Coverage for src/abcd_graph/models.py: 100%

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1# Copyright (c) 2024 Jordan Barrett & Aleksander Wojnarowicz 

2# 

3# Permission is hereby granted, free of charge, to any person obtaining a copy 

4# of this software and associated documentation files (the "Software"), to deal 

5# in the Software without restriction, including without limitation the rights 

6# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 

7# copies of the Software, and to permit persons to whom the Software is 

8# furnished to do so, subject to the following conditions: 

9# 

10# The above copyright notice and this permission notice shall be included in all 

11# copies or substantial portions of the Software. 

12# 

13# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 

14# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 

15# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 

16# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 

17# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 

18# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 

19# SOFTWARE. 

20 

21__all__ = [ 

22 "Model", 

23 "configuration_model", 

24 "chung_lu", 

25] 

26 

27from typing import Protocol 

28 

29import numpy as np 

30from numpy.typing import NDArray 

31 

32 

33class Model(Protocol): 

34 def __call__(self, degree_sequence: dict[int, int]) -> NDArray[np.int64]: ... 

35 

36 @property 

37 def __name__(self) -> str: ... 

38 

39 

40def configuration_model(degree_sequence: dict[int, int]) -> NDArray[np.int64]: 

41 labels = list(degree_sequence.keys()) 

42 counts = list(degree_sequence.values()) 

43 

44 vertices = np.repeat(labels, counts) 

45 

46 np.random.shuffle(vertices) 

47 

48 edges = np.array(vertices).reshape(-1, 2) 

49 

50 return edges 

51 

52 

53def normalize(degrees: list[int]) -> NDArray[np.float64]: 

54 """Normalize the degree sequence.""" 

55 degrees_array: NDArray[np.int64] = np.array(degrees) 

56 norm = degrees_array.sum() 

57 result: NDArray[np.float64] = np.divide(degrees_array, norm) 

58 return result 

59 

60 

61def chung_lu(degree_sequence: dict[int, int]) -> NDArray[np.int64]: 

62 """Generate a Chung-Lu random graph based on a given degree sequence.""" 

63 nodes = list(degree_sequence.keys()) 

64 degrees = list(degree_sequence.values()) 

65 

66 return np.random.choice(a=nodes, size=int(sum(degrees)), p=normalize(degrees)).reshape(-1, 2)