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我想使用@jit或@autojit加速我的 python 代码，在这里解释：http: //nbviewer.ipython.org/gist/harrism/f5707335f40af9463c43

然而，该页面上的示例是针对纯 python 函数的，而我的函数在一个类中，并且基于更多的搜索，看来为了使用类函数，我必须提供函数的显式签名。

我以前没有使用过签名，但我现在了解如何将它们用于具有简单参数的函数。但是我陷入了如何为二维数组等复杂参数编写它们的问题上。

下面是我需要明确签名的函数。我真的不知道该写什么@void...

""" Function: train
    Input parameters:
    #X =  shape: [n_samples, n_features]
    #y = classes corresponding to X , y's shape: [n_samples]
    #H = int, number of boosting rounds
    Returns: None
    Trains the model based on the training data and true classes
    """
    #@autojit
    #@void
    def train(self, X, y, H):
           # function code below
           # do lots of stuff...

编辑

鉴于我的参数类型，我试过这个：

@void(float_[:,:],int_[:],int_)

但出现以下错误：

Traceback (most recent call last):
  File "C:\Users\app\Documents\Python Scripts\gbc_carclassify.py", line 18, in <module>
    import gentleboost_c_class as gbc
  File "C:\Users\app\Documents\Python Scripts\gentleboost_c_class.py", line 20, in <module>
    @jit
  File "C:\Users\app\Anaconda\lib\site-packages\numba\decorators.py", line 272, in jit
    return jit_extension_class(cls, kws, env)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\entrypoints.py", line 20, in jit_extension_class
    return jitclass.create_extension(env, py_class, translator_kwargs)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\jitclass.py", line 98, in create_extension
    ext_type = typesystem.jit_exttype(py_class)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\types.py", line 55, in __call__
    return type.__call__(self, *args)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\types\extensiontype.py", line 37, in __init__
    assert isinstance(py_class, type), ("Must be a new-style class "
AssertionError: Must be a new-style class (inherit from 'object')

编辑 2

我已将课程的开头更改为 add (object)，所以现在看起来像这样：

import numba
from numba import jit, autojit, int_, void, float_

@jit
class GentleBoostC(object):
        def __init__(self):
        # init function

        @void(float_[:,:],int_[:],int_)
        def train(self, X, y, H): # this is the function I want to speed up
        # do stuff

但现在我收到了这个错误：

C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\validators.py:74: UserWarning: Constructor for class 'GentleBoostC' has no signature, assuming arguments have type 'object'
  ext_type.py_class.__name__)
Traceback (most recent call last):
  File "C:\Users\app\Documents\Python Scripts\gbc_carclassify.py", line 18, in <module>
    import gentleboost_c_class as gbc
  File "C:\Users\app\Documents\Python Scripts\gentleboost_c_class.py", line 21, in <module>
    class GentleBoostC(object):
  File "C:\Users\app\Anaconda\lib\site-packages\numba\decorators.py", line 272, in jit
    return jit_extension_class(cls, kws, env)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\entrypoints.py", line 20, in jit_extension_class
    return jitclass.create_extension(env, py_class, translator_kwargs)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\jitclass.py", line 110, in create_extension
    extension_compiler.infer()
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\compileclass.py", line 112, in infer
    self.type_infer_methods()
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\compileclass.py", line 145, in type_infer_methods
    self.type_infer_method(method)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\exttypes\compileclass.py", line 121, in type_infer_method
    **self.flags)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\pipeline.py", line 133, in compile2
    func_ast = functions._get_ast(func)
  File "C:\Users\app\Anaconda\lib\site-packages\numba\functions.py", line 89, in _get_ast
    ast.PyCF_ONLY_AST | flags, True)
  File "C:\Users\app\Documents\Python Scripts\gentleboost_c_class.py", line 1
    def train(self, X, y, H):
    ^
IndentationError: unexpected indent

object 我不认为我有缩进错误......在添加到类之前，我对这个完全相同的代码没有任何问题。

4

1 回答 1

1

您可以在数据类型上使用切片语法来表示数组。因此，您的示例可能类似于：

from numba import void, int_, float_, jit

...

@jit
class YourClass(object):

    ...

    @void(float_[:, :], int_[:], int_)
    def train(self, X, y, H):
         # X is typed as a 2D float array and y as a 1D int array.
         pass
于 2014-07-01T13:44:37.137 回答