概述

我正在使用Anacondas,Python 3.3

我试图自动执行的功能是

@autojit
def _generate_broadcasted_forecasts(self):
    x = np.array( self.residuals[1:],dtype=float)
    y = np.array( self.sigma[1:-1],dtype=float)
    errors = x/y
    self.errors = errors

数据是：

完整的错误是：


设置一个breakpoit


190行,我看了几行之前发现错误是因为


不幸的是,我对numba了解不足以了解这意味着什么.
为完整起见,发生错误的函数是：


我究竟做错了什么？我尝试在此处发布代码段,但无法重现错误.即以下工作在我的机器上：


self.residuals
Out[1]: 
array([ 0.00027274,0.
@autojit
def _generate_broadcasted_forecasts(self):
    x = np.array( self.residuals[1:],dtype=float)
    errors = x/y
    self.errors = errors   

376,0.01042219,0.02850773,-0.01411178,
 -0.01929838,0.00385101,-0.01630044,0.03715821,0.02258934,
 0.05662874,-0.02359702,-0.01823098,-0.03092986,0.02994069,
 0.01090546,-0.02475619,-0.01020354,-0.00332659,-0.01734819,
 0.01957363,-0.02706434,0.01215692,-0.05122325,-0.02016905,
 -0.0472204,0.0183388,0.00319104,-0.04198954,0.00586023,
 -0.03320624,0.0061127,-0.04101338,0.07630624,-0.04561496,
 -0.01602609,0.03317164,-0.02177255,-0.001892,-0.01752149,
 -0.01062089,-0.00036998,-0.02321696,-0.01139503,-0.04052122,
 0.01033072,-0.04154074,0.00703859,0.01299577,0.0532591,
 0.02145779,0.06422423,-0.02635143,-0.01230678,-0.00905247,
 -0.03167185,0.02384259,0.03086425,0.02310251,-0.003188,
 0.01928799,-0.02143164,-0.01694362,0.00274987,-0.03429799,
 0.00123984,-0.01234553,-0.01822308,0.01739987,0.05260887,
 -0.01671929,-0.0451185,-0.00253769,-0.01643629,0.0689997,
 0.07316202,-0.05564215,0.02830494,0.02396265,-0.00335834,
 -0.00328183,-0.01633152,-0.02944809,-0.01730992,0.02290413,
 0.03344062,-0.03211203,0.00115551,-0.0348293,0.01106866,
 -0.00334149,-0.000305,0.00499175,-0.00023061,0.00635803,
 -0.01883831,-0.01078026,0.01491779,0.04605717,-0.01274995,
 -0.00380779,0.03336405,-0.01263997,0.02967207,-0.00949215,
 -0.0239608,0.02311184,-0.03347211,-0.01047334,0.02570358,
 -0.02749113,0.01718504,-0.00012177,0.03942462,-0.02854834,
 -0.00689199,0.00769279,0.01237644,-0.02212167,0.02312826,
 -0.00335928,0.00626517,0.0127933,0.00980856,0.02102578,
 -0.00291102,0.02605287,0.00453684,0.01697849,-0.01733679,
 0.02957533,-0.00981372,0.01504038,-0.00308469,-0.01067471,
 0.02550573,0.01636283,0.00386063,0.01584089,0.02774164,
 0.01026229,-0.01319894,0.0047792,0.01137512,-0.00017363,
 -0.0026951,0.023888,-0.0178131,-0.00043418,0.00078961,
 -0.01046682,-0.01422187,0.02284845,0.01530962,-0.00277356,
 -0.047021,-0.0233437,-0.00700932,-0.00461674,0.04386189,
 0.03214708,0.02512427,0.05888554,0.00946264,-0.01649079,
 -0.01824022,0.00305155,0.02141179,0.02040107,0.01223521,
 0.04182406,0.01200173,-0.00666375,0.00839121,0.00601783,
 0.00727748,-0.01839253,-0.02627941,0.00040831,-0.00348844,
 0.0396223,0.01587524,0.05635961,0.00638051,0.03315842,
 -0.02278018,-0.00149056,-0.00028848,-0.09895528,0.02313064,
 0.02065743,-0.00115973,-0.00135562,-0.02609936,0.00146749,
 0.01020923,-0.07006086,0.04481519,0.02428593,0.00513643,
 0.00386419,-0.03904007,0.02584459,0.02042376,-0.00415313,
 0.0250591,0.0078504,0.01071272,0.01446379,0.04678684,
 -0.030569,0.00499873,-0.00121879,-0.00028605,-0.01341486,
 -0.00119668,0.0102857,-0.03408491,-0.01151752,0.02421846,
 -0.0126916,0.03427147,-0.00672604,0.00265135,0.04540966,
 -0.05231339,0.00802635,0.02506964,0.00188135,0.03521239,
 -0.01252548,-0.0487149,-0.01607805,0.06138833,-0.02267715,
 -0.00883121,0.04540312,-0.02248676,0.01033092,-0.01845178,
 -0.00950717,-0.03646932,0.05394227,-0.03149327,-0.02139244,
 -0.00629822,-0.01885609,-0.00724694,0.01627761,0.02120749])
@autojit
def _generate_broadcasted_forecasts(self):
    x = np.array( self.residuals[1:],dtype=float)
    errors = x/y
    self.errors = erroRSSelf.sigma
 Out[1]:
 array([ 0.02647903,0.02580863,0.02854044,0.02781347,0.02778348,
 0.02719822,0.02682252,0.02614003,0.02575487,0.02647783,
 0.02629712,0.0285969,0.02826486,0.02776125,0.0278659,
 0.02790585,0.02723722,0.02708099,0.02646327,0.02580474,
 0.02548335,0.025272,0.02542612,0.0249857,0.02698342,
 0.02665878,0.02803867,0.02755763,0.02681504,0.02775527,
 0.02702024,0.02733631,0.02663591,0.0275261,0.0317507,
 0.03234794,0.03145869,0.03131707,0.03068293,0.02970747,
 0.02906351,0.02830046,0.02749342,0.02724532,0.02663881,
 0.02749216,0.02684456,0.02774718,0.02702682,0.02647462,
 0.02842004,0.02801799,0.03078638,0.03037759,0.02954908,
 0.0287226,0.02876979,0.02843258,0.02846502,0.02812427,
 0.02733939,0.026951,0.02667852,0.02626748,0.02562064,
 0.02616582,0.02552095,0.02507717,0.02485235,0.02461849,
 0.0268124,0.02638272,0.02762759,0.02687628,0.02643228,
 0.03003171,0.0333827,0.03453727,0.03389183,0.03312942,
 0.03199099,0.03093019,0.03015629,0.02994792,0.02927847,
 0.02885815,0.0289911,0.02903535,0.0281757,0.02846391,
 0.02775531,0.02699883,0.02628887,0.0256573,0.02505049,
 0.02453268,0.02438172,0.02399769,0.02375888,0.02547854,
 0.02504846,0.02450438,0.02512236,0.02471973,0.02508061,
 0.02461088,0.02467567,0.02469342,0.02529554,0.02482779,
 0.02495733,0.02516796,0.02489793,0.02435106,0.02542535,
 0.02564393,0.02508549,0.02458393,0.02422082,0.02423858,
 0.02430145,0.0238144,0.02339728,0.02315016,0.02285179,
 0.02295964,0.02258121,0.02297639,0.02260993,0.02257341,
 0.02255411,0.02316487,0.02286531,0.0227356,0.02237744,
 0.0221682,0.02258031,0.02252393,0.02219001,0.02215312,
 0.02269849,0.02245043,0.02230218,0.02199653,0.02184009,
 0.02154876,0.02129164,0.02171611,0.02180259,0.02151474,
 0.02125285,0.02114355,0.02115455,0.02153887,0.02154778,
 0.02129126,0.02352853,0.02367564,0.0232808,0.02288927,
 0.02455152,0.02508442,0.02515797,0.02789379,0.0271991,
 0.0267295,0.02635739,0.02570532,0.02554735,0.02536272,
 0.02492956,0.02611233,0.02561108,0.02505219,0.02456478,
 0.02408222,0.0236572,0.02357261,0.02386815,0.0234048,
 0.02299286,0.02427669,0.0240433,0.02672357,0.02607349,
 0.02649293,0.02631903,0.02566302,0.02505579,0.03301026,
 0.03228805,0.03153822,0.03050111,0.02953701,0.02922853,
 0.02835562,0.02763884,0.03111266,0.03172814,0.03115373,
 0.03016414,0.02923511,0.02967298,0.02934095,0.02882213,
 0.0279922,0.0277788,0.02706721,0.02646064,0.02599358,
 0.02743368,0.02755225,0.02682387,0.02612851,0.0254851,
 0.02507182,0.02451255,0.02410654,0.02482254,0.02441787,
 0.02451516,0.02416618,0.0248875,0.02438786,0.02388931,
 0.02553014,0.02754089,0.02685012,0.02674541,0.026058,
 0.02661146,0.02608169,0.02767567,0.02715383,0.0297839,
 0.02931113,0.02849885,0.02948042,0.02902629,0.02826068,
 0.02776472,0.02708077,0.0275969,0.02942746,0.02939432,
 0.02890609,0.02808997,0.02762202,0.02691396,0.02646187,
 0.02622503])
Traceback (most recent call last):
  File "C:\Anaconda\envs\p33\lib\site-packages\IPython\core\interactiveshell.py",line 2732,in run_code
    exec(code_obj,self.user_global_ns,self.user_ns)
  File "<ipython-input-1-d063d30a36f8>",line 1,in <module>
    execfile('C:\\Users\\Jon\\workspace\\FundsAnalysis\\src\\examples\\ar-garch\\ar_garch3.py')
  File "C:\eclipse_kepler\plugins\org.python.pydev_2.7.5.2013052819\pysrc\_pydev_execfile.py",line 38,in execfile
    exec(compile(contents+"\n",file,'exec'),glob,loc) #execute the script
  File "C:\Users\Jon\workspace\FundsAnalysis\src\examples\ar-garch\ar_garch3.py",line 225,in <module>
    ar_garch.evaluate()
  File "C:\Users\Jon\workspace\FundsAnalysis\src\examples\ar-garch\ar_garch3.py",line 158,in evaluate
    self._generate_broadcasted_forecasts()
  File "numbawrapper.pyx",line 287,in numba.numbawrapper.BoundSpecializingWrapper.__call__ (numba\numbawrapper.c:5041)
  File "numbawrapper.pyx",line 189,in numba.numbawrapper._NumbaSpecializingWrapper.__call__ (numba\numbawrapper.c:3726)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\wrapping\compiler.py",line 68,in compile_from_args
    return self.compile(signature)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\wrapping\compiler.py",line 83,in compile
    compiled_function = dec(self.py_func)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\decorators.py",line 222,in _jit_decorator
    env,func,argtys,restype=return_type,nopython=nopython,**kwargs)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\decorators.py",line 133,in compile_function
    func_env = pipeline.compile2(env,restype,argtypes,**kwds)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\pipeline.py",line 134,in compile2
    post_ast = pipeline(func_ast,env)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\pipeline.py",line 181,in __call__
    ast = self.transform(ast,line 602,in transform
    ast = stage(ast,line 587,in _stage
    return _check_stage_object(stage_obj)(ast,line 184,line 499,in transform
    func_env.translator.translate()
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\codegen\translate.py",line 314,in translate
    self.visit(node)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\codegen\translate.py",line 128,in visit
    return fn(node)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\codegen\translate.py",line 554,in visit_Assign
    decref=decref,incref=incref)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\codegen\translate.py",line 566,in generate_assign_stack
    lvalue = self.caster.cast(lvalue,ltarget.type.pointee)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\llvm_types.py",line 91,in cast
    return self.build_cast(self.builder,lvalue,dst_ltype,*args,**kws)
  File "C:\Anaconda\envs\p33\lib\site-packages\numba\llvm_types.py",line 191,in build_cast
    (str(lty1),str(lty2)))
NotImplementedError: Unable to cast from { i64,i8* }* to { i64,i8* }.File "C:\Anaconda\envs\p33\lib\site-packages\numba\llvm_types.py",in build_castlkind1==14
lkind2==12@classmethod
def build_cast(cls,builder,lval1,lty2,**kws):
    ret_val = lval1
    lty1 = lval1.type
    lkind1 = lty1.kind
    lkind2 = lty2.kind

    # This looks like the wrong place to enforce this
    # TODO: We need to pass in the numba types instead
    # if lc.TYPE_INTEGER in (lkind1,lkind2) and 'unsigned' not in kws:
    #     # Be strict about having `unsigned` define when
    #     # we have integer types
    #     raise ValueError("UnkNown signedness for integer type",#                      '%s -> %s' % (lty1,lty2),args,kws)

    if lkind1 == lkind2:

        if lkind1 in cls.CAST_MAP:
            ret_val = cls.CAST_MAP[lkind1](cls,**kws)
        else:
            raise NotImplementedError(lkind1)
    else:
        map_index = (lkind1,lkind2)
        if map_index in cls.CAST_MAP:
            ret_val = cls.CAST_MAP[map_index](cls,**kws)
        else:
            raise NotImplementedError('Unable to cast from %s to %s.' %
                                      (str(lty1),str(lty2)))
    return ret_valfrom numba import autojit

import numpy as np

@autojit
def numba1():
    return np.array( [1,2,3] )

@autojit
def numba2():
    x = np.array([1.0,2.0,3.0,4.0])
    y = np.array([1.0,4,5.0])    
    z = x[1:] / y[1:-1]    
    return np.sqrt( z )


print( numba1() )
print( numba2() )

@autojit
def _generate_broadcasted_forecasts(self):
    x = np.array( self.residuals[1:],dtype=float)
    errors = x/y
    self.errors = errors   

完整的错误是：

设置一个breakpoit

190行,我看了几行之前发现错误是因为

不幸的是,我对numba了解不足以了解这意味着什么.

为完整起见,发生错误的函数是：

我究竟做错了什么？我尝试在此处发布代码段,但无法重现错误.即以下工作在我的机器上：

376,0.01042219,0.02850773,-0.01411178,
-0.01929838,0.00385101,-0.01630044,0.03715821,0.02258934,
0.05662874,-0.02359702,-0.01823098,-0.03092986,0.02994069,
0.01090546,-0.02475619,-0.01020354,-0.00332659,-0.01734819,
0.01957363,-0.02706434,0.01215692,-0.05122325,-0.02016905,
-0.0472204,0.0183388,0.00319104,-0.04198954,0.00586023,
-0.03320624,0.0061127,-0.04101338,0.07630624,-0.04561496,
-0.01602609,0.03317164,-0.02177255,-0.001892,-0.01752149,
-0.01062089,-0.00036998,-0.02321696,-0.01139503,-0.04052122,
0.01033072,-0.04154074,0.00703859,0.01299577,0.0532591,
0.02145779,0.06422423,-0.02635143,-0.01230678,-0.00905247,
-0.03167185,0.02384259,0.03086425,0.02310251,-0.003188,
0.01928799,-0.02143164,-0.01694362,0.00274987,-0.03429799,
0.00123984,-0.01234553,-0.01822308,0.01739987,0.05260887,
-0.01671929,-0.0451185,-0.00253769,-0.01643629,0.0689997,
0.07316202,-0.05564215,0.02830494,0.02396265,-0.00335834,
-0.00328183,-0.01633152,-0.02944809,-0.01730992,0.02290413,
0.03344062,-0.03211203,0.00115551,-0.0348293,0.01106866,
-0.00334149,-0.000305,0.00499175,-0.00023061,0.00635803,
-0.01883831,-0.01078026,0.01491779,0.04605717,-0.01274995,
-0.00380779,0.03336405,-0.01263997,0.02967207,-0.00949215,
-0.0239608,0.02311184,-0.03347211,-0.01047334,0.02570358,
-0.02749113,0.01718504,-0.00012177,0.03942462,-0.02854834,
-0.00689199,0.00769279,0.01237644,-0.02212167,0.02312826,
-0.00335928,0.00626517,0.0127933,0.00980856,0.02102578,
-0.00291102,0.02605287,0.00453684,0.01697849,-0.01733679,
0.02957533,-0.00981372,0.01504038,-0.00308469,-0.01067471,
0.02550573,0.01636283,0.00386063,0.01584089,0.02774164,
0.01026229,-0.01319894,0.0047792,0.01137512,-0.00017363,
-0.0026951,0.023888,-0.0178131,-0.00043418,0.00078961,
-0.01046682,-0.01422187,0.02284845,0.01530962,-0.00277356,
-0.047021,-0.0233437,-0.00700932,-0.00461674,0.04386189,
0.03214708,0.02512427,0.05888554,0.00946264,-0.01649079,
-0.01824022,0.00305155,0.02141179,0.02040107,0.01223521,
0.04182406,0.01200173,-0.00666375,0.00839121,0.00601783,
0.00727748,-0.01839253,-0.02627941,0.00040831,-0.00348844,
0.0396223,0.01587524,0.05635961,0.00638051,0.03315842,
-0.02278018,-0.00149056,-0.00028848,-0.09895528,0.02313064,
0.02065743,-0.00115973,-0.00135562,-0.02609936,0.00146749,
0.01020923,-0.07006086,0.04481519,0.02428593,0.00513643,
0.00386419,-0.03904007,0.02584459,0.02042376,-0.00415313,
0.0250591,0.0078504,0.01071272,0.01446379,0.04678684,
-0.030569,0.00499873,-0.00121879,-0.00028605,-0.01341486,
-0.00119668,0.0102857,-0.03408491,-0.01151752,0.02421846,
-0.0126916,0.03427147,-0.00672604,0.00265135,0.04540966,
-0.05231339,0.00802635,0.02506964,0.00188135,0.03521239,
-0.01252548,-0.0487149,-0.01607805,0.06138833,-0.02267715,
-0.00883121,0.04540312,-0.02248676,0.01033092,-0.01845178,
-0.00950717,-0.03646932,0.05394227,-0.03149327,-0.02139244,
-0.00629822,-0.01885609,-0.00724694,0.01627761,0.02120749])

完整的错误是：

设置一个breakpoit

190行,我看了几行之前发现错误是因为

不幸的是,我对numba了解不足以了解这意味着什么.

为完整起见,发生错误的函数是：

我究竟做错了什么？我尝试在此处发布代码段,但无法重现错误.即以下工作在我的机器上：

@autojit
def _generate_broadcasted_forecasts(self):
    x = np.array( self.residuals[1:],dtype=float)
    errors = x/y
    self.errors = errors   

376,0.01042219,0.02850773,-0.01411178,
-0.01929838,0.00385101,-0.01630044,0.03715821,0.02258934,
0.05662874,-0.02359702,-0.01823098,-0.03092986,0.02994069,
0.01090546,-0.02475619,-0.01020354,-0.00332659,-0.01734819,
0.01957363,-0.02706434,0.01215692,-0.05122325,-0.02016905,
-0.0472204,0.0183388,0.00319104,-0.04198954,0.00586023,
-0.03320624,0.0061127,-0.04101338,0.07630624,-0.04561496,
-0.01602609,0.03317164,-0.02177255,-0.001892,-0.01752149,
-0.01062089,-0.00036998,-0.02321696,-0.01139503,-0.04052122,
0.01033072,-0.04154074,0.00703859,0.01299577,0.0532591,
0.02145779,0.06422423,-0.02635143,-0.01230678,-0.00905247,
-0.03167185,0.02384259,0.03086425,0.02310251,-0.003188,
0.01928799,-0.02143164,-0.01694362,0.00274987,-0.03429799,
0.00123984,-0.01234553,-0.01822308,0.01739987,0.05260887,
-0.01671929,-0.0451185,-0.00253769,-0.01643629,0.0689997,
0.07316202,-0.05564215,0.02830494,0.02396265,-0.00335834,
-0.00328183,-0.01633152,-0.02944809,-0.01730992,0.02290413,
0.03344062,-0.03211203,0.00115551,-0.0348293,0.01106866,
-0.00334149,-0.000305,0.00499175,-0.00023061,0.00635803,
-0.01883831,-0.01078026,0.01491779,0.04605717,-0.01274995,
-0.00380779,0.03336405,-0.01263997,0.02967207,-0.00949215,
-0.0239608,0.02311184,-0.03347211,-0.01047334,0.02570358,
-0.02749113,0.01718504,-0.00012177,0.03942462,-0.02854834,
-0.00689199,0.00769279,0.01237644,-0.02212167,0.02312826,
-0.00335928,0.00626517,0.0127933,0.00980856,0.02102578,
-0.00291102,0.02605287,0.00453684,0.01697849,-0.01733679,
0.02957533,-0.00981372,0.01504038,-0.00308469,-0.01067471,
0.02550573,0.01636283,0.00386063,0.01584089,0.02774164,
0.01026229,-0.01319894,0.0047792,0.01137512,-0.00017363,
-0.0026951,0.023888,-0.0178131,-0.00043418,0.00078961,
-0.01046682,-0.01422187,0.02284845,0.01530962,-0.00277356,
-0.047021,-0.0233437,-0.00700932,-0.00461674,0.04386189,
0.03214708,0.02512427,0.05888554,0.00946264,-0.01649079,
-0.01824022,0.00305155,0.02141179,0.02040107,0.01223521,
0.04182406,0.01200173,-0.00666375,0.00839121,0.00601783,
0.00727748,-0.01839253,-0.02627941,0.00040831,-0.00348844,
0.0396223,0.01587524,0.05635961,0.00638051,0.03315842,
-0.02278018,-0.00149056,-0.00028848,-0.09895528,0.02313064,
0.02065743,-0.00115973,-0.00135562,-0.02609936,0.00146749,
0.01020923,-0.07006086,0.04481519,0.02428593,0.00513643,
0.00386419,-0.03904007,0.02584459,0.02042376,-0.00415313,
0.0250591,0.0078504,0.01071272,0.01446379,0.04678684,
-0.030569,0.00499873,-0.00121879,-0.00028605,-0.01341486,
-0.00119668,0.0102857,-0.03408491,-0.01151752,0.02421846,
-0.0126916,0.03427147,-0.00672604,0.00265135,0.04540966,
-0.05231339,0.00802635,0.02506964,0.00188135,0.03521239,
-0.01252548,-0.0487149,-0.01607805,0.06138833,-0.02267715,
-0.00883121,0.04540312,-0.02248676,0.01033092,-0.01845178,
-0.00950717,-0.03646932,0.05394227,-0.03149327,-0.02139244,
-0.00629822,-0.01885609,-0.00724694,0.01627761,0.02120749])

我要做的是在init(…)中声明self.errors

即

def __init__( self ):        
    self.errors = np.ndarray(0)

总结

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