Tugas3_Percobaan1
Nurmilasari
2022-10-25
library(readxl)
cmcData <- read_excel(path = "datakanker.xlsx")
diagnosis <- as.numeric(cmcData$diagnosis == "M")
cmcData$diagnosis <- diagnosis
cmcData## # A tibble: 569 × 32
## id diagnosis radius_m…¹ textu…² perim…³ area_…⁴ smoot…⁵ compa…⁶ conca…⁷
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 842302 1 18.0 10.4 123. 1001 0.118 0.278 0.300
## 2 842517 1 20.6 17.8 133. 1326 0.0847 0.0786 0.0869
## 3 84300903 1 19.7 21.2 130 1203 0.110 0.160 0.197
## 4 84348301 1 11.4 20.4 77.6 386. 0.142 0.284 0.241
## 5 84358402 1 20.3 14.3 135. 1297 0.100 0.133 0.198
## 6 843786 1 12.4 15.7 82.6 477. 0.128 0.17 0.158
## 7 844359 1 18.2 20.0 120. 1040 0.0946 0.109 0.113
## 8 84458202 1 13.7 20.8 90.2 578. 0.119 0.164 0.0937
## 9 844981 1 13 21.8 87.5 520. 0.127 0.193 0.186
## 10 84501001 1 12.5 24.0 84.0 476. 0.119 0.240 0.227
## # … with 559 more rows, 23 more variables: concave_points_mean <dbl>,
## # symmetry_mean <dbl>, fractal_dimension_mean <dbl>, radius_se <dbl>,
## # texture_se <dbl>, perimeter_se <dbl>, area_se <dbl>, smoothness_se <dbl>,
## # compactness_se <dbl>, concavity_se <dbl>, `concave points_se` <dbl>,
## # symmetry_se <dbl>, fractal_dimension_se <dbl>, radius_worst <dbl>,
## # texture_worst <dbl>, perimeter_worst <dbl>, area_worst <dbl>,
## # smoothness_worst <dbl>, compactness_worst <dbl>, concavity_worst <dbl>, …#data training diambil dari data index ke-1 sampai ke-90 dengan kolom ke-1 sampai ke-7
training <- cmcData[1:90,1:7]
#untuk testing kolom yang dipakai yaitu kolom ke-1 sampai ke-6
testing <- cmcData[91:nrow(cmcData),1:6]
#untuk validasi kolom yang dipakai adalah kolom ke-7
validasi <- cmcData[91:nrow(cmcData),7]
#data_research yang digunakan adalah kolom 3(radius_mean) dan 4(texture_mean)
data_research <- cmcData[91:nrow(cmcData),c(3,4)]
training## # A tibble: 90 × 7
## id diagnosis radius_mean texture_mean perimeter_mean area_mean smooth…¹
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 842302 1 18.0 10.4 123. 1001 0.118
## 2 842517 1 20.6 17.8 133. 1326 0.0847
## 3 84300903 1 19.7 21.2 130 1203 0.110
## 4 84348301 1 11.4 20.4 77.6 386. 0.142
## 5 84358402 1 20.3 14.3 135. 1297 0.100
## 6 843786 1 12.4 15.7 82.6 477. 0.128
## 7 844359 1 18.2 20.0 120. 1040 0.0946
## 8 84458202 1 13.7 20.8 90.2 578. 0.119
## 9 844981 1 13 21.8 87.5 520. 0.127
## 10 84501001 1 12.5 24.0 84.0 476. 0.119
## # … with 80 more rows, and abbreviated variable name ¹smoothness_meantesting## # A tibble: 479 × 6
## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 861648 0 14.6 24.0 94.6 663.
## 2 861799 1 15.4 22.8 100. 728.
## 3 861853 0 13.3 14.8 84.7 552.
## 4 862009 0 13.4 18.3 86.6 555.
## 5 862028 1 15.1 19.8 100. 706.
## 6 86208 1 20.3 23.0 132. 1264
## 7 86211 0 12.2 17.8 77.8 451.
## 8 862261 0 9.79 19.9 62.1 294.
## 9 862485 0 11.6 12.8 74.3 413.
## 10 862548 1 14.4 19.8 94.5 642.
## # … with 469 more rowsvalidasi## # A tibble: 479 × 1
## smoothness_mean
## <dbl>
## 1 0.0897
## 2 0.092
## 3 0.0736
## 4 0.102
## 5 0.104
## 6 0.0908
## 7 0.104
## 8 0.102
## 9 0.0898
## 10 0.0975
## # … with 469 more rowsdata_research ## # A tibble: 479 × 2
## radius_mean texture_mean
## <dbl> <dbl>
## 1 14.6 24.0
## 2 15.4 22.8
## 3 13.3 14.8
## 4 13.4 18.3
## 5 15.1 19.8
## 6 20.3 23.0
## 7 12.2 17.8
## 8 9.79 19.9
## 9 11.6 12.8
## 10 14.4 19.8
## # … with 469 more rows## Define interval of data
range.data <-apply(training, 2, range)
range.data## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## [1,] 85715 0 8.196 10.38 51.71 201.9
## [2,] 86135502 1 25.220 27.54 171.50 1878.0
## smoothness_mean
## [1,] 0.07685
## [2,] 0.14250## Set the method and its parameters,
## for example, we use Wang and Mendel's algorithm
method.type <- "WM"
control <- list(num.labels = 15, type.mf = "GAUSSIAN", type.defuz = "WAM",
type.tnorm = "MIN", type.snorm = "MAX", type.implication.func = "ZADEH",
name = "sim-0")library(frbs)
## Learning step: Generate an FRBS model
object.reg <- frbs.learn(training, range.data, method.type, control)
## Predicting step: Predict for newdata
res.test <- predict(object.reg, testing)## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"## Display the FRBS model
summary(object.reg)## The name of model: sim-0
## Model was trained using: WM
## The names of attributes: id diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## The interval of training data:
## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## min 85715 0 8.196 10.38 51.71 201.9
## max 86135502 1 25.220 27.54 171.50 1878.0
## smoothness_mean
## min 0.07685
## max 0.14250
## Type of FRBS model:
## [1] "MAMDANI"
## Type of membership functions:
## [1] "GAUSSIAN"
## Type of t-norm method:
## [1] "Standard t-norm (min)"
## Type of s-norm method:
## [1] "Standard s-norm"
## Type of defuzzification technique:
## [1] "Weighted average method"
## Type of implication function:
## [1] "ZADEH"
## The names of linguistic terms on the input variables:
## [1] "v.1_a.1" "v.1_a.2" "v.1_a.3" "v.1_a.4" "v.1_a.5" "v.1_a.6"
## [7] "v.1_a.7" "v.1_a.8" "v.1_a.9" "v.1_a.10" "v.1_a.11" "v.1_a.12"
## [13] "v.1_a.13" "v.1_a.14" "v.1_a.15" "v.2_a.1" "v.2_a.2" "v.2_a.3"
## [19] "v.2_a.4" "v.2_a.5" "v.2_a.6" "v.2_a.7" "v.2_a.8" "v.2_a.9"
## [25] "v.2_a.10" "v.2_a.11" "v.2_a.12" "v.2_a.13" "v.2_a.14" "v.2_a.15"
## [31] "v.3_a.1" "v.3_a.2" "v.3_a.3" "v.3_a.4" "v.3_a.5" "v.3_a.6"
## [37] "v.3_a.7" "v.3_a.8" "v.3_a.9" "v.3_a.10" "v.3_a.11" "v.3_a.12"
## [43] "v.3_a.13" "v.3_a.14" "v.3_a.15" "v.4_a.1" "v.4_a.2" "v.4_a.3"
## [49] "v.4_a.4" "v.4_a.5" "v.4_a.6" "v.4_a.7" "v.4_a.8" "v.4_a.9"
## [55] "v.4_a.10" "v.4_a.11" "v.4_a.12" "v.4_a.13" "v.4_a.14" "v.4_a.15"
## [61] "v.5_a.1" "v.5_a.2" "v.5_a.3" "v.5_a.4" "v.5_a.5" "v.5_a.6"
## [67] "v.5_a.7" "v.5_a.8" "v.5_a.9" "v.5_a.10" "v.5_a.11" "v.5_a.12"
## [73] "v.5_a.13" "v.5_a.14" "v.5_a.15" "v.6_a.1" "v.6_a.2" "v.6_a.3"
## [79] "v.6_a.4" "v.6_a.5" "v.6_a.6" "v.6_a.7" "v.6_a.8" "v.6_a.9"
## [85] "v.6_a.10" "v.6_a.11" "v.6_a.12" "v.6_a.13" "v.6_a.14" "v.6_a.15"
## The parameter values of membership function on the input variable (normalized):
## v.1_a.1 v.1_a.2 v.1_a.3 v.1_a.4 v.1_a.5 v.1_a.6 v.1_a.7
## [1,] 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
## [2,] 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429 0.4285714
## [3,] 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.1_a.8 v.1_a.9 v.1_a.10 v.1_a.11 v.1_a.12 v.1_a.13 v.1_a.14
## [1,] 5.000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
## [2,] 0.500 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429 0.9285714
## [3,] 0.025 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.1_a.15 v.2_a.1 v.2_a.2 v.2_a.3 v.2_a.4 v.2_a.5 v.2_a.6
## [1,] 5.000 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000
## [2,] 1.000 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429
## [3,] 0.025 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.2_a.7 v.2_a.8 v.2_a.9 v.2_a.10 v.2_a.11 v.2_a.12 v.2_a.13
## [1,] 5.0000000 5.000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000
## [2,] 0.4285714 0.500 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429
## [3,] 0.0250000 0.025 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.2_a.14 v.2_a.15 v.3_a.1 v.3_a.2 v.3_a.3 v.3_a.4 v.3_a.5
## [1,] 5.0000000 5.000 5.000 5.00000000 5.0000000 5.0000000 5.0000000
## [2,] 0.9285714 1.000 0.000 0.07142857 0.1428571 0.2142857 0.2857143
## [3,] 0.0250000 0.025 0.025 0.02500000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.3_a.6 v.3_a.7 v.3_a.8 v.3_a.9 v.3_a.10 v.3_a.11 v.3_a.12
## [1,] 5.0000000 5.0000000 5.000 5.0000000 5.0000000 5.0000000 5.0000000
## [2,] 0.3571429 0.4285714 0.500 0.5714286 0.6428571 0.7142857 0.7857143
## [3,] 0.0250000 0.0250000 0.025 0.0250000 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.3_a.13 v.3_a.14 v.3_a.15 v.4_a.1 v.4_a.2 v.4_a.3 v.4_a.4
## [1,] 5.0000000 5.0000000 5.000 5.000 5.00000000 5.0000000 5.0000000
## [2,] 0.8571429 0.9285714 1.000 0.000 0.07142857 0.1428571 0.2142857
## [3,] 0.0250000 0.0250000 0.025 0.025 0.02500000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.4_a.5 v.4_a.6 v.4_a.7 v.4_a.8 v.4_a.9 v.4_a.10 v.4_a.11
## [1,] 5.0000000 5.0000000 5.0000000 5.000 5.0000000 5.0000000 5.0000000
## [2,] 0.2857143 0.3571429 0.4285714 0.500 0.5714286 0.6428571 0.7142857
## [3,] 0.0250000 0.0250000 0.0250000 0.025 0.0250000 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.4_a.12 v.4_a.13 v.4_a.14 v.4_a.15 v.5_a.1 v.5_a.2 v.5_a.3
## [1,] 5.0000000 5.0000000 5.0000000 5.000 5.000 5.00000000 5.0000000
## [2,] 0.7857143 0.8571429 0.9285714 1.000 0.000 0.07142857 0.1428571
## [3,] 0.0250000 0.0250000 0.0250000 0.025 0.025 0.02500000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.5_a.4 v.5_a.5 v.5_a.6 v.5_a.7 v.5_a.8 v.5_a.9 v.5_a.10
## [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.000 5.0000000 5.0000000
## [2,] 0.2142857 0.2857143 0.3571429 0.4285714 0.500 0.5714286 0.6428571
## [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.025 0.0250000 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.5_a.11 v.5_a.12 v.5_a.13 v.5_a.14 v.5_a.15 v.6_a.1 v.6_a.2
## [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.000 5.000 5.00000000
## [2,] 0.7142857 0.7857143 0.8571429 0.9285714 1.000 0.000 0.07142857
## [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.025 0.025 0.02500000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.6_a.3 v.6_a.4 v.6_a.5 v.6_a.6 v.6_a.7 v.6_a.8 v.6_a.9
## [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000 5.0000000
## [2,] 0.1428571 0.2142857 0.2857143 0.3571429 0.4285714 0.500 0.5714286
## [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025 0.0250000
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## v.6_a.10 v.6_a.11 v.6_a.12 v.6_a.13 v.6_a.14 v.6_a.15
## [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000
## [2,] 0.6428571 0.7142857 0.7857143 0.8571429 0.9285714 1.000
## [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## The names of linguistic terms on the output variable:
## [1] "c.1" "c.2" "c.3" "c.4" "c.5" "c.6" "c.7" "c.8" "c.9" "c.10"
## [11] "c.11" "c.12" "c.13" "c.14" "c.15"
## The parameter values of membership function on the output variable (normalized):
## c.1 c.2 c.3 c.4 c.5 c.6 c.7 c.8
## [1,] 5.000 5.00000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000
## [2,] 0.000 0.07142857 0.1428571 0.2142857 0.2857143 0.3571429 0.4285714 0.500
## [3,] 0.025 0.02500000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025
## [4,] NA NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA NA
## c.9 c.10 c.11 c.12 c.13 c.14 c.15
## [1,] 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.0000000 5.000
## [2,] 0.5714286 0.6428571 0.7142857 0.7857143 0.8571429 0.9285714 1.000
## [3,] 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.0250000 0.025
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## The number of linguistic terms on each variables
## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## [1,] 15 15 15 15 15 15
## smoothness_mean
## [1,] 15
## The fuzzy IF-THEN rules:
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
## 1 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 2 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 3 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 4 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.4 and
## 5 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 6 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 7 IF id is v.1_a.15 and diagnosis is v.2_a.1 and radius_mean is v.3_a.1 and
## 8 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 9 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.11 and
## 10 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.6 and
## 11 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 12 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 13 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 14 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 15 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 16 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 17 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 18 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.12 and
## 19 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 20 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 21 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 22 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 23 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 24 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.1 and
## 25 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.2 and
## 26 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 27 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 28 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 29 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 30 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 31 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 32 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 33 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.2 and
## 34 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 35 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 36 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 37 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 38 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.11 and
## 39 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.15 and
## 40 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 41 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 42 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 43 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 44 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.11 and
## 45 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 46 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 47 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.2 and
## 48 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 49 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.1 and
## 50 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 51 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 52 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 53 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 54 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 55 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 56 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.9 and
## 57 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.11 and
## 58 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 59 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 60 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 61 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 62 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 63 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 64 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 65 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.3 and
## 66 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 67 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.3 and
## 68 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 69 IF id is v.1_a.2 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 70 IF id is v.1_a.2 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## 71 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.4 and
## 72 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.2 and
## 73 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 74 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 75 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 76 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.5 and
## 77 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.8 and
## 78 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 79 IF id is v.1_a.15 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 80 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.2 and
## 81 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.7 and
## 82 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 83 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.6 and
## 84 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.4 and
## 85 IF id is v.1_a.1 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 86 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.10 and
## 87 IF id is v.1_a.15 and diagnosis is v.2_a.15 and radius_mean is v.3_a.4 and
## 88 IF id is v.1_a.1 and diagnosis is v.2_a.1 and radius_mean is v.3_a.5 and
## V14 V15 V16 V17 V18 V19 V20 V21 V22 V23
## 1 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.5 and area_mean is
## 2 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.9 and area_mean is
## 3 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.8 and area_mean is
## 4 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.4 and area_mean is
## 5 texture_mean is v.4_a.15 and perimeter_mean is v.5_a.6 and area_mean is
## 6 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.9 and area_mean is
## 7 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.1 and area_mean is
## 8 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.6 and area_mean is
## 9 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.11 and area_mean is
## 10 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.6 and area_mean is
## 11 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.5 and area_mean is
## 12 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.4 and area_mean is
## 13 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.5 and area_mean is
## 14 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.7 and area_mean is
## 15 texture_mean is v.4_a.1 and perimeter_mean is v.5_a.9 and area_mean is
## 16 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.5 and area_mean is
## 17 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.7 and area_mean is
## 18 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.11 and area_mean is
## 19 texture_mean is v.4_a.14 and perimeter_mean is v.5_a.10 and area_mean is
## 20 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.6 and area_mean is
## 21 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.5 and area_mean is
## 22 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.6 and area_mean is
## 23 texture_mean is v.4_a.14 and perimeter_mean is v.5_a.10 and area_mean is
## 24 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.1 and area_mean is
## 25 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.2 and area_mean is
## 26 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.5 and area_mean is
## 27 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.6 and area_mean is
## 28 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.5 and area_mean is
## 29 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.5 and area_mean is
## 30 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.5 and area_mean is
## 31 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.8 and area_mean is
## 32 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.10 and area_mean is
## 33 texture_mean is v.4_a.3 and perimeter_mean is v.5_a.2 and area_mean is
## 34 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.6 and area_mean is
## 35 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.8 and area_mean is
## 36 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.4 and area_mean is
## 37 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.9 and area_mean is
## 38 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.12 and area_mean is
## 39 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.15 and area_mean is
## 40 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.4 and area_mean is
## 41 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.5 and area_mean is
## 42 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.8 and area_mean is
## 43 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.6 and area_mean is
## 44 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.10 and area_mean is
## 45 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.4 and area_mean is
## 46 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.9 and area_mean is
## 47 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.2 and area_mean is
## 48 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.9 and area_mean is
## 49 texture_mean is v.4_a.2 and perimeter_mean is v.5_a.1 and area_mean is
## 50 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.4 and area_mean is
## 51 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.6 and area_mean is
## 52 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.10 and area_mean is
## 53 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.6 and area_mean is
## 54 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.7 and area_mean is
## 55 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.8 and area_mean is
## 56 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.9 and area_mean is
## 57 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.10 and area_mean is
## 58 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.7 and area_mean is
## 59 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.10 and area_mean is
## 60 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.5 and area_mean is
## 61 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.6 and area_mean is
## 62 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.9 and area_mean is
## 63 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.5 and area_mean is
## 64 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.4 and area_mean is
## 65 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.3 and area_mean is
## 66 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.7 and area_mean is
## 67 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.3 and area_mean is
## 68 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.5 and area_mean is
## 69 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.7 and area_mean is
## 70 texture_mean is v.4_a.1 and perimeter_mean is v.5_a.5 and area_mean is
## 71 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.5 and area_mean is
## 72 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.2 and area_mean is
## 73 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.4 and area_mean is
## 74 texture_mean is v.4_a.11 and perimeter_mean is v.5_a.7 and area_mean is
## 75 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.8 and area_mean is
## 76 texture_mean is v.4_a.12 and perimeter_mean is v.5_a.5 and area_mean is
## 77 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.9 and area_mean is
## 78 texture_mean is v.4_a.5 and perimeter_mean is v.5_a.6 and area_mean is
## 79 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.4 and area_mean is
## 80 texture_mean is v.4_a.4 and perimeter_mean is v.5_a.2 and area_mean is
## 81 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.6 and area_mean is
## 82 texture_mean is v.4_a.7 and perimeter_mean is v.5_a.9 and area_mean is
## 83 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.5 and area_mean is
## 84 texture_mean is v.4_a.8 and perimeter_mean is v.5_a.3 and area_mean is
## 85 texture_mean is v.4_a.13 and perimeter_mean is v.5_a.10 and area_mean is
## 86 texture_mean is v.4_a.10 and perimeter_mean is v.5_a.10 and area_mean is
## 87 texture_mean is v.4_a.9 and perimeter_mean is v.5_a.4 and area_mean is
## 88 texture_mean is v.4_a.6 and perimeter_mean is v.5_a.5 and area_mean is
## V24 V25 V26 V27 V28
## 1 v.6_a.4 THEN smoothness_mean is c.7
## 2 v.6_a.8 THEN smoothness_mean is c.9
## 3 v.6_a.7 THEN smoothness_mean is c.10
## 4 v.6_a.3 THEN smoothness_mean is c.8
## 5 v.6_a.5 THEN smoothness_mean is c.9
## 6 v.6_a.8 THEN smoothness_mean is c.5
## 7 v.6_a.1 THEN smoothness_mean is c.3
## 8 v.6_a.5 THEN smoothness_mean is c.5
## 9 v.6_a.10 THEN smoothness_mean is c.6
## 10 v.6_a.5 THEN smoothness_mean is c.9
## 11 v.6_a.4 THEN smoothness_mean is c.9
## 12 v.6_a.3 THEN smoothness_mean is c.3
## 13 v.6_a.4 THEN smoothness_mean is c.2
## 14 v.6_a.6 THEN smoothness_mean is c.5
## 15 v.6_a.8 THEN smoothness_mean is c.10
## 16 v.6_a.4 THEN smoothness_mean is c.5
## 17 v.6_a.5 THEN smoothness_mean is c.7
## 18 v.6_a.11 THEN smoothness_mean is c.5
## 19 v.6_a.9 THEN smoothness_mean is c.5
## 20 v.6_a.5 THEN smoothness_mean is c.7
## 21 v.6_a.4 THEN smoothness_mean is c.12
## 22 v.6_a.5 THEN smoothness_mean is c.6
## 23 v.6_a.9 THEN smoothness_mean is c.11
## 24 v.6_a.1 THEN smoothness_mean is c.11
## 25 v.6_a.1 THEN smoothness_mean is c.5
## 26 v.6_a.4 THEN smoothness_mean is c.3
## 27 v.6_a.5 THEN smoothness_mean is c.9
## 28 v.6_a.4 THEN smoothness_mean is c.5
## 29 v.6_a.4 THEN smoothness_mean is c.8
## 30 v.6_a.3 THEN smoothness_mean is c.9
## 31 v.6_a.7 THEN smoothness_mean is c.7
## 32 v.6_a.9 THEN smoothness_mean is c.7
## 33 v.6_a.2 THEN smoothness_mean is c.6
## 34 v.6_a.5 THEN smoothness_mean is c.6
## 35 v.6_a.6 THEN smoothness_mean is c.10
## 36 v.6_a.3 THEN smoothness_mean is c.4
## 37 v.6_a.8 THEN smoothness_mean is c.7
## 38 v.6_a.10 THEN smoothness_mean is c.12
## 39 v.6_a.15 THEN smoothness_mean is c.7
## 40 v.6_a.3 THEN smoothness_mean is c.5
## 41 v.6_a.4 THEN smoothness_mean is c.2
## 42 v.6_a.7 THEN smoothness_mean is c.6
## 43 v.6_a.5 THEN smoothness_mean is c.7
## 44 v.6_a.10 THEN smoothness_mean is c.6
## 45 v.6_a.4 THEN smoothness_mean is c.6
## 46 v.6_a.9 THEN smoothness_mean is c.4
## 47 v.6_a.1 THEN smoothness_mean is c.7
## 48 v.6_a.8 THEN smoothness_mean is c.4
## 49 v.6_a.1 THEN smoothness_mean is c.5
## 50 v.6_a.3 THEN smoothness_mean is c.7
## 51 v.6_a.5 THEN smoothness_mean is c.10
## 52 v.6_a.9 THEN smoothness_mean is c.5
## 53 v.6_a.5 THEN smoothness_mean is c.5
## 54 v.6_a.6 THEN smoothness_mean is c.3
## 55 v.6_a.7 THEN smoothness_mean is c.9
## 56 v.6_a.8 THEN smoothness_mean is c.6
## 57 v.6_a.10 THEN smoothness_mean is c.3
## 58 v.6_a.5 THEN smoothness_mean is c.8
## 59 v.6_a.8 THEN smoothness_mean is c.7
## 60 v.6_a.4 THEN smoothness_mean is c.6
## 61 v.6_a.4 THEN smoothness_mean is c.9
## 62 v.6_a.8 THEN smoothness_mean is c.5
## 63 v.6_a.4 THEN smoothness_mean is c.4
## 64 v.6_a.3 THEN smoothness_mean is c.2
## 65 v.6_a.2 THEN smoothness_mean is c.11
## 66 v.6_a.6 THEN smoothness_mean is c.7
## 67 v.6_a.2 THEN smoothness_mean is c.9
## 68 v.6_a.4 THEN smoothness_mean is c.6
## 69 v.6_a.6 THEN smoothness_mean is c.4
## 70 v.6_a.4 THEN smoothness_mean is c.12
## 71 v.6_a.3 THEN smoothness_mean is c.12
## 72 v.6_a.2 THEN smoothness_mean is c.7
## 73 v.6_a.3 THEN smoothness_mean is c.8
## 74 v.6_a.6 THEN smoothness_mean is c.2
## 75 v.6_a.7 THEN smoothness_mean is c.5
## 76 v.6_a.3 THEN smoothness_mean is c.10
## 77 v.6_a.7 THEN smoothness_mean is c.10
## 78 v.6_a.4 THEN smoothness_mean is c.6
## 79 v.6_a.3 THEN smoothness_mean is c.5
## 80 v.6_a.1 THEN smoothness_mean is c.1
## 81 v.6_a.5 THEN smoothness_mean is c.4
## 82 v.6_a.8 THEN smoothness_mean is c.8
## 83 v.6_a.4 THEN smoothness_mean is c.10
## 84 v.6_a.3 THEN smoothness_mean is c.2
## 85 v.6_a.9 THEN smoothness_mean is c.4
## 86 v.6_a.9 THEN smoothness_mean is c.8
## 87 v.6_a.3 THEN smoothness_mean is c.15
## 88 v.6_a.4 THEN smoothness_mean is c.1## Plot the membership functions
plotMF(object.reg)
pred <- predict(object.reg, testing)## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"
## [1] "note: Some of your new data are out of the previously specified range"str(pred)## num [1:479, 1] 0.0862 0.0885 0.1029 0.0868 0.0984 ...str(data_research)## tibble [479 × 2] (S3: tbl_df/tbl/data.frame)
## $ radius_mean : num [1:479] 14.6 15.4 13.3 13.4 15.1 ...
## $ texture_mean: num [1:479] 24 22.8 14.8 18.3 19.8 ...data_research$predict <- pred
data_research## # A tibble: 479 × 3
## radius_mean texture_mean predict[,1]
## <dbl> <dbl> <dbl>
## 1 14.6 24.0 0.0862
## 2 15.4 22.8 0.0885
## 3 13.3 14.8 0.103
## 4 13.4 18.3 0.0868
## 5 15.1 19.8 0.0984
## 6 20.3 23.0 0.100
## 7 12.2 17.8 0.0821
## 8 9.79 19.9 0.109
## 9 11.6 12.8 0.105
## 10 14.4 19.8 0.100
## # … with 469 more rowsdata_research$residual <- (data_research$radius_mean-data_research$predict)
data_research## # A tibble: 479 × 4
## radius_mean texture_mean predict[,1] residual[,1]
## <dbl> <dbl> <dbl> <dbl>
## 1 14.6 24.0 0.0862 14.5
## 2 15.4 22.8 0.0885 15.3
## 3 13.3 14.8 0.103 13.2
## 4 13.4 18.3 0.0868 13.4
## 5 15.1 19.8 0.0984 15.0
## 6 20.3 23.0 0.100 20.2
## 7 12.2 17.8 0.0821 12.1
## 8 9.79 19.9 0.109 9.68
## 9 11.6 12.8 0.105 11.5
## 10 14.4 19.8 0.100 14.3
## # … with 469 more rowsmean(data_research$predict != data_research$radius_mean)## [1] 1accuracy <- table(data_research$predict, data_research$radius_mean)
sum(diag(accuracy))/sum(accuracy)## [1] 0.006263048library(ggplot2)
library(reshape2)x <- 1:1
real_data <- data_research$radius_mean
predict_data <- data_research$predict
df <- data.frame(x, real_data , predict_data )
# melt the data to a long format
df2 <- melt(data = df, id.vars = "x")
# plot, using the aesthetics argument 'colour'
ggplot(data = df2, aes(x = x, y = value, colour = variable))+
geom_point(alpha = 1/2,size=7) +
theme(legend.justification = "top") +
labs(title = "Graph of Prediction using Fuzzy logic",
subtitle = "Mamdani",
y = "Waktu Pompa", x = "Iterasi") +
theme(axis.text.x = element_text(angle = -45))
R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.