/// <summary>
/// Test and compare linear and binary searches, they should return identical results
/// </summary>
/// <returns></returns>
bool TestEqualityOfLinearVsBinarySearch()
{
var random = new System.Random();
for (int i = 0; i < 1000000; i++)
{
float u = i / 999999f;
float r = (float)random.NextDouble();
float[] randomWeights = RandomMath.RandomWeightsArray(random, 33);
RandomMath.BuildCumulativeDistribution(randomWeights);
if (randomWeights.SelectIndexLinearSearch(1f) != randomWeights.SelectIndexBinarySearch(1f))
{
return(false);
}
if (randomWeights.SelectIndexLinearSearch(u) != randomWeights.SelectIndexBinarySearch(u))
{
Debug.Log("Not matching u");
Debug.Log(u);
Debug.Log(randomWeights.SelectIndexLinearSearch(u));
Debug.Log(randomWeights.SelectIndexBinarySearch(u));
return(false);
}
if (randomWeights.SelectIndexLinearSearch(r) != randomWeights.SelectIndexBinarySearch(r))
{
Debug.Log("Not matching r");
Debug.Log(r);
Debug.Log(randomWeights.SelectIndexLinearSearch(r));
Debug.Log(randomWeights.SelectIndexBinarySearch(r));
return(false);
}
}
return(true);
}
// time both searches (linear and binary (log)), and find optimal breakpoint - where to use which for maximal performance
int FindOptimalBreakpointArray()
{
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();
float[] items = RandomMath.IdentityArray(optimalBreakpoint);
float selectedItem; //here just to simulate selecting from array
float[] arr = new float[optimalBreakpoint];
for (int k = 0; k < numOfDiffArrays; k++)
{
RandomMath.RandomWeightsArray(ref arr, random);
RandomMath.BuildCumulativeDistribution(arr);
stopwatchLinear.Start();
for (int i = 0; i < numOfTestPerArr; i++)
{
u = i / (numOfTestPerArr - 1f);
selectedItem = items[arr.SelectIndexLinearSearch(u)];
r = (float)random.NextDouble();
selectedItem = items[arr.SelectIndexLinearSearch(r)];
}
stopwatchLinear.Stop();
}
lin = stopwatchLinear.ElapsedMilliseconds;
/// Binary Search
stopwatchBinary.Stop();
stopwatchBinary.Reset();
for (int k = 0; k < numOfDiffArrays; k++)
{
RandomMath.RandomWeightsArray(ref arr, random);
RandomMath.BuildCumulativeDistribution(arr);
stopwatchBinary.Start();
for (int i = 0; i < numOfTestPerArr; i++)
{
u = i / (numOfTestPerArr - 1f);
selectedItem = items[arr.SelectIndexBinarySearch(u)];
r = (float)random.NextDouble();
selectedItem = items[arr.SelectIndexBinarySearch(r)];
}
stopwatchBinary.Stop();
}
log = stopwatchBinary.ElapsedMilliseconds;
optimalBreakpoint++;
}
return(optimalBreakpoint);
}
