TypeError：数组在赋值时必须具有一致的类型(TypeError: Arrays must have consistent types in assignment)

从这里开始，我有以下代码：

@jit(float_[:,:,:](float_[:,:], int_[:], int_)) def train_function(X, y, H): # do lots of stuff, including setting the arrays g and g_per_round like this: g = np.zeros((no_features, no_classes)) g_per_round = np.zeros((H, no_features, no_classes)) # do more stuff, then: g_h = None j = 0 print "Calculating regression coefficients per class. .." # building the parameters per j class for y1_w in zip(z.T, weights.T): y1, w = y1_w temp_g = sm.WLS(y1, X, w).fit() # Step 2(a)(ii) if g_h is None: # sometimes g *is* None, and that's fine g_h = temp_g.params # this is an array of floats else: g_h = np.c_[g_h, temp_g.params] j = j + 1 if np.allclose(g,0) or g is None: g = g_h else: g = g + g_h # do lots more stuff, then finally: return g_per_round class GentleBoostC(object): # init functions and stuff def train(self, X, y, H): self.g_per_round = train_function(X, y, H)

现在我收到以下错误：

@jit(float_[:,:,:](float_[:,:], int_[:], int_)) more lines, etc etc etc, last few lines: unresolved_types, var_name) File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 767, in promote_arrays assert_equal(non_array_types[0]) File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 764, in assert_equal var_name, result_type, other_type)) TypeError: Arrays must have consistent types in assignment for variable 'g': 'float64[:, :]' and 'none'

在尝试添加@jit以加速我的代码之前，我实际上没有遇到任何问题。

Following up from here, I've got code like the following:

@jit(float_[:,:,:](float_[:,:], int_[:], int_)) def train_function(X, y, H): # do lots of stuff, including setting the arrays g and g_per_round like this: g = np.zeros((no_features, no_classes)) g_per_round = np.zeros((H, no_features, no_classes)) # do more stuff, then: g_h = None j = 0 print "Calculating regression coefficients per class. .." # building the parameters per j class for y1_w in zip(z.T, weights.T): y1, w = y1_w temp_g = sm.WLS(y1, X, w).fit() # Step 2(a)(ii) if g_h is None: # sometimes g *is* None, and that's fine g_h = temp_g.params # this is an array of floats else: g_h = np.c_[g_h, temp_g.params] j = j + 1 if np.allclose(g,0) or g is None: g = g_h else: g = g + g_h # do lots more stuff, then finally: return g_per_round class GentleBoostC(object): # init functions and stuff def train(self, X, y, H): self.g_per_round = train_function(X, y, H)

Now I'm getting the following error:

@jit(float_[:,:,:](float_[:,:], int_[:], int_)) more lines, etc etc etc, last few lines: unresolved_types, var_name) File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 767, in promote_arrays assert_equal(non_array_types[0]) File "C:\Users\app\Anaconda\lib\site-packages\numba\typesystem\ssatypes.py", line 764, in assert_equal var_name, result_type, other_type)) TypeError: Arrays must have consistent types in assignment for variable 'g': 'float64[:, :]' and 'none'

I actually had no issues with this before trying to add @jit to speed up my code.