Question

我正在将一些高斯过程回归的代码从MATLAB转换为Python，而我的Python版本比Matlab版本慢得多。我从程序的cProfile中发现瓶颈是Linalg库中Numpy函数solve的瓶颈。此函数比MATLAB的对应函数（即mldivide或\运算符）慢得多，并且要慢得多，而且调用频率很高，以致整个代码的效率受到损害。我可以做些什么来加快solve的速度，还是在Python中有更好的替代方法？

这里是我的np.show_config（），以防万一：

blas_mkl_info:
  NOT AVAILABLE
blis_info:
  NOT AVAILABLE
openblas_info:
  NOT AVAILABLE
atlas_3_10_blas_threads_info:
  NOT AVAILABLE
atlas_3_10_blas_info:
  NOT AVAILABLE
atlas_blas_threads_info:
  NOT AVAILABLE
atlas_blas_info:
  NOT AVAILABLE
blas_opt_info:
  extra_compile_args = ['-msse3', '- 
  I/System/Library/Frameworks/vecLib.framework/Headers']
  extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
  define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]
lapack_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
  NOT AVAILABLE
openblas_clapack_info:
  NOT AVAILABLE
atlas_3_10_threads_info:
  NOT AVAILABLE
atlas_3_10_info:
  NOT AVAILABLE
atlas_threads_info:
  NOT AVAILABLE
atlas_info:
  NOT AVAILABLE
lapack_opt_info:
  extra_compile_args = ['-msse3']
  extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
  define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]

加速numpy解决

0 个答案: