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Searched +refs:sim +refs:MLPnet (Results 1 – 23 of 23) sorted by relevance

/dports/science/R-cran-AMORE/AMORE/
H A DNAMESPACE3 …sim,sim.MLPnet,ADAPTgd.MLPnet,ADAPTgdwm.MLPnet,BATCHgd.MLPnet,BATCHgdwm.MLPnet,error.LMS,error.LML…
5 S3method(sim, MLPnet)
H A DMD57 9e826c62ad3d3a65808dbb335d746a89 *R/sim.R
9 94bb63d02501e1ab9b694aa5453e9715 *man/ADAPTgd.MLPnet.Rd
10 6520dcf8ceff027ead287ddf6899a878 *man/ADAPTgdwm.MLPnet.Rd
11 46be61d708f27016a886c8d09d53f216 *man/BATCHgd.MLPnet.Rd
12 3d2b9ba0bd20fb13e0017249afaa3184 *man/BATCHgdwm.MLPnet.Rd
14 1649b80b9a28c22e43cc50fc39c4c52b *man/graphviz.MLPnet.Rd
17 c6d2b5042614600b2fdf10efbd9aba89 *man/random.init.MLPnet.Rd
20 5acb75de7643b306d4ec4a870e150208 *man/sim.MLPnet.Rd
32 4d4c87bbc5f7ae640b3f0052fbde49c9 *src/sim.c
H A DChangeLog4 sim.R
14 man/*.MLPnet.Rd train.Rd
/dports/science/R-cran-AMORE/AMORE/man/
H A Dsim.MLPnet.Rd1 \name{sim.MLPnet}
2 \alias{sim}
3 \alias{sim.MLPnet}
6 …rovided according to different degrees of C code conversion. The \emph{sim.MLPnet} function is the…
9 sim(net,P,...)
10 #sim.MLPnet(net,P,...)
H A Dnewff.Rd54 \code{\link{init.MLPneuron}}, \code{\link{random.init.MLPnet}}, \code{\link{random.init.MLPneuron}}…
72 y <- sim(result$net, P)
/dports/math/octave-forge-nnet/nnet/tests/MLP/example2/
H A Dmlp9_2_2_1_logsig.m77 # MLPnet.IW{1,1}(:) = 1.5;
78 # MLPnet.LW{2,1}(:) = 0.5;
79 # MLPnet.b{1,1}(:) = 1.5;
80 # MLPnet.b{2,1}(:) = 0.5;
82 saveMLPStruct(MLPnet,'MLP9-2-2-1.txt');
93 %[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
94 MLPnet.trainParam.show = NaN;
95 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
97 # % % make preparations for net test and test MLPnet
101 #[simOut,Pf,Af,simE,simPerf] = sim(net,mTestInputN);
[all …]
H A Dmlp9_5_3_logsig.m81 MLPnet.IW{1,1}(1,:) = 1.5;
82 MLPnet.IW{1,1}(2,:) = 0.5;
83 MLPnet.IW{1,1}(3:5,:) = 1;
84 MLPnet.LW{2,1}(1,:) = 1.5;
87 MLPnet.b{1,1}(1,:) = 0.5;
88 MLPnet.b{1,1}(2,:) = 1.5;
89 MLPnet.b{1,1}(3:5,:) = 1;
90 MLPnet.b{2,1}(1,:) = 0.5;
91 MLPnet.b{2,1}(2,:) = 0.5;
92 MLPnet.b{2,1}(3,:) = 0.1;
[all …]
H A Dmlp9_2_3_logsig.m82 MLPnet.IW{1,1}(1,:) = 1.5;
83 MLPnet.IW{1,1}(2,:) = 0.5;
84 MLPnet.LW{2,1}(1,:) = 1.5;
85 MLPnet.LW{2,1}(2,:) = 0.5;
86 MLPnet.LW{2,1}(3,:) = 0.1;
87 MLPnet.b{1,1}(1,:) = 0.5;
88 MLPnet.b{1,1}(2,:) = 1.5;
89 MLPnet.b{2,1}(1,:) = 0.5;
90 MLPnet.b{2,1}(2,:) = 0.5;
91 MLPnet.b{2,1}(3,:) = 0.1;
[all …]
H A Dmlp9_2_2_logsig.m81 MLPnet.IW{1,1}(1,:) = 1.5;
82 MLPnet.IW{1,1}(2,:) = 0.5;
83 MLPnet.LW{2,1}(1,:) = 1.5;
84 MLPnet.LW{2,1}(2,:) = 0.5;
85 MLPnet.b{1,1}(1,:) = 0.5;
86 MLPnet.b{1,1}(2,:) = 1.5;
87 MLPnet.b{2,1}(:) = 0.5;
89 # saveMLPStruct(MLPnet,"MLP3test.txt");
101 MLPnet.trainParam.show = NaN;
108 # % make preparations for net test and test MLPnet
[all …]
H A Dmlp9_1_1_logsig.m79 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"logsig","purelin"},"trainlm","lear… variable
81 MLPnet.IW{1,1}(:) = 1.5;
82 MLPnet.LW{2,1}(:) = 0.5;
83 MLPnet.b{1,1}(:) = 1.5;
84 MLPnet.b{2,1}(:) = 0.5;
86 #saveMLPStruct(MLPnet,"MLP3test.txt");
97 #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
98 MLPnet.trainParam.show = NaN;
99 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
104 # % make preparations for net test and test MLPnet
[all …]
H A Dmlp9_2_1_logsig.m81 MLPnet.IW{1,1}(1,:) = 0.5;
82 MLPnet.IW{1,1}(2,:) = 1.5;
83 MLPnet.LW{2,1}(:) = 0.5;
84 MLPnet.b{1,1}(1,:) = 0.5;
85 MLPnet.b{1,1}(2,:) = 1.5;
86 MLPnet.b{2,1}(:) = 0.5;
88 saveMLPStruct(MLPnet,"MLP3test.txt");
99 #[net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
100 MLPnet.trainParam.show = NaN;
108 # % make preparations for net test and test MLPnet
[all …]
/dports/math/octave-forge-nnet/nnet/tests/MLP/
H A DtestExample1_2.m49 %! MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","…
50 %! MLPnet.IW{1,1}(1,:) = 0.5;
51 %! MLPnet.IW{1,1}(2,:) = 1.5;
52 %! MLPnet.LW{2,1}(:) = 0.5;
53 %! MLPnet.b{1,1}(1,:) = 0.5;
54 %! MLPnet.b{1,1}(2,:) = 1.5;
55 %! MLPnet.b{2,1}(:) = 0.5;
59 %! [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
61 %! [simOut] = sim(net,mTestInputN);
H A DtestExample1_1.m50 %! MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","…
51 %! MLPnet.IW{1,1}(:) = 1.5;
52 %! MLPnet.LW{2,1}(:) = 0.5;
53 %! MLPnet.b{1,1}(:) = 1.5;
54 %! MLPnet.b{2,1}(:) = 0.5;
58 %! [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
60 %! [simOut] = sim(net,mTestInputN);
/dports/math/octave-forge-nnet/nnet/tests/MLP/example1/
H A Dmlp9_5_3_tansig.m84 MLPnet.IW{1,1}(1,:) = 1.5;
85 MLPnet.IW{1,1}(2,:) = 0.5;
86 MLPnet.IW{1,1}(3:5,:) = 1;
87 MLPnet.LW{2,1}(1,:) = 1.5;
90 MLPnet.b{1,1}(1,:) = 0.5;
91 MLPnet.b{1,1}(2,:) = 1.5;
92 MLPnet.b{1,1}(3:5,:) = 1;
93 MLPnet.b{2,1}(1,:) = 0.5;
94 MLPnet.b{2,1}(2,:) = 0.5;
95 MLPnet.b{2,1}(3,:) = 0.1;
[all …]
H A Dmlp9_2_3_tansig.m84 MLPnet.IW{1,1}(1,:) = 1.5;
85 MLPnet.IW{1,1}(2,:) = 0.5;
86 MLPnet.LW{2,1}(1,:) = 1.5;
87 MLPnet.LW{2,1}(2,:) = 0.5;
88 MLPnet.LW{2,1}(3,:) = 0.1;
89 MLPnet.b{1,1}(1,:) = 0.5;
90 MLPnet.b{1,1}(2,:) = 1.5;
91 MLPnet.b{2,1}(1,:) = 0.5;
92 MLPnet.b{2,1}(2,:) = 0.5;
93 MLPnet.b{2,1}(3,:) = 0.1;
[all …]
H A Dmlp9_2_2_tansig.m83 MLPnet.IW{1,1}(1,:) = 1.5;
84 MLPnet.IW{1,1}(2,:) = 0.5;
85 MLPnet.LW{2,1}(1,:) = 1.5;
86 MLPnet.LW{2,1}(2,:) = 0.5;
87 MLPnet.b{1,1}(1,:) = 0.5;
88 MLPnet.b{1,1}(2,:) = 1.5;
89 MLPnet.b{2,1}(:) = 0.5;
99 MLPnet.trainParam.show = NaN;
100 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
102 ## make preparations for net test and test MLPnet
[all …]
H A Dmlp9_2_1_tansig.m80 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","lear… variable
82 MLPnet.IW{1,1}(1,:) = 0.5;
83 MLPnet.IW{1,1}(2,:) = 1.5;
84 MLPnet.LW{2,1}(:) = 0.5;
85 MLPnet.b{1,1}(1,:) = 0.5;
86 MLPnet.b{1,1}(2,:) = 1.5;
87 MLPnet.b{2,1}(:) = 0.5;
97 MLPnet.trainParam.show = NaN;
98 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
100 ## make preparations for net test and test MLPnet
[all …]
H A Dmlp9_2_2_1_tansig.m81 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{'tansig','tansig','tansig','purelin… variable
83 # MLPnet.IW{1,1}(:) = 1.5;
84 # MLPnet.LW{2,1}(:) = 0.5;
85 # MLPnet.b{1,1}(:) = 1.5;
86 # MLPnet.b{2,1}(:) = 0.5;
96 MLPnet.trainParam.show = NaN;
97 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
99 ## make preparations for net test and test MLPnet
104 [simOut] = sim(net,mTestInputN);
H A Dmlp9_1_1_tansig.m83 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],{"tansig","purelin"},"trainlm","lear… variable
85 MLPnet.IW{1,1}(:) = 1.5;
86 MLPnet.LW{2,1}(:) = 0.5;
87 MLPnet.b{1,1}(:) = 1.5;
88 MLPnet.b{2,1}(:) = 0.5;
98 MLPnet.trainParam.show = NaN;
99 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
101 ## make preparations for net test and test MLPnet
106 [simOut] = sim(net,mTestInputN);
/dports/math/octave-forge-nnet/nnet/doc/latex/users/examples/2/
H A DMLP9_1_1.m_template56 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
59 MLPnet.IW{1,1}(:) = 1.5;
60 MLPnet.LW{2,1}(:) = 0.5;
61 MLPnet.b{1,1}(:) = 1.5;
62 MLPnet.b{2,1}(:) = 0.5;
64 saveMLPStruct(MLPnet,"MLP3test.txt");
73 [net] = train(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV);
75 # make preparations for net test and test MLPnet
79 [simOut] = sim(net,mTestInputN);
H A DMLP9_1_1.tex63 \hlline{00061\ }\hlstd{MLPnet = \hlkwc{newff}(mMinMaxElements,[nHiddenNeurons nOutputNeurons],$\bac…
66 \hlline{00064\ }\hlstd{MLPnet.IW\{1,1\}(:) = 1.5;}\\
67 \hlline{00065\ }\hlstd{MLPnet.LW\{2,1\}(:) = 0.5;}\\
68 \hlline{00066\ }\hlstd{MLPnet.b\{1,1\}(:) = 1.5;}\\
69 \hlline{00067\ }\hlstd{MLPnet.b\{2,1\}(:) = 0.5;}\\
71 \hlline{00069\ }\hlkwc{saveMLPStruct}\hlstd{(MLPnet,"MLP3test.txt");}\\
80 \hlline{00078\ }\hlstd{[net] = \hlkwc{train}(MLPnet,mTrainInputN,mTrain(end,:),[],[],VV);}\\
82 \hlline{00080\ }\hlslc{\# make preparations for net test and test MLPnet}\\
86 \hlline{00084\ }\hlstd{[simOut] = \hlkwc{sim}(net,mTestInputN);}\\
/dports/math/octave-forge-nnet/nnet/doc/latex/users/examples/1/
H A DMLP9_1_1.m_template74 MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
77 MLPnet.IW{1,1}(:) = 1.5;
78 MLPnet.LW{2,1}(:) = 0.5;
79 MLPnet.b{1,1}(:) = 1.5;
80 MLPnet.b{2,1}(:) = 0.5;
82 saveMLPStruct(MLPnet,"MLP3test.txt");
91 [net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
93 # make preparations for net test and test MLPnet
97 [simOut] = sim(net,mTestInputN);
H A DMLP9_1_1.tex77 \hlline{00075\ }MLPnet }\hlsym{= }\hlstd{}\hlkwc{newff}\hlstd{}\hlsym{(}\hlstd{mMinMaxElements}\hls…
80 \hlline{00078\ }\hlstd{MLPnet.IW}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlst…
81 \hlline{00079\ }\hlstd{MLPnet.LW}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlst…
82 \hlline{00080\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{1}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd…
83 \hlline{00081\ }\hlstd{MLPnet.b}\hlsym{\{}\hlstd{}\hlnum{2}\hlstd{}\hlsym{,}\hlstd{}\hlnum{1}\hlstd…
85 \hlline{00083\ }\hlkwc{saveMLPStruct}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{}\hlstr{"MLP3te…
94 …[}}\hlstd{net}\hlsym{{]} = }\hlstd{}\hlkwc{train}\hlstd{}\hlsym{(}\hlstd{MLPnet}\hlsym{,}\hlstd{mT…
96 \hlline{00094\ }\hlslc{\# make preparations for net test and test MLPnet}\\
100 \hlline{00098\ }\hlsym{{[}}\hlstd{simOut}\hlsym{{]} = }\hlstd{}\hlkwc{sim}\hlstd{}\hlsym{(}\hlstd{n…