使用 C# 和 "Accord.NET" 进行非线性支持向量回归
Non-linear Support Vector Regression with C# and "Accord.NET"
在 Accord 中使用 C# 进行非线性向量回归应该使用什么?
谢谢
(traininginputs double[][] and trainingoutput double[] NOT int[])
Accord.NET 为 SequentialMinimalOptimizationRegression class. There is an example application for this topic in the sample application's wiki page.
中的回归问题提供支持向量机学习算法
这里有一个如何使用它的例子:
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 20, 8
};
// Create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimizationRegression<Polynomial>()
{
Kernel = new Polynomial(degree: 2)
}
// Use the teacher to learn a new machine
var svm = teacher.Learn(inputs, outputs);
// Compute the answer for one particular example
double fxy = machine.Transform(inputs[0]); // 1.0003849827673186
// Compute the answer for all examples
double[] fxys = machine.Transform(inputs);
在 Accord 中使用 C# 进行非线性向量回归应该使用什么? 谢谢 (traininginputs double[][] and trainingoutput double[] NOT int[])
Accord.NET 为 SequentialMinimalOptimizationRegression class. There is an example application for this topic in the sample application's wiki page.
中的回归问题提供支持向量机学习算法这里有一个如何使用它的例子:
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 20, 8
};
// Create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimizationRegression<Polynomial>()
{
Kernel = new Polynomial(degree: 2)
}
// Use the teacher to learn a new machine
var svm = teacher.Learn(inputs, outputs);
// Compute the answer for one particular example
double fxy = machine.Transform(inputs[0]); // 1.0003849827673186
// Compute the answer for all examples
double[] fxys = machine.Transform(inputs);