//same as before, but for lists instead of arrays
int FindOptimalBreakpointList()
{
int optimalBreakpoint = 2;
var random = new System.Random();
Stopwatch stopwatchLinear = new Stopwatch();
Stopwatch stopwatchBinary = new Stopwatch();
float lin = 0f;
float log = 1f;
// continue increasing "optimalBreakpoint" until linear becomes slower than log
// result is around 15-16, varies a bit due to random nature of test
while (lin <= log)
{
int numOfDiffArrays = 100;
int numOfTestPerArr = 10000;
// u = uniform grid, r = uniform random
float u, r;
///Linear Search
stopwatchLinear.Stop();
stopwatchLinear.Reset();
List <float> items = RandomMath.IdentityList(optimalBreakpoint);
float selectedItem; //simulate selecting from array
List <float> list = new List <float>(optimalBreakpoint);
for (int i = 0; i < optimalBreakpoint; i++)
{
list.Add(0f);
}
for (int k = 0; k < numOfDiffArrays; k++)
{
RandomMath.RandomWeightsList(ref list, random);
RandomMath.BuildCumulativeDistribution(list);
stopwatchLinear.Start();
for (int i = 0; i < numOfTestPerArr; i++)
{
u = i / (numOfTestPerArr - 1f);
selectedItem = items[list.SelectIndexLinearSearch(u)];
r = (float)random.NextDouble();
selectedItem = items[list.SelectIndexLinearSearch(r)];
}
stopwatchLinear.Stop();
}
lin = stopwatchLinear.ElapsedMilliseconds;
/// Binary Search
stopwatchBinary.Stop();
stopwatchBinary.Reset();
for (int k = 0; k < numOfDiffArrays; k++)
{
RandomMath.RandomWeightsList(ref list, random);
RandomMath.BuildCumulativeDistribution(list);
stopwatchBinary.Start();
for (int i = 0; i < numOfTestPerArr; i++)
{
u = i / (numOfTestPerArr - 1f);
selectedItem = items[list.SelectIndexBinarySearch(u)];
r = (float)random.NextDouble();
selectedItem = items[list.SelectIndexBinarySearch(r)];
}
stopwatchBinary.Stop();
}
log = stopwatchBinary.ElapsedMilliseconds;
optimalBreakpoint++;
}
return(optimalBreakpoint);
}
