我通过以下nuget包在我的C＃代码中使用liblinear的.NET实现： https://www.nuget.org/packages/Liblinear/
但是在liblinear的自述文件中，x的格式是：
struct problem描述了问题：
struct problem
{
int l, n;
int *y;
struct feature_node **x;
double bias;
};
where `l` is the number of training data. If bias >= 0, we assume
that one additional feature is added to the end of each data
instance. `n` is the number of feature (including the bias feature
if bias >= 0). `y` is an array containing the target values. (integers
in classification, real numbers in regression) And `x` is an array
of pointers, each of which points to a sparse representation (array
of feature_node) of one training vector.
For example, if we have the following training data:
LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5
----- ----- ----- ----- ----- -----
1 0 0.1 0.2 0 0
2 0 0.1 0.3 -1.2 0
1 0.4 0 0 0 0
2 0 0.1 0 1.4 0.5
3 -0.1 -0.2 0.1 1.1 0.1
and bias = 1, then the components of problem are:
l = 5
n = 6
y -> 1 2 1 2 3
x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)
[ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)
[ ] -> (1,0.4) (6,1) (-1,?)
[ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)
[ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)
但是，在显示java实现的示例中： https://gist.github.com/hodzanassredin/6682771
problem.x <- [|
[|new FeatureNode(1,0.); new FeatureNode(2,1.)|]
[|new FeatureNode(1,2.); new FeatureNode(2,0.)|]
|]// feature nodes
problem.y <- [|1.;2.|] // target values
这意味着他的数据集是：
1 0 1
2 2 0
因此，他没有按照liblinear的稀疏格式存储节点。对于liblinear实现，是否有人知道x的正确格式？
答案 0 :(得分：1)
虽然它没有完全解决您提到的图书馆，但我可以为您提供另一种选择。该 Accord.NET Framework最近在其机器学习中融入了所有LIBLINEAR算法 命名空间。它也是available through NuGet。
在此库中，从内存数据创建线性支持向量机的直接语法是
// Create a simple binary AND
// classification problem:
double[][] problem =
{
// a b a + b
new double[] { 0, 0, 0 },
new double[] { 0, 1, 0 },
new double[] { 1, 0, 0 },
new double[] { 1, 1, 1 },
};
// Get the two first columns as the problem
// inputs and the last column as the output
// input columns
double[][] inputs = problem.GetColumns(0, 1);
// output column
int[] outputs = problem.GetColumn(2).ToInt32();
// However, SVMs expect the output value to be
// either -1 or +1. As such, we have to convert
// it so the vector contains { -1, -1, -1, +1 }:
//
outputs = outputs.Apply(x => x == 0 ? -1 : 1);
创建问题后，可以使用
学习线性SVM// Create a new linear-SVM for two inputs (a and b)
SupportVectorMachine svm = new SupportVectorMachine(inputs: 2);
// Create a L2-regularized L2-loss support vector classification
var teacher = new LinearDualCoordinateDescent(svm, inputs, outputs)
{
Loss = Loss.L2,
Complexity = 1000,
Tolerance = 1e-5
};
// Learn the machine
double error = teacher.Run(computeError: true);
// Compute the machine's answers for the learned inputs
int[] answers = inputs.Apply(x => Math.Sign(svm.Compute(x)));
但是，假设您的数据已经在内存中。如果您希望从中加载数据 磁盘，来自libsvm稀疏格式的文件，您可以使用框架的SparseReader class。 有关如何使用它的示例如下：
// Suppose we are going to read a sparse sample file containing
// samples which have an actual dimension of 4. Since the samples
// are in a sparse format, each entry in the file will probably
// have a much smaller number of elements.
//
int sampleSize = 4;
// Create a new Sparse Sample Reader to read any given file,
// passing the correct dense sample size in the constructor
//
SparseReader reader = new SparseReader(file, Encoding.Default, sampleSize);
// Declare a vector to obtain the label
// of each of the samples in the file
//
int[] labels = null;
// Declare a vector to obtain the description (or comments)
// about each of the samples in the file, if present.
//
string[] descriptions = null;
// Read the sparse samples and store them in a dense vector array
double[][] samples = reader.ReadToEnd(out labels, out descriptions);
之后，可以使用samples和labels向量作为问题的输入和输出，
分别
我希望它有所帮助。
免责声明：我是这个图书馆的作者。我真诚希望回答这个问题 可以对OP有用，因为不久前我也遇到了同样的问题。如果主持人认为 这看起来像垃圾邮件，随意删除。但是，我只是张贴这个，因为我认为它可能 帮助别人。在搜索现有的C＃时，我甚至错误地遇到了这个问题 LIBSVM的实现，而不是LIBLINEAR。