public void AddShotNoise()
{
// this really needs checking. the average noise should = sqrt(signal). that can be tested..
for (var x = 0; x < Image.Width; x++)
{
for (var y = 0; y < Image.Height; y++)
{
var value = Image.At <ushort>(y, x);
if (value > 0)
{
var poisson = new Poisson(value);
var noised = poisson.Sample();
if (noised > ushort.MaxValue)
{
noised = ushort.MaxValue;
}
Image.Set(y, x, (ushort)noised);
}
}
}
}
public DistribucionPoisson(double lambda)
{
this.generador = new GeneradorProvistoPorElLenguaje();
this.lambda = lambda;
var entero = new Poisson(lambda);
num = entero.Sample();
}
private void generateAttributes(Character character)
{
foreach (Attribute attribute in attributes.Select(x => x.Value))
{
int value = Poisson.Sample(StoreConfig.MODIFIER_LAMBDA * 2.0d);
character.setAttribute(attribute, value);
}
}
protected void Generate()
{
var amount = poisson.Sample();
var array = new int[amount];
DiscreteUniform.Samples(array, 0, Constants.ONE_SECOND_TIME - 1);
samples = new HashSet <int>(array);
}
/// <summary>
/// Determine the number of arrivals in time window (15 minutes) and their arrival times
/// </summary>
/// <param name="mean">Average number of arrivals</param>
/// <returns></returns>
public static int[] arrivingPassengers(double mean)
{
var arrivals = Poisson.Sample(mean);
var times = new int[arrivals];
for (int i = 0; i < arrivals; i++)
{
times[i] = DiscreteUniform.Sample(0, 899);
}
return(times);
}
public int SamplePoisson(double lambda)
{
if (lambda <= 0d)
{
throw new ArgumentException("Lambda parameter for Poisson distribution must be > 0");
}
var distribution = new Poisson(lambda, RandomSource);
return(distribution.Sample());
}
/// <summary>
/// Randomly create true theta and phi arrays
/// </summary>
/// <param name="numVocab">Vocabulary size</param>
/// <param name="numTopics">Number of topics</param>
/// <param name="numDocs">Number of documents</param>
/// <param name="averageDocLength">Average document length</param>
/// <param name="averageWordsPerTopic">Average number of words per topic</param>
/// <param name="trueTheta">Theta array (output)</param>
/// <param name="truePhi">Phi array (output)</param>
public static void CreateTrueThetaAndPhi(
int numVocab, int numTopics, int numDocs, int averageDocLength, int averageWordsPerTopic,
out Dirichlet[] trueTheta, out Dirichlet[] truePhi)
{
truePhi = new Dirichlet[numTopics];
for (int i = 0; i < numTopics; i++)
{
truePhi[i] = Dirichlet.Uniform(numVocab);
truePhi[i].PseudoCount.SetAllElementsTo(0.0);
// Draw the number of unique words in the topic.
int numUniqueWordsPerTopic = Poisson.Sample((double)averageWordsPerTopic);
if (numUniqueWordsPerTopic >= numVocab)
{
numUniqueWordsPerTopic = numVocab;
}
if (numUniqueWordsPerTopic < 1)
{
numUniqueWordsPerTopic = 1;
}
double expectedRepeatOfWordInTopic =
((double)numDocs) * averageDocLength / numUniqueWordsPerTopic;
int[] shuffledWordIndices = Rand.Perm(numVocab);
for (int j = 0; j < numUniqueWordsPerTopic; j++)
{
int wordIndex = shuffledWordIndices[j];
// Draw the count for that word
int cnt = Poisson.Sample(expectedRepeatOfWordInTopic);
truePhi[i].PseudoCount[wordIndex] = cnt + 1.0;
}
}
trueTheta = new Dirichlet[numDocs];
for (int i = 0; i < numDocs; i++)
{
trueTheta[i] = Dirichlet.Uniform(numTopics);
trueTheta[i].PseudoCount.SetAllElementsTo(0.0);
// Draw the number of unique topics in the doc.
int numUniqueTopicsPerDoc = Math.Min(1 + Poisson.Sample(1.0), numTopics);
double expectedRepeatOfTopicInDoc =
averageDocLength / numUniqueTopicsPerDoc;
int[] shuffledTopicIndices = Rand.Perm(numTopics);
for (int j = 0; j < numUniqueTopicsPerDoc; j++)
{
int topicIndex = shuffledTopicIndices[j];
// Draw the count for that topic
int cnt = Poisson.Sample(expectedRepeatOfTopicInDoc);
trueTheta[i].PseudoCount[topicIndex] = cnt + 1.0;
}
}
}
private void generateAttributes(Equipment equipment)
{
foreach (KeyValuePair <int, Attribute> entry in this.attributes)
{
bool success = Bernoulli.Sample(StoreConfig.ATTRIBUTE_PROB) == 1;
if (success)
{
int modifier = Poisson.Sample(StoreConfig.MODIFIER_LAMBDA) - 1;
// Clamp modifier in [1,inf)
equipment.setAttributeBonus(entry.Value, modifier > 0 ? modifier : 1);
}
}
}
//Generate Processes
private void ProcessGenerator()
{
Normal ATNormal = new Normal(ATmean, ATstdDev);
Normal BTNormal = new Normal(BTmean, BTstdDev);
Poisson PriPoisson = new Poisson(PriGamma);
using (StreamWriter sw = new StreamWriter("Processes.txt"))
{
sw.WriteLine(N);
double randomValue;
for (int i = 1; i <= N; ++i)
{
string line = "";
line += i.ToString();
line += " ";
randomValue = ATNormal.Sample();
if (randomValue < 0) // no negative AT
{
--i;
continue;
}
line += randomValue.ToString();
line += " ";
randomValue = BTNormal.Sample();
if (randomValue <= 0) // no negative or zero BT
{
--i;
continue;
}
line += randomValue.ToString();
line += " ";
randomValue = PriPoisson.Sample();
if (randomValue < 0) // no negative priority
{
--i;
continue;
}
line += randomValue.ToString();
sw.WriteLine(line);
}
}
MessageBox.Show("Processes Generated Successfully", "Notification",
MessageBoxButtons.OK, MessageBoxIcon.Information);
}
public void generateEquipment(double lambda)
{
int count = Poisson.Sample(lambda);
int countClamped = count > 1 ? count : 1;
Equipment[] newEquipment = Enumerable
.Range(1, countClamped)
.Select(i => this.generateEquipment())
.ToArray();
foreach (Equipment eq in newEquipment)
{
this.equipment.Add(eq.id, eq);
}
}
public void TestMeanAndVariacneConsistency()
{
const int numSamples = 100000;
double mean, stdev;
RunningStat rs = new RunningStat();
Random defaultrs = new Random();
Poisson poisson = new Poisson();
rs.Clear();
mean = 2000; stdev = Math.Sqrt(2000);
poisson.Lambda = 2000;
for (int i = 0; i < numSamples; ++i)
{
rs.Push(poisson.Sample(defaultrs));
}
PrintResult.CompareMeanAndVariance("Poisson Discrete", mean, stdev * stdev, rs.Mean(), rs.Variance());
}
/// Generate LDA data - returns an array of dictionaries mapping unique word index
/// to word count per document.
/// <param name="trueTheta">Known Theta</param>
/// <param name="truePhi">Known Phi</param>
/// <param name="averageNumWords">Average number of words to sample per doc</param>
/// <returns></returns>
public static Dictionary <int, int>[] GenerateLDAData(Dirichlet[] trueTheta, Dirichlet[] truePhi, int averageNumWords)
{
int numVocab = truePhi[0].Dimension;
int numTopics = truePhi.Length;
int numDocs = trueTheta.Length;
// Sample from the model
Vector[] topicDist = new Vector[numDocs];
Vector[] wordDist = new Vector[numTopics];
for (int i = 0; i < numDocs; i++)
{
topicDist[i] = trueTheta[i].Sample();
}
for (int i = 0; i < numTopics; i++)
{
wordDist[i] = truePhi[i].Sample();
}
var wordCounts = new Dictionary <int, int> [numDocs];
for (int i = 0; i < numDocs; i++)
{
int LengthOfDoc = Poisson.Sample((double)averageNumWords);
var counts = new Dictionary <int, int>();
for (int j = 0; j < LengthOfDoc; j++)
{
int topic = Discrete.Sample(topicDist[i]);
int w = Discrete.Sample(wordDist[topic]);
if (!counts.ContainsKey(w))
{
counts.Add(w, 1);
}
else
{
counts[w] = counts[w] + 1;
}
}
wordCounts[i] = counts;
}
return(wordCounts);
}
private IDistribution <Vector[]> GetInitialisation(double initMaxPseudoCount, double initWordsPerTopic, Sparsity sparsity, double beta)
{
Dirichlet[] initPhi = new Dirichlet[TotalTopics.ObservedValue];
Random r = new Random(12347);
for (int i = 0; i < TotalTopics.ObservedValue; i++)
{
Vector v = Vector.Constant(TotalWords.ObservedValue, beta, sparsity);
int[] perm = Rand.Perm(TotalWords.ObservedValue);
int numWords = Poisson.Sample(initWordsPerTopic);
for (int j = 0; j < numWords; j++)
{
v[perm[j]] += initMaxPseudoCount * r.NextDouble();
}
initPhi[i] = new Dirichlet(v);
}
return(Distribution <Vector> .Array(initPhi));
}
//Calculate the amount of passengers entering the tram at a given station, given the station, last tram arrival at that station of a previous tram and the current time
static public Queue <TimeSpan> getPassengersIn(Station station, Direction direction, TimeSpan lastArrival, TimeSpan now)
{
Queue <TimeSpan> passengers = new Queue <TimeSpan>();
if ((station.stationNumber == 1 && direction == Direction.UtrechtCentraal) || (station.stationNumber == 9 && direction == Direction.Uithof))
{
return(passengers);
} //empty queue }
if (lastArrival.Hours < 6)
{
lastArrival = TimeSpan.FromHours(6);
}
int firstInterval = (int)(lastArrival.TotalMinutes - 360) / 15;
int lastInterval = (int)(now.TotalMinutes - 360) / 15;
double rate;
for (int n = firstInterval; n <= lastInterval; n++)
{
if (direction == Direction.Uithof)
{
rate = double.Parse(enteringRatesA[n + 1][station.stationNumber - 1], System.Globalization.NumberStyles.AllowDecimalPoint, System.Globalization.NumberFormatInfo.InvariantInfo);
}
else
{
rate = double.Parse(enteringRatesB[n + 1][9 - station.stationNumber], System.Globalization.NumberStyles.AllowDecimalPoint, System.Globalization.NumberFormatInfo.InvariantInfo);
}
double minutesInInterval = Math.Min(now.TotalMinutes, (n + 1) * 15 + 360) - (Math.Max(lastArrival.TotalMinutes, (n * 15) + 360));
//Get the amount of passengers using the rate and time in this current interval
if (rate > 0 && minutesInInterval > 0)
{
int pii = Poisson.Sample(rate * minutesInInterval);
for (int i = 0; i < pii; i++)
{
passengers.Enqueue(TimeSpan.FromMinutes(
(Math.Max(lastArrival.TotalMinutes, (n * 15) + 360)) + minutesInInterval / 2));
}
}
}
return(passengers);
}
public static int[] SampleData(int N, double pi, double lambda)
{
int[] data = new int[N];
Bernoulli coin = new Bernoulli(pi);
Poisson poisson = new Poisson(lambda);
for (int i = 0; i < N; i++)
{
bool coin_value = coin.Sample();
if (coin_value)
{
data[i] = 0;
}
else
{
data[i] = poisson.Sample();
}
}
return(data);
}
public void Generate()
{
String[] line = new String[6];
StreamReader sr = new StreamReader(@"C:\\Users\\egypt2\\source\\repos\\WindowsFormsApp2\\input.txt");
line[0] = sr.ReadLine();
int i = 1;
while (line != null)
{
if (i == 4)
{
break;
}
line[i] = sr.ReadLine();
i++;
}
string[] ArrivalParams = line[1].Split(null);
string[] BurstParams = line[2].Split(null);
sr.Close();
var poisson = new Poisson(Convert.ToDouble(line[3]));
var arrivalTimeNormal = new Normal(Convert.ToDouble(ArrivalParams[0]), Convert.ToDouble(ArrivalParams[1]));
var burstTimeNormal = new Normal(Convert.ToDouble(BurstParams[0]), Convert.ToDouble(BurstParams[1]));
string[] text = new string[Convert.ToInt32(line[0]) + 1];
text[0] = Convert.ToString(line[0]);
for (i = 1; i <= Convert.ToInt32(line[0]); i++)
{
var arrivalT = Math.Abs(arrivalTimeNormal.Sample());
var burstT = Math.Abs(burstTimeNormal.Sample());
var prior = poisson.Sample();
text[i] = Convert.ToString(i) + " " + Convert.ToString(arrivalT) + " " + Convert.ToString(burstT) + " " + Convert.ToString(prior);
}
System.IO.File.WriteAllLines(@"C:\\Users\\egypt2\\source\\repos\\WindowsFormsApp2\\output.txt", text);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Poisson distribution class with parameter Lambda = 1
var poisson = new Poisson(1);
Console.WriteLine(@"1. Initialize the new instance of the Poisson distribution class with parameter Lambda = {0}", poisson.Lambda);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", poisson);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", poisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", poisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", poisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", poisson.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", poisson.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", poisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", poisson.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", poisson.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", poisson.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", poisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", poisson.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", poisson.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Poisson distribution
Console.WriteLine(@"3. Generate 10 samples of the Poisson distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(poisson.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Poisson(1) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Poisson(1) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = poisson.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Poisson(4) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Poisson(4) distribution and display histogram");
poisson.Lambda = 4;
for (var i = 0; i < data.Length; i++)
{
data[i] = poisson.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Poisson(10) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Poisson(10) distribution and display histogram");
poisson.Lambda = 10;
for (var i = 0; i < data.Length; i++)
{
data[i] = poisson.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
public override void ExecuteExample()
{
// <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial distribution</a>
MathDisplay.WriteLine("<b>Binomial distribution</b>");
// 1. Initialize the new instance of the Binomial distribution class with parameters P = 0.2, N = 20
var binomial = new Binomial(0.2, 20);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Binomial distribution class with parameters P = {0}, N = {1}", binomial.P, binomial.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", binomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", binomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Binomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Binomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Bernoulli distribution</a>
MathDisplay.WriteLine("<b>Bernoulli distribution</b>");
// 1. Initialize the new instance of the Bernoulli distribution class with parameter P = 0.2
var bernoulli = new Bernoulli(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Bernoulli distribution class with parameter P = {0}", bernoulli.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", bernoulli);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", bernoulli.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", bernoulli.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", bernoulli.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", bernoulli.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", bernoulli.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", bernoulli.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", bernoulli.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", bernoulli.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", bernoulli.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", bernoulli.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", bernoulli.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Bernoulli distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Bernoulli distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(bernoulli.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Categorical distribution</a>
MathDisplay.WriteLine("<b>Categorical distribution</b>");
// 1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)
var binomialC = new Categorical(new[] { 0.1, 0.2, 0.25, 0.45 });
MathDisplay.WriteLine(@"1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)");
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomialC);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomialC.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomialC.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomialC.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomialC.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomialC.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomialC.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomialC.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomialC.Median.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomialC.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomialC.StdDev.ToString(" #0.00000;-#0.00000"));
// 3. Generate 10 samples of the Categorical distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Categorical distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomialC.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
MathDisplay.WriteLine("<b>Conway Maxwell Poisson distribution</b>");
// 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
var conwayMaxwellPoisson = new ConwayMaxwellPoisson(2, 1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", conwayMaxwellPoisson.Lambda, conwayMaxwellPoisson.Nu);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", conwayMaxwellPoisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", conwayMaxwellPoisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", conwayMaxwellPoisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", conwayMaxwellPoisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", conwayMaxwellPoisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", conwayMaxwellPoisson.Mean.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", conwayMaxwellPoisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", conwayMaxwellPoisson.StdDev.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(conwayMaxwellPoisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Discrete_uniform">DiscreteUniform distribution</a>
MathDisplay.WriteLine("<b>Discrete Uniform distribution</b>");
// 1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = 2, UpperBound = 10
var discreteUniform = new DiscreteUniform(2, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = {0}, UpperBound = {1}", discreteUniform.LowerBound, discreteUniform.UpperBound);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", discreteUniform);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", discreteUniform.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", discreteUniform.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", discreteUniform.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", discreteUniform.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", discreteUniform.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", discreteUniform.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", discreteUniform.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", discreteUniform.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", discreteUniform.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", discreteUniform.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", discreteUniform.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", discreteUniform.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the DiscreteUniform distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the DiscreteUniform distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(discreteUniform.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution</a>
MathDisplay.WriteLine("<b>Geometric distribution</b>");
// 1. Initialize the new instance of the Geometric distribution class with parameter P = 0.2
var geometric = new Geometric(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Geometric distribution class with parameter P = {0}", geometric.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", geometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", geometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", geometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", geometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", geometric.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", geometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", geometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", geometric.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", geometric.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", geometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", geometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", geometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", geometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Geometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Geometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(geometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Hypergeometric distribution</a>
MathDisplay.WriteLine("<b>Hypergeometric distribution</b>");
// 1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = 10, M = 2, N = 8
var hypergeometric = new Hypergeometric(30, 15, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Hypergeometric distribution class with parameters Population = {0}, Success = {1}, Draws = {2}", hypergeometric.Population, hypergeometric.Success, hypergeometric.Draws);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", hypergeometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", hypergeometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", hypergeometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", hypergeometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", hypergeometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", hypergeometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", hypergeometric.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", hypergeometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", hypergeometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", hypergeometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", hypergeometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Hypergeometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Hypergeometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(hypergeometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Negative_binomial">NegativeBinomial distribution</a>
MathDisplay.WriteLine("<b>Negative Binomial distribution</b>");
// 1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = 0.2, R = 20
var negativeBinomial = new NegativeBinomial(20, 0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = {0}, N = {1}", negativeBinomial.P, negativeBinomial.R);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", negativeBinomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", negativeBinomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", negativeBinomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", negativeBinomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", negativeBinomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", negativeBinomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", negativeBinomial.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", negativeBinomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", negativeBinomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", negativeBinomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", negativeBinomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the NegativeBinomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the NegativeBinomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(negativeBinomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>
MathDisplay.WriteLine("<b>Poisson distribution</b>");
// 1. Initialize the new instance of the Poisson distribution class with parameter Lambda = 1
var poisson = new Poisson(1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Poisson distribution class with parameter Lambda = {0}", poisson.Lambda);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", poisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", poisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", poisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", poisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", poisson.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", poisson.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", poisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", poisson.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", poisson.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", poisson.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", poisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", poisson.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", poisson.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Poisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Poisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(poisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Zipf_distribution">Zipf distribution</a>
MathDisplay.WriteLine("<b>Zipf distribution</b>");
// 1. Initialize the new instance of the Zipf distribution class with parameters S = 5, N = 10
var zipf = new Zipf(5, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Zipf distribution class with parameters S = {0}, N = {1}", zipf.S, zipf.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", zipf);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", zipf.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", zipf.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", zipf.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", zipf.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", zipf.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", zipf.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", zipf.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", zipf.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", zipf.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", zipf.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", zipf.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Zipf distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Zipf distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(zipf.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
}
public void CanSample()
{
var d = new Poisson(0.3);
d.Sample();
}
public int GetEmbarkingPassengers(Tram tram, int time)
{
// Find the index in the array of lambdas for the poisson process.
var currDT = Utils.SecondsToDateTime(time);
int index = FindAppropriateInterval(currDT);
var lambda = 0.0;
var direction = 1;
// CS -> P+R Direction 1
if (Id == 0 && tram.PreviousTerminal.Id == 8)
{
// If for any reason we are delayed, we have to go back to the data that we know.
if (index > RouteCStoPR.GetLength(0) - 1)
{
index = RouteCStoPR.GetLength(0) - 1;
}
lambda = RouteCStoPR[index, Id];
// Special case we are Centraal (id = 0) but we came from P+R
}
else if (Id < 8)
{
direction = 1;
// If for any reason we are delayed, we have to go back to the data that we know.
if (index > RouteCStoPR.GetLength(0) - 1)
{
index = RouteCStoPR.GetLength(0) - 1;
}
lambda = RouteCStoPR[index, Id];
}
else if (Id >= 8)
{
direction = 0;
// If for any reason we are delayed, we have to go back to the data that we know.
if (index > RoutePRtoCS.GetLength(0) - 1)
{
index = RoutePRtoCS.GetLength(0) - 1;
}
lambda = RoutePRtoCS[index, Id - 8];
}
else // Depot
{
return(0);
}
if (lambda == 0.0)
{
lambda = 0.01;
}
// Generate the number of new arrival events *n* that will occur.
var pd = new Poisson(lambda);
var n = pd.Sample();
var my_index = GetIndex(time);
// Deduct from the expected people the n we computed.
var shouldEnter = 0;
if (direction == 0)
{
shouldEnter = (int)ComingDistrubutions[my_index].PassIn;
if (shouldEnter - n >= 0)
{
ComingDistrubutions[my_index].PassIn -= n;
}
else
{
// in direction zero take from this collection
n = (int)ComingDistrubutions[my_index].PassIn;
ComingDistrubutions[my_index].PassIn = 0;
}
}
else
{
shouldEnter = (int)GoingDistrubutions[my_index].PassIn;
if (shouldEnter - n >= 0)
{
GoingDistrubutions[my_index].PassIn -= n;
}
else
{
// in direction 1 take from this collection
n = (int)GoingDistrubutions[my_index].PassIn;
GoingDistrubutions[my_index].PassIn = 0;
}
}
// Initialize an array to store the arrival times t_i, i = {1,2,..., n}.
var arrivals = new int[n];
// We will get the arrival times from the uniform distribution. U[0,T]
var last = 0;
if (TimeOfLastTram.HasValue)
{
last = TimeOfLastTram.Value;
}
var ud = new DiscreteUniform(last, time);
for (int i = 0; i < n; ++i)
{
arrivals[i] = ud.Sample();
}
// Compute the total waiting time for the new passengers given the array arrivals. we also need to keep track of the people already waiting.
var total_waiting_time = 0;
for (int i = 0; i < n; ++i)
{
total_waiting_time += time - arrivals[i];
}
TotalWaitingTime += total_waiting_time;
return(n);
}
static void Main(string[] args)
{
Poisson order_num = new Poisson(9.375);
Bernoulli corn_bread = new Bernoulli(0.56667);
Bernoulli orange_cake = new Bernoulli(0.26667);
Bernoulli choco_cake = new Bernoulli(0.46667);
Bernoulli three_milks = new Bernoulli(0.43333);
Bernoulli turkey = new Bernoulli(0.73333);
Bernoulli pork_leg = new Bernoulli(0.8);
int corn_total = 0;
int orange_total = 0;
int choco_total = 0;
int milk_total = 0;
int turkey_total = 0;
int pork_total = 0;
List <Order> orders = new List <Order>();
Console.WriteLine("Order List:\n-------------------------");
for (int i = 0; i <= order_num.Sample(); i++)
{
Console.WriteLine("Order #" + (i + 1) + ":");
orders.Add(new Order(new List <Food>()));
if (Convert.ToBoolean(corn_bread.Sample()))
{
Console.WriteLine("* Corn Bread");
orders[i].Add_item(new Food("Corn Bread", 50, "salt", 1));
corn_total++;
}
if (Convert.ToBoolean(orange_cake.Sample()))
{
Console.WriteLine("* Orange Cake");
orders[i].Add_item(new Food("Orange Cake", 60, "sweet", 1));
orange_total++;
}
if (Convert.ToBoolean(choco_cake.Sample()))
{
Console.WriteLine("* Chocolate Cake");
orders[i].Add_item(new Food("Chocolate Cake", 60, "sweet", 1));
choco_total++;
}
if (Convert.ToBoolean(three_milks.Sample()))
{
Console.WriteLine("* Tres Leches");
orders[i].Add_item(new Food("Tres Leches", 40, "sweet", 1));
milk_total++;
}
if (Convert.ToBoolean(turkey.Sample()))
{
Console.WriteLine("* Turkey");
orders[i].Add_item(new Food("Turkey", 270, "salt", 2));
turkey_total++;
}
if (Convert.ToBoolean(pork_leg.Sample()))
{
Console.WriteLine("* Pork Leg");
orders[i].Add_item(new Food("Pork Leg", 270, "salt", 2));
pork_total++;
}
Console.WriteLine();
}
Console.WriteLine("\nTotals:\n-------------------------");
Console.WriteLine("* Corn Bread: " + corn_total);
Console.WriteLine("* Orange Cake: " + orange_total);
Console.WriteLine("* Chocolate Cake: " + choco_total);
Console.WriteLine("* Tres Leches: " + milk_total);
Console.WriteLine("* Turkey: " + turkey_total);
Console.WriteLine("* Pork Leg: " + pork_total);
Console.WriteLine("\n\n3-slot Oven Test:\n-------------------------");
//3-spot oven test
Food slot_1 = new Food("", 0, "", 1);
Food slot_2 = new Food("", 0, "", 1);
Food slot_3 = new Food("", 0, "", 1);
double option_a_time = 0;
int next_step = 0;
while (turkey_total > 0 || pork_total > 0 || corn_total > 0 || orange_total > 0 || choco_total > 0 || milk_total > 0 || next_step != 0)
{
if (slot_1.Cooktime_left == 0 && slot_1.Type != "")
{
Console.WriteLine(slot_1.Name + " comes out of Slot 1");
slot_1 = new Food("", 0, "", 1);
}
if (slot_2.Cooktime_left == 0 && slot_2.Type != "")
{
Console.WriteLine(slot_2.Name + " comes out of Slot 2");
slot_2 = new Food("", 0, "", 1);
}
if (slot_3.Cooktime_left == 0 && slot_3.Type != "")
{
Console.WriteLine(slot_3.Name + " comes out of Slot 3");
slot_3 = new Food("", 0, "", 1);
}
if (turkey_total > 0)
{
if (turkey_total > 0 && slot_1.Type == "" && slot_2.Type == "" && slot_3.Type != "sweet")
{
turkey_total--;
slot_1 = new Food("Turkey A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 1. " + turkey_total + " turkeys still left to cook...");
slot_2 = new Food("Turkey B", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Slot 2. " + turkey_total + " turkeys still left to cook...");
}
if (turkey_total > 0 && slot_1.Type != "sweet" && slot_2.Type == "" && slot_3.Type == "")
{
turkey_total--;
slot_2 = new Food("Turkey A", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Slot 2. " + turkey_total + " turkeys still left to cook...");
slot_3 = new Food("Turkey B", 270, "salt", 2);
Console.WriteLine(slot_3.Name + " goes into Slot 3. " + turkey_total + " turkeys still left to cook...");
}
if (turkey_total > 0 && slot_1.Type == "" && slot_2.Type == "salt" && slot_3.Type == "")
{
slot_3 = slot_2;
slot_2 = new Food("", 0, "", 1);
Console.WriteLine(slot_3.Name + " was swapped over from slot 2 to slot 3");
turkey_total--;
slot_1 = new Food("Turkey A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 1. " + turkey_total + " turkeys still left to cook...");
slot_2 = new Food("Turkey B", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 2. " + turkey_total + " turkeys still left to cook...");
}
}
if (pork_total > 0)
{
if (pork_total > 0 && slot_1.Type == "" && slot_2.Type == "" && slot_3.Type != "sweet")
{
pork_total--;
slot_1 = new Food("Pork Leg A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 1. " + pork_total + " pork legs still left to cook...");
slot_2 = new Food("Pork Leg B", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Slot 2. " + pork_total + " pork legs still left to cook...");
}
if (pork_total > 0 && slot_1.Type != "sweet" && slot_2.Type == "" && slot_3.Type == "")
{
pork_total--;
slot_2 = new Food("Pork Leg A", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Slot 2. " + pork_total + " pork legs still left to cook...");
slot_3 = new Food("Pork Leg B", 270, "salt", 2);
Console.WriteLine(slot_3.Name + " goes into Slot 3. " + pork_total + " pork legs still left to cook...");
}
if (pork_total > 0 && slot_1.Type == "" && slot_2.Type == "salt" && slot_3.Type == "")
{
slot_3 = slot_2;
slot_2 = new Food("", 0, "", 1);
Console.WriteLine(slot_3.Name + " was swapped over from slot 2 to slot 3");
pork_total--;
slot_1 = new Food("Pork Leg A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 1. " + pork_total + " pork legs still left to cook...");
slot_2 = new Food("Pork Leg B", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Slot 2. " + pork_total + " pork legs still left to cook...");
}
}
if (corn_total > 0)
{
if (corn_total > 0 && slot_1.Type == "" && slot_2.Type != "sweet" && slot_3.Type != "sweet")
{
corn_total--;
slot_1 = new Food("Corn Bread", 50, "salt", 1);
Console.WriteLine("Corn Bread goes into Slot 1. " + corn_total + " corn breads still left to cook...");
}
if (corn_total > 0 && slot_1.Type != "sweet" && slot_2.Type == "" && slot_3.Type != "sweet")
{
corn_total--;
slot_2 = new Food("Corn Bread", 50, "salt", 1);
Console.WriteLine("Corn Bread goes into Slot 2. " + corn_total + " corn breads still left to cook...");
}
if (corn_total > 0 && slot_1.Type != "sweet" && slot_2.Type != "sweet" && slot_3.Type == "")
{
corn_total--;
slot_3 = new Food("Corn Bread", 50, "salt", 1);
Console.WriteLine("Corn Bread goes into Slot 3. " + corn_total + " corn breads still left to cook...");
}
}
if (orange_total > 0)
{
if (orange_total > 0 && slot_1.Type == "" && slot_2.Type != "salt" && slot_3.Type != "salt")
{
orange_total--;
slot_1 = new Food("Orange Cake", 60, "sweet", 1);
Console.WriteLine("Orange Cake goes into Slot 1. " + orange_total + " orange cakes still left to cook...");
}
if (orange_total > 0 && slot_1.Type != "salt" && slot_2.Type == "" && slot_3.Type != "salt")
{
orange_total--;
slot_2 = new Food("Orange Cake", 60, "sweet", 1);
Console.WriteLine("Orange Cake goes into Slot 2. " + orange_total + " orange cakes still left to cook...");
}
if (orange_total > 0 && slot_1.Type != "salt" && slot_2.Type != "salt" && slot_3.Type == "")
{
orange_total--;
slot_3 = new Food("Orange Cake", 60, "sweet", 1);
Console.WriteLine("Orange Cake goes into Slot 3. " + orange_total + " orange cakes still left to cook...");
}
}
if (choco_total > 0)
{
if (choco_total > 0 && slot_1.Type == "" && slot_2.Type != "salt" && slot_3.Type != "salt")
{
choco_total--;
slot_1 = new Food("Chocolate Cake", 60, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Slot 1. " + choco_total + " chocolate cakes still left to cook...");
}
if (choco_total > 0 && slot_1.Type != "salt" && slot_2.Type == "" && slot_3.Type != "salt")
{
choco_total--;
slot_2 = new Food("Chocolate Cake", 60, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Slot 2. " + choco_total + " chocolate cakes still left to cook...");
}
if (choco_total > 0 && slot_1.Type != "salt" && slot_2.Type != "salt" && slot_3.Type == "")
{
choco_total--;
slot_3 = new Food("Chocolate Cake", 60, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Slot 3. " + choco_total + " chocolate cakes still left to cook...");
}
}
if (milk_total > 0)
{
if (milk_total > 0 && slot_1.Type == "" && slot_2.Type != "salt" && slot_3.Type != "salt")
{
milk_total--;
slot_1 = new Food("Tres Leches", 40, "sweet", 1);
Console.WriteLine("Tres Leches goes into Slot 1. " + milk_total + " tres leches still left to cook...");
}
if (milk_total > 0 && slot_1.Type != "salt" && slot_2.Type == "" && slot_3.Type != "salt")
{
milk_total--;
slot_2 = new Food("Tres Leches", 40, "sweet", 1);
Console.WriteLine("Tres Leches goes into Slot 2. " + milk_total + " tres leches still left to cook...");
}
if (milk_total > 0 && slot_1.Type != "salt" && slot_2.Type != "salt" && slot_3.Type == "")
{
milk_total--;
slot_3 = new Food("Tres Leches", 40, "sweet", 1);
Console.WriteLine("Tres Leches goes into Slot 3. " + milk_total + " tres leches still left to cook...");
}
}
next_step = Convert.ToInt16(slot_1.Cooktime_left);
if (next_step > slot_2.Cooktime_left && slot_2.Cooktime_left != 0)
{
next_step = Convert.ToInt16(slot_2.Cooktime_left);
}
if (next_step > slot_3.Cooktime_left && slot_3.Cooktime_left != 0)
{
next_step = Convert.ToInt16(slot_3.Cooktime_left);
}
if (slot_1.Cooktime_left > 0)
{
slot_1.Cooktime_left = slot_1.Cooktime_left - next_step;
}
if (slot_2.Cooktime_left > 0)
{
slot_2.Cooktime_left = slot_2.Cooktime_left - next_step;
}
if (slot_3.Cooktime_left > 0)
{
slot_3.Cooktime_left = slot_3.Cooktime_left - next_step;
}
option_a_time = option_a_time + next_step;
Console.WriteLine(next_step + " minutes pass...");
}
Console.WriteLine("Total Cook-time using 3-slot Oven: " + option_a_time + " Minutes (" + (option_a_time / 60) + " Hours or " + (option_a_time / 60 / 24) + " Days)");
for (int i = 0; i < orders.Count; i++)
{
for (int j = 0; j < orders[i].Contents.Count; j++)
{
switch (orders[i].Contents[j].Name)
{
case "Turkey":
turkey_total++;
break;
case "Pork Leg":
pork_total++;
break;
case "Corn Bread":
corn_total++;
break;
case "Orange Cake":
orange_total++;
break;
case "Chocolate Cake":
choco_total++;
break;
case "Tres Leches":
milk_total++;
break;
default:
break;
}
}
}
//Secondhand Oven Test
Console.WriteLine("\n\nSecondhand Oven Test:\n-------------------------");
Food slot_4 = new Food("", 0, "", 1);
double option_b_time = 0;
while (turkey_total > 0 || pork_total > 0 || corn_total > 0 || orange_total > 0 || choco_total > 0 || milk_total > 0 || next_step != 0)
{
if (slot_1.Cooktime_left == 0 && slot_1.Type != "")
{
Console.WriteLine(slot_1.Name + " comes out of Regular Oven Slot 1");
slot_1 = new Food("", 0, "", 1);
}
if (slot_2.Cooktime_left == 0 && slot_2.Type != "")
{
Console.WriteLine(slot_2.Name + " comes out of Regular Oven Slot 2");
slot_2 = new Food("", 0, "", 1);
}
if (slot_3.Cooktime_left == 0 && slot_3.Type != "")
{
Console.WriteLine(slot_3.Name + " comes out of Secondhand Oven Slot 1");
slot_3 = new Food("", 0, "", 1);
}
if (slot_4.Cooktime_left == 0 && slot_4.Type != "")
{
Console.WriteLine(slot_4.Name + " comes out of Secondhand Oven Slot 2");
slot_4 = new Food("", 0, "", 1);
}
if (turkey_total > 0)
{
if (turkey_total > 0 && ((slot_1.Type == "" && slot_2.Type != "sweet") || (slot_2.Type == "" && slot_1.Type != "sweet")))
{
turkey_total--;
slot_1 = new Food("Turkey A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Regular Oven Slot 1. " + turkey_total + " turkeys still left to cook...");
slot_2 = new Food("Turkey B", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Regular Oven Slot 2. " + turkey_total + " turkeys still left to cook...");
}
if (turkey_total > 0 && ((slot_3.Type != "sweet" && slot_4.Type == "") || (slot_4.Type != "sweet" && slot_3.Type == "")))
{
turkey_total--;
slot_3 = new Food("Turkey A", 290, "salt", 2);
Console.WriteLine(slot_3.Name + " goes into Secondhand Oven Slot 1. " + turkey_total + " turkeys still left to cook...");
slot_4 = new Food("Turkey B", 290, "salt", 2);
Console.WriteLine(slot_4.Name + " goes into Secondhand Oven Slot 2. " + turkey_total + " turkeys still left to cook...");
}
}
if (pork_total > 0)
{
if (pork_total > 0 && ((slot_1.Type == "" && slot_2.Type != "sweet") || (slot_2.Type == "" && slot_1.Type != "sweet")))
{
pork_total--;
slot_1 = new Food("Pork Leg A", 270, "salt", 2);
Console.WriteLine(slot_1.Name + " goes into Regular Oven Slot 1. " + pork_total + " pork legs still left to cook...");
slot_2 = new Food("Pork Leg B", 270, "salt", 2);
Console.WriteLine(slot_2.Name + " goes into Regular Oven Slot 2. " + pork_total + " pork legs still left to cook...");
}
if (pork_total > 0 && ((slot_3.Type != "sweet" && slot_4.Type == "") || (slot_4.Type != "sweet" && slot_3.Type == "")))
{
pork_total--;
slot_3 = new Food("Pork Leg A", 290, "salt", 2);
Console.WriteLine(slot_3.Name + " goes into Secondhand Oven Slot 1. " + pork_total + " pork legs still left to cook...");
slot_4 = new Food("Pork Leg B", 290, "salt", 2);
Console.WriteLine(slot_4.Name + " goes into Secondhand Oven Slot 2. " + pork_total + " pork legs still left to cook...");
}
}
if (corn_total > 0)
{
if (corn_total > 0 && slot_1.Type == "" && slot_2.Type != "sweet")
{
corn_total--;
slot_1 = new Food("Corn Bread", 50, "salt", 1);
Console.WriteLine("Corn Bread goes into Regular Oven Slot 1. " + corn_total + " corn breads still left to cook...");
}
if (corn_total > 0 && slot_1.Type != "sweet" && slot_2.Type == "")
{
corn_total--;
slot_2 = new Food("Corn Bread", 50, "salt", 1);
Console.WriteLine("Corn Bread goes into Regular Oven Slot 2. " + corn_total + " corn breads still left to cook...");
}
if (corn_total > 0 && slot_3.Type == "" && slot_4.Type != "sweet")
{
corn_total--;
slot_3 = new Food("Corn Bread", 70, "salt", 1);
Console.WriteLine("Corn Bread goes into Secondhand Oven Slot 1. " + corn_total + " corn breads still left to cook...");
}
if (corn_total > 0 && slot_3.Type != "sweet" && slot_4.Type == "")
{
corn_total--;
slot_3 = new Food("Corn Bread", 70, "salt", 1);
Console.WriteLine("Corn Bread goes into Secondhand Oven Slot 2. " + corn_total + " corn breads still left to cook...");
}
}
if (orange_total > 0)
{
if (orange_total > 0 && slot_1.Type == "" && slot_2.Type != "salt")
{
orange_total--;
slot_1 = new Food("Orange Cake", 60, "sweet", 1);
Console.WriteLine("Orange Cake goes into Regular Oven Slot 1. " + orange_total + " orange cakes still left to cook...");
}
if (orange_total > 0 && slot_1.Type != "salt" && slot_2.Type == "")
{
orange_total--;
slot_2 = new Food("Orange Cake", 60, "sweet", 1);
Console.WriteLine("Orange Cake goes into Regular Oven Slot 2. " + orange_total + " orange cakes still left to cook...");
}
if (orange_total > 0 && slot_3.Type == "" && slot_4.Type != "salt")
{
orange_total--;
slot_3 = new Food("Orange Cake", 80, "sweet", 1);
Console.WriteLine("Orange Cake goes into Secondhand Oven Slot 1. " + orange_total + " orange cakes still left to cook...");
}
if (orange_total > 0 && slot_4.Type == "" && slot_3.Type != "salt")
{
orange_total--;
slot_4 = new Food("Orange Cake", 80, "sweet", 1);
Console.WriteLine("Orange Cake goes into Secondhand Oven Slot 2. " + orange_total + " orange cakes still left to cook...");
}
}
if (choco_total > 0)
{
if (choco_total > 0 && slot_1.Type == "" && slot_2.Type != "salt")
{
choco_total--;
slot_1 = new Food("Chocolate Cake", 60, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Regular Oven Slot 1. " + choco_total + " chocolate cakes still left to cook...");
}
if (choco_total > 0 && slot_1.Type != "salt" && slot_2.Type == "")
{
choco_total--;
slot_2 = new Food("Chocolate Cake", 60, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Regular Oven Slot 2. " + choco_total + " chocolate cakes still left to cook...");
}
if (choco_total > 0 && slot_3.Type == "" && slot_4.Type != "salt")
{
choco_total--;
slot_3 = new Food("Chocolate Cake", 80, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Secondhand Oven Slot 1. " + choco_total + " chocolate cakes still left to cook...");
}
if (choco_total > 0 && slot_4.Type == "" && slot_3.Type != "salt")
{
choco_total--;
slot_4 = new Food("Chocolate Cake", 80, "sweet", 1);
Console.WriteLine("Chocolate Cake goes into Secondhand Oven Slot 2. " + choco_total + " chocolate cakes still left to cook...");
}
}
if (milk_total > 0)
{
if (milk_total > 0 && slot_1.Type == "" && slot_2.Type != "salt")
{
milk_total--;
slot_1 = new Food("Tres Leches", 40, "sweet", 1);
Console.WriteLine("Tres Leches goes into Regular Oven Slot 1. " + milk_total + " tres leches still left to cook...");
}
if (milk_total > 0 && slot_1.Type != "salt" && slot_2.Type == "")
{
milk_total--;
slot_2 = new Food("Tres Leches", 40, "sweet", 1);
Console.WriteLine("Tres Leches goes into Regular Oven Slot 2. " + milk_total + " tres leches still left to cook...");
}
if (milk_total > 0 && slot_3.Type == "" && slot_4.Type != "salt")
{
milk_total--;
slot_3 = new Food("Tres Leches", 60, "sweet", 1);
Console.WriteLine("Tres Leches goes into Secondhand Oven Slot 1. " + milk_total + " tres leches still left to cook...");
}
if (milk_total > 0 && slot_4.Type == "" && slot_3.Type != "salt")
{
milk_total--;
slot_4 = new Food("Tres Leches", 60, "sweet", 1);
Console.WriteLine("Tres Leches goes into Secondhand Oven Slot 2. " + milk_total + " tres leches still left to cook...");
}
}
next_step = Convert.ToInt16(slot_1.Cooktime_left);
if (next_step > slot_2.Cooktime_left && slot_2.Cooktime_left != 0)
{
next_step = Convert.ToInt16(slot_2.Cooktime_left);
}
if (next_step > slot_3.Cooktime_left && slot_3.Cooktime_left != 0)
{
next_step = Convert.ToInt16(slot_3.Cooktime_left);
}
if (slot_1.Cooktime_left > 0)
{
slot_1.Cooktime_left = slot_1.Cooktime_left - next_step;
}
if (slot_2.Cooktime_left > 0)
{
slot_2.Cooktime_left = slot_2.Cooktime_left - next_step;
}
if (slot_3.Cooktime_left > 0)
{
slot_3.Cooktime_left = slot_3.Cooktime_left - next_step;
}
if (slot_4.Cooktime_left > 0)
{
slot_4.Cooktime_left = slot_4.Cooktime_left - next_step;
}
option_b_time = option_b_time + next_step;
Console.WriteLine(next_step + " minutes pass...");
}
Console.WriteLine("Total Cook-time using 2 2-slot Ovens: " + option_b_time + " Minutes (" + (option_b_time / 60) + " Hours or " + (option_b_time / 60 / 24) + " Days)\n\n");
Console.WriteLine("Conclusions:\n-------------------------");
Console.WriteLine("Time using 3-slot Oven: " + option_a_time + " Minutes");
Console.WriteLine("Time using a Secondhand Oven: " + option_b_time + " Minutes");
double difference = Math.Abs(option_a_time - option_b_time);
Console.WriteLine("Difference: " + difference + " Minutes");
Console.Read();
}
static uint serverCapacity = 5816; // Number of simulataneous requests a server can handle
static void Main(string[] args)
{
for (uint requestCount = requestsPerMonthMin; requestCount < requestsPerMonthMax; requestCount += requestsPerMonthStep)
{
double avgRequestRate = (requestCount * 2d) / (60 * 60 * 9 * 21); // Average requests per second
// Initialize a Matrix Representing Available Capacity
List <double>[] serverUsage = new List <double> [serverCount];
for (int i = 0; i < serverCount; i++)
{
serverUsage[i] = new List <double>();
}
// Initialize Request Distributions
Poisson arrivalDist = new Poisson(avgRequestRate);
// Initialize Variables for Simulating Round-Robin Load Balancing
int nextServerToUse = 0; // Next Server to Receive a Request
// Initialize Measurement Variables
int requestsRejectedByServer = 0;
int totalRequestsInSimulation = 0;
// Iterate through Simulation Rounds
for (int round = 0; round < simulationTime; round++)
{
// Add New Requests to the Servers
int requestsThisRound = arrivalDist.Sample();
totalRequestsInSimulation += requestsThisRound;
for (int request = 0; request < requestsThisRound; request++)
{
// Determine if the Next Server can handle the Request
double requestServiceTime = avgServiceTime;
if (serverUsage[nextServerToUse].Count >= serverCapacity)
{
requestsRejectedByServer++;
}
else
{
serverUsage[nextServerToUse].Add(requestServiceTime);
}
// Use Round-Robin to Select Next Server
if (nextServerToUse == serverCount - 1)
{
nextServerToUse = 0;
}
else
{
nextServerToUse++;
}
}
for (int server = 0; server < serverCount; server++)
{
// Calculate Remaining Time needed for Requests
for (int request = 0; request < serverUsage[server].Count; request++)
{
serverUsage[server][request] -= 1d;
}
// Remove Completed Requests from the Servers
serverUsage[server].RemoveAll(requestTime => requestTime <= 0);
}
// Render Output
if (round % (int)Math.Round(simulationTime / 10d) == 0)
{
Console.SetCursorPosition(0, 0);
Console.ResetColor();
Console.WriteLine("Round {0}/{1} ({2}%)", round, simulationTime,
Math.Round((double)round * 100d / (double)simulationTime, 2));
for (int server = 0; server < serverCount; server++)
{
if (serverUsage[server].Count >= serverCapacity)
{
Console.ForegroundColor = ConsoleColor.Red;
}
else
{
Console.ForegroundColor = ConsoleColor.Green;
}
Console.WriteLine("Server {0}: [{1}]", server.ToString(),
String.Empty
.PadRight(serverUsage[server].Count, '|')
.PadRight((int)serverCapacity));
}
}
}
Console.WriteLine("Testing Monthly Request Count: " + requestCount);
Console.WriteLine("Simulated Request Count: " + totalRequestsInSimulation);
Console.WriteLine("Requests Rejected By Server: " + requestsRejectedByServer);
if (requestsRejectedByServer > 0)
{
Console.WriteLine("Found a Rejection at {0} Requests/Mo.", requestCount);
Console.ReadKey(true);
}
totalRequestsInSimulation = requestsRejectedByServer = 0;
// Console.ReadKey(true);
}
}
// Generate toy data - returns indices into vocab for each doc
private int[][] GenerateToyLDAData(
int numTopics, int numVocab, int numDocs, int expectedDocLength,
out Dirichlet[] trueTheta, out Dirichlet[] truePhi)
{
truePhi = new Dirichlet[numTopics];
for (int i = 0; i < numTopics; i++)
{
truePhi[i] = Dirichlet.Uniform(numVocab);
truePhi[i].PseudoCount.SetAllElementsTo(0.0);
// Draw the number of unique words in the topic
int numUniqueWordsPerTopic = Poisson.Sample((double)numVocab / numTopics);
if (numUniqueWordsPerTopic >= numVocab)
{
numUniqueWordsPerTopic = numVocab;
}
double expectedRepeatOfWordInTopic =
((double)numDocs) * expectedDocLength / numUniqueWordsPerTopic;
int[] shuffledWordIndices = Rand.Perm(numVocab);
for (int j = 0; j < numUniqueWordsPerTopic; j++)
{
int wordIndex = shuffledWordIndices[j];
// Draw the count for that word
int cnt = Poisson.Sample(expectedRepeatOfWordInTopic);
truePhi[i].PseudoCount[wordIndex] = cnt + 1.0;
}
}
trueTheta = new Dirichlet[numDocs];
for (int i = 0; i < numDocs; i++)
{
trueTheta[i] = Dirichlet.Uniform(numTopics);
trueTheta[i].PseudoCount.SetAllElementsTo(0.0);
// Draw the number of unique topics in the doc. We expect this to be
// very sparse
int numUniqueTopicsPerDoc = System.Math.Min(1 + Poisson.Sample(1.0), numTopics);
double expectedRepeatOfTopicInDoc =
expectedDocLength / numUniqueTopicsPerDoc;
int[] shuffledTopicIndices = Rand.Perm(numTopics);
for (int j = 0; j < numUniqueTopicsPerDoc; j++)
{
int topicIndex = shuffledTopicIndices[j];
// Draw the count for that topic
int cnt = Poisson.Sample(expectedRepeatOfTopicInDoc);
trueTheta[i].PseudoCount[topicIndex] = cnt + 1.0;
}
}
// Sample from the model
Vector[] topicDist = new Vector[numDocs];
Vector[] wordDist = new Vector[numTopics];
for (int i = 0; i < numDocs; i++)
{
topicDist[i] = trueTheta[i].Sample();
}
for (int i = 0; i < numTopics; i++)
{
wordDist[i] = truePhi[i].Sample();
}
int[][] wordsInDoc = new int[numDocs][];
for (int i = 0; i < numDocs; i++)
{
int LengthOfDoc = Poisson.Sample((double)expectedDocLength);
wordsInDoc[i] = new int[LengthOfDoc];
for (int j = 0; j < LengthOfDoc; j++)
{
int topic = Discrete.Sample(topicDist[i]);
wordsInDoc[i][j] = Discrete.Sample(wordDist[topic]);
}
}
return(wordsInDoc);
}
public void GeneratePackage()
{
_package = Poisson.Sample();
}
public void CanSample()
{
var d = new Poisson(0.3);
var s = d.Sample();
}
