本文整理汇总了C#中NormalDistribution类的典型用法代码示例。如果您正苦于以下问题：C# NormalDistribution类的具体用法？C# NormalDistribution怎么用？C# NormalDistribution使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
NormalDistribution类属于命名空间，在下文中一共展示了NormalDistribution类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的C#代码示例。
示例1: ProbabilityFunctionTest
public void ProbabilityFunctionTest()
{
var p1 = new NormalDistribution(4.2, 1);
var p2 = new NormalDistribution(7.0, 2);
Independent<NormalDistribution> target = new Independent<NormalDistribution>(p1, p2);
double[] x;
double actual, expected;
x = new double[] { 4.2, 7.0 };
actual = target.ProbabilityDensityFunction(x);
expected = p1.ProbabilityDensityFunction(x[0]) * p2.ProbabilityDensityFunction(x[1]);
Assert.AreEqual(expected, actual, 1e-10);
Assert.IsFalse(double.IsNaN(actual));
x = new double[] { 0.0, 0.0 };
actual = target.ProbabilityDensityFunction(x);
expected = p1.ProbabilityDensityFunction(x[0]) * p2.ProbabilityDensityFunction(x[1]);
Assert.AreEqual(expected, actual, 1e-10);
Assert.IsFalse(double.IsNaN(actual));
x = new double[] { 7.0, 4.2 };
actual = target.ProbabilityDensityFunction(x);
expected = p1.ProbabilityDensityFunction(x[0]) * p2.ProbabilityDensityFunction(x[1]);
Assert.AreEqual(expected, actual, 1e-10);
Assert.IsFalse(double.IsNaN(actual));
}示例2: ConstructorTest
public void ConstructorTest()
{
var p1 = new NormalDistribution(4.2, 1);
var p2 = new NormalDistribution(7.0, 2);
Independent<NormalDistribution> target = new Independent<NormalDistribution>(p1, p2);
Assert.AreEqual(target.Components[0], p1);
Assert.AreEqual(target.Components[1], p2);
Assert.AreEqual(2, target.Dimension);
Assert.AreEqual(4.2, target.Mean[0]);
Assert.AreEqual(7.0, target.Mean[1]);
Assert.AreEqual(1, target.Variance[0]);
Assert.AreEqual(4, target.Variance[1]);
Assert.AreEqual(1, target.Covariance[0, 0]);
Assert.AreEqual(4, target.Covariance[1, 1]);
Assert.AreEqual(0, target.Covariance[0, 1]);
Assert.AreEqual(0, target.Covariance[1, 0]);
var text = target.ToString("N2", System.Globalization.CultureInfo.InvariantCulture);
Assert.AreEqual("Independent(x0, x1; N(x0; μ = 4.20, σ² = 1.00) + N(x1; μ = 7.00, σ² = 4.00))", text);
}示例3: ConstructorTest5
public void ConstructorTest5()
{
var normal = new NormalDistribution(mean: 4, stdDev: 4.2);
double mean = normal.Mean; // 4.0
double median = normal.Median; // 4.0
double var = normal.Variance; // 17.64
double cdf = normal.DistributionFunction(x: 1.4); // 0.26794249453351904
double pdf = normal.ProbabilityDensityFunction(x: 1.4); // 0.078423391448155175
double lpdf = normal.LogProbabilityDensityFunction(x: 1.4); // -2.5456330358182586
double ccdf = normal.ComplementaryDistributionFunction(x: 1.4); // 0.732057505466481
double icdf = normal.InverseDistributionFunction(p: cdf); // 1.4
double hf = normal.HazardFunction(x: 1.4); // 0.10712736480747137
double chf = normal.CumulativeHazardFunction(x: 1.4); // 0.31189620872601354
string str = normal.ToString(CultureInfo.InvariantCulture); // N(x; μ = 4, σ² = 17.64)
Assert.AreEqual(4.0, mean);
Assert.AreEqual(4.0, median);
Assert.AreEqual(17.64, var);
Assert.AreEqual(0.31189620872601354, chf);
Assert.AreEqual(0.26794249453351904, cdf);
Assert.AreEqual(0.078423391448155175, pdf);
Assert.AreEqual(-2.5456330358182586, lpdf);
Assert.AreEqual(0.10712736480747137, hf);
Assert.AreEqual(0.732057505466481, ccdf);
Assert.AreEqual(1.4, icdf);
Assert.AreEqual("N(x; μ = 4, σ² = 17.64)", str);
}示例4: PinkNoise
public PinkNoise(IRandomGenerator randomGenerator, float rmsAmplitude)
{
if (null == randomGenerator)
throw new ArgumentNullException("randomGenerator");
_whiteGenerator = new NormalDistribution(randomGenerator, 0, RmsScale * rmsAmplitude);
}示例5: ConstructorTest2
public void ConstructorTest2()
{
var original = new NormalDistribution(mean: 4, stdDev: 4.2);
var normal = GeneralContinuousDistribution.FromDensityFunction(
original.Support, original.ProbabilityDensityFunction);
testNormal(normal);
}示例6: CreateModel1
public static HiddenMarkovClassifier<Independent> CreateModel1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0 },
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4 },
new double[] { 3 },
new double[] { 2 },
new double[] { 1 },
new double[] { 0 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution component = new NormalDistribution();
Independent density = new Independent(component);
var classifier = new HiddenMarkovClassifier<Independent>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<Independent>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
Assert.AreEqual(-13.271981026832929d, logLikelihood);
return classifier;
}示例7: LearnTest1
public void LearnTest1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0,1,2,3,4 }, // This is the first sequence with label = 0
new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood1, likelihood2;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood1);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-13.271981026832929, logLikelihood, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(likelihood1));
Assert.IsFalse(double.IsNaN(likelihood2));
}示例8: ConstructorTest5
public void ConstructorTest5()
{
var normal = new NormalDistribution(mean: 4, stdDev: 4.2);
double mean = normal.Mean; // 4.0
double median = normal.Median; // 4.0
double mode = normal.Mode; // 4.0
double var = normal.Variance; // 17.64
double cdf = normal.DistributionFunction(x: 1.4); // 0.26794249453351904
double pdf = normal.ProbabilityDensityFunction(x: 1.4); // 0.078423391448155175
double lpdf = normal.LogProbabilityDensityFunction(x: 1.4); // -2.5456330358182586
double ccdf = normal.ComplementaryDistributionFunction(x: 1.4); // 0.732057505466481
double icdf = normal.InverseDistributionFunction(p: cdf); // 1.4
double hf = normal.HazardFunction(x: 1.4); // 0.10712736480747137
double chf = normal.CumulativeHazardFunction(x: 1.4); // 0.31189620872601354
string str = normal.ToString(CultureInfo.InvariantCulture); // N(x; μ = 4, σ² = 17.64)
Assert.AreEqual(4.0, mean);
Assert.AreEqual(4.0, median);
Assert.AreEqual(4.0, mode);
Assert.AreEqual(17.64, var);
Assert.AreEqual(0.31189620872601354, chf);
Assert.AreEqual(0.26794249453351904, cdf);
Assert.AreEqual(0.078423391448155175, pdf);
Assert.AreEqual(-2.5456330358182586, lpdf);
Assert.AreEqual(0.10712736480747137, hf);
Assert.AreEqual(0.732057505466481, ccdf);
Assert.AreEqual(1.4, icdf);
Assert.AreEqual("N(x; μ = 4, σ² = 17.64)", str);
Assert.AreEqual(Accord.Math.Normal.Function(normal.ZScore(4.2)), normal.DistributionFunction(4.2));
Assert.AreEqual(Accord.Math.Normal.Derivative(normal.ZScore(4.2)) / normal.StandardDeviation, normal.ProbabilityDensityFunction(4.2), 1e-16);
Assert.AreEqual(Accord.Math.Normal.LogDerivative(normal.ZScore(4.2)) - Math.Log(normal.StandardDeviation), normal.LogProbabilityDensityFunction(4.2), 1e-15);
var range1 = normal.GetRange(0.95);
var range2 = normal.GetRange(0.99);
var range3 = normal.GetRange(0.01);
Assert.AreEqual(-2.9083852331961833, range1.Min);
Assert.AreEqual(10.908385233196183, range1.Max);
Assert.AreEqual(-5.7706610709715314, range2.Min);
Assert.AreEqual(13.770661070971531, range2.Max);
Assert.AreEqual(-5.7706610709715314, range3.Min);
Assert.AreEqual(13.770661070971531, range3.Max);
}示例9: ByParams
public static NormalDistribution ByParams(double expectation, double variance)
{
if (Double.IsInfinity(expectation) || Double.IsNaN(expectation))
throw new ArgumentException("The expectation must be a finite number");
if (variance <= 0 || Double.IsInfinity(expectation) || Double.IsNaN(expectation))
throw new ArgumentException("The variance must be a positive finite number");
NormalDistribution distribution = new NormalDistribution();
distribution.Expectation = expectation;
distribution.Variance = variance;
distribution.ComputeInternalParameters();
return distribution;
}示例10: NormalGenerateTest
public void NormalGenerateTest()
{
// Create a Normal with mean 2 and sigma 5
var normal = new NormalDistribution(2, 5);
// Generate 1000000 samples from it
double[] samples = normal.Generate(1000000);
// Try to estimate a new Normal distribution from the
// generated samples to check if they indeed match
var actual = NormalDistribution.Estimate(samples);
string result = actual.ToString("N2"); // N(x; μ = 2.01, σ² = 25.03)
Assert.AreEqual("N(x; μ = 2.01, σ² = 25.03)", result);
}示例11: Start
// Use this for initialization
void Start()
{
// Save a pointer to the fusion engine
fusionEngine = GetComponentInParent<FusionEngine>();
// Get a pointer to the target
targets = GameObject.FindGameObjectsWithTag("Target");
// Noise distribution
nd = new NormalDistribution(0, 1);
noiseCovariance = new Matrix(3, 3);
noiseCovariance[0, 0] = 1e-3;
noiseCovariance[1, 1] = 1e-3;
noiseCovariance[2, 2] = 1e-3;
noiseCovCholT = noiseCovariance.CholeskyDecomposition.TriangularFactor.Clone();
noiseCovCholT.Transpose();
// Reset time since the last update
timeSinceLastUpdateSec = 0.0f;
}示例12: ConstructorTest1
public void ConstructorTest1()
{
NormalDistribution normal = new NormalDistribution(4.2, 1.2);
MultivariateNormalDistribution target = new MultivariateNormalDistribution(new[] { 4.2 }, new[,] { { 1.2 * 1.2 } });
double[] mean = target.Mean;
double[] median = target.Median;
double[] var = target.Variance;
double[,] cov = target.Covariance;
double apdf1 = target.ProbabilityDensityFunction(new double[] { 2 });
double apdf2 = target.ProbabilityDensityFunction(new double[] { 4 });
double apdf3 = target.ProbabilityDensityFunction(new double[] { 3 });
double alpdf = target.LogProbabilityDensityFunction(new double[] { 3 });
double acdf = target.DistributionFunction(new double[] { 3 });
double accdf = target.ComplementaryDistributionFunction(new double[] { 3 });
double epdf1 = target.ProbabilityDensityFunction(new double[] { 2 });
double epdf2 = target.ProbabilityDensityFunction(new double[] { 4 });
double epdf3 = target.ProbabilityDensityFunction(new double[] { 3 });
double elpdf = target.LogProbabilityDensityFunction(new double[] { 3 });
double ecdf = target.DistributionFunction(new double[] { 3 });
double eccdf = target.ComplementaryDistributionFunction(new double[] { 3 });
Assert.AreEqual(normal.Mean, target.Mean[0]);
Assert.AreEqual(normal.Median, target.Median[0]);
Assert.AreEqual(normal.Variance, target.Variance[0]);
Assert.AreEqual(normal.Variance, target.Covariance[0, 0]);
Assert.AreEqual(epdf1, apdf1);
Assert.AreEqual(epdf2, apdf2);
Assert.AreEqual(epdf3, apdf3);
Assert.AreEqual(elpdf, alpdf);
Assert.AreEqual(ecdf, acdf);
Assert.AreEqual(eccdf, accdf);
Assert.AreEqual(1.0 - ecdf, eccdf);
}示例13: Combat
public Combat(Unit attacker, Unit defender, bool useRandomisedCombatEfficiency, List<Unit> antiAirUnits = null)
{
UseRandomisedCombatEfficiency = useRandomisedCombatEfficiency;
Generator = new NormalDistribution(GameConstants.CombatEfficiencyMean, GameConstants.CombatEfficiencyDeviation);
Attacker = new UnitCombatState(attacker, this);
Defender = new UnitCombatState(defender, this);
if (attacker.IsAirUnit())
{
AttackType = AttackType.AirAttack;
AntiAirUnits = antiAirUnits.Select((Unit x) => new UnitCombatState(x, this)).ToList();
foreach (var unit in AntiAirUnits)
unit.SetDamage(unit.Unit.Type.Stats.AirAttack.Value);
}
else if (attacker.IsArtillery())
AttackType = AttackType.ArtilleryAttack;
else
AttackType = AttackType.GroundAttack;
// Calculate the outcome right away
Attack();
}示例14: ConstructorTest
public void ConstructorTest()
{
var p1 = new NormalDistribution(4.2, 1);
var p2 = new NormalDistribution(7.0, 2);
Independent<NormalDistribution> target = new Independent<NormalDistribution>(p1, p2);
Assert.AreEqual(target.Components[0], p1);
Assert.AreEqual(target.Components[1], p2);
Assert.AreEqual(2, target.Dimension);
Assert.AreEqual(4.2, target.Mean[0]);
Assert.AreEqual(7.0, target.Mean[1]);
Assert.AreEqual(1, target.Variance[0]);
Assert.AreEqual(4, target.Variance[1]);
Assert.AreEqual(1, target.Covariance[0, 0]);
Assert.AreEqual(4, target.Covariance[1, 1]);
Assert.AreEqual(0, target.Covariance[0, 1]);
Assert.AreEqual(0, target.Covariance[1, 0]);
}示例15: MedianTest
public void MedianTest()
{
NormalDistribution target = new NormalDistribution(0.4, 2.2);
Assert.AreEqual(target.Median, target.InverseDistributionFunction(0.5));
}本文标签属性：
示例：示例是什么意思
代码：代码转换器
NormalDistribution：NormalDistribution
