1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
|
import pytest
np = pytest.importorskip("numpy")
import networkx as nx
from .base_test import (
BaseTestAttributeMixing,
BaseTestDegreeMixing,
BaseTestNumericMixing,
)
class TestDegreeMixingDict(BaseTestDegreeMixing):
def test_degree_mixing_dict_undirected(self):
d = nx.degree_mixing_dict(self.P4)
d_result = {1: {2: 2}, 2: {1: 2, 2: 2}}
assert d == d_result
def test_degree_mixing_dict_undirected_normalized(self):
d = nx.degree_mixing_dict(self.P4, normalized=True)
d_result = {1: {2: 1.0 / 3}, 2: {1: 1.0 / 3, 2: 1.0 / 3}}
assert d == d_result
def test_degree_mixing_dict_directed(self):
d = nx.degree_mixing_dict(self.D)
print(d)
d_result = {1: {3: 2}, 2: {1: 1, 3: 1}, 3: {}}
assert d == d_result
def test_degree_mixing_dict_multigraph(self):
d = nx.degree_mixing_dict(self.M)
d_result = {1: {2: 1}, 2: {1: 1, 3: 3}, 3: {2: 3}}
assert d == d_result
def test_degree_mixing_dict_weighted(self):
d = nx.degree_mixing_dict(self.W, weight="weight")
d_result = {0.5: {1.5: 1}, 1.5: {1.5: 6, 0.5: 1}}
assert d == d_result
class TestDegreeMixingMatrix(BaseTestDegreeMixing):
def test_degree_mixing_matrix_undirected(self):
# fmt: off
a_result = np.array([[0, 2],
[2, 2]]
)
# fmt: on
a = nx.degree_mixing_matrix(self.P4, normalized=False)
np.testing.assert_equal(a, a_result)
a = nx.degree_mixing_matrix(self.P4)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_degree_mixing_matrix_directed(self):
# fmt: off
a_result = np.array([[0, 0, 2],
[1, 0, 1],
[0, 0, 0]]
)
# fmt: on
a = nx.degree_mixing_matrix(self.D, normalized=False)
np.testing.assert_equal(a, a_result)
a = nx.degree_mixing_matrix(self.D)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_degree_mixing_matrix_multigraph(self):
# fmt: off
a_result = np.array([[0, 1, 0],
[1, 0, 3],
[0, 3, 0]]
)
# fmt: on
a = nx.degree_mixing_matrix(self.M, normalized=False)
np.testing.assert_equal(a, a_result)
a = nx.degree_mixing_matrix(self.M)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_degree_mixing_matrix_selfloop(self):
# fmt: off
a_result = np.array([[2]])
# fmt: on
a = nx.degree_mixing_matrix(self.S, normalized=False)
np.testing.assert_equal(a, a_result)
a = nx.degree_mixing_matrix(self.S)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_degree_mixing_matrix_weighted(self):
a_result = np.array([[0.0, 1.0], [1.0, 6.0]])
a = nx.degree_mixing_matrix(self.W, weight="weight", normalized=False)
np.testing.assert_equal(a, a_result)
a = nx.degree_mixing_matrix(self.W, weight="weight")
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_degree_mixing_matrix_mapping(self):
a_result = np.array([[6.0, 1.0], [1.0, 0.0]])
mapping = {0.5: 1, 1.5: 0}
a = nx.degree_mixing_matrix(
self.W, weight="weight", normalized=False, mapping=mapping
)
np.testing.assert_equal(a, a_result)
class TestAttributeMixingDict(BaseTestAttributeMixing):
def test_attribute_mixing_dict_undirected(self):
d = nx.attribute_mixing_dict(self.G, "fish")
d_result = {
"one": {"one": 2, "red": 1},
"two": {"two": 2, "blue": 1},
"red": {"one": 1},
"blue": {"two": 1},
}
assert d == d_result
def test_attribute_mixing_dict_directed(self):
d = nx.attribute_mixing_dict(self.D, "fish")
d_result = {
"one": {"one": 1, "red": 1},
"two": {"two": 1, "blue": 1},
"red": {},
"blue": {},
}
assert d == d_result
def test_attribute_mixing_dict_multigraph(self):
d = nx.attribute_mixing_dict(self.M, "fish")
d_result = {"one": {"one": 4}, "two": {"two": 2}}
assert d == d_result
class TestAttributeMixingMatrix(BaseTestAttributeMixing):
def test_attribute_mixing_matrix_undirected(self):
mapping = {"one": 0, "two": 1, "red": 2, "blue": 3}
a_result = np.array([[2, 0, 1, 0], [0, 2, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]])
a = nx.attribute_mixing_matrix(
self.G, "fish", mapping=mapping, normalized=False
)
np.testing.assert_equal(a, a_result)
a = nx.attribute_mixing_matrix(self.G, "fish", mapping=mapping)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_attribute_mixing_matrix_directed(self):
mapping = {"one": 0, "two": 1, "red": 2, "blue": 3}
a_result = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]])
a = nx.attribute_mixing_matrix(
self.D, "fish", mapping=mapping, normalized=False
)
np.testing.assert_equal(a, a_result)
a = nx.attribute_mixing_matrix(self.D, "fish", mapping=mapping)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_attribute_mixing_matrix_multigraph(self):
mapping = {"one": 0, "two": 1, "red": 2, "blue": 3}
a_result = np.array([[4, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
a = nx.attribute_mixing_matrix(
self.M, "fish", mapping=mapping, normalized=False
)
np.testing.assert_equal(a, a_result)
a = nx.attribute_mixing_matrix(self.M, "fish", mapping=mapping)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
class TestNumericMixingMatrix(BaseTestNumericMixing):
def test_numeric_mixing_matrix_negative(self):
mapping = {-2: 0, -3: 1, -4: 2}
a_result = np.array([[4.0, 1.0, 1.0], [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]])
a = nx.numeric_mixing_matrix(
self.N, "margin", mapping=mapping, normalized=False
)
np.testing.assert_equal(a, a_result)
a = nx.numeric_mixing_matrix(self.N, "margin", mapping=mapping)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
def test_numeric_mixing_matrix_float(self):
mapping = {0.5: 1, 1.5: 0}
a_result = np.array([[6.0, 1.0], [1.0, 0.0]])
a = nx.numeric_mixing_matrix(
self.F, "margin", mapping=mapping, normalized=False
)
np.testing.assert_equal(a, a_result)
a = nx.numeric_mixing_matrix(self.F, "margin", mapping=mapping)
np.testing.assert_equal(a, a_result / float(a_result.sum()))
|
