Log of /trunk/math/mlp/src/TMLPAnalyzer.cxx
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Revision
48992 -
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Modified
Thu Mar 28 15:26:26 2013 UTC (21 months, 3 weeks ago) by
rdm
File length: 19621 byte(s)
Diff to
previous 44507
From Lifeng Sun:
The attached patchset fixes a bunch of typo in the source:
0001-succes-success.patch
0002-preceed-preced.patch
0003-informations-information.patch
0004-childs-children.patch
0005-avaliable-available.patch
0006-writeable-writable.patch
0007-comand-command.patch
0008-unkown-unknown.patch
0009-wierd-weird.patch
0010-wheter-whether.patch
0011-unecessary-unnecessary.patch
0012-splitted-split.patch
0013-registerd-registered.patch
0014-recieve-receive.patch
0015-processsing-processing.patch
0016-ouput-output.patch
0017-mutiple-multiple.patch
0018-lenght-length.patch
0019-interupted-interrupted.patch
0020-independant-independent.patch
0021-inconsistant-inconsistent.patch
0022-expresion-expression.patch
0023-explicitely-explicitly.patch
0024-enviroment-environment.patch
0025-deafult-default.patch
0026-continous-continuous.patch
0027-completly-completely.patch
0028-commited-committed.patch
0029-choosen-chosen.patch
0030-backgroud-background.patch
0031-auxilliary-auxiliary.patch
0032-authentification-authentication.patch
0033-appropiate-appropriate.patch
0034-an-other-another.patch
0035-environement-environment.patch
0036-targetting-targeting.patch
0037-suppported-supported.patch
0038-paramater-parameter.patch
Revision
44507 -
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Modified
Mon Jun 4 12:30:41 2012 UTC (2 years, 7 months ago) by
axel
File length: 19622 byte(s)
Diff to
previous 35902
Remove
using namespace std;
from Riostream.h, which has huge consequences for all of ROOT.
Riostream.h is now a simple wrapper for fstream, iostream, iomanip for backward compatibility; Riosfwd.h simply wraps iosfwd.
Because of templates and their inline functions, Riostream.h needed to be included in headers, too (e.g. TParameter.h), which violated the assumption that Riostream.h is not exposing its using namespace std to headers.
ROOT now requires R__ANSISTREAM, R__SSTREAM, which does not change the set of supported compilers.
Without "using namespace std", several identifiers are now prefixed by std::; e.g. roofit/* source files now have a using namespace std to keep their coding style.
TFile::MakeProject() now generates "using namespace std" to convert the CINT-style class names into C++ ones.
Revision
22885 -
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Modified
Fri Mar 28 13:57:25 2008 UTC (6 years, 9 months ago) by
rdm
File length: 18712 byte(s)
Diff to
previous 22419
move the following directories under the new "math" meta directory:
mathcore
mathmore
fftw
foam
fumili
genvector
matrix
minuit
minuit2
mlp
physics
smatrix
splot
unuran
quadp
Revision
22419 -
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Modified
Mon Mar 3 00:25:01 2008 UTC (6 years, 10 months ago) by
rdm
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 18712 byte(s)
Diff to
previous 20882
From Andrew Savchenko:
ROOT can not be compiled with gcc-4.3.
Some ROOT source files doesn't contain required #include directives,
for example, they use strlen(), but #include <string.h> is missed or
malloc() is used and #include <stdlib.h> is missed.
Earlier versions of gcc allowed some headers to be included implicitly,
but issued a warning (-Wimplicit-function-declaration). Newer one,
gcc-4.3 denies such silly behaviour: all required headers must be explicitly
included.
Attached patch fixes this. Also it fixes another issue, which disallows
ROOT to compile under gcc-4.3: C functions don't belong to namespace std,
so expressions like std::memcpy() are no longer valid and plain memcpy()
should be used instead.
Revision
13804 -
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Modified
Mon Jan 9 15:47:30 2006 UTC (9 years ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 18747 byte(s)
Diff to
previous 12622
From Christophe Delaere and Andrea Bocci:
Andrea has extended a bit ROOT's TMultiLayerPerceptron to
optionally use cross-entropy errors, which allows to train a network
for pattern classification based on Bayesian posterior probability.
Reference: [Bishop 1995 , Neural Networks for Pattern Recognition], in
particular chapter 6.
In order to achieve this, I had to add the softmax (generalized
sigmoid) neuron function, which in turn required a bit of changes to
the neuron itself.
Also, I added softmax and sigmoid as possible output neurons, requiring
some changes to how error back propagation is performed.
Currently, softmax neurons are used only in the output layer, but
everything is setup so that they should be OK as hidden units, too,
provided they form a whole layer.
Revision
12329 -
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Modified
Mon Jul 18 12:02:02 2005 UTC (9 years, 6 months ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 18541 byte(s)
Diff to
previous 11032
From Christophe Delaere:
- input normalization is now optional. A "@" must be added at the beginning of
the input description to enforce normalization.
- the input/output normalization is now saved/loaded with the weight
(DumpWeights() / LoadWeights() )
- the input/output normalization is taken into account when a function is
exported (C++, FORTRAN, PYTHON)
- The neuron transfer function can now be chosen, either as a predefined
function (sigmoid(default), tanh, gauss, linear) or as an external function
(TFormula).
- arrays can now be used as input. If no index is specified, a neuron will be
created for each element in the array. Only fixed-size arrays are handled
this way.
- TChains can now be used without crash.
- bugfix in TMultiLayerPerceptron::DrawResult() (thanks to Axel): the training
sample was always used, ignoring the option field.
Revision
11024 -
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Modified
Thu Feb 3 07:29:32 2005 UTC (9 years, 11 months ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 17980 byte(s)
Diff to
previous 10831
From Axel Nauman & Christophe Delaere
This patch fixes a bug in DrawNetwork, where the hists' upper edge was smaller than the lower edge (this was causing the corrupted histos in the mlpHiggs tutorial). I updated the new regression methods (some null pointer checks, better labels) and their doc. I added the following comment to the doc of TMultiLayerPerceptron: "(One should still try to pass normalized inputs, e.g. between [0.,1])", and added labels for the output nodes in Draw.
Revision
10831 -
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Modified
Fri Dec 17 22:34:01 2004 UTC (10 years, 1 month ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 15468 byte(s)
Diff to
previous 10824
From Axel Naumann:
As you pointed out, MSVC found a real problem. Your quick correction
still left the underlying logic problem. Fixed now, see attachment. I
also renamed the vars.
On the mlpHiggs result: the result is (within the randomization
variations) unchanged - I did not touch any of the internals of the MLP
algorithm. Only DrawNetwork didn't manage to display the stack of
histos. This looks like a problem with THStack, its side effect shows up
in mlpHiggs. I replaced the THStack->Draw by sigh->Draw(),
bgh->Draw("same") for now, leaving a reminder comment that this needs to
be fixed. I'll look into that later.
My new methods don't work on the mlpHiggs example (they do work on my
private use case, though). There is a problem creating the TProfile
histos for the mlpHiggs tutorial which I didn't find after chasing it
down for hours. I need more time for that.
I forgot one deletion, now the legends are only created if options
doesn't contain "goff". The profiles are now filled with O-T:T, not
(O-T)/T:T (O: output value, T: truth value), to avoid div by 0. One
THStack had an invalid Form()'ed title.
TMLP::Train now tells how many test and train events are used.
Revision
10822 -
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Modified
Thu Dec 16 21:20:47 2004 UTC (10 years, 1 month ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 14503 byte(s)
Diff to
previous 10186
From Axel Naumann
I've added four utility methods to TMLPAnalyzer. ANNs used to represent
an unknown function (i.e. not for classification) had no appropriate
tools for testing the output quality.
For the sake of clarity I made the TNeurons derive from TNamed; they now
get names assigned. First, last layer: their TTreeFormula, hidden layer:
Form("HiddenL%d:N%d",layer,i). This allows quick access to the nodes'
names when drawing their input / output for e.g. axis labels.
There was a small bug in the "vary the inputs by a bit, look what
happens to the output" algorithm. The input wasn't reset to its original
value before the next node's input was modified, creating "cross talk".
The loops are also a bit more efficient now.
The status graph is now only updated once per round (works for me, even
with the zoomed axis).
The example in the class descr is fixed (no type specifiers "/F").
The c'tor doc for const char* test/train cuts is fixed.
TMLP::DrawResult now has the option "nocanv" which noesn't create a new
canvas (it's this way around for backwards comp).
tutorials/mlpHiggs now has the proper orthogonal train and test cut.
TMLPAnalyzer now creates a TTree containing the input, true output and
real output - good for quick plots of any possible dependencies one can
think of. This is used to plot the relative difference of (output,true)
vs true (by DrawTruthDeviation) and (output,true) vs input (by
DrawTruthDeviationInOut). The former shows how dependent the error is on
the output value, the latter how much it depends on tyhe input. These
histos are plotted (and returned) as TProfiles, showing the mean
deviation (and the std dev of the deviation) vs true or input value.
Revision
8766 -
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Added
Mon May 3 16:30:12 2004 UTC (10 years, 8 months ago) by
brun
Original Path:
trunk/mlp/src/TMLPAnalyzer.cxx
File length: 8927 byte(s)
From Christophe Delaere
New class TMLPAnalyzer: This class grouping several utilities is designed
for analyzing a Neural Network.
The tutorial mlpHiggs.C has been modified to illustrate this new class.
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