// Update is called once per frame
void Update()
{
timeToNextMonster -= Time.deltaTime;
if (timeToNextMonster < 0 && !monster.active)
{
cenario = GenerateRandoms.cenarioSelecionado;
//monster.GetComponent<Animator>().Play(cenario.ToString());
timeToNextMonster = cosine(4, 8);
double[] probs = new double[4];
probs[0] = 0.4;
probs[1] = 0.3;
probs[2] = 0.2;
probs[3] = 0.1;
typeOfMonster = Categorical.Sample(probs);
monster.SetActive(true);
monster.transform.position = new Vector3(Camera.main.transform.position.x + 5, transform.position.y, transform.position.z);
// monster.GetComponent<SpriteRenderer>().sprite = Resources.Load<Sprite>("inimigos/" + cenario.ToString() + "/monstro" + typeOfMonster);
monster.GetComponent <Animator>().Play(cenario.ToString() + typeOfMonster.ToString());
Debug.Log(cenario.ToString() + typeOfMonster.ToString());
}
}
public void TrainModel3()
{
var trainer = BrightWireProvider.CreateMarkovTrainer3 <string>();
_Train(trainer);
var model = trainer.Build().AsDictionary;
// generate some text
var rand = new Random();
string prevPrev = default(string), prev = default(string), curr = default(string);
var output = new List <string>();
for (var i = 0; i < 1024; i++)
{
var transitions = model.GetTransitions(prevPrev, prev, curr);
var distribution = new Categorical(transitions.Select(d => Convert.ToDouble(d.Probability)).ToArray());
var next = transitions[distribution.Sample()].NextState;
output.Add(next);
if (SimpleTokeniser.IsEndOfSentence(next))
{
break;
}
prevPrev = prev;
prev = curr;
curr = next;
}
Assert.IsTrue(output.Count < 1024);
}
public static T uniformFromArray <T>(T[] items)
{
double[] weights = (from i in Enumerable.Range(1, items.Length) select 1.0d).ToArray();
int idx = Categorical.Sample(weights);
return(items[idx]);
}
/// <summary>
/// sample negative level non-uniforly with respect to the frequency items in the level and importance of level
/// </summary>
/// <param name="allowedLevels">The levels that user has items in</param>
/// <returns></returns>
protected virtual int SampleNegativeLevel(List <int> allowedLevels)
{
int sum = 0;
foreach (int level in allowedLevels)
{
sum += LevelPosFeedback[level].Count * level;
}
double[] probs = new double[allowedLevels.Count + 1];
for (int i = 0; i < allowedLevels.Count; i++)
{
probs[i] = (1 - UnobservedRatio) * allowedLevels[i] * LevelPosFeedback[allowedLevels[i]].Count / sum;
}
probs[allowedLevels.Count] = UnobservedRatio;
int levelIndex = new Categorical(probs).Sample();
if (levelIndex == allowedLevels.Count)
{
return(0);
}
return(allowedLevels[levelIndex]);
}
public void ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var b = new Categorical(_smallP);
Assert.AreEqual("Categorical(Dimension = 3)", b.ToString());
}
public void TrainModel2()
{
var trainer = BrightWireProvider.CreateMarkovTrainer2 <string>();
_Train(trainer);
// test serialisation/deserialisation
using (var buffer = new MemoryStream()) {
trainer.SerialiseTo(buffer);
buffer.Position = 0;
trainer.DeserialiseFrom(buffer, true);
}
var dictionary = trainer.Build().AsDictionary;
// generate some text
var rand = new Random();
string prev = default(string), curr = default(string);
var output = new List <string>();
for (var i = 0; i < 1024; i++)
{
var transitions = dictionary.GetTransitions(prev, curr);
var distribution = new Categorical(transitions.Select(d => Convert.ToDouble(d.Probability)).ToArray());
var next = transitions[distribution.Sample()].NextState;
output.Add(next);
if (SimpleTokeniser.IsEndOfSentence(next))
{
break;
}
prev = curr;
curr = next;
}
Assert.IsTrue(output.Count < 1024);
}
public KMeans(int k, IReadOnlyList <IVector> data, DistanceMetric distanceMetric = DistanceMetric.Euclidean, int?randomSeed = null)
{
_k = k;
_distanceMetric = distanceMetric;
_cluster = new ClusterData();
_data = data;
// use kmeans++ to find best initial positions
// https://normaldeviate.wordpress.com/2012/09/30/the-remarkable-k-means/
var rand = randomSeed.HasValue ? new Random(randomSeed.Value) : new Random();
var data2 = data.ToList();
// pick the first at random
var firstIndex = rand.Next(0, data2.Count);
_cluster.Add(data2[firstIndex]);
data2.RemoveAt(firstIndex);
// create a categorical distribution for each subsequent pick
for (var i = 1; i < _k && data2.Count > 0; i++)
{
var probabilityList = new List <double>();
foreach (var item in data2)
{
using (var distance = _cluster.CalculateDistance(item, _distanceMetric)) {
var minIndex = distance.MinimumIndex();
probabilityList.Add(distance.AsIndexable()[minIndex]);
}
}
var distribution = new Categorical(probabilityList.ToArray());
var nextIndex = distribution.Sample();
_cluster.Add(data2[nextIndex]);
data2.RemoveAt(nextIndex);
}
}
/// <summary>
/// Creates a new split-set validation algorithm.
/// </summary>
///
/// <param name="size">The total number of available samples.</param>
/// <param name="proportion">The desired proportion of samples in the training
/// set in comparison with the testing set.</param>
///
public SplitSetValidation(int size, double proportion)
{
this.Proportion = proportion;
this.IsStratified = false;
this.Indices = Categorical.Random(size, proportion);
this.ValidationSet = Indices.Find(x => x == 0);
this.TrainingSet = Indices.Find(x => x == 1);
}
private Multinomial(int total_count, Categorical categorical, torch.Generator generator = null) : base(generator)
{
this.total_count = total_count;
this.categorical = categorical;
this.batch_shape = categorical.batch_shape;
var ps = categorical.param_shape;
this.event_shape = new long[] { ps[ps.Length - 1] };
}
private Multinomial(int total_count, Categorical categorical)
{
this.total_count = total_count;
this.categorical = categorical;
this.batch_shape = categorical.batch_shape;
var ps = categorical.param_shape;
this.event_shape = new long[] { ps[ps.Length - 1] };
}
public void StartEpoch()
{
var distribution = new Categorical(_weightFunc().Select(d => Convert.ToDouble(d)).ToArray());
_rowMap.Clear();
for (int i = 0, len = _dataProvider.Count; i < len; i++)
{
_rowMap[i] = distribution.Sample();
}
}
public void CanCreateCategoricalFromHistogram()
{
double[] smallDataset = { 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5 };
Histogram hist = new Histogram(smallDataset, 10, 0.0, 10.0);
var m = new Categorical(hist);
for (int i = 0; i <= m.Maximum; i++)
{
AssertEx.AreEqual<double>(1.0/10.0, m.P[i]);
}
}
public RandomProjection(ILinearAlgebraProvider lap, int fixedSize, int reducedSize, int s = 3)
{
_lap = lap;
_fixedSize = fixedSize;
_reducedSize = reducedSize;
var c1 = Math.Sqrt(3);
var distribution = new Categorical(new[] { 1.0 / (2 * s), 1 - (1.0 / s), 1.0 / (2 * s) });
_matrix = _lap.CreateMatrix(fixedSize, reducedSize, (i, j) => Convert.ToSingle((distribution.Sample() - 1) * c1));
}
public void CanCreateCategoricalFromHistogram()
{
double[] smallDataset = { 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5 };
var hist = new Histogram(smallDataset, 10, 0.0, 10.0);
var m = new Categorical(hist);
for (var i = 0; i <= m.Maximum; i++)
{
Assert.AreEqual(1.0 / 10.0, m.P[i]);
}
}
IReadOnlyList <int> _GetNextSamples()
{
var distribution = new Categorical(_rowWeight.Select(d => Convert.ToDouble(d)).ToArray());
var ret = new List <int>();
for (int i = 0, len = _rowWeight.Length; i < len; i++)
{
ret.Add(distribution.Sample());
}
return(ret.OrderBy(v => v).ToList());
}
/// <summary>
/// Builds a n-gram based language model and generates new text from the model
/// </summary>
public static void MarkovChains()
{
// tokenise the novel "The Beautiful and the Damned" by F. Scott Fitzgerald
List <IReadOnlyList <string> > sentences;
using (var client = new WebClient()) {
var data = client.DownloadString("http://www.gutenberg.org/cache/epub/9830/pg9830.txt");
var pos = data.IndexOf("CHAPTER I");
sentences = SimpleTokeniser.FindSentences(SimpleTokeniser.Tokenise(data.Substring(pos))).ToList();
}
// create a markov trainer that uses a window of size 3
var trainer = BrightWireProvider.CreateMarkovTrainer3 <string>();
foreach (var sentence in sentences)
{
trainer.Add(sentence);
}
var model = trainer.Build().AsDictionary;
// generate some text
var rand = new Random();
for (var i = 0; i < 50; i++)
{
var sb = new StringBuilder();
string prevPrev = default(string), prev = default(string), curr = default(string);
for (var j = 0; j < 256; j++)
{
var transitions = model.GetTransitions(prevPrev, prev, curr);
var distribution = new Categorical(transitions.Select(d => Convert.ToDouble(d.Probability)).ToArray());
var next = transitions[distribution.Sample()].NextState;
if (Char.IsLetterOrDigit(next[0]) && sb.Length > 0)
{
var lastChar = sb[sb.Length - 1];
if (lastChar != '\'' && lastChar != '-')
{
sb.Append(' ');
}
}
sb.Append(next);
if (SimpleTokeniser.IsEndOfSentence(next))
{
break;
}
prevPrev = prev;
prev = curr;
curr = next;
}
Console.WriteLine(sb.ToString());
}
}
public CategoricalPrimitive(T[] items, double[] probabilities, Random gen)
{
Gen = gen;
Items = items;
ProbabilityMass = probabilities;
categorical = new Categorical(ProbabilityMass, gen);
ItemIndex = new Dictionary <T, int>();
for (int c = 0; c < items.Length; c++)
{
ItemIndex.Add(items[c], c);
}
}
protected override void InitModel()
{
base.InitModel();
CacheSplitData();
// initialize dynamic sampler
_factorBasedRank = new Dictionary <int, List <string> >();
double[] rankingPro = new double[AllItems.Count];
double sum = 0;
for (int i = 0; i < AllItems.Count; i++)
{
rankingPro[i] = Math.Exp(-(i + 1.0) / Lambda);
sum += rankingPro[i];
}
for (int i = 0; i < AllItems.Count; i++)
{
rankingPro[i] /= sum;
}
_rankSampler = new Categorical(rankingPro);
_itemFactorsStdev = new float[NumFactors];
double[] levelPro = new double[PosLevels.Count];
double[] levelProUniFeedback = new double[PosLevels.Count];
// initialize dynamic level sampler
//if (PosSampler == PosSampler.DynamicLevel || PosSampler == PosSampler.AdaptedWeight)
{
// key is levelNo, value id list of feedback in that level
sum = LevelPosFeedback.Sum(kv => kv.Key * kv.Value.Count);
for (int i = 0; i < PosLevels.Count; i++)
{
levelPro[i] = 1.0f * PosLevels[i] * LevelPosFeedback[PosLevels[i]].Count / sum;
levelProUniFeedback[i] = 1 / sum;
}
_posLevelSampler = new Categorical(levelPro);
_posLevelUniFeedbackSampler = new Categorical(levelProUniFeedback);
}
// this sampler specifies whether the negative sample should be sampled from observed or unobserved feedback
// the parameter of this distributio is parameter beta in Loni_RecSys2016
// with beta=1 always unobserved will be sampled but with beta = 0 always observed will be sampled (if possible)
_unobservedOrNegativeSampler = new Categorical(new double[] { (1 - UnobservedRatio), UnobservedRatio });
}
protected virtual void UpdatePosSampler()
{
double[] levelsAvg = new double[PosLevels.Count];
for (int i = 0; i < PosLevels.Count; i++)
{
foreach (Feedback f in LevelPosFeedback[PosLevels[i]])
{
int user_id = UsersMap.ToInternalID(f.User.Id);
int item_id = ItemsMap.ToInternalID(f.Item.Id);
levelsAvg[i] += MatrixExtensions.RowScalarProduct(user_factors, user_id, item_factors, item_id);
}
//Console.WriteLine(levelsAvg[i]);
levelsAvg[i] /= LevelPosFeedback[PosLevels[i]].Count;
}
double avgSum = levelsAvg.Sum();
double[] levelWeights = new double[PosLevels.Count];
for (int i = 0; i < PosLevels.Count; i++)
{
levelWeights[i] = levelsAvg[i] / avgSum;
}
double sum = 0;
for (int i = 0; i < PosLevels.Count; i++)
{
sum += levelWeights[i] * LevelPosFeedback[PosLevels[i]].Count;
}
double[] levelPros = new double[PosLevels.Count];
for (int i = 0; i < PosLevels.Count; i++)
{
levelPros[i] = levelWeights[i] * LevelPosFeedback[PosLevels[i]].Count / sum;
}
string weights = levelWeights.Select(p => string.Format("{0:0.00}", p)).Aggregate((a, b) => a + " " + b);
Logger.Current.Info(weights);
//var temp = SampledCount.Values.Take(10).Select(i => i.ToString()).Aggregate((a, b) => a + " " + b);
//Console.WriteLine(temp);
_posLevelSampler = new Categorical(levelPros);
}
public virtual Feedback SampleLeastPopularPosFeedback()
{
int user_id = SampleUser();
var user = Split.Container.Users[UsersMap.ToOriginalID(user_id)];
var userFeedback = user.Feedbacks.Where(f => f.SliceType == FeedbackSlice.TRAIN).ToList();
double[] itemProbs = userFeedback.Select(f => 1.0 / Math.Log(SampledCount[f.Item.Id] + 1, 2)).ToArray();
var fIx = new Categorical(itemProbs).Sample();
Feedback posFeedback = userFeedback[fIx];
SampledCount[posFeedback.Item.Id]++;
return(posFeedback);
}
protected virtual string SampleNegItemDynamic(Feedback posFeedback)
{
// sample r
int r;
do
{
r = _rankSampler.Sample();
} while (r >= AllItems.Count);
int user_id = UsersMap.ToInternalID(posFeedback.User.Id);
var u = user_factors.GetRow(user_id);
// sample f from p(f|c)
double sum = 0;
for (int i = 0; i < NumFactors; i++)
{
sum += Math.Abs(u[i]) * _itemFactorsStdev[i];
}
double[] probs = new double[NumFactors];
for (int i = 0; i < NumFactors; i++)
{
probs[i] = Math.Abs(u[i]) * _itemFactorsStdev[i] / sum;
}
int f = new Categorical(probs).Sample();
// take item j (negItemId) from position r of the list of sampled f
string negItemId;
if (Math.Sign(user_factors[user_id, f]) > 0)
{
negItemId = _factorBasedRank[f][r];
}
else
{
negItemId = _factorBasedRank[f][AllItems.Count - r - 1];
}
return(negItemId);
}
/// <summary>
/// Constructs a Multinomial Logistic Regression Analysis.
/// </summary>
///
/// <param name="inputs">The input data for the analysis.</param>
/// <param name="outputs">The output data for the analysis.</param>
///
public MultinomialLogisticRegressionAnalysis(double[][] inputs, int[] outputs)
{
// Initial argument checking
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (inputs.Length != outputs.Length)
{
throw new ArgumentException("The number of rows in the input array must match the number of given outputs.");
}
init(inputs, Categorical.OneHot(outputs));
}
/// <summary>
/// Processes the current filter.
/// </summary>
///
protected override DataTable ProcessFilter(DataTable data)
{
if (!lockGroups)
{
// Check if we should balance label proportions
if (Columns.Count == 0)
{
// No. Just generate assign groups at random
groupIndices = Categorical.Random(data.Rows.Count, Proportion);
}
else
{
// Yes, we must balance the occurrences in a data column
groupIndices = balancedGroups(data);
}
}
return(apply(data));
}
public List <Quiz> GetQuizCategory(List <Quiz> UnsortedList, QuizCategory category)
{//Function to return list of quiz questions based off of category selected by user
List <Quiz> Categorical = new List <Quiz>();
if (category == QuizCategory.All)
{
Categorical = UnsortedList;
}
else
{
foreach (Quiz quiz in UnsortedList)
{
if (quiz.Category == category)
{
Categorical.Add(quiz);
}
}
}
return(Categorical);
}
public IList <NeuralNetwork> ApplySelection(IList <GeneticAlgorithmAgent> agents)
{
int populationCount = agents.Count;
float fitnessSum = agents.Select(agent => agent.Fitness).Sum();
var probabilities = new double[populationCount];
for (int i = 0; i < populationCount; i++)
{
probabilities[i] = (double)agents[i].Fitness / (double)fitnessSum;
}
var distribution = new Categorical(probabilities);
var newPopulation = new List <NeuralNetwork>();
for (int i = 0; i < populationCount; i++)
{
newPopulation.Add(agents[distribution.Sample()].Network.Clone());
}
return(newPopulation);
}
/// <summary>
/// Runs the learning algorithm.
/// </summary>
///
/// <remarks>
/// Learning problem. Given some training observation sequences O = {o1, o2, ..., oK}
/// and general structure of HMM (numbers of hidden and visible states), determine
/// HMM parameters M = (A, B, pi) that best fit training data.
/// </remarks>
///
public double Run(params T[] observations)
{
convergence.Clear();
double newLogLikelihood = Double.NegativeInfinity;
int[][] paths = new int[observations.Length][];
do // Until convergence or max iterations is reached
{
if (batches == 1)
{
RunEpoch(observations, paths);
}
else
{
// Divide in batches
int[] groups = Categorical.Random(observations.Length, batches);
// For each batch
for (int j = 0; j < batches; j++)
{
var idx = groups.Find(x => x == j);
var inputs = observations.Submatrix(idx);
var outputs = paths.Submatrix(idx);
RunEpoch(inputs, outputs);
}
}
// Compute log-likelihood
newLogLikelihood = ComputeLogLikelihood(observations);
// Check convergence
convergence.NewValue = newLogLikelihood;
} while (!convergence.HasConverged);
return(newLogLikelihood);
}
// Update is called once per frame
void Update()
{
if (!item.active)
{
timeToNextItem -= Time.deltaTime;
if (timeToNextItem < 0)
{
timeToNextItem = cosine(30, 45);
double[] probs = new double[4];
probs[0] = 0.4;
probs[1] = 0.3;
probs[2] = 0.2;
probs[3] = 0.1;
int typeOfItem = Categorical.Sample(probs);
Camera cam = Camera.main;
float height = 2f * cam.orthographicSize;
float width = height * cam.aspect;
item.SetActive(true);
item.transform.position = new Vector3(cam.transform.position.x + 4, transform.position.y, transform.position.z);
if (typeOfItem == 0)
{
item.GetComponent <SpriteRenderer>().sprite = Resources.Load <Sprite>("Itens/potion");
}
else if (typeOfItem == 1)
{
item.GetComponent <SpriteRenderer>().sprite = Resources.Load <Sprite>("Itens/pill");
}
else if (typeOfItem == 2)
{
item.GetComponent <SpriteRenderer>().sprite = Resources.Load <Sprite>("Itens/medicine");
}
else if (typeOfItem == 3)
{
item.GetComponent <SpriteRenderer>().sprite = Resources.Load <Sprite>("Itens/backpack");
}
}
}
}
static void Main(string[] args)
{
int increment = 10; // how much inverse probabilities are updated per sample
int guidanceParameter = 1000000; // Small one - consequtive sampling is more affected by outcome. Large one - closer to uniform sampling
int[] invprob = new int [6];
double[] probabilities = new double [6];
int[] counter = new int [] { 0, 0, 0, 0, 0, 0 };
int[] repeat = new int [] { 0, 0, 0, 0, 0, 0 };
int prev = -1;
for (int k = 0; k != 100000; ++k)
{
if (k % 60 == 0) // drop accumulation, important for low guidance
{
for (int i = 0; i != 6; ++i)
{
invprob[i] = guidanceParameter;
}
}
for (int i = 0; i != 6; ++i)
{
probabilities[i] = 1.0 / (double)invprob[i];
}
var cat = new Categorical(probabilities);
var q = cat.Sample();
counter[q] += 1;
invprob[q] += increment;
if (q == prev)
{
repeat[q] += 1;
}
prev = q;
}
counter.ToList().ForEach(Console.WriteLine);
repeat.ToList().ForEach(Console.WriteLine);
}
public IList <NeuralNetwork> ApplySelection(IList <GeneticAlgorithmAgent> agents)
{
IList <GeneticAlgorithmAgent> agentsSorted = agents
.OrderBy(agent => agent.Fitness)
.ToList();
int populationCount = agentsSorted.Count;
double[] probabilities = Enumerable
.Range(0, populationCount)
.Select(i => (double)(2 * i) / (populationCount * (populationCount - 1)))
.ToArray();
var distribution = new Categorical(probabilities);
var newPopulation = new List <NeuralNetwork>();
for (int i = 0; i < populationCount; i++)
{
newPopulation.Add(agentsSorted[distribution.Sample()].Network.Clone());
}
return(newPopulation);
}
public void SetProbabilityFails()
{
var b = new Categorical(_largeP);
Assert.Throws<ArgumentOutOfRangeException>(() => b.P = _badP);
}
public void SetProbabilityFails()
{
var b = new Categorical(_largeP);
Assert.That(() => b.P = _badP, Throws.ArgumentException);
}
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 CategoricalCreateFailsWithNegativeRatios()
{
var m = new Categorical(badP);
}
public void CategoricalCreateFailsWithNullHistogram()
{
Histogram h = null;
var m = new Categorical(h);
}
public void SetProbabilityFails()
{
var b = new Categorical(largeP);
b.P = badP;
}
public void CanSample()
{
var n = new Categorical(_largeP);
n.Sample();
}
public void CanCreateCategorical()
{
var m = new Categorical(largeP);
}
public void CanSample()
{
var n = new Categorical(_largeP);
n.Sample();
}
public void CategoricalCreateFailsWithAllZeroRatios()
{
var m = new Categorical(badP2);
}
public void ValidateToString()
{
var b = new Categorical(_smallP);
Assert.AreEqual("Categorical(Dimension = 3)", b.ToString());
}
public void CanSample()
{
var n = new Categorical(largeP);
var d = n.Sample();
}
public void CanSetProbability()
{
var b = new Categorical(largeP);
b.P = smallP;
}