Sourcery Starbot ⭐ refactored Tybirk/OCAIM#1
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SourceryAI wants to merge 1 commit intoTybirk:masterfrom
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Sourcery Starbot ⭐ refactored Tybirk/OCAIM#1SourceryAI wants to merge 1 commit intoTybirk:masterfrom
SourceryAI wants to merge 1 commit intoTybirk:masterfrom
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SourceryAI
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Apr 6, 2022
| feature_matrix = np.zeros(numberOfFeatures*numberOfNodes).reshape(numberOfNodes, numberOfFeatures) | ||
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| featureToIndex = dict() | ||
| featureToIndex = {} |
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Function get_feature_matrix refactored with the following changes:
- Replace dict() with {} (
dict-literal)
| G = nx.Graph() | ||
| else: | ||
| G = nx.DiGraph() | ||
| G = nx.DiGraph() if directed else nx.Graph() |
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Function read_graph refactored with the following changes:
- Swap if/else branches of if expression to remove negation (
swap-if-expression) - Replace if statement with if expression (
assign-if-exp)
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| for i in range(numberOfEdges): | ||
| for _ in range(numberOfEdges): |
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Function create_graph refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
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| if w is None: w = np.zeros(len(X[1])) | ||
| for i in range(epochs): | ||
| for _ in range(epochs): |
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Function mini_batch_grad_descent refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
This removes the following comments ( why? ):
# lr = lr * np.linalg.norm(grad)/len(y)
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| if not linear: | ||
| return sigmoid(feature_mat @ parameters) | ||
| else: | ||
| edge_probs = [max(min(feature_mat[i, :]@linear_parameters, 1),0) for i in range(feature_mat.shape[0])] | ||
| return edge_probs | ||
| return ( | ||
| [ | ||
| max(min(feature_mat[i, :] @ linear_parameters, 1), 0) | ||
| for i in range(feature_mat.shape[0]) | ||
| ] | ||
| if linear | ||
| else sigmoid(feature_mat @ parameters) | ||
| ) |
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Function get_edge_probabilities refactored with the following changes:
- Swap if/else branches of if expression to remove negation (
swap-if-expression) - Replace if statement with if expression (
assign-if-exp) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| while len(new_activations) > 0: | ||
| while new_activations: |
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Function simulate_activations refactored with the following changes:
- Simplify sequence length comparison (
simplify-len-comparison)
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| message = list(bestMessage) | ||
| for feature in range(1, numberFeatures): | ||
| index += 1 | ||
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| currentResult = 0 | ||
| for index, feature in enumerate(range(1, numberFeatures), start=1): |
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Function greedy refactored with the following changes:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Replace manual loop counter with call to enumerate (
convert-to-enumerate) - Swap if/else branches (
swap-if-else-branches) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif) - Replace unused for index with underscore (
for-index-underscore)
| plt.xlabel(xlabel, fontsize=40) | ||
| if title: | ||
| plt.title('%s' %(title), fontsize = 48) | ||
| plt.title(f'{title}', fontsize = 48) |
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Function visualiseResults refactored with the following changes:
- Replace interpolated string formatting with f-string (
replace-interpolation-with-fstring)
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