /dev/null
+# Created by Octave 3.6.1, Sun Apr 01 17:24:32 2012 UTC <root@t61>
+# name: cache
+# type: cell
+# rows: 3
+# columns: 28
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 8
+dhardlim
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 39
+ -- Function File: [A = dhardlim (N)
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 0
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 10
+dividerand
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 80
+Divide the vectors in training, validation and test group according to
+the infor
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+dposlin
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 117
+ -- Function File: A= poslin (N)
+ `poslin' is a positive linear transfer function used by neural
+ networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 72
+`poslin' is a positive linear transfer function used by neural networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+dsatlin
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 38
+ -- Function File: [A = dsatlin (N)
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 0
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 8
+dsatlins
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 105
+ -- Function File: [A = satlins (N)
+ A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 61
+A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+hardlim
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 38
+ -- Function File: [A = hardlim (N)
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 0
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 8
+hardlims
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 39
+ -- Function File: [A = hardlims (N)
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 0
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+ind2vec
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 36
+`vec2ind' convert indices to vector
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 8
+isposint
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 52
+`isposint' returns true for positive integer values.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+logsig
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 73
+`logsig' is a non-linear transfer function used to train neural
+networks.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+mapstd
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 50
+Map values to mean 0 and standard derivation to 1.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+min_max
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 56
+`min_max' returns variable Pr with range of matrix rows
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 5
+newff
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 54
+`newff' create a feed-forward backpropagation network
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 4
+newp
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 27
+`newp' create a perceptron
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+poslin
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 117
+ -- Function File: A= poslin (N)
+ `poslin' is a positive linear transfer function used by neural
+ networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 72
+`poslin' is a positive linear transfer function used by neural networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+poststd
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 73
+`poststd' postprocesses the data which has been preprocessed by
+`prestd'.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+prestd
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 80
+`prestd' preprocesses the data so that the mean is 0 and the standard
+deviation
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+purelin
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 105
+ -- Function File: A= purelin (N)
+ `purelin' is a linear transfer function used by neural networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 64
+`purelin' is a linear transfer function used by neural networks
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+radbas
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 100
+ -- Function File: radbas (N)
+ Radial basis transfer function.
+
+ `radbas(n) = exp(-n^2)'
+
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 31
+Radial basis transfer function.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+satlin
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 104
+ -- Function File: [A = satlin (N)
+ A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 61
+A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+satlins
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 105
+ -- Function File: [A = satlins (N)
+ A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 61
+A neural feed-forward network will be trained with `trainlm'
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 13
+saveMLPStruct
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 119
+ -- Function File: saveMLPStruct (NET,STRFILENAME)
+ `saveStruct' saves a neural network structure to *.txt files
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 51
+`saveStruct' saves a neural network structure to *.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 3
+sim
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 61
+`sim' is usuable to simulate a before defined neural network.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+subset
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 80
+`subset' splits the main data matrix which contains inputs and targets
+into 2 or
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+tansig
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 73
+`tansig' is a non-linear transfer function used to train neural
+networks.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 5
+train
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 59
+A neural feed-forward network will be trained with `train'
+
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 6
+trastd
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 66
+`trastd' preprocess additional data for neural network simulation.
+
+
+
+# name: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 7
+vec2ind
+
+
+# name: <cell-element>
+# 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: <cell-element>
+# type: sq_string
+# elements: 1
+# length: 37
+`vec2ind' convert vectors to indices
+
+
+
+
+
+
git://git.creatis.insa-lyon.fr / CreaPhase.git / blobdiff