-
Notifications
You must be signed in to change notification settings - Fork 37
/
Isomap.py
70 lines (62 loc) · 2.54 KB
/
Isomap.py
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
import numpy as np
# 用Floyd_Warshall算法算出的dist和sklearn有差异
# MDS也有差异
class Isomap:
def __init__(self,k=5,d_=2):
self.d_=d_
self.k=k
self.dist_matrix_=None
@staticmethod
def Floyd_Warshall(Dist):
m = Dist.shape[0]
for k in range(m):
for i in range(m):
for j in range(m):
Dist[i, j] = min(Dist[i,j],Dist[i, k] + Dist[k, j])
return Dist
def fit(self,X):
m = X.shape[0]
Dist = np.zeros((m, m), dtype=np.float32)
self.Omega = np.zeros((m, m), dtype=np.float32)
for i in range(m):
Dist[i, :] = np.sqrt(np.sum((X[i] - X) ** 2, axis=1))
inf_index=np.argsort(Dist[i,:])[self.k+1:]
Dist[i,inf_index]=float('inf')
Dist=Isomap.Floyd_Warshall(Dist)
self.dist_matrix_=Dist
# 使用MDS中的步骤
Dist_i2 = np.mean(Dist, axis=1).reshape(-1, 1)
Dist_j2 = np.mean(Dist, axis=0).reshape(1, -1)
dist_2 = np.mean(Dist)
B_new = -0.5 * (Dist - Dist_i2 - Dist_j2 + dist_2)
# 用eig和eigh函数分解出的结果符号位不同
#values, vectors = np.linalg.eig(B_new)
values,vectors=np.linalg.eigh(B_new)
idx = np.argsort(values)[::-1]
self.values_ = values[idx][:self.d_]
# print('values:',self.values_)
self.vectors_ = vectors[:, idx][:, :self.d_]
self.Z = self.vectors_.dot(np.diag(np.sqrt(self.values_))).real
def fit_transform(self,X):
self.fit(X)
return self.Z
pass
if __name__=='__main__':
X=np.array([[0.697,0.460],[0.774,0.376],[0.634,0.264],[0.608,0.318],[0.556,0.215],
[0.403,0.237],[0.481,0.149],[0.437,0.211],[0.666,0.091],[0.243,0.267],
[0.245,0.057],[0.343,0.099],[0.639,0.161],[0.657,0.198],[0.360,0.370],
[0.593,0.042],[0.719,0.103],[0.359,0.188],[0.339,0.241],[0.282,0.257],
[0.748,0.232],[0.714,0.346],[0.483,0.312],[0.478,0.437],[0.525,0.369],
[0.751,0.489],[0.532,0.472],[0.473,0.376],[0.725,0.445],[0.446,0.459]])
X=np.c_[X,X]
isomap=Isomap(k=5,d_=2)
Z=isomap.fit_transform(X)
print('tinyml:')
print(Z)
import sklearn.manifold as manifold
sklearn_Isomap=manifold.Isomap(n_neighbors=5, n_components=2,path_method='auto')
Z2=sklearn_Isomap.fit_transform(X)
print('sklearn')
print(Z2)
print('dist_matrix_diff:',np.sum((isomap.dist_matrix_-sklearn_Isomap.dist_matrix_)**2))
print('Z diff:',np.sum((Z-Z2)**2))