PolynomialLeastSquares Class 
Namespace: Accord.Statistics.Models.Regression.Linear
[SerializableAttribute] public class PolynomialLeastSquares : ISupervisedLearning<PolynomialRegression, double, double>
The PolynomialLeastSquares type exposes the following members.
Name  Description  

PolynomialLeastSquares  Initializes a new instance of the PolynomialLeastSquares class 
Name  Description  

Degree 
Gets or sets the polynomial degree to use
in the polynomial regression.
 
IsRobust 
Gets or sets whether to always use a robust LeastSquares
estimate using the SingularValueDecomposition.
Default is false.
 
Token 
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.

Name  Description  

Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Learn 
Learns a model that can map the given inputs to the given outputs.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual  Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)  
ToT 
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 
// Let's say we would like to learn 2nd degree polynomial that // can map the first column X into its second column Y. We have // 5 examples of those (x,y) pairs that we can use to learn this // function: double[,] data = { // X Y { 12, 144 }, // example #1 { 15, 225 }, // example #2 { 20, 400 }, // example #3 { 25, 625 }, // example #4 { 35, 1225 }, // example #5 }; // Let's retrieve the input and output data: double[] inputs = data.GetColumn(0); // X double[] outputs = data.GetColumn(1); // Y // We can create a learning algorithm var ls = new PolynomialLeastSquares() { Degree = 2 }; // Now, we can use the algorithm to learn a polynomial PolynomialRegression poly = ls.Learn(inputs, outputs); // The learned polynomial will be given by string str = poly.ToString("N1"); // "y(x) = 1.0x^2 + 0.0x^1 + 0.0" // Where its weights can be accessed using double[] weights = poly.Weights; // { 1.0000000000000024, 1.2407665029287351E13 } double intercept = poly.Intercept; // 1.5652369518855253E12 // Finally, we can use this polynomial // to predict values for the input data double[] pred = poly.Transform(inputs); // Where the meansquarederror (MSE) should be double error = new SquareLoss(outputs).Loss(pred); // 0.0