-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain.py
169 lines (155 loc) · 6.94 KB
/
train.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
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
import pandas as pd
import numpy as np
from evolve import train
from math import *
filepath = './dataset/syntheticdataforexperimentsom/gingerBreadman.txt'
if 'glass.data' in filepath:
number_of_columns_csv = 11
features = 9
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(1,features+1) ]).T
print data.shape
train(dataset_name="glass_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'car.data' in filepath:
number_of_columns_csv = 7
features = 6
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="car_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'iris.data' in filepath:
number_of_columns_csv = 5
features = 4
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="iris_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'abalone.data' in filepath:
number_of_columns_csv = 9
features = 8
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="abalone_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'sonar-uniquestring.all-data' in filepath:
number_of_columns_csv = 61
features = 60
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="sonar_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'wine.data' in filepath:
number_of_columns_csv = 14
features = 14
density = 0.5
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)])
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
data = np.array( [ df[str(i)] for i in range(1,features) ]).T
print data.shape
train(dataset_name="wine_result_dhalf_new",no_generation=10,population_size=5,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="realWorld",
final_rough_train=1600,final_fine_train=400)
if 'corner.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
density = 2
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
# rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="corner_dtwo_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)
if 'crescentFullmoon.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
density = 2
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
# rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="crescentFullmoon_dtwo_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)
if 'gingerBreadman.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
# density = 2
# dlen = df.shape[0]
# rangemax = int(ceil(sqrt(dlen/density)))
rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="gingerBreadman_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)
if 'half_kernel.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
density = 2
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
# rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="half_kernel_dtwo_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)
if 'outliers.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
# density = 2
# dlen = df.shape[0]
# rangemax = int(ceil(sqrt(dlen/density)))
rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="outliers_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)
if 'two_spirals.txt' in filepath:
number_of_columns_csv = 3
features = 2
df = pd.read_csv(filepath,names=[str(i) for i in range(number_of_columns_csv)], sep='\t')
density = 2
dlen = df.shape[0]
rangemax = int(ceil(sqrt(dlen/density)))
# rangemax=20
data = np.array( [ df[str(i)] for i in range(features) ]).T
print data.shape
train(dataset_name="two_spirals_dtwo_result",no_generation=15,population_size=10,
dim_rangemin=1,dim_rangemax=rangemax,data1=data,len_train_rough1=800,len_train_finetune1=200,type_of_problem="syntheticWorld",
final_rough_train=1600,final_fine_train=400)