private double CalculateCoverage()
{
var range = MaximumToolCoverage - MininumToolCoverage;
var rawCoverage = _random.Generate(MininumToolCoverage, range);
return(rawCoverage.Clip(0, 100));
}
/// <summary>
/// Creates a square matrix matrix with random data.
/// </summary>
///
public static T[][] Random <T>(int size, IRandomNumberGenerator <T> generator,
bool symmetric = false, T[][] result = null)
{
if (result == null)
{
result = Jagged.Create <T>(size, size);
}
if (!symmetric)
{
for (int i = 0; i < size; i++)
{
result[i] = generator.Generate(size);
}
}
else
{
for (int i = 0; i < size; i++)
{
T[] row = generator.Generate(size / 2, result[i]);
for (int start = 0, end = size - 1; start < size / 2; start++, end--)
{
row[end] = row[start];
}
}
}
return(result);
}
///<inheritdoc/>
public ChunkInfo Build(long chunkSize)
{
var entryInfos = new List <EntryInfo>();
var diff = _encodingInfoProvider.GetStringLength(chunkSize);
//First entry of chunk should be repeated
var repeatedEntryLength = _randomNumberGenerator.Generate(EntryInfo.MinLength, EntryInfo.MaxEntryLength);
if (repeatedEntryLength * 2 >= diff - EntryInfo.MinLength)
{
// In this case chunk will be small and contain only row and repeated row
return(new ChunkInfo(new List <EntryInfo>(), CreateEntryInfo((int)diff / 2)));
}
var repeatedEntryInfo = CreateEntryInfo(repeatedEntryLength);
diff -= repeatedEntryLength * 2;
while (diff > 0)
{
var nextLength = _randomNumberGenerator.Generate(EntryInfo.MinLength, EntryInfo.MaxEntryLength);
var totalLength = nextLength < diff - EntryInfo.MinLength ? nextLength : diff;
entryInfos.Add(CreateEntryInfo((int)totalLength));
diff -= totalLength;
}
return(new ChunkInfo(entryInfos, repeatedEntryInfo));
}
public void SetWinningDoor(List <Door> allDoors)
{
var randomNumber = _randomNumberGenerator.Generate();
var currentDoor = allDoors.Find(door => door.Number == randomNumber);
currentDoor.Winner = true;
}
public PlanFeature BuildPlanFeature(Feature feature)
{
var planFeature = new PlanFeature("Plan" + feature.Name, feature);
var count = _random.Generate(1, NumberOfMeasurementSets);
var measurementSets = Enumerable.Range(0, count)
.Select(i => BuildMeasurementSet(planFeature)).ToImmutableList();
return(planFeature.With(measurementSets));
}
public ActionResult <IEnumerable <int> > Get()
{
return(new int[]
{
_rand.Generate(),
_rand.Generate(),
_rand.Generate(),
_rand.Generate()
});
}
private Feature BuildFeature(int partSize, int index)
{
var name = "Feature" + index;
var center = _random.Generate(Point3D.Origin, partSize);
var direction = _random.Generate(Point3D.Origin, 1)
.ToVector3D().ToDirection3D();
var featureSize = _random.Generate(0, partSize);
return(new Feature(name, center, direction, featureSize));
}
private IEnumerable <MeasurementSetNode> SortMeasurementSetNodes(IImmutableList <MeasurementSetNodeResult> measurementSetNodeResults)
{
foreach (var measurementSetNodeResult in measurementSetNodeResults)
{
var measurementSetNodes = measurementSetNodeResult.MeasurementSetNodes;
if (measurementSetNodes.Count == 1)
{
yield return(measurementSetNodes.First());
}
var index = _random.Generate(0, measurementSetNodes.Count);
yield return(measurementSetNodes[index]);
}
}
public SymbolOptions ChooseSymbol()
{
// generates a value in [0,maxValue) so we add 1 to create a [1,100] percentage
int percentage = _generator.Generate(max: 100) + 1;
SymbolOptions chosenSymbol = null;
// since symbols are a low count an O(N) algorithm is good enough
// an O(logN) one may result in a more complex implementation which is harder to support with no actual benefits
foreach (SymbolOptions currentSymbol in _options.Symbols)
{
if (percentage <= currentSymbol.Probability)
{
chosenSymbol = currentSymbol;
break;
}
else
{
percentage -= currentSymbol.Probability;
}
}
if (chosenSymbol == null)
{
throw new InvalidOperationException("Failed to generate a symbol.");
}
_logger.LogDebug("Generated symbol {0}.", chosenSymbol);
return(chosenSymbol);
}
/// <summary>
/// Process the filter on the specified image.
/// </summary>
///
/// <param name="image">Source image data.</param>
/// <param name="rect">Image rectangle for processing by the filter.</param>
///
protected override unsafe void ProcessFilter(UnmanagedImage image, Rectangle rect)
{
int pixelSize = (image.PixelFormat == PixelFormat.Format8bppIndexed) ? 1 : 3;
int startY = rect.Top;
int stopY = startY + rect.Height;
int startX = rect.Left * pixelSize;
int stopX = startX + rect.Width * pixelSize;
int offset = image.Stride - (stopX - startX);
// do the job
byte *ptr = (byte *)image.ImageData.ToPointer();
// allign pointer to the first pixel to process
ptr += (startY * image.Stride + rect.Left * pixelSize);
// for each line
for (int y = startY; y < stopY; y++)
{
// for each pixel
for (int x = startX; x < stopX; x++, ptr++)
{
*ptr = Math.Max((byte)0, Math.Min((byte)255, (byte)(*ptr + generator.Generate())));
}
ptr += offset;
}
}
public BankResponse processPayment(BankRequest request)
{
var seed = _randomSeed.Generate();
var transactionId = new DateTimeOffset().ToUnixTimeMilliseconds().ToString();
// very dangerous, don't use it on production :D
if (seed < 7 || request.CardNumber == "0000-0000-0000-0000")
{
return(new BankResponse {
id = transactionId,
successful = true,
message = "successful transaction",
statusCode = 1
});
}
if (seed < 9)
{
return(new BankResponse {
id = transactionId,
successful = false,
message = "not enough credit to perform transaction",
statusCode = 3
});
}
throw new System.Exception("Bank Service Unavailable");
}
public ActionResult SessionManagement()
{
var siteCookie = GetSessionCookie();
if (siteCookie != null)
{
ViewData["session_id"] = siteCookie.Value;
return(View());
}
var sessionId = _passwordHashProvidcer.Hash(Guid.NewGuid() + _randomNumberGenerator.GenerateLong().ToString(), _salt);
var custom = _passwordHashProvidcer.Hash(Guid.NewGuid() + _randomNumberGenerator.Generate().ToString(), _salt);
var cookieName = string.Format("{0}{1}", SessionCookieNamePrefix, custom);
var sessionCookie = new HttpCookie(cookieName)
{
Value = sessionId,
Domain = Request.Url.Host,
Path = "~/good/",
// Expires = End of the session,
HttpOnly = true,
Shareable = false
};
Response.Cookies.Add(sessionCookie);
ViewData["session_id"] = sessionId;
return(View());
}
public void Shuffle()
{
var cardList = _cards.ToArray();
_cards.Clear();
_cards = new Stack <ICard>(cardList.OrderBy(x => _randomNumberGenerator.Generate(cardList.Length)));
}
public void CurrentPlayerPlay()
{
while (!_gameBoard.Play(CurrentPlayer.PlayCharacter, _randomNumberGenerator.Generate(), _randomNumberGenerator.Generate()))
{
;
}
}
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <param name="result">The location where to store the samples.</param>
///
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public virtual double[][] Generate(int samples, double[][] result)
{
if (generator == null)
{
generator = new MetropolisHasting(Dimension, LogProbabilityDensityFunction);
}
return(generator.Generate(samples, result));
}
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <param name="result">The location where to store the samples.</param>
///
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public virtual int[][] Generate(int samples, int[][] result)
{
if (generator == null)
{
generator = MetropolisHasting.Discrete(Dimension, this);
}
return(generator.Generate(samples, result));
}
/// <summary>
/// Generate random chromosome value.
/// </summary>
///
/// <remarks><para>Regenerates chromosome's value using random number generator.</para>
/// </remarks>
///
public override void Generate()
{
for (int i = 0; i < length; i++)
{
// generate next value
val[i] = chromosomeGenerator.Generate();
}
}
/// <summary>
/// Randomizes (initializes) the weights of
/// the network using a Gaussian distribution.
/// </summary>
///
public void Randomize()
{
foreach (ActivationLayer layer in network.Layers)
{
foreach (ActivationNeuron neuron in layer.Neurons)
{
for (int i = 0; i < neuron.Weights.Length; i++)
{
neuron.Weights[i] = random.Generate();
}
if (UpdateThresholds)
{
neuron.Threshold = random.Generate();
}
}
}
}
public string[] Generate()
{
var codePegs = new string[4];
for (int i = 0; i < 4; i++)
{
codePegs[i] = PossibleColors.Options[_randomNumberGenerator.Generate()];
}
return(codePegs);
}
public void ShuffleDeck()
{
_currentCardIndex = 0;
for (var i = 0; i < _cards.Length; ++i)
{
int j = _randomNumberGenerator.Generate(0, _cards.Length);
var temp = _cards[j];
_cards[i] = _cards[j];
_cards[j] = temp;
}
}
private Point3D CreateToolLocation(double size)
{
var position = _random.Generate(0, 1) * size;
var side = _random.Generate(0, 4);
switch (side)
{
case 0:
return(new Point3D(0, position, 0));
case 1:
return(new Point3D(size, position, 0));
case 2:
return(new Point3D(position, 0, 0));
default:
return(new Point3D(position, size, 0));
}
}
/// <summary>
/// Mutation operator.
/// </summary>
///
/// <remarks><para>The method performs chromosome's mutation, adding random number
/// to chromosome's gene or multiplying the gene by random number. These random
/// numbers are generated with help of <see cref="mutationMultiplierGenerator">mutation
/// multiplier</see> and <see cref="mutationAdditionGenerator">mutation
/// addition</see> generators.</para>
///
/// <para>The exact type of mutation applied to the particular gene
/// is selected randomly each time and depends on <see cref="MutationBalancer"/>.
/// Before mutation is done a random number is generated in [0, 1] range - if the
/// random number is smaller than <see cref="MutationBalancer"/>, then multiplication
/// mutation is done, otherwise addition mutation.
/// </para></remarks>
///
public override void Mutate()
{
int mutationGene = Generator.Random.Next(length);
if (Generator.Random.NextDouble() < mutationBalancer)
{
val[mutationGene] *= mutationMultiplierGenerator.Generate();
}
else
{
val[mutationGene] += mutationAdditionGenerator.Generate();
}
}
/// <summary>
/// Creates a rows-by-cols matrix with random data.
/// </summary>
///
public static T[][] Random <T>(int rows, int cols,
IRandomNumberGenerator <T> generator, T[][] result = null)
{
if (result == null)
{
result = Jagged.Create <T>(rows, cols);
}
for (int i = 0; i < rows; i++)
{
result[i] = generator.Generate(cols);
}
return(result);
}
/// <summary>
/// 获取随机长度
/// </summary>
private int GetRandomLength(int maxLength)
{
return(_random.Generate(1, maxLength));
}
/// <summary>
/// 获取随机长度
/// </summary>
/// <param name="maxLength">最大值（默认为int.MaxValue）</param>
/// <param name="minLength">最小值（默认为0）</param>
/// <returns></returns>
private int GetRandomLength(int maxLength = int.MaxValue, int minLength = 1)
{
return(_random.Generate(minLength, maxLength));
}
/// <summary>
/// 生成随机布尔值
/// </summary>
/// <returns><c>true</c> if XXXX, <c>false</c> otherwise.</returns>
public bool GenerateBool()
{
var random = _random.Generate(1, 100);
if (random % 2 == 0)
{
return(false);
}
return(true);
}
/// <summary>
/// Randomize neuron.
/// </summary>
///
/// <remarks>
/// Initialize neuron's weights with random values specified
/// by the <see cref="RandGenerator"/>.</remarks>
///
public virtual void Randomize()
{
rand.Generate(weights.Length, weights);
}
public static void Generate(this IRandomNumberGenerator rng, byte[] data) => rng.Generate(new ArraySegment <byte>(data));
public static void Generate(this IRandomNumberGenerator rng, byte[] data, int offset, int count) => rng.Generate(new ArraySegment <byte>(data, offset, count));
public static byte[] Generate(this IRandomNumberGenerator rng, int howmuch)
{
byte[] data = new byte[howmuch];
rng.Generate(data);
return(data);
}
