线性使用格式

本文关键字：格式 线性 | 更新日期: 2023-09-27 18:34:19

我正在通过以下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描述了这个问题：

    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。有没有人知道用于自由线性实现的 x 的正确格式？

线性使用格式

虽然它没有完全解决你提到的库，但我可以为你提供一个替代方案。这Accord.NET Framework最近将LIBLINEAR的所有算法整合到其机器学习中命名空间。它也可以通过NuGet获得。

在此库中，从内存中数据创建线性支持向量机的直接语法为

// 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，可以使用以下方法学习线性 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，这假定您的数据已在内存中。如果您希望从磁盘，从 libsvm 稀疏格式的文件中，您可以使用框架的 SparseReader 类。有关如何使用它的示例可以在下面找到：

// 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：我是这个库的作者。我真诚地希望回答这个问题对OP很有用，因为不久前我也遇到了同样的问题。如果版主认为这看起来像垃圾邮件，请随时删除。但是，我只是发布这个，因为我认为它可能帮助他人。我什至在搜索现有的 C# 时错误地遇到了这个问题LIBSVM 的实现，而不是 LIBLINEAR。

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