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hub / github.com/FastMAS/KVCOMM / generate_layered_graph

Function generate_layered_graph

experiments/run_humaneval.py:230–244  ·  view source on GitHub ↗
(n, layer_num=2)

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228 node_kwargs = None
229
230 def generate_layered_graph(n, layer_num=2):
231 adj_matrix = [[0 for _ in range(n)] for _ in range(n)]
232 base_size = n // layer_num
233 remainder = n % layer_num
234 layers: List[int] = []
235 for i in range(layer_num):
236 size = base_size + (1 if i < remainder else 0)
237 layers.extend([i] * size)
238 random.shuffle(layers)
239 for i in range(n):
240 current_layer = layers[i]
241 for j in range(n):
242 if layers[j] == current_layer + 1:
243 adj_matrix[i][j] = 1
244 return adj_matrix
245
246 def generate_star_graph(n):
247 matrix = [[0] * n for _ in range(n)]

Callers 1

get_kwargsFunction · 0.70

Calls

no outgoing calls

Tested by

no test coverage detected