https://github.com/anirban166/gsoc20-tests
Test solutions for candidature @ GSOC'20 for the R org. with topic 'Asymptotic Complexity Testing' undertaken with the supervision of mentor Dr. Toby Dylan Hocking.
https://github.com/anirban166/gsoc20-tests
Last synced: 7 months ago
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Test solutions for candidature @ GSOC'20 for the R org. with topic 'Asymptotic Complexity Testing' undertaken with the supervision of mentor Dr. Toby Dylan Hocking.
- Host: GitHub
- URL: https://github.com/anirban166/gsoc20-tests
- Owner: Anirban166
- Created: 2019-09-20T15:58:49.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-06T19:21:19.000Z (almost 6 years ago)
- Last Synced: 2025-01-24T18:09:47.671Z (9 months ago)
- Language: R
- Homepage:
- Size: 639 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Easy Test
---
Package dependencies: PeakSegDP, PeakSegOptimal, tidyverse (ggplot2), dplyr (data.table) and microbenchmark.
### Third Revision:
```
library(PeakSegDP)
library(PeakSegOptimal)
library(ggplot2)
library(microbenchmark)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)
for(i in 1:l)
{
s <- as.data.frame(microbenchmark(
"PeakSegPDPA" = PeakSegPDPA(rpois(datasetsizes[i],10),max.segments=3L),
"cDPA" = cDPA(rpois(datasetsizes[i],10),maxSegments=3)))
if(i==1) # for the first iteration we append datasetsize as the third column to s and initialize combinator as s:
{
s$datasetsize<-datasetsizes[i]
combinator<-s
}
# we must add/append datasetsize to other data frames as well: (in order to plot datasetsize vs time from resulting data frame)
s$datasetsize<-datasetsizes[i] # add datasetsize
if(i!=1) # bind/combine all data frames after first iteration to combinator (final data frame) - gets +200x2 obs per iteration.
{ combinator<-rbind(combinator,s) }
}
#dt <- data.table(combinator)
# Plot as per suggestion (3):
# data table (combinator) , mapping = datasetsize vs time , geometry = point (+line) based on algorithm (PeakSegPDPA/cDPA)
ggplot(combinator, aes(x=datasetsize,y=time)) + geom_point(aes(color = expr)) + geom_line(aes(color = expr)) + labs(x="N", y="Runtime") + scale_x_log10() + scale_y_log10()
```
Here's my idea: We know that the data frame returned by microbenchmark is a 200x2 one (with expr and time being the 2 variables), and since its already a data frame from which we need all the columns (unlike the previous case, when I needed to exclude neval), there isn't a need to change that and instead I should just append the dataset size to them for plotting purposes.
Now the looping part - for one iteration we would only get one data frame (stored in s) and for multiple iterations we will need an array of data frames i.e. a list using which when I tried the code became a bit messy and I eventually dropped the idea trying to think of a simpler solution.
I found that it would be easier to just append each data frame collected in s to consecutive iterations - something which rbind() can do. After this point it was relatively simple - I took a dataframe by the name of 'combinator' and for the first iteration I simply had the data in s collected to it (i.e. the first 200 obs. from dataset size 10) for the purpose of initializing combinator size. (since I didn't explicitly declare it outside)
Then I append the dataset size column to that as combinator would be a 200x3 dataframe (expr,time,datasetsizes). This condition of adding the datasetsize column is there for every iteration since s is 200x2 as returned by microbenchmark.
After the first iteration I use rbind to join the corresponding 200x3 data frames for different data set sizes together.
Hence the final result would be a (200x7)x3 or 1400x3 data frame. (7 dataset sizes)
Lastly I did the plotting with the dataframe as a datatable vs microbenchmark timings, distinguished by expr which I set to PeakSegPDPA and cDPA inside the microbenchmark catering to respective algorithm calls (just like last revision).
Output Plot:
Data from microbenchmark:
Initially when I ran ```> combinator``` it showed uptil obs. 333 with ```[ reached 'max' / getOption("max.print") -- omitted 267 rows ]``` at the end.
Going by the statement I checked ```getOption("max.print")``` which returned 1000, so I figured if I get 333 obs.for 1000, 5000 should be a safe number to print 1400 obs. and so I set my max print accordingly: ```options(max.print=5000)```
And here is the complete data from microbenchmark with dataset size appended to it: (Super long!)
```
> combinator
expr time datasetsize
1 PeakSegPDPA 191120 10.00000
2 cDPA 38437 10.00000
3 PeakSegPDPA 115659 10.00000
4 cDPA 27505 10.00000
5 PeakSegPDPA 123064 10.00000
6 PeakSegPDPA 119537 10.00000
7 cDPA 26800 10.00000
8 PeakSegPDPA 116717 10.00000
9 PeakSegPDPA 139990 10.00000
10 PeakSegPDPA 130469 10.00000
11 cDPA 28563 10.00000
12 cDPA 23625 10.00000
13 cDPA 23626 10.00000
14 cDPA 24331 10.00000
15 cDPA 23273 10.00000
16 PeakSegPDPA 112485 10.00000
17 PeakSegPDPA 112133 10.00000
18 cDPA 26094 10.00000
19 PeakSegPDPA 122711 10.00000
20 PeakSegPDPA 117774 10.00000
21 PeakSegPDPA 114954 10.00000
22 cDPA 25742 10.00000
23 PeakSegPDPA 132584 10.00000
24 cDPA 25037 10.00000
25 PeakSegPDPA 118833 10.00000
26 PeakSegPDPA 117069 10.00000
27 PeakSegPDPA 118833 10.00000
28 PeakSegPDPA 105433 10.00000
29 cDPA 25742 10.00000
30 PeakSegPDPA 113190 10.00000
31 PeakSegPDPA 118127 10.00000
32 PeakSegPDPA 108960 10.00000
33 cDPA 25741 10.00000
34 cDPA 24332 10.00000
35 PeakSegPDPA 93444 10.00000
36 cDPA 25037 10.00000
37 cDPA 23979 10.00000
38 cDPA 23626 10.00000
39 cDPA 25036 10.00000
40 cDPA 23979 10.00000
41 PeakSegPDPA 112838 10.00000
42 PeakSegPDPA 102260 10.00000
43 PeakSegPDPA 138931 10.00000
44 PeakSegPDPA 113191 10.00000
45 PeakSegPDPA 122359 10.00000
46 cDPA 26095 10.00000
47 cDPA 23625 10.00000
48 cDPA 24331 10.00000
49 PeakSegPDPA 104375 10.00000
50 cDPA 23626 10.00000
51 cDPA 23626 10.00000
52 cDPA 23274 10.00000
53 cDPA 22568 10.00000
54 PeakSegPDPA 108254 10.00000
55 PeakSegPDPA 106843 10.00000
56 cDPA 23626 10.00000
57 cDPA 22567 10.00000
58 cDPA 23274 10.00000
59 cDPA 22569 10.00000
60 PeakSegPDPA 122006 10.00000
61 cDPA 24684 10.00000
62 PeakSegPDPA 99086 10.00000
63 cDPA 24685 10.00000
64 cDPA 23978 10.00000
65 PeakSegPDPA 104375 10.00000
66 cDPA 23626 10.00000
67 cDPA 23626 10.00000
68 cDPA 22567 10.00000
69 cDPA 22568 10.00000
70 cDPA 24684 10.00000
71 cDPA 21863 10.00000
72 cDPA 23626 10.00000
73 PeakSegPDPA 138579 10.00000
74 cDPA 24684 10.00000
75 PeakSegPDPA 116364 10.00000
76 PeakSegPDPA 110017 10.00000
77 PeakSegPDPA 101201 10.00000
78 cDPA 25389 10.00000
79 cDPA 24331 10.00000
80 cDPA 23273 10.00000
81 cDPA 22921 10.00000
82 cDPA 21863 10.00000
83 PeakSegPDPA 117775 10.00000
84 PeakSegPDPA 130116 10.00000
85 PeakSegPDPA 143868 10.00000
86 PeakSegPDPA 95913 10.00000
87 cDPA 26799 10.00000
88 PeakSegPDPA 104023 10.00000
89 cDPA 26094 10.00000
90 cDPA 26094 10.00000
91 cDPA 24331 10.00000
92 PeakSegPDPA 117070 10.00000
93 cDPA 25036 10.00000
94 PeakSegPDPA 131526 10.00000
95 cDPA 24684 10.00000
96 PeakSegPDPA 126590 10.00000
97 PeakSegPDPA 106491 10.00000
98 cDPA 24683 10.00000
99 cDPA 25389 10.00000
100 PeakSegPDPA 89213 10.00000
101 PeakSegPDPA 105786 10.00000
102 cDPA 24685 10.00000
103 cDPA 25036 10.00000
104 cDPA 22920 10.00000
105 PeakSegPDPA 119890 10.00000
106 PeakSegPDPA 129058 10.00000
107 PeakSegPDPA 125532 10.00000
108 PeakSegPDPA 114954 10.00000
109 PeakSegPDPA 102964 10.00000
110 PeakSegPDPA 114601 10.00000
111 cDPA 25742 10.00000
112 PeakSegPDPA 125532 10.00000
113 cDPA 26447 10.00000
114 cDPA 24683 10.00000
115 cDPA 22568 10.00000
116 PeakSegPDPA 124827 10.00000
117 cDPA 23979 10.00000
118 PeakSegPDPA 110722 10.00000
119 cDPA 26094 10.00000
120 PeakSegPDPA 128001 10.00000
121 cDPA 25389 10.00000
122 cDPA 23626 10.00000
123 PeakSegPDPA 109665 10.00000
124 cDPA 23979 10.00000
125 cDPA 22568 10.00000
126 PeakSegPDPA 113191 10.00000
127 cDPA 25389 10.00000
128 PeakSegPDPA 107901 10.00000
129 cDPA 23978 10.00000
130 cDPA 23273 10.00000
131 cDPA 24331 10.00000
132 PeakSegPDPA 131174 10.00000
133 cDPA 25389 10.00000
134 cDPA 23626 10.00000
135 PeakSegPDPA 117422 10.00000
136 PeakSegPDPA 104729 10.00000
137 cDPA 25036 10.00000
138 cDPA 23626 10.00000
139 cDPA 22921 10.00000
140 PeakSegPDPA 135405 10.00000
141 PeakSegPDPA 107196 10.00000
142 cDPA 25741 10.00000
143 PeakSegPDPA 130469 10.00000
144 PeakSegPDPA 110018 10.00000
145 cDPA 26095 10.00000
146 PeakSegPDPA 102259 10.00000
147 PeakSegPDPA 108606 10.00000
148 cDPA 25037 10.00000
149 cDPA 23626 10.00000
150 PeakSegPDPA 115306 10.00000
151 cDPA 24331 10.00000
152 cDPA 22922 10.00000
153 PeakSegPDPA 83924 10.00000
154 PeakSegPDPA 113544 10.00000
155 cDPA 26799 10.00000
156 PeakSegPDPA 100496 10.00000
157 PeakSegPDPA 118480 10.00000
158 PeakSegPDPA 101907 10.00000
159 cDPA 25741 10.00000
160 PeakSegPDPA 107902 10.00000
161 cDPA 24684 10.00000
162 PeakSegPDPA 110722 10.00000
163 PeakSegPDPA 102259 10.00000
164 cDPA 24331 10.00000
165 cDPA 22922 10.00000
166 PeakSegPDPA 123416 10.00000
167 PeakSegPDPA 108960 10.00000
168 PeakSegPDPA 109311 10.00000
169 cDPA 25036 10.00000
170 PeakSegPDPA 125532 10.00000
171 cDPA 24684 10.00000
172 PeakSegPDPA 107196 10.00000
173 cDPA 24684 10.00000
174 cDPA 24685 10.00000
175 cDPA 22567 10.00000
176 PeakSegPDPA 108254 10.00000
177 PeakSegPDPA 106843 10.00000
178 PeakSegPDPA 112485 10.00000
179 PeakSegPDPA 116012 10.00000
180 PeakSegPDPA 111076 10.00000
181 PeakSegPDPA 129411 10.00000
182 cDPA 26095 10.00000
183 PeakSegPDPA 133289 10.00000
184 PeakSegPDPA 121654 10.00000
185 PeakSegPDPA 98734 10.00000
186 PeakSegPDPA 107549 10.00000
187 cDPA 26093 10.00000
188 cDPA 24332 10.00000
189 PeakSegPDPA 115306 10.00000
190 cDPA 25389 10.00000
191 cDPA 24332 10.00000
192 PeakSegPDPA 123416 10.00000
193 PeakSegPDPA 89566 10.00000
194 cDPA 25036 10.00000
195 PeakSegPDPA 116011 10.00000
196 PeakSegPDPA 114954 10.00000
197 cDPA 26093 10.00000
198 cDPA 23626 10.00000
199 PeakSegPDPA 118480 10.00000
200 PeakSegPDPA 116364 10.00000
201 PeakSegPDPA 497544 31.62278
202 PeakSegPDPA 490845 31.62278
203 PeakSegPDPA 568421 31.62278
204 cDPA 70172 31.62278
205 cDPA 61357 31.62278
206 PeakSegPDPA 454525 31.62278
207 cDPA 65589 31.62278
208 PeakSegPDPA 496486 31.62278
209 PeakSegPDPA 483791 31.62278
210 PeakSegPDPA 454525 31.62278
211 cDPA 66646 31.62278
212 cDPA 53952 31.62278
213 PeakSegPDPA 486260 31.62278
214 PeakSegPDPA 485556 31.62278
215 cDPA 62063 31.62278
216 cDPA 62062 31.62278
217 cDPA 57478 31.62278
218 PeakSegPDPA 552200 31.62278
219 cDPA 62415 31.62278
220 cDPA 66294 31.62278
221 cDPA 52894 31.62278
222 PeakSegPDPA 521874 31.62278
223 PeakSegPDPA 542680 31.62278
224 PeakSegPDPA 482030 31.62278
225 cDPA 65588 31.62278
226 PeakSegPDPA 415384 31.62278
227 PeakSegPDPA 447473 31.62278
228 PeakSegPDPA 562073 31.62278
229 PeakSegPDPA 495781 31.62278
230 PeakSegPDPA 502129 31.62278
231 PeakSegPDPA 413622 31.62278
232 cDPA 66646 31.62278
233 PeakSegPDPA 449940 31.62278
234 PeakSegPDPA 439716 31.62278
235 PeakSegPDPA 459461 31.62278
236 PeakSegPDPA 533511 31.62278
237 cDPA 63473 31.62278
238 PeakSegPDPA 465456 31.62278
239 cDPA 62415 31.62278
240 PeakSegPDPA 459462 31.62278
241 PeakSegPDPA 471450 31.62278
242 cDPA 67704 31.62278
243 PeakSegPDPA 460520 31.62278
244 cDPA 64530 31.62278
245 PeakSegPDPA 498249 31.62278
246 PeakSegPDPA 440773 31.62278
247 cDPA 67350 31.62278
248 cDPA 54305 31.62278
249 PeakSegPDPA 442888 31.62278
250 cDPA 57478 31.62278
251 PeakSegPDPA 509181 31.62278
252 cDPA 59594 31.62278
253 PeakSegPDPA 582878 31.62278
254 PeakSegPDPA 593808 31.62278
255 cDPA 86745 31.62278
256 cDPA 68409 31.62278
257 cDPA 60652 31.62278
258 cDPA 76167 31.62278
259 PeakSegPDPA 611440 31.62278
260 PeakSegPDPA 550436 31.62278
261 PeakSegPDPA 492255 31.62278
262 PeakSegPDPA 491549 31.62278
263 cDPA 71583 31.62278
264 cDPA 63120 31.62278
265 cDPA 54305 31.62278
266 PeakSegPDPA 526459 31.62278
267 PeakSegPDPA 438305 31.62278
268 cDPA 68409 31.62278
269 cDPA 58183 31.62278
270 cDPA 56421 31.62278
271 cDPA 60300 31.62278
272 PeakSegPDPA 466867 31.62278
273 PeakSegPDPA 448531 31.62278
274 cDPA 63473 31.62278
275 cDPA 56420 31.62278
276 PeakSegPDPA 464046 31.62278
277 PeakSegPDPA 566305 31.62278
278 PeakSegPDPA 399869 31.62278
279 cDPA 65236 31.62278
280 cDPA 66294 31.62278
281 PeakSegPDPA 588872 31.62278
282 PeakSegPDPA 561016 31.62278
283 cDPA 65941 31.62278
284 cDPA 66646 31.62278
285 cDPA 56420 31.62278
286 PeakSegPDPA 495076 31.62278
287 PeakSegPDPA 459109 31.62278
288 cDPA 56420 31.62278
289 cDPA 64178 31.62278
290 cDPA 57125 31.62278
291 cDPA 55362 31.62278
292 cDPA 55715 31.62278
293 PeakSegPDPA 482382 31.62278
294 cDPA 70877 31.62278
295 PeakSegPDPA 525048 31.62278
296 cDPA 59946 31.62278
297 cDPA 68409 31.62278
298 cDPA 58183 31.62278
299 cDPA 65236 31.62278
300 PeakSegPDPA 534216 31.62278
301 cDPA 71583 31.62278
302 PeakSegPDPA 418205 31.62278
303 cDPA 69467 31.62278
304 PeakSegPDPA 452762 31.62278
305 PeakSegPDPA 543385 31.62278
306 cDPA 73699 31.62278
307 cDPA 64178 31.62278
308 cDPA 58888 31.62278
309 PeakSegPDPA 606856 31.62278
310 PeakSegPDPA 490492 31.62278
311 cDPA 64179 31.62278
312 PeakSegPDPA 540564 31.62278
313 cDPA 68762 31.62278
314 PeakSegPDPA 436542 31.62278
315 cDPA 64178 31.62278
316 PeakSegPDPA 493313 31.62278
317 cDPA 64531 31.62278
318 cDPA 59594 31.62278
319 cDPA 63120 31.62278
320 cDPA 58889 31.62278
321 PeakSegPDPA 449588 31.62278
322 cDPA 78635 31.62278
323 cDPA 57831 31.62278
324 cDPA 52189 31.62278
325 cDPA 61357 31.62278
326 cDPA 51484 31.62278
327 cDPA 54305 31.62278
328 cDPA 53952 31.62278
329 PeakSegPDPA 495076 31.62278
330 cDPA 65941 31.62278
331 PeakSegPDPA 424905 31.62278
332 cDPA 61710 31.62278
333 cDPA 62415 31.62278
334 PeakSegPDPA 424905 31.62278
335 cDPA 61356 31.62278
336 PeakSegPDPA 514823 31.62278
337 cDPA 61005 31.62278
338 cDPA 60299 31.62278
339 PeakSegPDPA 477092 31.62278
340 cDPA 62062 31.62278
341 PeakSegPDPA 466514 31.62278
342 cDPA 61710 31.62278
343 cDPA 111428 31.62278
344 PeakSegPDPA 476387 31.62278
345 PeakSegPDPA 482734 31.62278
346 PeakSegPDPA 559605 31.62278
347 cDPA 91329 31.62278
348 PeakSegPDPA 503891 31.62278
349 cDPA 72640 31.62278
350 PeakSegPDPA 432310 31.62278
351 cDPA 70878 31.62278
352 cDPA 67351 31.62278
353 PeakSegPDPA 581467 31.62278
354 PeakSegPDPA 497897 31.62278
355 cDPA 65941 31.62278
356 cDPA 58889 31.62278
357 PeakSegPDPA 419616 31.62278
358 PeakSegPDPA 482382 31.62278
359 cDPA 68410 31.62278
360 PeakSegPDPA 449589 31.62278
361 PeakSegPDPA 455230 31.62278
362 cDPA 63120 31.62278
363 PeakSegPDPA 446415 31.62278
364 PeakSegPDPA 492255 31.62278
365 cDPA 64883 31.62278
366 cDPA 56067 31.62278
367 cDPA 63826 31.62278
368 cDPA 65941 31.62278
369 PeakSegPDPA 541269 31.62278
370 PeakSegPDPA 471452 31.62278
371 cDPA 75462 31.62278
372 PeakSegPDPA 472508 31.62278
373 cDPA 66999 31.62278
374 cDPA 61005 31.62278
375 PeakSegPDPA 513764 31.62278
376 PeakSegPDPA 628012 31.62278
377 cDPA 73699 31.62278
378 cDPA 63472 31.62278
379 PeakSegPDPA 391759 31.62278
380 cDPA 57125 31.62278
381 PeakSegPDPA 532453 31.62278
382 PeakSegPDPA 462988 31.62278
383 PeakSegPDPA 558547 31.62278
384 PeakSegPDPA 458051 31.62278
385 PeakSegPDPA 469335 31.62278
386 PeakSegPDPA 443593 31.62278
387 cDPA 68762 31.62278
388 cDPA 61709 31.62278
389 cDPA 61709 31.62278
390 cDPA 54658 31.62278
391 PeakSegPDPA 531042 31.62278
392 cDPA 63120 31.62278
393 PeakSegPDPA 500718 31.62278
394 PeakSegPDPA 494723 31.62278
395 PeakSegPDPA 587814 31.62278
396 PeakSegPDPA 472862 31.62278
397 PeakSegPDPA 453467 31.62278
398 PeakSegPDPA 507065 31.62278
399 PeakSegPDPA 460520 31.62278
400 PeakSegPDPA 455231 31.62278
401 PeakSegPDPA 2111824 100.00000
402 cDPA 439714 100.00000
403 PeakSegPDPA 1847010 100.00000
404 PeakSegPDPA 1651659 100.00000
405 cDPA 360376 100.00000
406 PeakSegPDPA 1845599 100.00000
407 cDPA 369191 100.00000
408 cDPA 344156 100.00000
409 PeakSegPDPA 1716189 100.00000
410 cDPA 388938 100.00000
411 cDPA 363197 100.00000
412 PeakSegPDPA 1914007 100.00000
413 PeakSegPDPA 2082558 100.00000
414 cDPA 420322 100.00000
415 PeakSegPDPA 2135450 100.00000
416 cDPA 378712 100.00000
417 PeakSegPDPA 2249346 100.00000
418 PeakSegPDPA 1857940 100.00000
419 PeakSegPDPA 1775076 100.00000
420 cDPA 464751 100.00000
421 PeakSegPDPA 1961257 100.00000
422 PeakSegPDPA 1862878 100.00000
423 cDPA 435131 100.00000
424 cDPA 349092 100.00000
425 PeakSegPDPA 2112883 100.00000
426 PeakSegPDPA 2135450 100.00000
427 cDPA 431604 100.00000
428 PeakSegPDPA 1914007 100.00000
429 PeakSegPDPA 1873456 100.00000
430 cDPA 413269 100.00000
431 cDPA 360728 100.00000
432 PeakSegPDPA 1645312 100.00000
433 cDPA 383649 100.00000
434 PeakSegPDPA 2101599 100.00000
435 PeakSegPDPA 1920706 100.00000
436 PeakSegPDPA 1967604 100.00000
437 PeakSegPDPA 2050822 100.00000
438 cDPA 352266 100.00000
439 PeakSegPDPA 1957731 100.00000
440 PeakSegPDPA 1596651 100.00000
441 PeakSegPDPA 2042359 100.00000
442 cDPA 392112 100.00000
443 cDPA 394580 100.00000
444 PeakSegPDPA 1719714 100.00000
445 cDPA 396695 100.00000
446 PeakSegPDPA 1938690 100.00000
447 cDPA 442183 100.00000
448 PeakSegPDPA 1988057 100.00000
449 PeakSegPDPA 1995109 100.00000
450 cDPA 415031 100.00000
451 cDPA 398812 100.00000
452 PeakSegPDPA 1863936 100.00000
453 cDPA 398812 100.00000
454 PeakSegPDPA 1756739 100.00000
455 cDPA 467572 100.00000
456 PeakSegPDPA 1972894 100.00000
457 cDPA 365313 100.00000
458 PeakSegPDPA 1924938 100.00000
459 cDPA 398812 100.00000
460 PeakSegPDPA 2214436 100.00000
461 PeakSegPDPA 1754976 100.00000
462 PeakSegPDPA 1942921 100.00000
463 cDPA 398812 100.00000
464 cDPA 349445 100.00000
465 cDPA 339219 100.00000
466 cDPA 343804 100.00000
467 PeakSegPDPA 1854414 100.00000
468 cDPA 375538 100.00000
469 cDPA 363197 100.00000
470 cDPA 461578 100.00000
471 PeakSegPDPA 1940806 100.00000
472 cDPA 407980 100.00000
473 cDPA 419263 100.00000
474 cDPA 431957 100.00000
475 cDPA 374833 100.00000
476 PeakSegPDPA 1581842 100.00000
477 cDPA 427726 100.00000
478 PeakSegPDPA 2078679 100.00000
479 cDPA 416089 100.00000
480 cDPA 368487 100.00000
481 cDPA 372366 100.00000
482 cDPA 354029 100.00000
483 PeakSegPDPA 1768729 100.00000
484 PeakSegPDPA 1812453 100.00000
485 cDPA 374129 100.00000
486 PeakSegPDPA 1817743 100.00000
487 PeakSegPDPA 2009566 100.00000
488 PeakSegPDPA 1939396 100.00000
489 cDPA 357556 100.00000
490 PeakSegPDPA 1776486 100.00000
491 cDPA 428078 100.00000
492 PeakSegPDPA 1775781 100.00000
493 PeakSegPDPA 1985235 100.00000
494 PeakSegPDPA 1544111 100.00000
495 cDPA 386117 100.00000
496 PeakSegPDPA 1862524 100.00000
497 cDPA 432663 100.00000
498 PeakSegPDPA 2058932 100.00000
499 cDPA 411505 100.00000
500 cDPA 428431 100.00000
501 cDPA 346977 100.00000
502 PeakSegPDPA 1767317 100.00000
503 cDPA 403043 100.00000
504 cDPA 399869 100.00000
505 cDPA 425257 100.00000
506 cDPA 412916 100.00000
507 cDPA 348740 100.00000
508 PeakSegPDPA 1775075 100.00000
509 cDPA 452409 100.00000
510 PeakSegPDPA 1879450 100.00000
511 cDPA 430194 100.00000
512 PeakSegPDPA 1948916 100.00000
513 PeakSegPDPA 2006392 100.00000
514 cDPA 383649 100.00000
515 cDPA 433367 100.00000
516 cDPA 451704 100.00000
517 cDPA 411858 100.00000
518 PeakSegPDPA 2053290 100.00000
519 PeakSegPDPA 1772960 100.00000
520 cDPA 406921 100.00000
521 cDPA 423495 100.00000
522 PeakSegPDPA 1736641 100.00000
523 PeakSegPDPA 1706667 100.00000
524 cDPA 478503 100.00000
525 PeakSegPDPA 1726767 100.00000
526 cDPA 409743 100.00000
527 PeakSegPDPA 1750039 100.00000
528 cDPA 392112 100.00000
529 PeakSegPDPA 2084322 100.00000
530 PeakSegPDPA 1983472 100.00000
531 PeakSegPDPA 1780718 100.00000
532 PeakSegPDPA 1910481 100.00000
533 PeakSegPDPA 1995814 100.00000
534 PeakSegPDPA 1725709 100.00000
535 cDPA 371661 100.00000
536 PeakSegPDPA 1907660 100.00000
537 PeakSegPDPA 2012035 100.00000
538 cDPA 445710 100.00000
539 PeakSegPDPA 1980652 100.00000
540 cDPA 439715 100.00000
541 PeakSegPDPA 1885444 100.00000
542 PeakSegPDPA 1967957 100.00000
543 PeakSegPDPA 1880155 100.00000
544 cDPA 379417 100.00000
545 cDPA 422437 100.00000
546 PeakSegPDPA 2018381 100.00000
547 PeakSegPDPA 1798348 100.00000
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1130 cDPA 345567500 3162.27766
1131 PeakSegPDPA 97251775 3162.27766
1132 PeakSegPDPA 105424733 3162.27766
1133 cDPA 390414037 3162.27766
1134 PeakSegPDPA 95005605 3162.27766
1135 cDPA 312292795 3162.27766
1136 cDPA 378465609 3162.27766
1137 cDPA 418351884 3162.27766
1138 cDPA 378326679 3162.27766
1139 PeakSegPDPA 87724064 3162.27766
1140 cDPA 421942936 3162.27766
1141 cDPA 379597863 3162.27766
1142 cDPA 405216547 3162.27766
1143 PeakSegPDPA 93585969 3162.27766
1144 cDPA 368582813 3162.27766
1145 cDPA 353450253 3162.27766
1146 cDPA 330262511 3162.27766
1147 cDPA 331108086 3162.27766
1148 PeakSegPDPA 85213077 3162.27766
1149 cDPA 374892542 3162.27766
1150 cDPA 364329194 3162.27766
1151 PeakSegPDPA 91949120 3162.27766
1152 PeakSegPDPA 85285717 3162.27766
1153 cDPA 399613111 3162.27766
1154 PeakSegPDPA 86510356 3162.27766
1155 cDPA 345224051 3162.27766
1156 cDPA 379750898 3162.27766
1157 cDPA 359556522 3162.27766
1158 PeakSegPDPA 90632095 3162.27766
1159 PeakSegPDPA 87242389 3162.27766
1160 cDPA 327787139 3162.27766
1161 cDPA 350289747 3162.27766
1162 PeakSegPDPA 90803468 3162.27766
1163 cDPA 346687059 3162.27766
1164 PeakSegPDPA 87895435 3162.27766
1165 cDPA 406694012 3162.27766
1166 cDPA 381347901 3162.27766
1167 cDPA 305688278 3162.27766
1168 cDPA 335069033 3162.27766
1169 cDPA 378062568 3162.27766
1170 cDPA 315302733 3162.27766
1171 cDPA 399555986 3162.27766
1172 PeakSegPDPA 94856448 3162.27766
1173 PeakSegPDPA 97577240 3162.27766
1174 cDPA 352517934 3162.27766
1175 PeakSegPDPA 96480249 3162.27766
1176 cDPA 384489366 3162.27766
1177 PeakSegPDPA 92572547 3162.27766
1178 cDPA 380091174 3162.27766
1179 PeakSegPDPA 78936494 3162.27766
1180 PeakSegPDPA 88228305 3162.27766
1181 PeakSegPDPA 86412328 3162.27766
1182 PeakSegPDPA 87808339 3162.27766
1183 PeakSegPDPA 96273967 3162.27766
1184 cDPA 331675447 3162.27766
1185 PeakSegPDPA 94351501 3162.27766
1186 cDPA 457258587 3162.27766
1187 cDPA 388520837 3162.27766
1188 PeakSegPDPA 104038243 3162.27766
1189 cDPA 336811314 3162.27766
1190 PeakSegPDPA 89658520 3162.27766
1191 PeakSegPDPA 94032029 3162.27766
1192 PeakSegPDPA 86526224 3162.27766
1193 cDPA 348608470 3162.27766
1194 PeakSegPDPA 89401816 3162.27766
1195 cDPA 303725965 3162.27766
1196 cDPA 429972731 3162.27766
1197 PeakSegPDPA 93653671 3162.27766
1198 PeakSegPDPA 85397144 3162.27766
1199 cDPA 344030442 3162.27766
1200 cDPA 464920603 3162.27766
1201 PeakSegPDPA 285903995 10000.00000
1202 cDPA 3092790817 10000.00000
1203 PeakSegPDPA 340789539 10000.00000
1204 cDPA 3547161470 10000.00000
1205 cDPA 4057759374 10000.00000
1206 PeakSegPDPA 332698037 10000.00000
1207 cDPA 3951132220 10000.00000
1208 cDPA 3218484326 10000.00000
1209 cDPA 3368468917 10000.00000
1210 PeakSegPDPA 307705953 10000.00000
1211 PeakSegPDPA 319841620 10000.00000
1212 PeakSegPDPA 334567965 10000.00000
1213 PeakSegPDPA 349170541 10000.00000
1214 PeakSegPDPA 327593553 10000.00000
1215 cDPA 3057998096 10000.00000
1216 PeakSegPDPA 297844665 10000.00000
1217 PeakSegPDPA 311387980 10000.00000
1218 PeakSegPDPA 300482241 10000.00000
1219 cDPA 3225415014 10000.00000
1220 cDPA 3171311222 10000.00000
1221 PeakSegPDPA 321355758 10000.00000
1222 cDPA 4128344133 10000.00000
1223 PeakSegPDPA 307800101 10000.00000
1224 cDPA 3783761495 10000.00000
1225 cDPA 4031943578 10000.00000
1226 cDPA 3291478165 10000.00000
1227 cDPA 3047302165 10000.00000
1228 PeakSegPDPA 313852773 10000.00000
1229 PeakSegPDPA 327898214 10000.00000
1230 PeakSegPDPA 351326441 10000.00000
1231 cDPA 3870222835 10000.00000
1232 PeakSegPDPA 381237532 10000.00000
1233 cDPA 4199929267 10000.00000
1234 cDPA 4461664891 10000.00000
1235 cDPA 3368673082 10000.00000
1236 cDPA 3633267372 10000.00000
1237 cDPA 3015338489 10000.00000
1238 cDPA 3233493823 10000.00000
1239 PeakSegPDPA 325853740 10000.00000
1240 cDPA 3747632707 10000.00000
1241 cDPA 3738241105 10000.00000
1242 PeakSegPDPA 342611159 10000.00000
1243 PeakSegPDPA 299229745 10000.00000
1244 PeakSegPDPA 319890986 10000.00000
1245 PeakSegPDPA 298294252 10000.00000
1246 cDPA 3071500859 10000.00000
1247 PeakSegPDPA 281149660 10000.00000
1248 cDPA 3114354053 10000.00000
1249 cDPA 3445602480 10000.00000
1250 PeakSegPDPA 311159484 10000.00000
1251 cDPA 3253253775 10000.00000
1252 cDPA 3144671712 10000.00000
1253 cDPA 4140879668 10000.00000
1254 cDPA 4092687148 10000.00000
1255 PeakSegPDPA 310679221 10000.00000
1256 cDPA 3145442886 10000.00000
1257 cDPA 2983463674 10000.00000
1258 cDPA 3813808696 10000.00000
1259 cDPA 3944502667 10000.00000
1260 cDPA 3738863827 10000.00000
1261 PeakSegPDPA 326343877 10000.00000
1262 PeakSegPDPA 307836068 10000.00000
1263 PeakSegPDPA 323139294 10000.00000
1264 cDPA 3285412094 10000.00000
1265 cDPA 3521180647 10000.00000
1266 cDPA 4548480611 10000.00000
1267 PeakSegPDPA 319316221 10000.00000
1268 cDPA 3453014136 10000.00000
1269 PeakSegPDPA 336728801 10000.00000
1270 PeakSegPDPA 337939336 10000.00000
1271 PeakSegPDPA 331711413 10000.00000
1272 cDPA 2974500150 10000.00000
1273 cDPA 2943948707 10000.00000
1274 cDPA 3789671708 10000.00000
1275 cDPA 4457072053 10000.00000
1276 cDPA 3650393275 10000.00000
1277 PeakSegPDPA 326864340 10000.00000
1278 cDPA 3168146131 10000.00000
1279 cDPA 4053312169 10000.00000
1280 cDPA 4087866521 10000.00000
1281 cDPA 4323768010 10000.00000
1282 PeakSegPDPA 295694055 10000.00000
1283 cDPA 4037546309 10000.00000
1284 PeakSegPDPA 349150089 10000.00000
1285 PeakSegPDPA 348422288 10000.00000
1286 PeakSegPDPA 326959547 10000.00000
1287 cDPA 3486301183 10000.00000
1288 PeakSegPDPA 327276550 10000.00000
1289 cDPA 3768972031 10000.00000
1290 PeakSegPDPA 327079438 10000.00000
1291 PeakSegPDPA 325764881 10000.00000
1292 PeakSegPDPA 289726364 10000.00000
1293 PeakSegPDPA 329413762 10000.00000
1294 PeakSegPDPA 341019798 10000.00000
1295 PeakSegPDPA 303045061 10000.00000
1296 PeakSegPDPA 358101977 10000.00000
1297 cDPA 3237681855 10000.00000
1298 PeakSegPDPA 307720058 10000.00000
1299 PeakSegPDPA 311540311 10000.00000
1300 cDPA 3594529925 10000.00000
1301 cDPA 4278586840 10000.00000
1302 cDPA 4457635183 10000.00000
1303 cDPA 3821380441 10000.00000
1304 PeakSegPDPA 327505750 10000.00000
1305 cDPA 3722451973 10000.00000
1306 cDPA 3153556603 10000.00000
1307 PeakSegPDPA 318057025 10000.00000
1308 PeakSegPDPA 311244465 10000.00000
1309 cDPA 3994803132 10000.00000
1310 cDPA 3194559967 10000.00000
1311 PeakSegPDPA 358504665 10000.00000
1312 cDPA 4209206973 10000.00000
1313 PeakSegPDPA 309001820 10000.00000
1314 PeakSegPDPA 310593534 10000.00000
1315 PeakSegPDPA 314571053 10000.00000
1316 PeakSegPDPA 297399662 10000.00000
1317 cDPA 4513521807 10000.00000
1318 PeakSegPDPA 329094291 10000.00000
1319 PeakSegPDPA 300799243 10000.00000
1320 cDPA 3710188658 10000.00000
1321 PeakSegPDPA 308195738 10000.00000
1322 cDPA 3372229931 10000.00000
1323 cDPA 3182450042 10000.00000
1324 PeakSegPDPA 313632035 10000.00000
1325 cDPA 3912768545 10000.00000
1326 cDPA 3666529030 10000.00000
1327 cDPA 3453590313 10000.00000
1328 PeakSegPDPA 293131939 10000.00000
1329 cDPA 3205850765 10000.00000
1330 PeakSegPDPA 317041841 10000.00000
1331 PeakSegPDPA 341850211 10000.00000
1332 cDPA 4174927309 10000.00000
1333 cDPA 3974457836 10000.00000
1334 cDPA 4347050607 10000.00000
1335 cDPA 3534033890 10000.00000
1336 PeakSegPDPA 362737127 10000.00000
1337 PeakSegPDPA 313395428 10000.00000
1338 PeakSegPDPA 312041026 10000.00000
1339 PeakSegPDPA 340987005 10000.00000
1340 PeakSegPDPA 329913772 10000.00000
1341 PeakSegPDPA 323152694 10000.00000
1342 PeakSegPDPA 357440468 10000.00000
1343 cDPA 3282390167 10000.00000
1344 cDPA 4090396195 10000.00000
1345 PeakSegPDPA 336503127 10000.00000
1346 cDPA 3780987104 10000.00000
1347 PeakSegPDPA 321545818 10000.00000
1348 cDPA 4062961533 10000.00000
1349 cDPA 4215429253 10000.00000
1350 PeakSegPDPA 343125274 10000.00000
1351 PeakSegPDPA 321688628 10000.00000
1352 PeakSegPDPA 292259212 10000.00000
1353 PeakSegPDPA 405213374 10000.00000
1354 PeakSegPDPA 300926185 10000.00000
1355 PeakSegPDPA 299222693 10000.00000
1356 PeakSegPDPA 316683935 10000.00000
1357 PeakSegPDPA 311789963 10000.00000
1358 cDPA 3721216051 10000.00000
1359 PeakSegPDPA 297592192 10000.00000
1360 PeakSegPDPA 342984933 10000.00000
1361 PeakSegPDPA 313594657 10000.00000
1362 cDPA 3569873533 10000.00000
1363 cDPA 4231294198 10000.00000
1364 PeakSegPDPA 314884177 10000.00000
1365 cDPA 3885082468 10000.00000
1366 PeakSegPDPA 314949412 10000.00000
1367 cDPA 3633801234 10000.00000
1368 PeakSegPDPA 318969246 10000.00000
1369 PeakSegPDPA 334782356 10000.00000
1370 cDPA 4042677592 10000.00000
1371 cDPA 3831726577 10000.00000
1372 cDPA 3026784085 10000.00000
1373 PeakSegPDPA 297732180 10000.00000
1374 PeakSegPDPA 315827781 10000.00000
1375 PeakSegPDPA 326851999 10000.00000
1376 cDPA 3743201017 10000.00000
1377 cDPA 4244398151 10000.00000
1378 cDPA 3728132634 10000.00000
1379 cDPA 3582314918 10000.00000
1380 cDPA 4250308012 10000.00000
1381 PeakSegPDPA 323949961 10000.00000
1382 PeakSegPDPA 320580705 10000.00000
1383 cDPA 3293345272 10000.00000
1384 cDPA 4352447763 10000.00000
1385 cDPA 4455091051 10000.00000
1386 cDPA 3344740966 10000.00000
1387 PeakSegPDPA 342385131 10000.00000
1388 PeakSegPDPA 310447903 10000.00000
1389 PeakSegPDPA 303562351 10000.00000
1390 cDPA 3918033824 10000.00000
1391 PeakSegPDPA 335859954 10000.00000
1392 PeakSegPDPA 329193024 10000.00000
1393 cDPA 3494999187 10000.00000
1394 cDPA 4840201021 10000.00000
1395 PeakSegPDPA 300530549 10000.00000
1396 cDPA 4230564633 10000.00000
1397 PeakSegPDPA 327447922 10000.00000
1398 PeakSegPDPA 378498051 10000.00000
1399 cDPA 3335136383 10000.00000
1400 PeakSegPDPA 311229302 10000.00000
```
### Second Revision:
```
library(PeakSegDP)
library(PeakSegOptimal)
library(microbenchmark)
library(ggplot2)
require(dplyr)
library(data.table)
# Intializing data set sizes as required:
datasetsizes<-c(10^seq(1,4,by=0.5))
l <- length(datasetsizes)
# Condition vector to categorize/classify between PeakSegPDPA and cDPA (or for any another algorithm based function as well):
conds <- c("PeakSegPDPA", "cDPA")
# Making a data frame which assigns dataset size (1:7) and creates replicates:
dat <- data.frame(
datasetsizes = rep(datasetsizes, each = length(conds)), # make replicates for each condition (algo based function).
cond = rep(NA, l*length(conds))
)
# Adding parameters (microbenchmark ones) and initializing them to zero for the start:
dat[, c("min", "lq", "mean", "median", "uq", "max")] <- 0
dat$cond <- factor(dat$cond, levels = conds)
# debug statement/check for required data table format:
# head(dat)
for(i in 1:l)
{
s <- summary(microbenchmark(
"PeakSegPDPA" = PeakSegPDPA(rpois(datasetsizes[i],10),max.segments=3L),
"cDPA" = cDPA(rpois(datasetsizes[i],10),maxSegments=3)
))
dat[which(dat$datasetsizes == datasetsizes[i]), # selecting rows of current dataset size.
c("cond", "min", "lq", "mean", "median", "uq", "max")] <- s[, !colnames(s)%in%c("neval")] # excluding neval column and expr->cond.
}
# Converting our data frame into a data table for plotting:
dat <- data.table(dat)
# Plotting: (for all 6 values using both point and line geometry on a log-log scale)
# (i) Plotting with aesthetic mapping of datasetsizes by min:
ggplot(dat, aes(x=datasetsizes,y=min))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
# (ii) Plotting with aesthetic mapping of datasetsizes by lq:
ggplot(dat, aes(x=datasetsizes,y=lq))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
# (iii) Plotting with aesthetic mapping of datasetsizes by mean:
ggplot(dat, aes(x=datasetsizes,y=mean))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
# (iv) Plotting with aesthetic mapping of datasetsizes by median:
ggplot(dat, aes(x=datasetsizes,y=median))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
# (v) Plotting with aesthetic mapping of datasetsizes by uq:
ggplot(dat, aes(x=datasetsizes,y=uq))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
# (vi) Plotting with aesthetic mapping of datasetsizes by max:
ggplot(dat, aes(x=datasetsizes,y=max))
+ geom_point(aes(color = cond))
+ geom_line(aes(color = cond)) # added to see a clear difference between conditions.
+ labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
```
Apart from your suggestions I also thought it would be better to generalize every part of the code. Hence instead of making seperate vectors each to hold values for (min..max) and then extracting each parameter seperately into them (which I did initially), I created a data frame before entering the loop and set the dataset sizes along with a condition vector with replicates x2 for holding data for both cDPA and PeakSegPDPA and to classify among them for ease in plotting. Then I added column vectors (min<->max) to my data frame and initialized them to 0. Then I factored out the data frame into levels based on my condition vector (conds).
Now coming to the for loop I did the usual required calculation via microbenchmark, but placed PeakSegPDPA and cDPA computed values differently based on conds. Then I selected the rows from each iteration and from the summary of microbenchmark (which includes expr, min..max, neval) I removed expr and neval for obvious reasons and collected the required parameters (min<->max, along with condition vector PeakSegPDPA/cDPA) into my data frame.
Then I converted that data frame into a data table as you suggested and plotted the dataset size against the parameters accordingly. I used a geom_line as well apart from the geom_point.
Data table with dataset sizes, condition (classification/distinction between PeakSegPDPA and cDPA) and microbenchmark computed values (min, lq, mean, median, uq, max) for reference:
Output Plots:
(i) Dataset size vs min:
(ii) Dataset size vs lq:
(iii) Dataset size vs mean:
(iv) Dataset size vs median:
(v) Dataset size vs uq:
(i) Dataset size vs max:
The previously noted observation can be inferred from each. (PeakSegPDPA>cDPA for the start, then cDPA>PeakSegPDPA for greater dataset sizes which shows the actual complexity trend.
Click here to view previous versions of my submissions to the Easy Test
Initial setup for second revision: (kept for my reference)
```
library(PeakSegDP)
library(PeakSegOptimal)
library(microbenchmark)
library(ggplot2)
require(dplyr)
library(data.table)
datasetsizes<-c(10^seq(1,4,by=0.5))
peaksegpdpa_min<-integer(length(datasetsizes))
peaksegpdpa_lq<-integer(length(datasetsizes))
peaksegpdpa_mean<-integer(length(datasetsizes))
peaksegpdpa_median<-integer(length(datasetsizes))
peaksegpdpa_uq<-integer(length(datasetsizes))
peaksegpdpa_max<-integer(length(datasetsizes))
cdpa_min<-integer(length(datasetsizes))
cdpa_lq<-integer(length(datasetsizes))
cdpa_mean<-integer(length(datasetsizes))
cdpa_median<-integer(length(datasetsizes))
cdpa_uq<-integer(length(datasetsizes))
cdpa_max<-integer(length(datasetsizes))
for(loopvar in 1:(length(datasetsizes)))
{
s<-summary(microbenchmark(PeakSegPDPA(rpois(datasetsizes[loopvar],10), max.segments=3L),cDPA(rpois(datasetsizes[loopvar],10), maxSegments=3)))
peaksegpdpa_min[loopvar] <- s$min[1]
cdpa_min[loopvar] <- s$min[2]
peaksegpdpa_lq[loopvar] <- s$lq[1]
cdpa_lq[loopvar] <- s$lq[2]
peaksegpdpa_mean[loopvar] <- s$mean[1]
cdpa_mean[loopvar] <- s$mean[2]
peaksegpdpa_median[loopvar] <- s$median[1]
cdpa_median[loopvar] <- s$median[2]
peaksegpdpa_uq[loopvar] <- s$uq[1]
cdpa_uq[loopvar] <- s$uq[2]
peaksegpdpa_max[loopvar] <- s$max[1]
cdpa_max[loopvar] <- s$max[2]
}
algorithm<-data.table( peaksegpdpa_min, cdpa_min,
peaksegpdpa_lq, cdpa_lq,
peaksegpdpa_mean, cdpa_mean, peaksegpdpa_median, cdpa_median,
peaksegpdpa_uq, cdpa_uq,
peaksegpdpa_max, cdpa_max, datasetsizes)
algorithm$algorithm <- 1:nrow(algorithm)
ggplot(algorithm, aes(x=algorithm,y=datasetsizes)) + geom_point(aes(color=algorithm)) + labs(x="N", y="Runtime") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
```
Microbenchmark computed values (min, lq, mean, median, uq, max) for reference:
Output Plot:
### First Revision:
Input: N=10 (lambda/mean for rpois values set to N(L) as well)
```
library(PeakSegDP)
library(PeakSegOptimal)
library(ggplot2)
library(microbenchmark)
N <- readline(prompt="Enter size: ")
N <- as.integer(N)
Nvalues=c(N,N*10,N*100,N*1000)
peaksegpdpa<-integer(4)
cdpa<-integer(4)
for(loopvar in 1:4)
{
s<-summary(microbenchmark(PeakSegPDPA(rpois(Nvalues[loopvar],N), max.segments=3L),
cDPA(rpois(Nvalues[loopvar],N), maxSegments=3)))
peaksegpdpa[loopvar] <- s$mean[1]
cdpa[loopvar] <- s$mean[2]
# debug/check benchmark values with print statements below:
# cat("Mean values computed from microbenchmark for PeakSegPDPA :",s$mean[1],"\n",
# "Mean values computed from microbenchmark for cDPA :",s$mean[2],"\n")
}
# plot the 4x3 data frame on a log-log scale: (log10)
ggplot(data.frame(peaksegpdpa, cdpa, Nvalues), aes(x=Nvalues, y=cdpa)) + geom_line(color = 'red') + geom_line(y = peaksegpdpa, color='blue') + labs(x="N", y="Runtime") + scale_x_log10() + scale_y_log10() + scale_y_continuous(lim=c(0,10000))
# can use coord_trans(x="log2",y="log2") or continuous scale transform for log2 scale as well)
```
Output plot:
### Initial Submission:
(1) Using autoplot:
Case I: Using small dataset sizes: (N,N+10,N+20,N+30)
```
library(PeakSegDP)
library(PeakSegOptimal)
library(ggplot2)
library(microbenchmark)
N <- readline(prompt="Enter size: ")
N <- as.integer(N)
# fits(i) where i is odd pertain to cDPA (quadratic) and where i is even pertain to PDPA (log-linear):
fit1 <- function() cDPA(rpois(N,N), maxSegments=3)
fit2 <- function() PeakSegPDPA(rpois(N,N), max.segments=3L)
fit3 <- function() cDPA(rpois(N+10,N), maxSegments=3)
fit4 <- function() PeakSegPDPA(rpois(N+10,N), max.segments=3L)
fit5 <- function() cDPA(rpois(N+20,N), maxSegments=3)
fit6 <- function() PeakSegPDPA(rpois(N+20,N), max.segments=3L)
fit7 <- function() cDPA(rpois(N+30,N), maxSegments=3)
fit8 <- function() PeakSegPDPA(rpois(N+30,N), max.segments=3L)
# The difference in time is observed at each alternate fit (cDPA
Microbenchmark computed values:
We can infer from the above data that PeakSegPDPA is consuming more time than cDPA (reverse of actual complexity) for small values in the dataset.
Case II: Using large dataset sizes: (N,N+90,N+990,N+9990)
```
library(PeakSegDP)
library(PeakSegOptimal)
library(microbenchmark)
library(ggplot2)
N <- readline(prompt="Enter size: ")
N <- as.integer(N)
fit1 <- function() cDPA(rpois(N,N), maxSegments=3)
fit2 <- function() PeakSegPDPA(rpois(N,N), max.segments=3L)
fit3 <- function() cDPA(rpois(N+90,N), maxSegments=3)
fit4 <- function() PeakSegPDPA(rpois(N+90,N), max.segments=3L)
fit5 <- function() cDPA(rpois(N+990,N), maxSegments=3)
fit6 <- function() PeakSegPDPA(rpois(N+990,N), max.segments=3L)
fit7 <- function() cDPA(rpois(N+9990,N), maxSegments=3)
fit8 <- function() PeakSegPDPA(rpois(N+9990,N), max.segments=3L)
# The difference in time is observed at each alternate fit (cDPA>PeakSegPDPA for this case):
micrograph <- microbenchmark(fit1(),fit2(),fit3(),fit4(),fit5(),fit6(),fit7(),fit8())
autoplot(micrograph,title="PeakSegDP::cDPA (quadratic) vs PeakSegOptimal::PeakSegPDPA (log-linear)")
```
Microbenchmark computed values:
From the data above we can infer that cDPA is consuming more time than PeakSegPDPA (which is expected) for large values in the dataset.
The observations made above won't be noticeable if we used autoplot and directly jumped into a dataset with possibly more of large values, wherein only the expected trend in time complexity will be shown (and not the initial reverse trend) in the violin plots.
(2) Using ggplot:
```
library(PeakSegDP)
library(PeakSegOptimal)
library(ggplot2)
library(microbenchmark)
N <- readline(prompt="Enter size: ")
N <- as.integer(N)
# Specifying dataset values in a vector and assigning its length as size to PeakSegPDPA and cDPA (arrays):
Nvalues=c(N,N+90,N+990,N+9990)
peaksegpdpa<-integer(length(Nvalues))
cdpa<-integer(length(Nvalues))
# Iterating through 'Nvalues' and computing microbenchmark values:
for (loopvariable in 1:length(Nvalues))
{ x <- rpois(Nvalues[loopvariable], N)
s <- summary(microbenchmark( PeakSegPDPA(x, rep(1, length(x)), 3L),cDPA(x, rep(1, length(x)), 3L)))
# Extract the mean from the microbenchmarked values to an array of PeakSegPDPA and cDPA values respectively:
peaksegpdpa[loopvariable] <- s$mean[1]
cdpa[loopvariable] <- s$mean[2]
}
ggplot(data.frame(peaksegpdpa, cdpa, Nvalues), aes(x=Nvalues, y=cdpa)) + geom_line(color = 'red') + geom_line(y = peaksegpdpa, color='blue') + labs(x="N", y="Runtime") + scale_y_continuous(lim=c(0,10000))
# The difference in time is seen by the growth rate in plot (cDPA>PDPA), which at start however has a higher rate for PDPA:
```
Output plot:
It is clear from the plot above that initially for small values of dataset sizes : PDPA>cDPA. After that for larger dataset sizes it follows the estimated trend of cDPA>PDPA in terms of runtime complexity.
Hence we can safely conclude that computation times of PeakSegDP::cDPA follow a greater time complexity (quadratic) as compared to PeakSegOptimal::PeakSegPDPA (log-linear) taking into consideration the trend for large dataset sizes N following the plot(s) obtained.
Medium Test
---
Function Name: loglinearvsquadraticClassifier
Function Body/Definition:
```
loglinearvsquadraticClassifier<-function(f1,f2)
{
f1name<-as.character(substitute(f1))
f2name<-as.character(substitute(f2))
if(f1name!=f2name)
{
cat("Among the two functions passed as parameters, (calculating..please wait)\n")
s <- summary(microbenchmark( f1(rpois(1000,10),rep(1,length(rpois(1000,10))),3L),f2(rpois(1000,10),rep(1,length(rpois(1000,10))),3L)))
if(s$mean[1]>s$mean[2])
cat(f1name,"follows quadratic time complexity whereas",f2name,"follows log-linear time complexity.")
else if(s$mean[1]
This is just a preliminary function to comply with the test, classifying asymptotic time complexity of two functions (considering one of them follows a log-linear trend while the remaining follows a quadratic one) based on microbenchmark timings.
From the easy test I could figure out that from around N=1000 observations the trend is quadratic for the tested PeakSegDP::cDPA (and similar functions) and further tests by increasing the range would be unnecessary. (I started with a for loop ranging from N values 1e+01 to 1e+06 with two counters each for f1 and f2, with them incrementing by one if s$mean[1]>s$mean[2] and reverse for the later respectively, with f1/f2 being quadratic in nature if their respective count value is higher than the other - but that was unecessary since a single point i.e. at N=1000 itself there was a clear distinction of the quadratic trend remaining the same for higher N values, so thereafter I considered to use that point only)
However to classify on a broader range with all complexities taken into account (linear, log, cubic, exponential etc) we will require to loop through increasing sizes of data, and benchmark them accordingly - maybe through sampling of a model.
Hard Test
---
Package name: testComplexity
Function(s): loglinearvsquadraticClassifier(function1,function2)
Documentation: (roxygen format)
```
#' Preliminary function to distinguish between log-linear vs quadratic time complexity
#' of two algorithms/functions with parameters : count.vec, <- integer vector of count data.
#' weight.vec=rep(1, length(count.vec)), <- numeric vector (same length as count.vec) of positive weights.
#' and max.segments. <- integer of length 1: maximum number of segments (must be >= 2, default value: 3).
#'
#' @title Log-linear vs Quadratic time complexity classifier
#'
#' @param f1 Function based on an algorithm which is known or expected to run at either log-linear or quadratic time complexity, different from that of f2.
#' @param f2 Function based on an algorithm which is known or expected to run at either log-linear or quadratic time complexity, different from that of f1.
#'
#' @return This function does not return any value but a statement indicating which among the two is log-linear and which is quadratic in nature.
#'
#' @export
#' @import microbenchmark
#' @importFrom stats rpois
```
R CMD check() results:
Installation:
Might be prompted for updates:
Successful installation and an example run:
Install locally using :
```install.packages("testComplexity")```
Install via Github using (repository not linked yet) :
```if(!require(devtools))install.packages("devtools") devtools::install_github("Anirban166/RGSOC_20_Tests")```
Tests: (using testthat)
Thanks for reading! - Anirban