python - 使用 SWIG 在 Python 中包装 C++ 类

Question

我有以下 C++ 类，我希望用 SWIG 将其包装在 Python 中。

typedef std::map<std::string, double> ParamaterSet;

class GPRegressor{
public:
    double runRegression(const double *trainData, const double *trainTruth, int trainRows, int trainCols,
                             const double *testData, const double *testTruth, int testRows, int testCols,
                             const ParamaterSet &params);

    GPRegressor(KernelType kernType = SQUARED_EXPONENTIAL);
    ~GPRegressor();
    };

我编写了以下 SWIG 接口(interface)文件，以允许我将 numpy 数组传递给 runRegression 成员函数。

%module pyGP
%{
#define SWIG_FILE_WITH_INIT
#include "lib/typedefs.h"
#include "lib/Kernels.h"
#include "lib/GPRegressor.h"
%}

%include "numpy.i"
%include "std_string.i"
%include "std_map.i"
%template(map_string_double) std::map<std::string, double>;

%init %{
  import_array();
%}

%apply (const double *arr, int dim1, int dim2) {(const double *data, int dimx, int dimy)}
%apply (double *arr, int dim1, int dim2) {(double *data, int dimx, int dimy)}

%include "lib/typedefs.h"
%include "lib/Kernels.h"
%include "lib/GPRegressor.h"

我相信我已经正确编写了 SWIG 类型映射以允许我执行此操作。我编写了以下 Python 测试代码。

#!/usr/bin/python3
import sys, os
sys.path.append(os.path.realpath('../build'))
import numpy as np

from pyGP import *

def runRegression():
    X = np.random.rand(30, 2)
    Y = np.random.rand(30, 1)
    X_s = np.random.rand(30, 2)
    Y_s = np.random.rand(30, 1)

    regressor = GPRegressor()
    regressor.runRegression(X, Y, 30, 2, X_s, Y_s, 30, 1, {'a' : 0.0, 'b' : 0.0})

if __name__ == "__main__":
    runRegression()

但是我收到以下错误，暗示我实际上在 numpy 数组的类型映射中犯了一个错误。

Traceback (most recent call last):
  File "./demo.py", line 22, in <module>
    runRegression()
  File "./demo.py", line 18, in runRegression
    regressor.runRegression(X, Y, 30, 2, X_s, Y_s, 30, 1, {'a' : 0.0, 'b' : 0.0})
  File "/home/jack/GitRepos/GaussianProcess/build/pyGP.py", line 337, in runRegression
    return _pyGP.GPRegressor_runRegression(self, trainData, trainTruth, trainRows, trainCols, testData, testTruth, testRows, testCols, params)
TypeError: in method 'GPRegressor_runRegression', argument 2 of type 'double const *'

总而言之，我想知道我尝试包装类及其成员函数以便将 numpy 数组传递给 const double* 的方式实际上是否正确。如果不是，约定是什么？

编辑:-我已将 SWIG 文件更新为包含以下内容:

%apply (double *IN_ARRAY2, int DIM1, int DIM2) {(const double *trainData, int trainCols, int trainRows)};
%apply (double *IN_ARRAY1, int DIM1) {(const double *trainTruth, int trainRows)};
%apply (double *IN_ARRAY2, int DIM1, int DIM2) {(const double *testData, int testCols, int testRows)};
%apply (double *IN_ARRAY1, int DIM1) {(const double *testTruth, int testRows)};

并将要包装的函数的签名更改为以下内容:

double runRegression(const double *trainData, int trainCols, int trainRows, const double *trainTruth,
                     int trainTruthRows, const double *testData, int testCols, int testRows,
                     const double *testTruth, int testTruthRows, const ParamaterSet &params);

以便参数的顺序与类型映射的顺序相匹配。但是，我仍然收到以下错误:

Traceback (most recent call last):
  File "./demo.py", line 23, in <module>
    runRegression()
  File "./demo.py", line 19, in runRegression
    regressor.runRegression(X, 2, 30, Y, 30, X_s, 2, 30, Y_s, 30, {'a' : 0.0, 'b' : 0.0})
TypeError: runRegression() takes 6 positional arguments but 12 were given

Answer 1

如果查看 numpy.i header 中的示例，您将看到有关如何应用 NumPy 类型映射的示例。

在你的情况下，你应该改变

%apply (double *arr, int dim1, int dim2) {(double *data, int dimx, int dimy)}

进入

%apply (double* IN_ARRAY2, int DIM1, int DIM2) {(const double *testTruth, int testRows, int testColsdouble)};

%apply (double* IN_ARRAY2, int DIM1, int DIM2) {(const double *trainTruth, int trainRows, int trainCols)};

注意 IN_ARRAY2 是如何使用的