zomux /
deepy
| Conditions | 20 |
| Total Lines | 58 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 3 | ||
| Bugs | 0 | Features | 0 |
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like convert_to_theano_var() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | #!/usr/bin/env python |
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| 7 | def convert_to_theano_var(obj): |
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| 8 | """ |
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| 9 | Convert neural vars to theano vars. |
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| 10 | :param obj: NeuralVariable or list or dict or tuple |
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| 11 | :return: theano var, test var, tensor found, neural var found |
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| 12 | """ |
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| 13 | from deepy.core.neural_var import NeuralVariable |
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| 14 | if type(obj) == tuple: |
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| 15 | return tuple(convert_to_theano_var(list(obj))) |
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| 16 | if type(obj) == list: |
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| 17 | unpacked_list = map(convert_to_theano_var, obj) |
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| 18 | normal_list = [] |
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| 19 | test_list = [] |
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| 20 | theano_var_found = False |
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| 21 | neural_var_found = False |
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| 22 | for normal_var, tensor_found, neural_found in unpacked_list: |
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| 23 | normal_list.append(normal_var) |
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| 24 | if tensor_found: theano_var_found = True |
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| 25 | if neural_found: neural_var_found = True |
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| 26 | return normal_list, theano_var_found, neural_var_found |
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| 27 | elif type(obj) == dict: |
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| 28 | normal_map = {} |
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| 29 | theano_var_found = False |
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| 30 | neural_var_found = False |
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| 31 | for key in obj: |
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| 32 | normal_var, tensor_found, neural_found = convert_to_theano_var(obj[key]) |
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| 33 | normal_map[key] = normal_var |
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| 34 | if tensor_found: theano_var_found = True |
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| 35 | if neural_found: neural_var_found = True |
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| 36 | return normal_map, theano_var_found, neural_var_found |
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| 37 | elif type(obj) == MapDict: |
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| 38 | normal_map = {} |
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| 39 | theano_var_found = False |
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| 40 | neural_var_found = False |
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| 41 | for key in obj: |
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| 42 | normal_var, tensor_found, neural_found = convert_to_theano_var(obj[key]) |
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| 43 | normal_map[key] = normal_var |
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| 44 | if tensor_found: theano_var_found = True |
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| 45 | if neural_found: neural_var_found = True |
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| 46 | return MapDict(normal_map), theano_var_found, neural_var_found |
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| 47 | elif type(obj) == NeuralVariable: |
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| 48 | theano_tensor = obj.tensor |
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| 49 | theano_tensor.tag.last_dim = obj.dim() |
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| 50 | return theano_tensor, False, True |
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| 51 | elif type(obj) == TensorVariable: |
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| 52 | return obj, True, False |
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| 53 | elif type(obj) == slice: |
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| 54 | normal_args = [] |
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| 55 | theano_var_found = False |
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| 56 | neural_var_found = False |
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| 57 | for arg in [obj.start, obj.stop, obj.step]: |
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| 58 | normal_var, tensor_found, neural_found = convert_to_theano_var(arg) |
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| 59 | normal_args.append(normal_var) |
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| 60 | if tensor_found: theano_var_found = True |
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| 61 | if neural_found: neural_var_found = True |
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| 62 | return slice(*normal_args), theano_var_found, neural_var_found |
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| 63 | else: |
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| 64 | return obj, False, False |
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| 65 | |||
| 132 | return neural_computation(original_func, prefer_tensor=True) |
