deinega5
.docx
zavdann9 1
> library(e1071)
Загрузка требуемого пакета: class
>
> SVMData<-read.table("svmdata1.txt")
> SVMData
X1 X2 Color
1 0.1487474069 0.131288343 red
2 -0.0488152465 0.036423198 red
3 -0.0623905823 -0.234859663 red
4 0.3548392535 -0.177402872 red
5 -0.1456167560 0.081265308 red
6 0.0877447693 -0.291933867 red
7 -0.0742950518 -0.072756035 red
8 0.0696747719 0.104949374 red
9 0.0007751977 -0.142144682 red
10 0.1802746409 0.291522533 red
11 -0.1328878583 0.013373291 red
12 -0.0611663996 0.123317237 red
13 -0.1125849669 0.042594690 red
14 0.1261414992 -0.256180543 red
15 0.1079931463 -0.005926504 red
16 0.0401478019 -0.057325920 red
17 -0.0194609009 -0.096386362 red
18 0.1286997646 0.062265038 red
19 0.2045427345 0.007401814 red
20 -0.1384021365 -0.238766758 red
21 0.9689590106 0.807352437 green
22 1.0096519003 1.070619400 green
23 1.1823329495 1.086272274 green
24 0.9035634323 0.722369514 green
25 0.9851006663 0.684300488 green
26 1.0878503124 0.844380462 green
27 0.8083535092 0.821261836 green
28 1.0429336070 0.939052636 green
29 1.1262640781 0.985588494 green
30 1.2316934522 0.927650634 green
31 1.1617160371 0.866711376 green
32 1.0640343536 1.295645605 green
33 0.7587539403 1.081325570 green
34 1.0502065818 1.068037533 green
35 1.0761530988 1.221281317 green
36 1.2709731752 0.949211329 green
37 1.0126267790 1.111827922 green
38 1.0711410214 1.127448984 green
39 1.0319027618 0.738628183 green
40 0.9811110744 0.889771031 green
> model<-svm(SVMData$Color~.,SVMData,kernel="linear",cost=2)
> plot(model,SVMData)
> x<-subset(SVMData,select= -Color)
> Color_pred<-predict(model,x)
> TABLE(svmdATA$cOLOR,cOLOR_PRED)
Ошибка: не могу найти функцию "TABLE"
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 20 0
red 0 20
> SVMDataTest<-read.table("svmdata1test.txt")
> x<-subset(SVMDataTest,select= -Color)
> Color_pred<-predict(model,x)
> table(SVMDataTest$Color,Color_pred)
Color_pred
green red
green 20 0
red 0 20
zavdann9 2
SVMData<-read.table("svmdata2.txt")
> SVMData
X1 X2 Colors
1 0.076975920 -3.932089e-01 red
2 -0.553391696 2.123627e-01 red
3 0.535482769 -5.316915e-02 red
4 -0.001037057 -9.769743e-06 red
5 -0.121083157 4.646396e-01 red
6 -0.201952019 -1.437049e-01 red
7 0.474111523 3.139432e-01 red
8 -0.464306270 1.992086e-02 red
9 -0.701445378 -3.730172e-01 red
10 0.354699394 1.243998e-01 red
11 0.608739369 6.256046e-01 red
12 0.292494575 -4.197986e-01 red
13 0.023572998 -2.951492e-01 red
14 0.249018893 -2.369266e-01 red
15 0.397321782 -6.237173e-01 red
16 -0.023581975 1.067593e-01 red
17 -0.102501258 3.847678e-01 red
18 0.064590900 9.227248e-02 red
19 -0.261130652 3.352433e-01 red
20 -0.668198012 -1.495844e-01 red
21 0.123760884 4.660110e-01 red
22 0.274256956 -2.214181e-01 red
23 0.312036417 -4.903453e-01 red
24 -0.189323062 9.919215e-02 red
25 -0.591697402 -3.967874e-02 red
26 1.064467856 9.584308e-01 green
27 1.434909027 1.102927e+00 green
28 1.177983531 8.944166e-01 green
29 1.739353050 8.231773e-01 green
30 0.546185688 9.190220e-01 green
31 0.804564200 1.532705e+00 green
32 1.063747587 1.865665e+00 green
33 1.050275135 5.860192e-01 green
34 1.184455227 4.147339e-01 green
35 0.764254983 1.102790e+00 green
36 1.396203307 1.004134e+00 green
37 1.077056751 7.402940e-01 green
38 0.796690734 1.151478e+00 green
39 1.011257518 8.984277e-01 green
40 1.266692514 7.443719e-01 green
41 1.504513493 1.424907e+00 green
42 0.470063105 1.300626e+00 green
43 0.554867359 1.234510e+00 green
44 1.270645050 4.375717e-01 green
45 1.448780933 1.044336e+00 green
46 1.214542788 6.417231e-01 green
47 1.417506695 1.278895e+00 green
48 0.607374974 9.532071e-01 green
49 0.200202367 8.607130e-01 green
50 1.241756756 1.151513e+00 green
> model<-svm(SVMData$Color~.,SVMData,kernel="linear",cost=0)
Ошибка в svm.default(x, y, scale = scale, ..., na.action = na.action) :
C <= 0!
> model<-svm(SVMData$Color~.,SVMData,kernel="linear",cost=1)
> plot(model,SVMData)
Ошибка в Summary.factor(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, :
min not meaningful for factors
> x<-subset(SVMData,select= -Color)
Ошибка в eval(expr, envir, enclos) : объект 'Color' не найден
> x<-subset(SVMData,select= -Colors)
> Color_pred<-predict(model,x)
Ошибка в eval(expr, envir, enclos) : объект 'Colors' не найден
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=1)
> Color_pred<-predict(model,x)
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=2)
> Color_pred<-predict(model,x)
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=3)
> Color_pred<-predict(model,x)
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=20)
> Color_pred<-predict(model,x)
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=30)
> Color_pred<-predict(model,x)
> table(SVMData$Color, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=30)
> table(SVMData$Colors, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMData$Colors~.,SVMData,kernel="linear",cost=30)
> Color_pred<-predict(model,X2)
Ошибка в inherits(newdata, "Matrix") : объект 'X2' не найден
> model
Call:
svm(formula = SVMData$Colors ~ ., data = SVMData, kernel = "linear",
cost = 30)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 30
gamma: 0.5
Number of Support Vectors: 4
Змінюючи С в завданні 2.1 кількість помилок не змінюється
SVMDataTest<-read.table("svmdata2test.txt")
> SVMDataTest
X1 X2 Colors
1 0.46893143 0.36620090 red
2 -0.38249171 -0.53404323 red
3 0.19607747 0.11956491 red
4 -0.06520800 -0.74986826 red
5 -0.18367616 -0.73310468 red
6 0.07005766 -0.07536280 red
7 -0.34398347 0.39754377 red
8 -0.12273903 0.05087431 red
9 0.01871148 0.40491470 red
10 0.15002588 -0.14355071 red
11 0.04281974 -0.74164683 red
12 0.19339754 -0.51219950 red
13 -0.82833273 0.23713295 red
14 0.10029556 0.34709828 red
15 -0.41603353 -0.76215972 red
16 -0.62953631 -0.22058582 red
17 0.16631160 0.19842551 red
18 0.06899162 0.02187579 red
19 -0.63030120 -0.22023958 red
20 0.37352068 -0.10766499 red
21 0.45826670 0.34228141 red
22 0.42973430 -0.73686030 red
23 0.07121908 0.47978255 red
24 0.12011827 0.43764330 red
25 -0.11260687 -0.09159020 red
26 0.69653870 0.61390050 green
27 0.67615330 0.97632319 green
28 0.85975561 1.00715433 green
29 1.05217374 0.34746000 green
30 0.58177477 1.42092628 green
31 1.03900600 0.36452800 green
32 1.03811930 0.88410696 green
33 0.75327304 0.82380787 green
34 0.95985915 0.91125849 green
35 1.78350045 0.85238943 green
36 1.13262093 1.06265202 green
37 1.41654080 0.90530920 green
38 1.45237885 0.67463445 green
39 1.40639342 0.99319942 green
40 1.38284737 0.90931751 green
41 0.63750032 0.79868266 green
42 1.39329727 0.62015833 green
43 0.94410095 1.50894800 green
44 0.65308056 1.41042075 green
45 1.14852765 1.10352456 green
46 0.68202170 0.94252091 green
47 1.15590726 1.40818642 green
48 0.55379360 0.64525940 green
49 0.68505910 0.84377548 green
50 0.72131792 1.36107898 green
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model,X1)
Ошибка в inherits(newdata, "Matrix") : объект 'X1' не найден
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMDataTest$Colors~.,SVMData,kernel="linear",cost=30)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 25 0
red 1 24
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="linear",cost=40)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 25 0
red 0 25
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="linear",cost=1)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 25 0
red 0 25
Вибірка не має помилок
Завдання 3
SVMDataTest<-read.table("svmdata3test.txt")
> SVMDataTest
X1 X2 Colors
1 -2.242460042 0.470488715 green
2 -0.305601118 0.194386931 red
3 -1.039244651 1.328225617 green
4 -0.635354620 -1.025658148 green
5 -1.078191313 -0.881511994 green
6 0.321438348 -0.404281649 red
7 1.497861292 1.253777265 green
8 0.403182073 1.377522611 green
9 0.802650744 -0.384938550 red
10 1.794116618 -1.866867079 green
11 -0.531092910 0.689793561 red
12 -1.038337911 1.265833050 green
13 -1.823329422 -2.140070892 green
14 -0.616858079 -1.604195630 green
15 1.511091087 1.706003631 green
16 -0.153808861 -0.006347670 red
17 -0.493017311 -0.709841523 red
18 0.858748033 0.650833013 green
19 -0.144312123 -0.647620107 red
20 -0.438536438 1.053046805 green
21 -0.275495473 -1.605762971 green
22 -0.518204587 0.227903013 red
23 -0.215131498 0.135938477 red
24 0.441778008 1.619399828 green
25 1.188071032 -1.573812931 green
26 -0.958970914 -0.897771913 green
27 1.393460185 0.182491351 green
28 -0.531808149 0.134641437 red
29 0.082380623 -0.772573266 red
30 0.340826639 -0.974430969 green
31 1.000473610 0.148583760 green
32 0.863065638 -0.096976016 red
33 -0.268064387 1.766520499 green
34 1.228752570 0.418542520 green
35 -2.646910793 0.111512489 green
36 0.656997778 0.977371590 green
37 -0.187114715 0.245620040 red
38 -0.641559691 -0.236651493 red
39 1.581933109 -1.386089500 green
40 -0.978687774 0.135961807 red
41 -0.622511212 0.792679486 green
42 0.465570412 0.070172789 red
43 1.358052589 -1.338801366 green
44 0.433025208 0.671335806 red
45 0.720768003 -0.149355355 red
46 0.279945294 -0.173260621 red
47 -1.106071048 -1.406381973 green
48 -0.356093754 2.055822455 green
49 0.869202404 -0.101178487 red
50 1.177297104 0.798187435 green
51 -0.734447476 0.805940233 green
52 -1.305755503 0.902866832 green
53 -1.051953637 0.465404549 green
54 0.391465196 0.122833391 red
55 1.494362500 -0.339050964 green
56 1.595346640 0.213681645 green
57 1.081284964 -0.433142049 green
58 0.817748137 -0.116370589 red
59 0.699598211 0.255024075 red
60 -0.669377560 0.833250730 green
61 -1.450610001 1.726902738 green
62 -0.921590879 -0.831616843 green
63 1.592462076 -1.442881428 green
64 0.006103176 1.881612775 green
65 1.913734920 -0.792143514 green
66 -0.030083537 -0.939474553 red
67 -1.593393028 -0.219864520 green
68 -0.951907702 0.091723877 red
69 -0.314932905 -0.526919847 red
70 -1.883646752 0.298276574 green
71 -1.161024310 0.224000280 green
72 0.220564956 0.116555211 red
73 -0.669977998 -0.306471701 red
74 0.581502495 1.538296819 green
75 1.579482916 -0.671795739 green
76 0.296274053 -1.327213930 green
77 -0.540616237 0.995740317 green
78 1.717081064 1.238953110 green
79 0.362953999 -0.796291507 red
80 -0.754331619 -0.299784726 red
81 -0.608632157 0.227470985 red
82 -0.246830504 1.052598444 green
83 -0.086544813 -1.485079765 green
84 1.149361618 -0.146746209 green
85 -0.271865695 0.049858957 red
86 0.558060782 0.197436669 red
87 0.083525509 -0.009300762 red
88 1.406809304 -1.199270158 green
89 -1.185456963 -0.435643281 green
90 -2.668667526 0.174760926 green
91 0.226635248 1.026139689 green
92 0.923209214 0.764557130 green
93 0.937081967 -0.665747637 green
94 -0.358298693 -0.259381855 red
95 2.285807980 -1.705130117 green
96 -1.027140710 0.595640716 green
97 0.279214055 -0.112859927 red
98 0.410184666 -1.272044612 green
99 -2.166653577 -0.505980665 green
100 0.873211322 0.699790505 green
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="polynomial",cost=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 64 0
red 36 0
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="radial",cost=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 63 1
red 3 33
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="sigmoid",cost=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 43 21
red 33 3
Найкращим являється тип ядра radial, має найменшу кількість помилок
Завдання 5
> SVMDataTest<-read.table("svmdata5test.txt")
> SVMDataTest
X1 X2 Colors
1 -0.481784568 0.68548524 red
2 -0.284473464 0.01469602 red
3 -0.324098462 0.99189159 red
4 -0.078877016 0.66346874 red
5 -0.021214181 0.01493104 red
6 -0.254058023 0.29313090 red
7 -0.679841200 0.82277976 red
8 -0.602103755 0.57264466 red
9 -0.106885302 0.75305932 red
10 -0.574473554 0.27903737 red
11 -0.715530011 0.28201639 red
12 -0.459999850 0.42745570 red
13 -0.186205595 0.87418472 red
14 -0.959525548 0.91336070 red
15 -0.023136445 0.46623378 red
16 -0.386536725 0.05907895 red
17 -0.415478723 0.69303507 red
18 -0.600721030 0.92983246 red
19 -0.897501578 0.80048506 red
20 -0.407888186 0.64579971 red
21 -0.943341804 0.10928400 red
22 -0.708053988 0.34040822 red
23 -0.790432356 0.88703848 red
24 -0.887747257 0.18256054 red
25 -0.949768782 0.40210996 red
26 -0.567327457 0.52782838 red
27 -0.945909683 0.29632176 red
28 -0.312632524 0.26180085 red
29 -0.588554757 0.88743061 red
30 -0.882928790 0.10882618 red
31 0.053436367 -0.49295162 red
32 0.525034426 -0.23551285 red
33 0.493532783 -0.04338757 red
34 0.963411601 -0.39622082 red
35 0.262749036 -0.62868939 red
36 0.435606358 -0.42090743 red
37 0.192741067 -0.43373039 red
38 0.085833105 -0.91474636 red
39 0.518767821 -0.57409196 red
40 0.478699503 -0.28727032 red
41 0.872259526 -0.48360100 red
42 0.749928291 -0.49315726 red
43 0.546449612 -0.60152275 red
44 0.788889577 -0.78688209 red
45 0.671603258 -0.97382132 red
46 0.942156224 -0.50087774 red
47 0.343013956 -0.97096712 red
48 0.590155921 -0.35359392 red
49 0.157795625 -0.84924977 red
50 0.547031456 -0.21567735 red
51 0.778691685 -0.15749285 red
52 0.551370601 -0.84838589 red
53 0.290384809 -0.14096979 red
54 0.706980640 -0.79172207 red
55 0.086284209 -0.02175502 red
56 0.489940311 -0.32462453 red
57 0.346134514 -0.45644946 red
58 0.933667479 -0.61632120 red
59 0.177482622 -0.99384048 red
60 0.137133956 -0.42343188 red
61 -0.924531813 -0.56173289 green
62 -0.730102711 -0.46472122 green
63 -0.177238950 -0.34374712 green
64 -0.061459735 -0.15410420 green
65 -0.827913110 -0.48677938 green
66 -0.608966181 -0.77217501 green
67 -0.079821747 -0.82672341 green
68 -0.484919243 -0.77327388 green
69 -0.872942010 -0.09255706 green
70 -0.070719550 -0.70951020 green
71 -0.379049916 -0.48927064 green
72 -0.397855220 -0.83161862 green
73 -0.667312099 -0.50691908 green
74 -0.108664609 -0.05973963 green
75 -0.123015842 -0.46678552 green
76 -0.619526254 -0.34172493 green
77 -0.103038867 -0.91556910 green
78 -0.603033867 -0.97191834 green
79 -0.801051923 -0.98787276 green
80 -0.721380081 -0.30397141 green
81 -0.372186757 -0.97791848 green
82 -0.440096577 -0.72178337 green
83 -0.590824575 -0.37416125 green
84 -0.178591632 -0.01401279 green
85 -0.284665289 -0.92817540 green
86 -0.006257840 -0.05372754 green
87 -0.921970228 -0.10519845 green
88 -0.326213700 -0.49090036 green
89 -0.370784881 -0.74077662 green
90 -0.859034752 -0.35080480 green
91 0.633302033 0.15886997 green
92 0.815000165 0.08792545 green
93 0.142150231 0.54287911 green
94 0.925999049 0.38914842 green
95 0.887039525 0.87164656 green
96 0.749408175 0.29291694 green
97 0.126153190 0.18033642 green
98 0.675174182 0.41745784 green
99 0.619931438 0.34558105 green
100 0.954427186 0.59941886 green
101 0.253364075 0.26588236 green
102 0.488137671 0.03069267 green
103 0.801721962 0.91202056 green
104 0.515588088 0.22159971 green
105 0.023544391 0.63218008 green
106 0.549983344 0.39365679 green
107 0.453260251 0.92421078 green
108 0.015943220 0.27386889 green
109 0.060326651 0.68906899 green
110 0.638690129 0.53457456 green
111 0.828861076 0.73862456 green
112 0.936033244 0.14341058 green
113 0.725998555 0.73138222 green
114 0.143624864 0.81346642 green
115 0.937720003 0.10389448 green
116 0.864839987 0.48313355 green
117 0.561949824 0.39304887 green
118 0.008488237 0.70643129 green
119 0.007324778 0.55748398 green
120 0.730787246 0.20326414 green
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="sigmoid",gamma=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 28 32
red 34 26
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="radial",gamma=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 59 1
red 4 56
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="polynomial",gamma=1)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 27 33
red 15 45
>
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="sigmoid",gamma=10)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 25 35
red 34 26
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="radial",gamma=10)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 60 0
red 2 58
> model<-svm(SVMDataTest$Colors~.,SVMDataTest,kernel="polynomial",gamma=10)
> x<-subset(SVMDataTest,select= -Colors)
> Color_pred<-predict(model)
> table(SVMDataTest$Colors, Color_pred)
Color_pred
green red
green 27 33
red 15 45
>
Zavdann9 6
SVMData<-read.table("svmdata6.txt")
> SVMData
X Y
1 0.00 -0.03566182
2 0.05 0.05978404
3 0.10 -0.11134412
4 0.15 0.09972835
5 0.20 0.29780782
6 0.25 0.30622463
7 0.30 0.20847428
8 0.35 0.30891137
9 0.40 0.35565093
10 0.45 0.51553855
11 0.50 0.58776188
12 0.55 0.61026845
13 0.60 0.48693221
14 0.65 0.81102334
15 0.70 0.58674182
16 0.75 0.73443044
17 0.80 0.72057228
18 0.85 0.69293089
19 0.90 0.84743978
20 0.95 0.83961848
21 1.00 0.76839038
22 1.05 0.95903101
23 1.10 0.88008363
24 1.15 0.99857538
25 1.20 0.68064542
26 1.25 0.75795782
27 1.30 1.06507723
28 1.35 1.02801762
29 1.40 0.84870053
30 1.45 1.06681649
31 1.50 0.87252252
32 1.55 1.20016327
33 1.60 1.03411428
34 1.65 0.90794376
35 1.70 0.99977314
36 1.75 1.00749804
37 1.80 1.01860185
38 1.85 0.95491637
39 1.90 1.08395959
40 1.95 0.81931680
41 2.00 0.73403545
42 2.05 0.94781101
43 2.10 0.81552428
44 2.15 0.92596234
45 2.20 0.76017525
46 2.25 0.84636088
47 2.30 0.77419427
48 2.35 0.73926198
49 2.40 0.72424329
50 2.45 0.63809174
51 2.50 0.51642457
52 2.55 0.67533242
53 2.60 0.51337986
54 2.65 0.32126796
55 2.70 0.43808380
56 2.75 0.22888165
57 2.80 0.27919777
58 2.85 0.39096174
59 2.90 0.13879461
60 2.95 0.35752683
61 3.00 0.16036942
62 3.05 0.11770151
63 3.10 -0.01758271
64 3.15 -0.05099682
65 3.20 -0.14639221
66 3.25 -0.02938006
67 3.30 -0.13820798
68 3.35 -0.33220297
69 3.40 -0.11346667
70 3.45 -0.21309142
71 3.50 -0.35054216
72 3.55 -0.33335569
73 3.60 -0.42441298
74 3.65 -0.59198730
75 3.70 -0.47649112
76 3.75 -0.49414025
77 3.80 -0.76809658
78 3.85 -0.41793545
79 3.90 -0.56975399
80 3.95 -0.85622142
81 4.00 -0.83734993
82 4.05 -0.72996809
83 4.10 -0.95141228
84 4.15 -0.92884341
85 4.20 -0.80372416
86 4.25 -0.77004043
87 4.30 -1.03773683
88 4.35 -0.92908917
89 4.40 -0.99051372
90 4.45 -1.06090580
91 4.50 -0.88556153
92 4.55 -1.09461668
93 4.60 -1.09906908
94 4.65 -0.88549495
95 4.70 -1.05191110
96 4.75 -0.79921257
97 4.80 -1.19966322
98 4.85 -0.92610358
99 4.90 -0.85584691
100 4.95 -0.86752856
101 5.00 -1.09817111
> X
Ошибка: объект 'X' не найден
> x<-subset(SVMData,select=X)
> x
X
1 0.00
2 0.05
3 0.10
4 0.15
5 0.20
6 0.25
7 0.30
8 0.35
9 0.40
10 0.45
11 0.50
12 0.55
13 0.60
14 0.65
15 0.70
16 0.75
17 0.80
18 0.85
19 0.90
20 0.95
21 1.00
22 1.05
23 1.10
24 1.15
25 1.20
26 1.25
27 1.30
28 1.35
29 1.40
30 1.45
31 1.50
32 1.55
33 1.60
34 1.65
35 1.70
36 1.75
37 1.80
38 1.85
39 1.90
40 1.95
41 2.00
42 2.05
43 2.10
44 2.15
45 2.20
46 2.25
47 2.30
48 2.35
49 2.40
50 2.45
51 2.50
52 2.55
53 2.60
54 2.65
55 2.70
56 2.75
57 2.80
58 2.85
59 2.90
60 2.95
61 3.00
62 3.05
63 3.10
64 3.15
65 3.20
66 3.25
67 3.30
68 3.35
69 3.40
70 3.45
71 3.50
72 3.55
73 3.60
74 3.65
75 3.70
76 3.75
77 3.80
78 3.85
79 3.90
80 3.95
81 4.00
82 4.05
83 4.10
84 4.15
85 4.20
86 4.25
87 4.30
88 4.35
89 4.40
90 4.45
91 4.50
92 4.55
93 4.60
94 4.65
95 4.70
96 4.75
97 4.80
98 4.85
99 4.90
100 4.95
101 5.00
> x<-subset(SVMData,select=Y)
> x<-subset(SVMData,select=X)
> y<-subset(SVMData,select=Y)
> x
X
1 0.00
2 0.05
3 0.10
4 0.15
5 0.20
6 0.25
7 0.30
8 0.35
9 0.40
10 0.45
11 0.50
12 0.55
13 0.60
14 0.65
15 0.70
16 0.75
17 0.80
18 0.85
19 0.90
20 0.95
21 1.00
22 1.05
23 1.10
24 1.15
25 1.20
26 1.25
27 1.30
28 1.35
29 1.40
30 1.45
31 1.50
32 1.55
33 1.60
34 1.65
35 1.70
36 1.75
37 1.80
38 1.85
39 1.90
40 1.95
41 2.00
42 2.05
43 2.10
44 2.15
45 2.20
46 2.25
47 2.30
48 2.35
49 2.40
50 2.45
51 2.50
52 2.55
53 2.60
54 2.65
55 2.70
56 2.75
57 2.80
58 2.85
59 2.90
60 2.95
61 3.00
62 3.05
63 3.10
64 3.15
65 3.20
66 3.25
67 3.30
68 3.35
69 3.40
70 3.45
71 3.50
72 3.55
73 3.60
74 3.65
75 3.70
76 3.75
77 3.80
78 3.85
79 3.90
80 3.95
81 4.00
82 4.05
83 4.10
84 4.15
85 4.20
86 4.25
87 4.30
88 4.35
89 4.40
90 4.45
91 4.50
92 4.55
93 4.60
94 4.65
95 4.70
96 4.75
97 4.80
98 4.85
99 4.90
100 4.95
101 5.00
> y
Y
1 -0.03566182
2 0.05978404
3 -0.11134412
4 0.09972835
5 0.29780782
6 0.30622463
7 0.20847428
8 0.30891137
9 0.35565093
10 0.51553855
11 0.58776188
12 0.61026845
13 0.48693221
14 0.81102334
15 0.58674182
16 0.73443044
17 0.72057228
18 0.69293089
19 0.84743978
20 0.83961848
21 0.76839038
22 0.95903101
23 0.88008363
24 0.99857538
25 0.68064542
26 0.75795782
27 1.06507723
28 1.02801762
29 0.84870053
30 1.06681649
31 0.87252252
32 1.20016327
33 1.03411428
34 0.90794376
35 0.99977314
36 1.00749804
37 1.01860185
38 0.95491637
39 1.08395959
40 0.81931680
41 0.73403545
42 0.94781101
43 0.81552428
44 0.92596234
45 0.76017525
46 0.84636088
47 0.77419427
48 0.73926198
49 0.72424329
50 0.63809174
51 0.51642457
52 0.67533242
53 0.51337986
54 0.32126796
55 0.43808380
56 0.22888165
57 0.27919777
58 0.39096174
59 0.13879461
60 0.35752683
61 0.16036942
62 0.11770151
63 -0.01758271
64 -0.05099682
65 -0.14639221
66 -0.02938006
67 -0.13820798
68 -0.33220297
69 -0.11346667
70 -0.21309142
71 -0.35054216
72 -0.33335569
73 -0.42441298
74 -0.59198730
75 -0.47649112
76 -0.49414025
77 -0.76809658
78 -0.41793545
79 -0.56975399
80 -0.85622142
81 -0.83734993
82 -0.72996809
83 -0.95141228
84 -0.92884341
85 -0.80372416
86 -0.77004043
87 -1.03773683
88 -0.92908917
89 -0.99051372
90 -1.06090580
91 -0.88556153
92 -1.09461668
93 -1.09906908
94 -0.88549495
95 -1.05191110
96 -0.79921257
97 -1.19966322
98 -0.92610358
99 -0.85584691
100 -0.86752856
101 -1.09817111
> y_pred<-predict(model,x)
Ошибка в eval(expr, envir, enclos) : объект 'X1' не найден
> plot(x,y)
Ошибка в stripchart.default(x1, ...) : неправильный метод рисования
> model<-svm(x,y,type="eps-regression",eps=0.15)
> y_pred<-predict(model,x)
> plot(x,y)
Ошибка в stripchart.default(x1, ...) : неправильный метод рисования
> points(x,log(x),col=2)
Ошибка в xy.coords(x, y) :
объект (список) не может быть преобразован в тип 'double'
> X
Ошибка: объект 'X' не найден
> points(x,col=2)
> points(y,col=4)
> plot(x,y)
Ошибка в stripchart.default(x1, ...) : неправильный метод рисования
> x
X
1 0.00
2 0.05
3 0.10
4 0.15
5 0.20
6 0.25
7 0.30
8 0.35
9 0.40
10 0.45
11 0.50
12 0.55
13 0.60
14 0.65
15 0.70
16 0.75
17 0.80
18 0.85
19 0.90
20 0.95
21 1.00
22 1.05
23 1.10
24 1.15
25 1.20
26 1.25
27 1.30
28 1.35
29 1.40
30 1.45
31 1.50
32 1.55
33 1.60
34 1.65
35 1.70
36 1.75
37 1.80
38 1.85
39 1.90
40 1.95
41 2.00
42 2.05
43 2.10
44 2.15
45 2.20
46 2.25
47 2.30
48 2.35
49 2.40
50 2.45
51 2.50
52 2.55
53 2.60
54 2.65
55 2.70
56 2.75
57 2.80
58 2.85
59 2.90
60 2.95
61 3.00
62 3.05
63 3.10
64 3.15
65 3.20
66 3.25
67 3.30
68 3.35
69 3.40
70 3.45
71 3.50
72 3.55
73 3.60
74 3.65
75 3.70
76 3.75
77 3.80
78 3.85
79 3.90
80 3.95
81 4.00
82 4.05
83 4.10
84 4.15
85 4.20
86 4.25
87 4.30
88 4.35
89 4.40
90 4.45
91 4.50
92 4.55
93 4.60
94 4.65
95 4.70
96 4.75
97 4.80
98 4.85
99 4.90
100 4.95
101 5.00
> y
Y
1 -0.03566182
2 0.05978404
3 -0.11134412
4 0.09972835
5 0.29780782
6 0.30622463
7 0.20847428
8 0.30891137
9 0.35565093
10 0.51553855
11 0.58776188
12 0.61026845
13 0.48693221
14 0.81102334
15 0.58674182
16 0.73443044
17 0.72057228
18 0.69293089
19 0.84743978
20 0.83961848
21 0.76839038
22 0.95903101
23 0.88008363
24 0.99857538
25 0.68064542
26 0.75795782
27 1.06507723
28 1.02801762
29 0.84870053
30 1.06681649
31 0.87252252
32 1.20016327
33 1.03411428
34 0.90794376
35 0.99977314
36 1.00749804
37 1.01860185
38 0.95491637
39 1.08395959
40 0.81931680
41 0.73403545
42 0.94781101
43 0.81552428
44 0.92596234
45 0.76017525
46 0.84636088
47 0.77419427
48 0.73926198
49 0.72424329
50 0.63809174
51 0.51642457
52 0.67533242
53 0.51337986
54 0.32126796
55 0.43808380
56 0.22888165
57 0.27919777
58 0.39096174
59 0.13879461
60 0.35752683
61 0.16036942
62 0.11770151
63 -0.01758271
64 -0.05099682
65 -0.14639221
66 -0.02938006
67 -0.13820798
68 -0.33220297
69 -0.11346667
70 -0.21309142
71 -0.35054216
72 -0.33335569
73 -0.42441298
74 -0.59198730
75 -0.47649112
76 -0.49414025
77 -0.76809658
78 -0.41793545
79 -0.56975399
80 -0.85622142
81 -0.83734993
82 -0.72996809
83 -0.95141228
84 -0.92884341
85 -0.80372416
86 -0.77004043
87 -1.03773683
88 -0.92908917
89 -0.99051372
90 -1.06090580
91 -0.88556153
92 -1.09461668
93 -1.09906908
94 -0.88549495
95 -1.05191110
96 -0.79921257
97 -1.19966322
98 -0.92610358
99 -0.85584691
100 -0.86752856
101 -1.09817111
> plot(x,y)
Ошибка в stripchart.default(x1, ...) : неправильный метод рисования
> SVMData<-read.table("svmdata6.txt",header = TRUE)
> save.image("G:\\j")
> x
X
1 0.00
2 0.05
3 0.10
4 0.15
5 0.20
6 0.25
7 0.30
8 0.35
9 0.40
10 0.45
11 0.50
12 0.55
13 0.60
14 0.65
15 0.70
16 0.75
17 0.80
18 0.85
19 0.90
20 0.95
21 1.00
22 1.05
23 1.10
24 1.15
25 1.20
26 1.25
27 1.30
28 1.35
29 1.40
30 1.45
31 1.50
32 1.55
33 1.60
34 1.65
35 1.70
36 1.75
37 1.80
38 1.85
39 1.90
40 1.95
41 2.00
42 2.05
43 2.10
44 2.15
45 2.20
46 2.25
47 2.30
48 2.35
49 2.40
50 2.45
51 2.50
52 2.55
53 2.60
54 2.65
55 2.70
56 2.75
57 2.80
58 2.85
59 2.90
60 2.95
61 3.00
62 3.05
63 3.10
64 3.15
65 3.20
66 3.25
67 3.30
68 3.35
69 3.40
70 3.45
71 3.50
72 3.55
73 3.60
74 3.65
75 3.70
76 3.75
77 3.80
78 3.85
79 3.90
80 3.95
81 4.00
82 4.05
83 4.10
84 4.15
85 4.20
86 4.25
87 4.30
88 4.35
89 4.40
90 4.45
91 4.50
92 4.55
93 4.60
94 4.65
95 4.70
96 4.75
97 4.80
98 4.85
99 4.90
100 4.95
101 5.00
> y
Y
1 -0.03566182
2 0.05978404
3 -0.11134412
4 0.09972835
5 0.29780782
6 0.30622463
7 0.20847428
8 0.30891137
9 0.35565093
10 0.51553855
11 0.58776188
12 0.61026845
13 0.48693221
14 0.81102334
15 0.58674182
16 0.73443044
17 0.72057228
18 0.69293089
19 0.84743978
20 0.83961848
21 0.76839038
22 0.95903101
23 0.88008363
24 0.99857538
25 0.68064542
26 0.75795782
27 1.06507723
28 1.02801762
29 0.84870053
30 1.06681649
31 0.87252252
32 1.20016327
33 1.03411428
34 0.90794376
35 0.99977314
36 1.00749804
37 1.01860185
38 0.95491637
39 1.08395959
40 0.81931680
41 0.73403545
42 0.94781101
43 0.81552428
44 0.92596234
45 0.76017525
46 0.84636088
47 0.77419427
48 0.73926198
49 0.72424329
50 0.63809174
51 0.51642457
52 0.67533242
53 0.51337986
54 0.32126796
55 0.43808380
56 0.22888165
57 0.27919777
58 0.39096174
59 0.13879461
60 0.35752683
61 0.16036942
62 0.11770151
63 -0.01758271
64 -0.05099682
65 -0.14639221
66 -0.02938006
67 -0.13820798
68 -0.33220297
69 -0.11346667
70 -0.21309142
71 -0.35054216
72 -0.33335569
73 -0.42441298
74 -0.59198730
75 -0.47649112
76 -0.49414025
77 -0.76809658
78 -0.41793545
79 -0.56975399
80 -0.85622142
81 -0.83734993
82 -0.72996809
83 -0.95141228
84 -0.92884341
85 -0.80372416
86 -0.77004043
87 -1.03773683
88 -0.92908917
89 -0.99051372
90 -1.06090580
91 -0.88556153
92 -1.09461668
93 -1.09906908
94 -0.88549495
95 -1.05191110
96 -0.79921257
97 -1.19966322
98 -0.92610358
99 -0.85584691
100 -0.86752856
101 -1.09817111
> model<-svm(x,y,type="eps-regression",eps=0.15)
> y_pred<-predict(model,x)
> plot(x,y)
Ошибка в stripchart.default(x1, ...) : неправильный метод рисования
> plot(x)
> plot(y)
> lines(x)
>
zavdann9 6
SVMData<-read.table("svmdata6.txt",header=TRUE)
> SVMData
X Y
1 0.00 -0.03566182
2 0.05 0.05978404
3 0.10 -0.11134412
4 0.15 0.09972835
5 0.20 0.29780782
6 0.25 0.30622463
7 0.30 0.20847428
8 0.35 0.30891137
9 0.40 0.35565093
10 0.45 0.51553855
11 0.50 0.58776188
12 0.55 0.61026845
13 0.60 0.48693221
14 0.65 0.81102334
15 0.70 0.58674182
16 0.75 0.73443044
17 0.80 0.72057228
18 0.85 0.69293089
19 0.90 0.84743978
20 0.95 0.83961848
21 1.00 0.76839038
22 1.05 0.95903101
23 1.10 0.88008363
24 1.15 0.99857538
25 1.20 0.68064542
26 1.25 0.75795782
27 1.30 1.06507723
28 1.35 1.02801762
29 1.40 0.84870053
30 1.45 1.06681649
31 1.50 0.87252252
32 1.55 1.20016327
33 1.60 1.03411428
34 1.65 0.90794376
35 1.70 0.99977314
36 1.75 1.00749804
37 1.80 1.01860185
38 1.85 0.95491637
39 1.90 1.08395959
40 1.95 0.81931680
41 2.00 0.73403545
42 2.05 0.94781101
43 2.10 0.81552428
44 2.15 0.92596234
45 2.20 0.76017525
46 2.25 0.84636088
47 2.30 0.77419427
48 2.35 0.73926198
49 2.40 0.72424329
50 2.45 0.63809174
51 2.50 0.51642457
52 2.55 0.67533242
53 2.60 0.51337986
54 2.65 0.32126796
55 2.70 0.43808380
56 2.75 0.22888165
57 2.80 0.27919777
58 2.85 0.39096174
59 2.90 0.13879461
60 2.95 0.35752683
61 3.00 0.16036942
62 3.05 0.11770151
63 3.10 -0.01758271
64 3.15 -0.05099682
65 3.20 -0.14639221
66 3.25 -0.02938006
67 3.30 -0.13820798
68 3.35 -0.33220297
69 3.40 -0.11346667
70 3.45 -0.21309142
71 3.50 -0.35054216
72 3.55 -0.33335569
73 3.60 -0.42441298
74 3.65 -0.59198730
75 3.70 -0.47649112
76 3.75 -0.49414025
77 3.80 -0.76809658
78 3.85 -0.41793545
79 3.90 -0.56975399
80 3.95 -0.85622142
81 4.00 -0.83734993
82 4.05 -0.72996809
83 4.10 -0.95141228
84 4.15 -0.92884341
85 4.20 -0.80372416
86 4.25 -0.77004043
87 4.30 -1.03773683
88 4.35 -0.92908917
89 4.40 -0.99051372
90 4.45 -1.06090580
91 4.50 -0.88556153
92 4.55 -1.09461668
93 4.60 -1.09906908
94 4.65 -0.88549495
95 4.70 -1.05191110
96 4.75 -0.79921257
97 4.80 -1.19966322
98 4.85 -0.92610358
99 4.90 -0.85584691
100 4.95 -0.86752856
101 5.00 -1.09817111
> lm(SVMData)
Call:
lm(formula = SVMData)
Coefficients:
(Intercept) Y
2.720 -1.612
> summary(lm(SVMData))
Call:
lm(formula = SVMData)
Residuals:
Min 1Q Median 3Q Max
-2.7995 -0.1534 0.2846 0.5504 0.9269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.7201 0.0902 30.16 <2e-16 ***
Y -1.6116 0.1230 -13.10 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8906 on 99 degrees of freedom
Multiple R-squared: 0.6341, Adjusted R-squared: 0.6304
F-statistic: 171.6 on 1 and 99 DF, p-value: < 2.2e-16
> SVMData
X Y
1 0.00 -0.03566182
2 0.05 0.05978404
3 0.10 -0.11134412
4 0.15 0.09972835
5 0.20 0.29780782
6 0.25 0.30622463
7 0.30 0.20847428
8 0.35 0.30891137
9 0.40 0.35565093
10 0.45 0.51553855
11 0.50 0.58776188
12 0.55 0.61026845
13 0.60 0.48693221
14 0.65 0.81102334
15 0.70 0.58674182
16 0.75 0.73443044
17 0.80 0.72057228
18 0.85 0.69293089
19 0.90 0.84743978
20 0.95 0.83961848
21 1.00 0.76839038
22 1.05 0.95903101
23 1.10 0.88008363
24 1.15 0.99857538
25 1.20 0.68064542
26 1.25 0.75795782
27 1.30 1.06507723
28 1.35 1.02801762
29 1.40 0.84870053
30 1.45 1.06681649
31 1.50 0.87252252
32 1.55 1.20016327
33 1.60 1.03411428
34 1.65 0.90794376
35 1.70 0.99977314
36 1.75 1.00749804
37 1.80 1.01860185
38 1.85 0.95491637
39 1.90 1.08395959
40 1.95 0.81931680
41 2.00 0.73403545
42 2.05 0.94781101
43 2.10 0.81552428
44 2.15 0.92596234
45 2.20 0.76017525
46 2.25 0.84636088
47 2.30 0.77419427
48 2.35 0.73926198
49 2.40 0.72424329
50 2.45 0.63809174
51 2.50 0.51642457
52 2.55 0.67533242
53 2.60 0.51337986
54 2.65 0.32126796
55 2.70 0.43808380
56 2.75 0.22888165
57 2.80 0.27919777
58 2.85 0.39096174
59 2.90 0.13879461
60 2.95 0.35752683
61 3.00 0.16036942
62 3.05 0.11770151
63 3.10 -0.01758271
64 3.15 -0.05099682
65 3.20 -0.14639221
66 3.25 -0.02938006
67 3.30 -0.13820798
68 3.35 -0.33220297
69 3.40 -0.11346667
70 3.45 -0.21309142
71 3.50 -0.35054216
72 3.55 -0.33335569
73 3.60 -0.42441298
74 3.65 -0.59198730
75 3.70 -0.47649112
76 3.75 -0.49414025
77 3.80 -0.76809658
78 3.85 -0.41793545
79 3.90 -0.56975399
80 3.95 -0.85622142
81 4.00 -0.83734993
82 4.05 -0.72996809
83 4.10 -0.95141228
84 4.15 -0.92884341
85 4.20 -0.80372416
86 4.25 -0.77004043
87 4.30 -1.03773683
88 4.35 -0.92908917
89 4.40 -0.99051372
90 4.45 -1.06090580
91 4.50 -0.88556153
92 4.55 -1.09461668
93 4.60 -1.09906908
94 4.65 -0.88549495
95 4.70 -1.05191110
96 4.75 -0.79921257
97 4.80 -1.19966322
98 4.85 -0.92610358
99 4.90 -0.85584691
100 4.95 -0.86752856
101 5.00 -1.09817111
> x=SVMData$X