-
Notifications
You must be signed in to change notification settings - Fork 1
/
generate.R
328 lines (294 loc) · 11.7 KB
/
generate.R
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
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
##for consistent results, set the seed.
#setwd("C:/Users/phug7649/Desktop/TXTBIN")
source("./functions/qhull_algorithm.R")
library(ggplot2)
set.seed(20120927)
# de<-2
# points<-5
# ends<-1
# a<-data.frame(id="EM1",x=rnorm(ends, 5,de), y=rnorm(ends, 5,de),class=1)
# b<-data.frame(id="EM2",x=rnorm(ends, 100,de), y=rnorm(ends, 5,de),class=1)
# c<-data.frame(id="EM3",x=rnorm(ends, 50,de), y=rnorm(ends, 95,de),class=1)
# d<-data.frame(id="d",x=rnorm(points, 50,de), y=rnorm(points, 15,de),class=2)
# e<-data.frame(id="e",x=rnorm(points, 30,de), y=rnorm(points, 40,de),class=2)
# f<-data.frame(id="f",x=rnorm(points, 50,de), y=rnorm(points, 40,de),class=2)
# g<-data.frame(id="g",x=rnorm(points, 70,de), y=rnorm(points, 40,de),class=2)
# h<-data.frame(id="h",x=rnorm(points, 50,de), y=rnorm(points, 66.7,de),class=2)
# i<-data.frame(id="EX1",x=rnorm(1, 80,de), y=rnorm(1, 15,de),class=3)
# j<-data.frame(id="EX2",x=rnorm(1, 20,de), y=rnorm(1, 15,de),class=3)
#
#
#
# colours()
#
# data<-rbind(a,b,c,d,e,f,g,h,i,j)
# plot(data[,2],data[,3],main="soil classification by fuzzy sets",xlab="x",ylab="y",col=0)
# text(data$x,data$y,data[,1])
# hull<-quick_hull(data[,2:3])
# lines(data[c(hull,hull[1]),2:3],col="red")
# hist (data[,3],main="Histogram of y column",nclass=10,col="cornflowerblue")
# hist (data[,2],main="Histogram of x column",nclass=10,col="steelblue")
#
# write.csv(data,file="triangle.csv")
#
# ##funky plot time
#
# hull<-quick_hull(data[,2:3])
# hull_data <- data[c(hull,hull[1]),]
#
# ggplot(data, aes(x=x, y=y))+
# #theme_grey()+
# theme_bw()+
# geom_text(aes(label=id))+#, colour=factor(class)))+
# # scale_colour_manual(values=rbgpal(3))+
# # scale_colour_brewer(palette="Set1")+
# # geom_rug(aes(colour=factor(class)))+
# geom_path(data=hull_data,colour="red")+
# coord_equal()
#
# #text(data$x,data$y,data[,1])
#
# qplot(data=data,y, type=)
# ?ggplot2
##Alex/budis suggestion- A square data set.
set.seed(20120927)
de<-2
points<-20
ends<-1
f<-data.frame(id="EM1",x=rnorm(ends, 5,de), y=rnorm(ends, 5,de))
g<-data.frame(id="EM2",x=rnorm(ends, 100,de), y=rnorm(ends, 5,de))
h<-data.frame(id="EM3",x=rnorm(ends, 5,de), y=rnorm(ends, 95,de))
i<-data.frame(id="EM4",x=rnorm(ends, 95,de), y=rnorm(ends, 95,de))
a<-data.frame(id="a",x=rnorm(points, 50,de), y=rnorm(points, 25,de))
b<-data.frame(id="b",x=rnorm(points, 25,de), y=rnorm(points, 50,de))
c<-data.frame(id="c",x=rnorm(points, 50,de), y=rnorm(points, 50,de))
d<-data.frame(id="d",x=rnorm(points, 75,de), y=rnorm(points, 50,de))
e<-data.frame(id="e",x=rnorm(points, 50,de), y=rnorm(points, 75,de))
w<-data.frame(id="EX1",x=rnorm(1, 80,de), y=rnorm(1, 15,de))
y<-data.frame(id="EX2",x=rnorm(1, 20,de), y=rnorm(1, 15,de))
x<-data.frame(id="EX3",x=rnorm(1, 80,de), y=rnorm(1, 80,de))
z<-data.frame(id="EX4",x=rnorm(1, 20,de), y=rnorm(1, 80,de))
data<-rbind(a,b,c,d,e,f,g,h,i,w,x,y,z)
##plotting
hull<-quick_hull(data[,2:3])
hull_data <- data[c(hull,hull[1]),]
ggplot(data, aes(x=x, y=y))+
#theme_grey()+
theme_bw()+
geom_text(aes(label=id))+
geom_path(data=hull_data,colour="red")+
coord_equal()
hist (data[,3],main="Histogram of y column",nclass=10,col="cornflowerblue")
hist (data[,2],main="Histogram of x column",nclass=10,col="steelblue")
write.csv(data,file="square.csv")
ggplot(data, aes(x=x, y=y))+
#theme_grey()+
theme_bw()+
geom_text(aes(label=id,colour=id))+
geom_path(data=hull_data,colour="red")+
coord_equal()
##Input the text output from fuzzy k means.
##5_class
data2<-read.table(text="id MaxCls CI 5a 5b 5c 5d 5e 5*
a 5d 0.19363 0.02631 0.04049 0.04417 0.85053 0.01713 0.02137
a 5d 0.08901 0.01215 0.02039 0.02254 0.93353 0.00790 0.00349
a 5d 0.04958 0.00716 0.01152 0.01266 0.96308 0.00447 0.00110
a 5d 0.08437 0.01160 0.01940 0.02143 0.93706 0.00745 0.00306
a 5d 0.37571 0.05180 0.06932 0.07456 0.69952 0.02957 0.07523
b 5* 0.57573 0.04948 0.11794 0.12522 0.07971 0.07816 0.54949
b 5* 0.64893 0.05332 0.13569 0.14485 0.08561 0.08461 0.49592
b 5* 0.71004 0.05600 0.14944 0.16245 0.09800 0.08171 0.45241
b 5* 0.60155 0.05081 0.12317 0.13223 0.08810 0.07501 0.53068
b 5* 0.67028 0.05450 0.14156 0.14989 0.08213 0.09232 0.47961
c 5b 0.37320 0.00985 0.79877 0.17197 0.00919 0.00934 0.00088
c 5b 0.18232 0.00260 0.90489 0.08722 0.00264 0.00258 0.00006
c 5c 0.80853 0.01400 0.38042 0.57189 0.01580 0.01516 0.00273
c 5b 0.39630 0.00647 0.79178 0.18808 0.00653 0.00670 0.00045
c 5c 0.19412 0.00399 0.09068 0.89657 0.00478 0.00380 0.00017
d 5a 0.55765 0.55273 0.11037 0.10457 0.07226 0.05337 0.10670
d 5a 0.22240 0.82700 0.04940 0.04564 0.02749 0.03063 0.01984
d 5a 0.15379 0.87928 0.03306 0.03114 0.02187 0.02146 0.01319
d 5a 0.30484 0.75616 0.06100 0.05669 0.03600 0.04332 0.04684
d 5a 0.28066 0.77766 0.05832 0.05526 0.04003 0.03300 0.03575
e 5e 0.06540 0.00986 0.01620 0.01477 0.00624 0.95080 0.00212
e 5e 0.12826 0.01885 0.02975 0.02746 0.01235 0.90149 0.01010
e 5e 0.03320 0.00514 0.00832 0.00760 0.00325 0.97512 0.00058
e 5e 0.06548 0.01026 0.01581 0.01453 0.00647 0.95033 0.00259
e 5e 0.03213 0.00499 0.00805 0.00735 0.00315 0.97592 0.00054
EM1 5* 0.16363 0.01805 0.02601 0.02721 0.03713 0.01811 0.87349
EM2 5* 0.15973 0.03268 0.02320 0.02331 0.03129 0.01657 0.87296
EM3 5* 0.17712 0.01937 0.02869 0.02851 0.01985 0.04035 0.86323
EM4 5* 0.18474 0.03687 0.02686 0.02573 0.01852 0.03838 0.85364
EX1 5* 0.40893 0.08768 0.05660 0.05709 0.08558 0.03430 0.67875
EX3 5* 0.44406 0.09691 0.06184 0.05805 0.03669 0.09366 0.65285
EX2 5* 0.38518 0.03530 0.05677 0.06059 0.09947 0.03357 0.71429
EX4 5* 0.32813 0.03174 0.05202 0.05147 0.03202 0.08043 0.75230"
,header=TRUE)
data<-rbind(a,b,c,d,e,f,g,h,i,w,x,y,z)
square_mem <- cbind(data2[,1:2],data[c("x","y")])
centroids <- read.csv("output.csv")
names(centroids)[1] <- "MaxCls"
ggplot(square_mem, aes(x=x, y=y, colour=MaxCls))+
theme_bw()+
geom_text(aes(label=id))+
geom_path(data=hull_data,colour="red")+
geom_point(data=centroids, colour="black", size=4)+
coord_equal()
data<-rbind(centroids,data)
ggplot(data, aes(x=x, y=y))+
#theme_grey()+
theme_bw()+
geom_text(aes(label=id,colour=id))+
geom_path(data=hull_data,colour="red")+
coord_equal()
data3<-1:9
data3<-read.table(text="
5.34763995739063 6.30850486340641
99.2727579856495 1.89752825066717
7.13103086981294 90.1718221955097
94.8196867565331 95.3581000272730
50.1318047930393 23.4707188977868
75.2746403046578 48.2415777375755
50.6140589081322 74.9821186099562
25.4550234230729 48.8920780603341
49.8546504636677 48.9590239074639")
cent<-read.table("clipboard",sep="")
###Non-normal y column
# set.seed(20120927)
# de<-2
# a<-data.frame(id="a",x=rnorm(1, 2,de), y=rnorm(1, 2,de))
# b<-data.frame(id="b",x=rnorm(20, 20,de), y=rnorm(20, 2,de))
# c<-data.frame(id="c",x=rnorm(3, 40,de), y=rnorm(3, 2,de))
# d<-data.frame(id="d",x=rnorm(5, 2,de), y=rnorm(5, 15,de))
# e<-data.frame(id="e",x=rnorm(30, 20,de), y=rnorm(30, 14,de))
# f<-data.frame(id="f",x=rnorm(10, 36,de), y=rnorm(10, 13,de))
# g<-data.frame(id="g",x=rnorm(20, 30,de), y=rnorm(20, 2,de))
# h<-data.frame(id="h",x=rnorm(25, 10,de), y=rnorm(25, 14,de))
# i<-data.frame(id="a",x=rnorm(17, 15,de), y=rnorm(17, 2,de))
# j<-data.frame(id="a",x=rnorm(15, 33,de), y=rnorm(15, 15,de))
#
# data<-rbind(a,b,c,d,e,f,g,h)
# plot(data[,2],data[,3])
# hist(data[,2])
# hist(data[,3])
# qqnorm(data[,2])
# qqline(data[,2])
# qqnorm(data[,3])
# qqline(data[,3])
#
# plot(data[,2],data[,3])
# NNhull<-quick_hull(data[2:3])
# lines(data[c(NNhull,NNhull[1]),2:3],col="red")
##Convex bicycle script
# z<-data
#
# source(file.path(getwd(), "R-scripts", "point_euclid.R"))
#
#
#
# z<- na.exclude(z)
# z<-z[,2:3]
#
# ##creating a file to dump values
# file.create("bin.csv")
# bin<-matrix(NA, 1,1)
#
# ##Using sebs script to create hulls
# cz<-quick_hull(z) ############
# ##CONTROLS##
# ############
# ## there are two control methods atm; the first is to define the length of the yardstick. Provides an undefined number
# ## of end-members. the second is to use an equation which most likely is data specific.
#
# ##While the script below runs, the number in "eq1" is the number of end members the algorythm gets (approximately)
#
# # eq1 <- function (value) {exp((value + 4.7671)/-64.85)}
# # factor<-eq1(35)
#
# ys<-10 ##starting parameter for yardstick
# factor<-.5 ##creating the factor by which the yardstick length is modified
#
# ##I want the loop to start here. Set the threshold value (0 will include too many close points)
#
# while (ys>1)
#
# {
# ##sum of rows
# czr<-z[cz,]
# czr<-czr^2
# czrsum<-rowSums(czr)
# fin<-sqrt(czrsum)
# finm<-as.matrix(fin)
#
# ##rows with max and min euclidean distance from zero
# refmax<-(which.max(finm))
#
# ##getting maximum value and anchoring it to the row number in the master data set (z)
# BLARG<-rownames(z[cz,])==cz[refmax]
# BLARG<-as.matrix(cz[BLARG])
#
# ##retrieving all the principal component data from rows that contain maximum and minimum euclidean distances
# rowx<-z[BLARG,]
# #rowy<-z[BLURG,]
#
# ## retrieving all pc data from cz
# object<-z[cz,]
# #object<-z[finm,]
#
# ##getting distances
# pcdist<-as.matrix(point_euclid(object,rowx))
# #pcdisty<-as.matrix(point_euclid(object,rowy))
#
# ##yardstick
# b<-as.numeric(pcdist[which.max(pcdist),])
# ys<-b*factor
#
# ##compare yardstick to the convex hull
# new <- ys < as.vector(pcdist)
# new <- as.matrix(new)
#
# ##Placing maximum (maxi) and minimum (origin) points in the final file
#
# origin <- as.matrix(pcdist == 0)
# or <- as.matrix(pcdist[origin,])
# bin <- rbind(or,bin)
#
# # max <- as.matrix(pcdist==b)
# # maxi <- as.matrix(pcdist[max,])
# # bin <-rbind(maxi,bin)
#
#
# ##Exclude any values inferior to yardstick (this file should be renamed cz when its time to reiterate)
# finm <- as.matrix(pcdist[new,])
#
# ##Create a new object to replace the previous convex hull list
# cz<-as.numeric(rownames(finm))
#
# print(ys)
#
#
# }
#
# # ## removing duplicates
# # a<-rownames(bin)
# # s<-as.matrix(unique(a))
# #
# # ##Output
# #
# # write.csv(s, file = "ems.csv")
# #
# #
# # sz<-z[s,]
# # write.csv(sz,file="sz.csv")
# # ##test accuracy of hull. If data is greater than any hull points
# #
# # ##matlab code
# # ##Use only if you are performing an analysis on extrogrades.
# # y<-sz[1:20,]
# # write.csv(y,file="bend.csv")
# ##centroids from fuzzy k w/extragrades
# j<-data.frame(id="X",x= 28,y=-50)
# k<-data.frame(id="X",x= 50,y=-50.6074)
# l<-data.frame(id="X",x=- 1.35437,y=-4.39542)
# m<-data.frame(id="X",x= 70.0477,y= -50.1104)
# n<-data.frame(id="X",x= 0,y= 0)