-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathGRIMMER_VAR.py
233 lines (216 loc) · 9.47 KB
/
GRIMMER_VAR.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
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
import math
import re
## this is a stand alone function for variances along with code for testing
## I realize variables are labeled "sd" or "std" instead of "var"
## if it bothers you change it
def round_up(number,places):
if type(number)==type('string'):
pass
else:
number=repr(number)
if number[-1]=='5':
decimals=len(number.split('.')[-1])
if places<decimals:
return round(float(number[:-1]+'6'),places)
else:
return round(float(number),places)
else:
return round(float(number),places)
var_precision=12
#use floats for values except for size and Type, size is int Type is either "Sample", "Population", or random string
def grimmer(sd,sd_decimals,size,Type,mean=None,mean_decimals=None):
global possibilities
global averages_even
global averages_odd
global pattern_even
global pattern_odd
global averages_zero
global low
global high
global pattern
import importlib
mod = importlib.import_module('patterns.'+str(size))
pattern_zero=mod.pattern_zero[:]
pattern_even=mod.pattern_even[:]
pattern_odd=mod.pattern_odd[:]
averages_even=sorted(zip(mod.averages_even.copy().keys(),mod.averages_even.copy().values()))
averages_odd=sorted(zip(mod.averages_odd.copy().keys(),mod.averages_odd.copy().values()))
pattern_zero_rounded=[round_up('.'+repr(n).split('.')[1],5) for n in pattern_zero]
averages_zero={round_up('.'+repr(n).split('.')[1],5):mod.averages_even.copy()[n] for n in mod.averages_even.copy() if round_up('.'+repr(n).split('.')[1],5) in pattern_zero_rounded}
averages_zero=sorted(zip(averages_zero.copy().keys(),averages_zero.copy().values()))
def loop(low,high):
possibilities=[]
if low==0:
for index,i in enumerate(pattern_zero):
possibilities.append([i,index])
low=1
loop=0
X=True
if low%2==0:
pattern_1=pattern_even
pattern_2=pattern_odd
else:
pattern_1=pattern_odd
pattern_2=pattern_even
while True:
if X==True:
for index,i in enumerate(pattern_1):
value=low+i+loop
possibilities.append([value,index])
if value>=high:
X=False
break
loop+=1
if X==False:
break
for index,i in enumerate(pattern_2):
value=low+i+loop
possibilities.append([value,index])
if value>=high:
X=False
break
loop+=1
else:
break
return possibilities
if mean==None:
grim=False
else:
if round_up(round_up(mean*size,0)/size,mean_decimals)==mean:
grim=True
else:
return "GRIM failed"
lower=sd-.5/(10**sd_decimals)
if lower<0:
lower=0
higher=sd+.5/(10**sd_decimals)
low=math.floor(lower)
high=math.ceil(higher)
sample_count=0
population_count=0
if Type!='Sample':
possibilities=loop(low,high)
if grim:
for j in possibilities:
## print j
if int(j[0])==0:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_zero[j[1]][1]]:
if round_up(j[0],sd_decimals)==sd:
population_count+=1
elif int(j[0])%2==0:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_even[j[1]][1]]:
if round_up(j[0],sd_decimals)==sd:
population_count+=1
else:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_odd[j[1]][1]]:
if round_up(j[0],sd_decimals)==sd:
population_count+=1
else:
for j in possibilities:
if round_up(j[0],sd_decimals)==sd:
population_count+=1
if Type!='Population':
#recalculate low and high for sample variance
low=math.floor(low*(size-1)/size)
high=math.ceil(high*(size-1)/size)
possibilities=loop(low,high)
if grim:
for j in possibilities:
if int(j[0])==0:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_zero[j[1]][1]]:
if round_up(j[0]*size/(size-1),sd_decimals)==sd:
sample_count+=1
else:
if round_up(round(j[0]*size/(size-1),10),sd_decimals)==sd:
sample_count+=1
elif int(j[0])%2==0:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_even[j[1]][1]]:
if round_up(j[0]*size/(size-1),sd_decimals)==sd:
sample_count+=1
else:
if round_up(round(j[0]*size/(size-1),10),sd_decimals)==sd:
sample_count+=1
else:
if round_up('.'+repr(mean).split('.')[1],mean_decimals) in [round_up(ave,mean_decimals) for ave in averages_odd[j[1]][1]]:
if round_up(j[0]*size/(size-1),sd_decimals)==sd:
sample_count+=1
else:
if round_up(round(j[0]*size/(size-1),10),sd_decimals)==sd:
sample_count+=1
else:
for j in possibilities:
if round_up(round(j[0]*size/(size-1),10),sd_decimals)==sd:
sample_count+=1
if mean:
return sample_count,population_count
else:
return sample_count,population_count
#thorough testing
std_places=2
ave_places=2
##for r in range(5,100):
## import importlib
## mod = importlib.import_module('patterns.'+str(r))
## global_pattern_zero=mod.pattern_zero[:]
## global_pattern_even=mod.pattern_even[:]
## global_pattern_odd=mod.pattern_odd[:]
## global_averages_even=mod.averages_even.copy()
## global_averages_odd=mod.averages_odd.copy()
## global_pattern_zero_rounded=[round_up('.'+repr(n).split('.')[1],5) for n in global_pattern_zero]
## global_averages_zero={round_up('.'+repr(n).split('.')[1],5):global_averages_even[n] for n in global_averages_even if round_up('.'+repr(n).split('.')[1],5) in global_pattern_zero_rounded}
## test=global_pattern_zero[:]
## for n in range(1,3):
## if n%2==0:
## for value in global_pattern_even:
## test.append(value+n)
## else:
## for value in global_pattern_odd:
## test.append(value+n)
## print r, len(test),test[-1]
## pattern_zero=mod.pattern_zero[:]
## pattern_even=mod.pattern_even[:]
## pattern_odd=mod.pattern_odd[:]
## averages_even=mod.averages_even.copy()
## averages_odd=mod.averages_odd.copy()
## pattern_zero_rounded=[round_up('.'+repr(n).split('.')[1],5) for n in pattern_zero]
## averages_zero={round_up('.'+repr(n).split('.')[1],5):averages_even[n] for n in averages_even if round_up('.'+repr(n).split('.')[1],5) in pattern_zero_rounded}
## for i in test:
## #for 'Population':
#### std=round_up(i,std_places)
## ##for 'Sample':
## std=round_up(i*float(r)/(r-1),std_places)
## if int(i)==0:
## ave=round_up(repr(global_averages_zero[round_up('.'+repr(i).split('.')[1],5)][0]),ave_places)
## elif int(i)%2==0:
## ave=round_up(repr(global_averages_even[round_up('.'+repr(i).split('.')[1],var_precision)][0]),ave_places)
## else:
## ave=round_up(repr(global_averages_odd[round_up('.'+repr(i).split('.')[1],var_precision)][0]),ave_places)
## ##for 'Population'
#### if grimmer(std,std_places,r,'Population',ave,ave_places)[1]!=1:
#### print i,std,ave,r,low,high,grimmer(std,std_places,r,'Population',ave,ave_places)
##
## ##for 'Sample':
## if grimmer(std,std_places,r,'Sample',ave,ave_places)[0]!=1:
## print i,std,ave,r,low,high,grimmer(std,std_places,r,'Sample',ave,ave_places)
######random testing
import random
def variance(data,u):
return sum([(i-u)**2 for i in data])/len(data)
for r in range(5,100):
print r
std_places=2
ave_places=2
for i in range(1):
test=[random.randint(0,10) for k in range(r)]
mean=round_up(sum(test)/float(len(test)),ave_places)
true_mean=round_up(sum(test)/float(len(test)),16)
#for 'Population':
## std=round_up(variance(test,true_mean),std_places)
## if grimmer(std,std_places,r,'Population',mean,ave_places)[1]<1:
## print grimmer(std,std_places,r,'Population',mean,ave_places),test
## print grimmer(std,std_places,r,'Population',mean,ave_places),std,mean
#for 'Sample'
std=round_up((variance(test,true_mean)*len(test)/(len(test)-1)),std_places)
## if grimmer(std,std_places,r,'Sample',mean,ave_places)[0]<1:
## print grimmer(std,std_places,r,'Sample',mean,ave_places),std,mean,test
print grimmer(std,std_places,r,'Sample',mean,ave_places),std,mean