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Artificial Neural Networks made easy with the FANN library

, 28 Aug 2013 CPOL
Neural networks are typically associated with specialised applications, developed only by select groups of experts. This misconception has had a highly negative effect on its popularity. Hopefully, the FANN library will help fill this gap.
fann-1_2_0.zip
fann-1.2.0
debian
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
doc
fann_doc_complete_1.0.pdf
Makefile
html
src
include
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
win32_dll
examples
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
benchmarks
datasets
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
examples
Makefile
xor.data
python
README
examples
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
MSVC++
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
fann_win32_dll-1_2_0.zip
changelog
compat
control
copyright
docs
libfann1-dev.dirs
libfann1-dev.examples
libfann1-dev.files
libfann1-dev.install
libfann1.dirs
libfann1.files
libfann1.install
rules
fann_doc_complete_1.0.pdf
Makefile
Makefile.in
Makefile.am
Makefile.in
COPYING
Makefile.am
makefile
README
Makefile.in
configure
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile.am
NEWS
TODO
aclocal.m4
config.guess
config.sub
configure.in
depcomp
fann.pc.in
fann.spec.in
install-sh
ltmain.sh
missing
mkinstalldirs
building.test
building.train
diabetes.test
diabetes.train
gene.test
gene.train
mushroom.test
mushroom.train
robot.test
robot.train
soybean.test
soybean.train
thyroid.test
thyroid.train
two-spiral.train
pumadyn-32fm.test
pumadyn-32fm.train
two-spiral.test
parity8.train
parity8.test
parity13.test
parity13.train
Makefile
README
benchmark.sh
benchmarks.pdf
gnuplot
performance.cc
quality.cc
.cvsignore
Makefile
xor.data
README
libfann.i
makefile.gnu
makefile.msvc
libfann.pyc
libfann.dsp
all.dsw
simple_test.dsp
simple_train.dsp
steepness_train.dsp
xor_test.dsp
xor_train.dsp
config.in
bin
fanndoubled.dll
fanndoubled.lib
fanndoubleMTd.dll
fanndoubleMTd.lib
fannfixedd.dll
fannfixedd.lib
fannfixedMTd.dll
fannfixedMTd.lib
fannfloatd.dll
fannfloatd.lib
fannfloatMTd.dll
fannfloatMTd.lib
fanndouble.dll
fanndouble.lib
fanndoubleMT.dll
fanndoubleMT.lib
fannfixed.dll
fannfixed.lib
fannfixedMT.dll
fannfixedMT.lib
fannfloat.dll
fannfloat.lib
fannfloatMT.dll
fannfloatMT.lib
vs_net2003.zip
VS.NET2003
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<HTML
><HEAD
><TITLE
>Running a Fixed Point ANN</TITLE
><link href="../style.css" rel="stylesheet" type="text/css"><META
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TITLE="Fixed Point Usage"
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TITLE="Fixed Point Usage"
HREF="c189.html"><LINK
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TITLE="Precision of a Fixed Point ANN"
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><BODY
CLASS="section"
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><DIV
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><DIV
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><H1
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><A
NAME="fixed.run"
>3.2. Running a Fixed Point ANN</A
></H1
><P
>&#13;	Running a fixed point ANN is done much like running an ordinary ANN. The difference is that the inputs and outputs should be in fixed point
	representation. Furthermore the inputs should be restricted to be between -<VAR
CLASS="parameter"
>multiplier</VAR
> and <VAR
CLASS="parameter"
>multiplier</VAR
> to
	avoid integer overflow, where the <VAR
CLASS="parameter"
>multiplier</VAR
> is the value returned from
	<A
HREF="r1483.html"
><CODE
CLASS="function"
>fann_get_multiplier</CODE
></A
>. This multiplier is the value that a floating point number should
	be multiplied with, in order to be a fixed point number, likewise the output of the ANN should be divided by this multiplier in order to be between zero
	and one.
      </P
><P
>&#13;	To help using fixed point numbers, another function is provided.
	<A
HREF="r1467.html"
><CODE
CLASS="function"
>fann_get_decimal_point</CODE
></A
> which returns the decimal point. The decimal point is the
	position dividing the integer and fractional part of the fixed point number and is useful for doing operations on the fixed point inputs and outputs.
      </P
><DIV
CLASS="example"
><A
NAME="example.exec_fixed"
></A
><P
><B
>Example 3-2. An example of a program written to support both fixed point and floating point numbers</B
></P
><PRE
CLASS="programlisting"
>&#13;
#include &#60;time.h&#62;
#include &#60;sys/time.h&#62;
#include &#60;stdio.h&#62;

#include "fann.h"

int main()
{
	fann_type *calc_out;
	unsigned int i;
	int ret = 0;

	struct fann *ann;
	struct fann_train_data *data;

	printf("Creating network.\n");

#ifdef FIXEDFANN
	ann = fann_create_from_file("xor_fixed.net");
#else
	ann = fann_create_from_file("xor_float.net");
#endif
	
	if(!ann){
		printf("Error creating ann --- ABORTING.\n");
		return 0;
	}

	printf("Testing network.\n");

#ifdef FIXEDFANN
	data = fann_read_train_from_file("xor_fixed.data");
#else
	data = fann_read_train_from_file("xor.data");
#endif

	for(i = 0; i &#60; data-&#62;num_data; i++){
		fann_reset_MSE(ann);
		calc_out = fann_test(ann, data-&#62;input[i], data-&#62;output[i]);
#ifdef FIXEDFANN
		printf("XOR test (%d, %d) -&#62; %d, should be %d, difference=%f\n",
		data-&#62;input[i][0], data-&#62;input[i][1], *calc_out, data-&#62;output[i][0], (float)fann_abs(*calc_out - data-&#62;output[i][0])/fann_get_multiplier(ann));

		if((float)fann_abs(*calc_out - data-&#62;output[i][0])/fann_get_multiplier(ann) &#62; 0.1){
			printf("Test failed\n");
			ret = -1;
		}
#else
		printf("XOR test (%f, %f) -&#62; %f, should be %f, difference=%f\n",
		data-&#62;input[i][0], data-&#62;input[i][1], *calc_out, data-&#62;output[i][0], (float)fann_abs(*calc_out - data-&#62;output[i][0]));
#endif
	}

	printf("Cleaning up.\n");
	fann_destroy_train(data);
	fann_destroy(ann);

	return ret;
}

	</PRE
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><TR
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WIDTH="33%"
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>

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