-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtd6.py
137 lines (103 loc) · 3.98 KB
/
td6.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
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
"""
Copyright 2018 LIN Lu (ncble)
"""
# _*_ coding: utf-8 _*_
import numpy as np
import pandas as pd
from time import time
from numpy import linalg as LA
from tqdm import tqdm
import os, glob
from time import time
def centrer(X):
moy = np.mean(X, axis = 0)
# print("Mean : ", moy)
return X-moy, moy
def PCA_lu(X):
row = len(X) # == 72
val_propre, vec_propre = np.linalg.eig((1./row) * X.T.dot(X))
D_eig = np.diag(val_propre)
print()
return
def MDS_lu(X):
val_propre, vec_propre = np.linalg.eig(X.dot(X.T))
D_eig_sqrt = np.diag(np.sqrt(val_propre))
vec_propre.dot()
return
def load_cloud_to_matrix(filepath):
# filepath = "./clouds/cloud1.txt"
object_list = []
with open(filepath, "rb") as file:
for line in file:
object_list.append(map(float, line.split()))
return np.vstack(object_list)
# np.loadtxt()
# A = np.loadtxt(open("./clouds/cloud1.txt", "rb"), delimiter=" ")
# data = pd.read_csv("./clouds/cloud1.txt", header = None, delimiter=" ")
# print(data)
def Rips_filtration(filepath, truncate= np.arange(0,100), save2dir = "./"):
# if not os.path.exists(save2dir):
# os.mkdir(save2dir)
A = load_cloud_to_matrix(filepath)
A = A[truncate, :] # first 'truncate' points
# print(A.shape)
row = len(A)
distance_matrix = np.zeros((row,row))
# all_dist = []
# get_ij_from_index = {}
count = 0
print("Calculation distance matrix...")
for i in tqdm(xrange(row)):
for j in xrange(i+1, row):
distance_matrix[i, j] = LA.norm(A[i,:]-A[j,:])
distance_matrix[j, i] = distance_matrix[i, j]
# all_dist.append(distance_matrix[i, j])
# get_ij_from_index[count] = (i,j)
count += 1
print("Done.")
# all_dist = np.array(all_dist)
# ind = all_dist.argsort()
# all_dist= all_dist[ind]
# for k, item in enumerate(all_dist):
# i, j = get_ij_from_index[ind[k]] # i
# print item, 1, i, j
filename = filepath.split("/")[-1].split(".")[0]
print("Writing down Rips filtration to file..")
with open(save2dir+"filtration_"+filename+".txt", "ab") as file:
for i in tqdm(xrange(row)):
# print distance_matrix[i,i], 0, i
file.write(str(distance_matrix[i,i])+" "+str(0)+" "+str(i)+"\n")
for j in xrange(i+1, row):
# print distance_matrix[i,j], 1, i, j
file.write(str(distance_matrix[i,j])+" "+str(1)+" "+str(i)+" "+str(j)+"\n")
for k in xrange(j+1, row):
# print np.max((distance_matrix[i,j], distance_matrix[i,k], distance_matrix[j,k])), 2, i, j, k
file.write(str(np.max((distance_matrix[i,j], distance_matrix[i,k], distance_matrix[j,k])))\
+" "+str(2)+" "+str(i)+" "+str(j)+" "+str(k)+"\n")
print("Done.")
# return distance_matrix
def write_config_file(dir_path = "", number_of_points = 100, dir_number = 0):
with open(dir_path+"/ReadMe", "wb") as file:
file.write("{} points of Sobol sequence of range ({}, {}).\n".format(number_of_points, dir_number*100, (dir_number+1)*100))
def COCO_filtrations(truncate = np.arange(0,100), save2dir = "./coco_Rips_filtrations/", read_clouds_from_dir = "./coco_data/"):
counter = 0
Dir_number = truncate[0]/len(truncate)
save2dir = save2dir[:-1] + str(Dir_number)+"/"
if not os.path.exists(save2dir):
os.mkdir(save2dir)
total = len(glob.glob(read_clouds_from_dir+"*.txt")) # COCO clouds of points (1e4 points sample from Sobol sequence.)
write_config_file(dir_path = save2dir, number_of_points = len(truncate), dir_number =Dir_number)
for filepath in glob.glob(read_clouds_from_dir+"*.txt"):
counter += 1
print("Proceeding {}/{} of batch {}...".format(counter, total,Dir_number))
Rips_filtration(filepath, truncate = truncate, save2dir = save2dir)
if __name__ =="__main__":
print(17)
# Rips_filtratsion("./clouds/cloud1.txt")
# Rips_filtration("./coco_data/bbob_f001_i01_d02.txt", save2dir="./coco_Rips_filtrations/")
# Rips_filtration("./coco_data/bbob_f021_i03_d02.txt", save2dir="./coco_Rips_filtrations/")
st = time()
# for i in range(99,100):
# COCO_filtrations(truncate = np.arange(100*i,100*(i+1)), read_clouds_from_dir = "./coco_data/")
et = time()
print("Total elapsed time: "+str(et-st))