LibSVM之C# Wrapper

　double svm_predict(const svm_model *model, const svm_node *x)

 [DllImport("libsvm.dll", CallingConvention = CallingConvention.Cdecl)]
 public static extern double svm_predict(IntPtr model, IntPtr x);

 1         public static double Predict(SVMModel model, SVMNode[] x)
 2         {
 3             if (model == null)
 4             {
 5                 throw new ArgumentNullException("model");
 6             }
 7             if (x == null)
 8             {
 9                 throw new ArgumentNullException("x");
10             }
11             IntPtr intPtr = SVMModel.Allocate(model);
12             double result = SVM.Predict(intPtr, x);
13             SVMModel.Free(intPtr);
14             return result;
15         }
16 
17         public static double Predict(IntPtr ptr_model, SVMNode[] x)
18         {
19             if (ptr_model == IntPtr.Zero)
20             {
21                 throw new ArgumentNullException("ptr_model");
22             }
23             if (x == null)
24             {
25                 throw new ArgumentNullException("x");
26             }
27             List<SVMNode> list = (from a in x
28             select a.Clone()).ToList<SVMNode>();
29             list.Add(new SVMNode(-1, 0.0));
30             IntPtr intPtr = SVMNode.Allocate(list.ToArray());
31             double result = libsvm.svm_predict(ptr_model, intPtr);
32             SVMNode.Free(intPtr);
33             return result;
34         }

            C++            C#
        =====================================
        WORD              ushort
        DWORD             uint
        UCHAR             int/byte   大部分情况都可以使用int代替,而如果需要严格对齐的话则应该用bytebyte 
        UCHAR*            string/IntPtr
        unsigned char*    [MarshalAs(UnmanagedType.LPArray)]byte[]/？（Intptr）
        char*             string
        LPCTSTR           string
        LPTSTR            [MarshalAs(UnmanagedType.LPTStr)] string
        long              int
        ulong             uint
        Handle            IntPtr
        HWND              IntPtr
        void*             IntPtr
        int               int
        int*              ref int
        *int              IntPtr
        unsigned int      uint
        COLORREF          uint

 1 struct svm_node
 2 {
 3     int index;
 4     double value;
 5 };
 6 
 7 struct svm_problem
 8 {
 9     int l;
10     double *y;
11     struct svm_node **x;
12 };
13 
14 enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };    /* svm_type */
15 enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */
16 
17 struct svm_parameter
18 {
19     int svm_type;
20     int kernel_type;
21     int degree;    /* for poly */
22     double gamma;    /* for poly/rbf/sigmoid */
23     double coef0;    /* for poly/sigmoid */
24 
25     /* these are for training only */
26     double cache_size; /* in MB */
27     double eps;    /* stopping criteria */
28     double C;    /* for C_SVC, EPSILON_SVR and NU_SVR */
29     int nr_weight;        /* for C_SVC */
30     int *weight_label;    /* for C_SVC */
31     double* weight;        /* for C_SVC */
32     double nu;    /* for NU_SVC, ONE_CLASS, and NU_SVR */
33     double p;    /* for EPSILON_SVR */
34     int shrinking;    /* use the shrinking heuristics */
35     int probability; /* do probability estimates */
36 };
37 
38 //
39 // svm_model
40 // 
41 struct svm_model
42 {
43     struct svm_parameter param;    /* parameter */
44     int nr_class;        /* number of classes, = 2 in regression/one class svm */
45     int l;            /* total #SV */
46     struct svm_node **SV;        /* SVs (SV[l]) */
47     double **sv_coef;    /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
48     double *rho;        /* constants in decision functions (rho[k*(k-1)/2]) */
49     double *probA;        /* pariwise probability information */
50     double *probB;
51     int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
52 
53     /* for classification only */
54 
55     int *label;        /* label of each class (label[k]) */
56     int *nSV;        /* number of SVs for each class (nSV[k]) */
57                 /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
58     /* XXX */
59     int free_sv;        /* 1 if svm_model is created by svm_load_model*/
60                 /* 0 if svm_model is created by svm_train */
61 };

    public struct svm_node
    {
        /// <summary>
        /// 索引
        /// </summary>
        public int index;

        /// <summary>
        /// 值
        /// </summary>
        public double value;

        /// <summary>
        /// 构造函数
        /// </summary>
        /// <param name="i"></param>
        /// <param name="v"></param>
        public svm_node(int i,double v)
        {
            this.index = i;
            this.value = v;
        }
        public bool Equals(svm_node x)
        {
            return this.index.Equals(x.index) && this.value.Equals(x.value);
        }
    }
    public struct svm_problem
    {
        /// <summary>
        /// 支持向量个数
        /// </summary>
        public int l;

        /// <summary>
        /// 标签值
        /// </summary>
        public IntPtr y;

        /// <summary>
        /// 节点情况
        /// </summary>
        public IntPtr x;
    }
    ……