public RlmCacheDataArray Execute(long[] rneurons, double[][] inputs, double[] from, double[] to, bool[] rneuronsCache, double[] fromCache, double[] toCache)
{
RlmCacheDataArray retVal = new RlmCacheDataArray(inputs.Length);
Parallel.For(0, rneurons.Length, (r) =>
{
bool isMatch = true;
bool isMatchCache = true;
for (int i = 0; i < inputs.Length; i++)
{
if (!(inputs[i][r] >= from[i] && inputs[i][r] <= to[i]))
{
isMatch = false;
}
if (!(inputs[i][r] >= fromCache[i] && inputs[i][r] <= toCache[i]))
{
isMatchCache = false;
}
if (!isMatch && !isMatchCache)
{
break;
}
}
if (isMatch)
{
var rneuronId = rneurons[r];
ManagerReference.FindBestSolution(rneuronId);
}
if (isMatchCache)
{
double[] values = new double[inputs.Length];
for (int j = 0; j < inputs.Length; j++)
{
values[j] = inputs[j][r];
}
retVal.Add(rneurons[r], values);
}
});
return(retVal);
}
public virtual RlmCacheDataArray FindBestSolutionAndBuildCache(long[] rneurons, long[] results, double[][] inputs, bool[] rneuronsCache)
{
RlmCacheDataArray cachedData = new RlmCacheDataArray(inputs.Length);
Parallel.For(0, results.Length, (i) =>
{
if (results[i] != 0)
{
ManagerReference.FindBestSolution(results[i]);
}
if (rneuronsCache[i])
{
double[] values = new double[inputs.Length];
for (int j = 0; j < inputs.Length; j++)
{
values[j] = inputs[j][i];
}
cachedData.Add(rneurons[i], values);
}
});
return(cachedData);
}
