def map_data(data_map, axis_num): data_map = np.max(data_map, axis=axis_num) data_map -= data_map.min() data_map = data_map / data_map.max() data_map = np.array(data_map * 255, dtype=np.uint8) return data_map Eigon实现

以下是Eigen实现：

#include <iostream>
#include <Eigen/Dense>

using namespace Eigen;
using namespace std;

MatrixXi map_data(MatrixXi data_map, int axis_num) {
    int height = data_map.rows();
    int width = data_map.cols();

    // reduce the data_map along the given axis_num
    VectorXi max_vals(width);
    if (axis_num == 0) {
        max_vals = data_map.rowwise().maxCoeff();
    } else if (axis_num == 1) {
        max_vals = data_map.colwise().maxCoeff();
    }

    // normalize the result
    int min_val = max_vals.minCoeff();
    int max_val = max_vals.maxCoeff();
    MatrixXi result = (data_map.array() - min_val) * 255 / (max_val - min_val);

    return result;
}

int main() {
    // example usage
    MatrixXi data_map(3, 3);
    data_map << 1, 2, 3,
                4, 5, 6,
                7, 8, 9;
    MatrixXi result = map_data(data_map, 0);
    cout << result << endl;
    return 0;
}

需要注意的是，Eigen中的Matrix类模板参数中有两个参数，第一个参数是矩阵元素的类型，第二个参数是矩阵的行数和列数，用RowMajor表示按行存储，用ColMajor表示按列存储。在这个实现中，我们用MatrixXi表示矩阵元素为整型，且按列存储。