double DoNNET(NNetStruct locnnetstruct)
{
// string errorcontext = "CPU:NNET";
// int systemerror = 0;
long accumtime = 0;
double iterations = 0.0;
/*
** Init random number generator.
** NOTE: It is important that the random number generator
** be re-initialized for every pass through this test.
** The NNET algorithm uses the random number generator
** to initialize the net. Results are sensitive to
** the initial neural net state.
*/
ByteMark.randnum(3);
/*
** Read in the input and output patterns. We'll do this
** only once here at the beginning. These values don't
** change once loaded.
*/
read_data_file();
/*
** See if we need to perform self adjustment loop.
*/
if (locnnetstruct.adjust == 0)
{
/*
** Do self-adjustment. This involves initializing the
** # of loops and increasing the loop count until we
** get a number of loops that we can use.
*/
for (locnnetstruct.loops = 1;
locnnetstruct.loops < MAXNNETLOOPS;
locnnetstruct.loops++)
{
ByteMark.randnum(3);
if (DoNNetIteration(locnnetstruct.loops) > global.min_ticks)
{
break;
}
}
}
/*
** All's well if we get here. Do the test.
*/
accumtime = 0L;
iterations = (double)0.0;
do
{
ByteMark.randnum(3); /* Gotta do this for Neural Net */
accumtime += DoNNetIteration(locnnetstruct.loops);
iterations += (double)locnnetstruct.loops;
} while (ByteMark.TicksToSecs(accumtime) < locnnetstruct.request_secs);
/*
** Clean up, calculate results, and go home. Be sure to
** show that we don't have to rerun adjustment code.
*/
locnnetstruct.iterspersec = iterations / ByteMark.TicksToFracSecs(accumtime);
if (locnnetstruct.adjust == 0)
{
locnnetstruct.adjust = 1;
}
return(locnnetstruct.iterspersec);
}
double DoNNET(NNetStruct locnnetstruct)
{
// string errorcontext = "CPU:NNET";
// int systemerror = 0;
long accumtime = 0;
double iterations = 0.0;
/*
** Init random number generator.
** NOTE: It is important that the random number generator
** be re-initialized for every pass through this test.
** The NNET algorithm uses the random number generator
** to initialize the net. Results are sensitive to
** the initial neural net state.
*/
ByteMark.randnum(3);
/*
** Read in the input and output patterns. We'll do this
** only once here at the beginning. These values don't
** change once loaded.
*/
read_data_file();
/*
** See if we need to perform self adjustment loop.
*/
if (locnnetstruct.adjust == 0)
{
/*
** Do self-adjustment. This involves initializing the
** # of loops and increasing the loop count until we
** get a number of loops that we can use.
*/
for (locnnetstruct.loops = 1;
locnnetstruct.loops < MAXNNETLOOPS;
locnnetstruct.loops++)
{
ByteMark.randnum(3);
if (DoNNetIteration(locnnetstruct.loops) > global.min_ticks)
break;
}
}
/*
** All's well if we get here. Do the test.
*/
accumtime = 0L;
iterations = (double)0.0;
do
{
ByteMark.randnum(3); /* Gotta do this for Neural Net */
accumtime += DoNNetIteration(locnnetstruct.loops);
iterations += (double)locnnetstruct.loops;
} while (ByteMark.TicksToSecs(accumtime) < locnnetstruct.request_secs);
/*
** Clean up, calculate results, and go home. Be sure to
** show that we don't have to rerun adjustment code.
*/
locnnetstruct.iterspersec = iterations / ByteMark.TicksToFracSecs(accumtime);
if (locnnetstruct.adjust == 0)
locnnetstruct.adjust = 1;
return locnnetstruct.iterspersec;
}