/// <summary>
/// Gets all zodiac signs that are associated with either Yin or Yang.
/// This is a fixed yinyang-to-zodiac-sign association.
/// </summary>
/// <param name="yinOrYang">Supply either Yin or Yang.</param>
/// <returns>Returns a list of Chinese zodiac signs that match the YinYang filter.</returns>
public static List <ChineseZodiacModel> GetAllZodiacSignsForYinYang(YinYang yinOrYang)
{
return(ChineseZodiacHelper.InitializeAndGetAllZodiacSigns().Values
.Where(z => z.ZodiacYinYang == yinOrYang)
.Select(c => c)
.ToList());
}
public void yinyang_test()
{
// https://github.com/accord-net/framework/issues/451
double[][] observations = new YinYang().Instances;
Accord.Math.Random.Generator.Seed = 0;
var kmeans = new BalancedKMeans(2)
{
// If a limit is not set, the following Learn call does not return....
MaxIterations = 1000
};
var clusters = kmeans.Learn(observations);
int[] labels = kmeans.Labels;
int[] expected = new int[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1 };
string str = labels.ToCSharp();
Assert.IsTrue(labels.IsEqual(expected));
int[] hist = Accord.Math.Vector.Histogram(labels);
for (int i = 0; i < hist.Length; i++)
{
Assert.AreEqual(hist[i], 50);
}
}
internal ChineseZodiacModel(string name, string english, string zodiacPersonality, ChineseZodiacElements chineseZodiacFixedElement, YinYang yinYang)
: base(name, english)
{
ZodiacPersonality = zodiacPersonality;
ZodiacFixedElement = chineseZodiacFixedElement;
ZodiacYinYang = yinYang;
}
public void LearnTest2()
{
var dataset = new YinYang();
double[][] inputs = dataset.Instances;
int[] outputs = dataset.ClassLabels.Apply(x => x ? 1 : -1);
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Polynomial(3), inputs[0].Length);
// Create the Least Squares Support Vector Machine teacher
LeastSquaresLearning learn = new LeastSquaresLearning(machine, inputs, outputs);
learn.Complexity = 1 / 0.1;
// Run the learning algorithm
learn.Run();
int[] output = inputs.Apply(p => Math.Sign(machine.Compute(p)));
for (int i = 0; i < output.Length; i++)
{
Assert.AreEqual(System.Math.Sign(outputs[i]), System.Math.Sign(output[i]));
}
}
public void learn_logistic_regression()
{
#region doc_learn_lr
// This example shows how to use AdaBoost to train more complex
// models than a simple DecisionStump. For example, we will use
// it to train a boosted Logistic Regression classifier.
// Let's use some synthetic data for that: The Yin-Yang dataset is
// a simple 2D binary non-linear decision problem where the points
// belong to each of the classes interwine in a Yin-Yang shape:
var dataset = new YinYang();
double[][] inputs = dataset.Instances;
int[] outputs = Classes.ToZeroOne(dataset.ClassLabels);
// Create an AdaBoost for Logistic Regression as:
var teacher = new AdaBoost <LogisticRegression>()
{
// Here we can specify how each regression should be learned:
Learner = (param) => new IterativeReweightedLeastSquares <LogisticRegression>()
{
ComputeStandardErrors = false,
MaxIterations = 50,
Tolerance = 0
},
// Train until:
MaxIterations = 50,
Tolerance = 1e-5,
};
// Now, we can use the Learn method to learn a boosted classifier
Boost <LogisticRegression> classifier = teacher.Learn(inputs, outputs);
// And we can test its performance using (error should be 0.11):
ConfusionMatrix cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);
double error = cm.Error; // should be 0.11
double acc = cm.Accuracy; // should be 0.89
double kappa = cm.Kappa; // should be 0.78
// And compute a decision for a single data point using:
bool y = classifier.Decide(inputs[0]); // result should false
#endregion
Assert.AreEqual(false, y);
Assert.AreEqual(0.11, error);
Assert.AreEqual(0.89, acc);
Assert.AreEqual(0.78, kappa);
Assert.AreEqual(2, classifier.Models.Count);
Assert.AreEqual(0.63576818449825168, classifier.Models[0].Weight);
Assert.AreEqual(0.36423181550174832, classifier.Models[1].Weight);
int[] actual = new int[outputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = classifier.Compute(inputs[i]);
}
}
public void learn_decision_trees()
{
#region doc_learn_dt
// This example shows how to use AdaBoost to train more complex
// models than a simple DecisionStump. For example, we will use
// it to train a boosted Decision Trees.
// Let's use some synthetic data for that: The Yin-Yang dataset is
// a simple 2D binary non-linear decision problem where the points
// belong to each of the classes interwine in a Yin-Yang shape:
var dataset = new YinYang();
double[][] inputs = dataset.Instances;
int[] outputs = Classes.ToZeroOne(dataset.ClassLabels);
// Create an AdaBoost for Logistic Regression as:
var teacher = new AdaBoost <DecisionTree>()
{
// Here we can specify how each regression should be learned:
Learner = (param) => new C45Learning()
{
// i.e.
// MaxHeight =
// MaxVariables =
},
// Train until:
MaxIterations = 50,
Tolerance = 1e-5,
};
// Now, we can use the Learn method to learn a boosted classifier
Boost <DecisionTree> classifier = teacher.Learn(inputs, outputs);
// And we can test its performance using (error should be 0.11):
double error = ConfusionMatrix.Estimate(classifier, inputs, outputs).Error;
// And compute a decision for a single data point using:
bool y = classifier.Decide(inputs[0]); // result should false
#endregion
Assert.AreEqual(false, y);
Assert.AreEqual(0, error);
Assert.AreEqual(22, classifier.Models.Count);
Assert.AreEqual(0.063497989403001331, classifier.Models[0].Weight);
Assert.AreEqual(0.081129615464770655, classifier.Models[1].Weight);
Assert.AreEqual(0.083062765085567689, classifier.Models[2].Weight);
Assert.AreEqual(0.050307480220333232, classifier.Models[3].Weight);
Assert.AreEqual(0.044287142080877882, classifier.Models[4].Weight);
Assert.AreEqual(0.042772219812778081, classifier.Models[5].Weight);
int[] actual = new int[outputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = classifier.Compute(inputs[i]);
}
}
internal ZhouyiTrigram(int index, string name, string nature,
YinYang yinYang, FiveElement fiveElement)
{
this.Index = index;
this.Name = name;
this.Nature = nature;
this.YinYang = yinYang;
this.FiveElement = fiveElement;
}
public void ConstructorTest2()
{
var dataset = new YinYang();
double[][] inputs = dataset.Instances;
bool[] outputs2 = dataset.ClassLabels;
int[] outputs = outputs2.Apply(x => x ? 1 : 0);
var classifier = new Boost <Weak <LogisticRegression> >();
var teacher = new AdaBoost <Weak <LogisticRegression> >(classifier)
{
Creation = (weights) =>
{
LogisticRegression reg = new LogisticRegression(2, intercept: 1);
IterativeReweightedLeastSquares irls = new IterativeReweightedLeastSquares(reg)
{
ComputeStandardErrors = false
};
for (int i = 0; i < 50; i++)
{
irls.Run(inputs, outputs, weights);
}
return(new Weak <LogisticRegression>(reg, (s, x) => Math.Sign(s.Compute(x) - 0.5)));
},
Iterations = 50,
Tolerance = 1e-5,
};
double error = teacher.Run(inputs, outputs);
Assert.AreEqual(0.11, error);
Assert.AreEqual(2, classifier.Models.Count);
Assert.AreEqual(0.63576818449825168, classifier.Models[0].Weight);
Assert.AreEqual(0.36423181550174832, classifier.Models[1].Weight);
int[] actual = new int[outputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = classifier.Compute(inputs[i]);
}
//for (int i = 0; i < actual.Length; i++)
// Assert.AreEqual(outputs[i], actual[i]);
}
internal HorseChineseZodiacModel(string name, string english, string personality, ChineseZodiacElements chineseZodiacFixedElement, YinYang yinYang)
: base(name, english, personality, chineseZodiacFixedElement, yinYang)
{
}
public string GetYinYang()
{
var attr = YinYang.GetAttribute <EnumDescriptionAttribute>();
return(attr.GetDescription());
}
