public void RandomAllRanges()
{
for (int n = 3; n < 20; n++)
{
var data = new RandomDistribution(42).IntegerUniform(n, 0, 100).Select(x => (double)x).ToArray();
Output.WriteLine("[DATA] " + string.Join("; ", data));
var buffer = new double[data.Length];
var rqq = new Rqq(data);
for (int i = 0; i < n; i++)
{
for (int j = i; j < n; j++)
{
for (int k = 0; k < j - i; k++)
{
double actual = rqq.Select(i, j, k);
Array.Copy(data, i, buffer, 0, j - i + 1);
Array.Sort(buffer, 0, j - i + 1);
double expected = buffer[k];
Output.WriteLine($"n = {n}, i = {i}, j = {j}, k = {k}, expected = {expected}, actual = {actual}");
if (actual != expected)
{
DumpRqqTree(rqq);
}
Assert.Equal(expected, actual);
}
}
}
}
}
private void InitializeSyllables()
{
nameSyllables = new RandomDistribution<string>();
var longSyllables = new StringCombinator();
longSyllables.AddSet("b", "d", "g", "n", "r", "t");
longSyllables.AddSet("o", "u");
longSyllables.AddSet("f", "g", "l", "k", "m", "n", "s", "r");
foreach (var s in longSyllables.Enumerate())
{
if (s.StartsWith("f") || s.StartsWith("s"))
nameSyllables.Set(s, 0.4);
else
nameSyllables.Set(s, 1.0);
}
var shortSyllables = new StringCombinator();
shortSyllables.AddSet("a", "i", "e", "o", "u");
shortSyllables.AddSet("f", "g", "l", "k", "m", "n", "s", "r");
foreach (var s in shortSyllables.Enumerate())
{
if (s.EndsWith("f") || s.EndsWith("s"))
nameSyllables.Set(s, 1.0);
else
nameSyllables.Set(s, 3.5);
}
}
/// <summary>
/// Returns a random number [0f,1f) with linear probability
/// </summary>
public float Next(RandomDistribution distribution = RandomDistribution.Linear)
{
if (!_initialzed)
{
throw new Exception($"{typeof(GlobalPRNG).Name} needs to be initialized. Call Init() first");
}
switch (distribution)
{
case RandomDistribution.Linear:
return(Linear());
break;
case RandomDistribution.EaseInEaseOut:
return(EaseInEaseOut(2));
break;
case RandomDistribution.EaseInEaseOutCubic:
return(EaseInEaseOut(3));
break;
case RandomDistribution.Cubic:
return(Cubic());
break;
default:
throw new ArgumentOutOfRangeException(nameof(distribution), distribution, null);
}
}
public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
const int n = 20;
for (int i = 0; i < n; i++)
{
data.AddRange(random.Gaussian(100, mean: 20 * i, stdDev: 5));
}
var rqqIndexes = RqqPeltChangePointDetector.Instance.GetChangePointIndexes(data.ToArray());
var edIndexes = EdPeltChangePointDetector.Instance.GetChangePointIndexes(data.ToArray());
Console.WriteLine("RqqPelt EdPelt");
for (int i = 0, j = 0, k = 1; k < n; k++)
{
string rqqIndex = "-", edIndex = "-";
if (i < rqqIndexes.Length && rqqIndexes[i] < k * 100 + 10)
{
rqqIndex = rqqIndexes[i++].ToString();
}
if (j < edIndexes.Length && edIndexes[j] < k * 100 + 10)
{
edIndex = edIndexes[j++].ToString();
}
Console.WriteLine($"{rqqIndex.PadLeft(7)} {edIndex.PadLeft(6)}");
}
}
private void InitializeLengths()
{
nameLengths = new RandomDistribution<int>();
nameLengths.Set(2, 0.70);
nameLengths.Set(3, 0.29);
nameLengths.Set(4, 0.01);
}
void Initialize()
{
// dirty find distribution to visualize if none is set
if (visualizeDistribution == null)
{
visualizeDistribution = FindObjectOfType <RandomDistribution>();
}
numberLabels = GetComponentsInChildren <Text>();
}
public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
data.AddRange(random.Gaussian(200, mean: 20, stdDev: 1));
data.AddRange(random.Gaussian(200, mean: 22, stdDev: 1));
const double binSize = 0.5;
Console.WriteLine("*** Simple Histogram ***");
Console.WriteLine(SimpleHistogramBuilder.Instance.Build(data, binSize).ToString());
Console.WriteLine("*** Adaptive Histogram ***");
Console.WriteLine(AdaptiveHistogramBuilder.Instance.Build(data, binSize).ToString());
}
public void GaussianMeanProgression(int count, int meanFactor, int stdDev, int error)
{
var random = new RandomDistribution(42);
var data = new List <double>();
for (int i = 0; i < count; i++)
{
data.AddRange(random.Gaussian(100, mean: meanFactor * i, stdDev: stdDev));
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(count, error, indexes);
}
void OnEnable()
{
// get target script
targetScript = (RandomDistribution)target;
serializedScript = new SerializedObject(targetScript);
// get serialized properties
distributionCurve = serializedScript.FindProperty("distributionCurve");
randomizeMode = serializedScript.FindProperty("randomizeMode");
prebakeResolution = serializedScript.FindProperty("prebakeResolution");
// instantiate the gui contents only once
contentRandomizeMode = new GUIContent("Algorithm", "Pregenerate will generate a set of numbers on initialization. Brute Force does not do that, but may have worse performance.");
contentPrebakeResolution = new GUIContent("Pregeneration Resolution");
}
public void GaussianStdDevProgression(int error, [NotNull] string stdDevValuesString)
{
var random = new RandomDistribution(42);
var stdDevValues = stdDevValuesString.Split(';').Select(double.Parse).ToArray();
var data = new List <double>();
foreach (double stdDev in stdDevValues)
{
data.AddRange(random.Gaussian(100, mean: 0, stdDev: stdDev));
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(stdDevValues.Length, error, indexes);
}
public void BimodalProgression(int count, int meanFactor, int stdDev, int error)
{
var random = new RandomDistribution(42);
var shuffler = new Shuffler(42);
var data = new List <double>();
for (int i = 0; i < count; i++)
{
data.AddRange(random.Gaussian(30, 0, stdDev));
data.AddRange(random.Gaussian(70, (i + 1) * meanFactor, stdDev));
shuffler.Shuffle(data, i * 100, 100);
}
var indexes = detector.GetChangePointIndexes(data.ToArray(), 20);
Check100(count, error, indexes);
}
public void Run()
{
var random = new RandomDistribution(42);
var data = new List <double>();
data.AddRange(random.Gaussian(200, mean: 20));
data.AddRange(random.Gaussian(200, mean: 22));
List <double>
unimodal = new List <double>(),
bimodal = new List <double>(),
trimodal = new List <double>();
unimodal.AddRange(random.Gaussian(200, mean: 20));
bimodal.AddRange(random.Gaussian(200, mean: 20));
bimodal.AddRange(random.Gaussian(200, mean: 30));
trimodal.AddRange(random.Gaussian(200, mean: 20));
trimodal.AddRange(random.Gaussian(200, mean: 30));
trimodal.AddRange(random.Gaussian(200, mean: 40));
Console.WriteLine("Unimodal : " + MValueCalculator.Calculate(unimodal));
Console.WriteLine("Bimodal : " + MValueCalculator.Calculate(bimodal));
Console.WriteLine("Trimodal : " + MValueCalculator.Calculate(trimodal));
}
public void SetRandomDistribution(RandomDistribution distribution)
{
TCOD_random_set_distribution(Handle, distribution);
}
private extern static void TCOD_random_set_distribution(IntPtr mersenne, RandomDistribution distribution);
private void InitializeTitles()
{
titleBearing = new RandomDistribution<bool>();
titleBearing.Set(true, 0.2);
titleBearing.Set(false, 0.8);
var monikers = new StringCombinator();
monikers.AddSet("rock", "stone", "mountain", "ale", "battle", "axe", "hammer", "shield", "blood", "iron", "steel");
monikers.AddSet("smasher", "climber", "hunter", "drinker", "seeker", "bearer", "cracker", "thirsty", "fist", "head", "skin", "foot", "beard");
titles = new RandomDistribution<string>();
foreach (var m in monikers.Enumerate())
titles.Set(string.Concat("the ", m.Capitalize()), 1.0);
}
