-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtoeplitz.py
More file actions
97 lines (75 loc) · 2.04 KB
/
toeplitz.py
File metadata and controls
97 lines (75 loc) · 2.04 KB
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
# -*- coding: utf-8 -*-
"""
DOCTEXT
"""
import numpy as np
def toeplitz_inverse(m):
"""
Computes a fast inverse of a Toeplitz matrix (Alg. 1.1) and
log det m
mat = numpy.ndarray with Toeplitz form
"""
c = m[:, 0]
N = len(c)
c_inv = np.zeros((N, N)) # intialising the matrix
v, l = modified_trench_helper(c)
c_inv[0, :] = v[::-1]
c_inv[:, 0] = v[::-1]
c_inv[-1, :] = v
c_inv[:, -1] = v
for ii in xrange(1, int(np.floor((N-1)/2)+1)):
for jj in xrange(ii, N - ii):
c_inv[ii, jj] = (c_inv[ii-1, jj-1]
+ (v[N-jj-1] * v[N-ii-1] - v[ii-1] * v[jj-1])
/ v[-1])
c_inv[jj, ii] = c_inv[ii, jj]
c_inv[N-ii-1, N-jj-1] = c_inv[ii, jj]
c_inv[N-jj-1, N-ii-1] = c_inv[ii, jj]
return c_inv, l
def modified_trench_helper(c):
"""
The modified Trench algorithm (Alg. 1.2)
c is a vector containing the first column of the matrix
"""
N = len(c)
wiggle = c[1:]/c[0]
z, l = modified_durbin(len(wiggle), wiggle)
l = l + N * np.log(c[0])
v = np.zeros(N) # initialising
v[-1] = 1/((1 + wiggle.dot(z)) * c[0])
v[:-1] = v[-1] * z[::-1]
return v, l
def modified_durbin(m, wiggle):
"""
Modified Durbin algorithm
(Alg. 1.3)
"""
z = np.zeros(m)
z[0] = -wiggle[0]
beta = 1
alpha = -wiggle[0]
l = 0
for ii in xrange(m-1):
beta = (1 - alpha**2) * beta
l = l + np.log(beta)
if ii == 0: # there has to be a better way than this...?
alpha = - (wiggle[ii+1] + wiggle[0] * z[0]) / beta
z[0] = z[0] + alpha*z[0]
else:
# print alpha
alpha = - (wiggle[ii+1] + wiggle[ii::-1].dot(z[:ii+1])) / beta
z[:ii+1] = z[:ii+1] + alpha*z[ii::-1]
z[ii+1] = alpha
beta = (1 - alpha**2) * beta
l = l + np.log(beta)
return z, l
def vec_diag_sum(c):
"""
Alg. 3.1
"""
pass
def vec_solve_trace(c):
"""
Alg. 3.2
"""
pass