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-100 sampai ke-300 dengan kolom ke-10 sampai ke-16
training <- cmcData[100:200,10:16]
#untuk testing kolom yang dipakai adalah kolom ke-10 sampai ke-15
testing <- cmcData[201:nrow(cmcData),10:15]
#untuk validasi kolom yang dipakai adalah kolom ke-16
validasi <- cmcData[201:nrow(cmcData),16]
#data_research yang digunakan adalah kolom 10(concave_points_mean) dan 15(perimeter_se)
data_research <- cmcData[201:nrow(cmcData),c(10,15)]
training
## # A tibble: 101 × 7
## concave_points_mean symmetry_mean fractal_d…¹ radiu…² textu…³ perim…⁴ area_se
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.0584 0.188 0.0639 0.290 1.85 2.38 26.8
## 2 0.0449 0.161 0.0587 0.456 1.29 2.86 43.1
## 3 0 0.193 0.0782 0.224 1.51 1.55 9.83
## 4 0.0177 0.174 0.0568 0.192 1.57 1.18 14.7
## 5 0.0303 0.194 0.0632 0.180 1.22 1.53 11.8
## 6 0.0120 0.222 0.0648 0.355 1.53 2.30 23.1
## 7 0.0960 0.192 0.0769 0.391 0.924 2.41 34.7
## 8 0.0348 0.180 0.0652 0.306 1.66 2.15 20.6
## 9 0.0192 0.160 0.0607 0.120 0.894 0.848 9.23
## 10 0.182 0.256 0.0704 1.22 1.54 10.0 170
## # … with 91 more rows, and abbreviated variable names ¹fractal_dimension_mean,
## # ²radius_se, ³texture_se, ⁴perimeter_se
testing
## # A tibble: 369 × 6
## concave_points_mean symmetry_mean fractal_dimension…¹ radiu…² textu…³ perim…⁴
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.0411 0.198 0.0601 0.353 1.33 2.31
## 2 0.0749 0.151 0.0549 0.397 0.828 3.09
## 3 0.162 0.22 0.0623 0.554 1.56 4.67
## 4 0.0918 0.225 0.0742 0.565 1.93 3.91
## 5 0.0382 0.192 0.0637 0.396 1.04 2.50
## 6 0.0408 0.159 0.0599 0.271 0.362 1.97
## 7 0.0195 0.193 0.0628 0.214 1.34 1.52
## 8 0.0539 0.203 0.0522 0.586 0.855 4.11
## 9 0.0510 0.185 0.0731 0.193 0.922 1.49
## 10 0.0316 0.136 0.0553 0.213 0.363 1.52
## # … with 359 more rows, and abbreviated variable names ¹fractal_dimension_mean,
## # ²radius_se, ³texture_se, ⁴perimeter_se
validasi
## # A tibble: 369 × 1
## area_se
## <dbl>
## 1 27.2
## 2 40.7
## 3 83.2
## 4 52.7
## 5 30.3
## 6 26.4
## 7 12.3
## 8 68.5
## 9 15.1
## 10 20
## # … with 359 more rows
data_research
## # A tibble: 369 × 2
## concave_points_mean perimeter_se
## <dbl> <dbl>
## 1 0.0411 2.31
## 2 0.0749 3.09
## 3 0.162 4.67
## 4 0.0918 3.91
## 5 0.0382 2.50
## 6 0.0408 1.97
## 7 0.0195 1.52
## 8 0.0539 4.11
## 9 0.0510 1.49
## 10 0.0316 1.52
## # … with 359 more rows
## Define interval of data
range.data <-apply(training, 2, range)
range.data
## concave_points_mean symmetry_mean fractal_dimension_mean radius_se
## [1,] 0.0000 0.1167 0.05025 0.1199
## [2,] 0.2012 0.2678 0.09296 1.5090
## texture_se perimeter_se area_se
## [1,] 0.4064 0.8484 8.966
## [2,] 4.8850 10.0500 233.000
## 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 = "LUKASIEWICZ",
name = "fourhill")
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: fourhill
## Model was trained using: WM
## The names of attributes: concave_points_mean symmetry_mean fractal_dimension_mean radius_se texture_se perimeter_se area_se
## The interval of training data:
## concave_points_mean symmetry_mean fractal_dimension_mean radius_se
## min 0.0000 0.1167 0.05025 0.1199
## max 0.2012 0.2678 0.09296 1.5090
## texture_se perimeter_se area_se
## min 0.4064 0.8484 8.966
## max 4.8850 10.0500 233.000
## 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] "LUKASIEWICZ"
## 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
## concave_points_mean symmetry_mean fractal_dimension_mean radius_se
## [1,] 15 15 15 15
## texture_se perimeter_se area_se
## [1,] 15 15 15
## The fuzzy IF-THEN rules:
## V1 V2 V3 V4 V5 V6 V7 V8 V9
## 1 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.8 and
## 2 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.5 and
## 3 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.8 and
## 4 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.6 and
## 5 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.8 and
## 6 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.11 and
## 7 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 8 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 9 IF concave_points_mean is v.1_a.14 and symmetry_mean is v.2_a.14 and
## 10 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 11 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.6 and
## 12 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.6 and
## 13 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.8 and
## 14 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 15 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.6 and
## 16 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.10 and
## 17 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.10 and
## 18 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.10 and
## 19 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.6 and
## 20 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.8 and
## 21 IF concave_points_mean is v.1_a.15 and symmetry_mean is v.2_a.15 and
## 22 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.7 and
## 23 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.3 and
## 24 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 25 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.9 and
## 26 IF concave_points_mean is v.1_a.9 and symmetry_mean is v.2_a.11 and
## 27 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.8 and
## 28 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.8 and
## 29 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.10 and
## 30 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.7 and
## 31 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.6 and
## 32 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.5 and
## 33 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.3 and
## 34 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.6 and
## 35 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.8 and
## 36 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.7 and
## 37 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.8 and
## 38 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.7 and
## 39 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 40 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.3 and
## 41 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.9 and
## 42 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.15 and
## 43 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.6 and
## 44 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.6 and
## 45 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.4 and
## 46 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.14 and
## 47 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.11 and
## 48 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.14 and
## 49 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.7 and
## 50 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 51 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.8 and
## 52 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 53 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 54 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.4 and
## 55 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.9 and
## 56 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.6 and
## 57 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.5 and
## 58 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.7 and
## 59 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.3 and
## 60 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.8 and
## 61 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.4 and
## 62 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.7 and
## 63 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.5 and
## 64 IF concave_points_mean is v.1_a.9 and symmetry_mean is v.2_a.8 and
## 65 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.5 and
## 66 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.8 and
## 67 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.6 and
## 68 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.6 and
## 69 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.7 and
## 70 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.3 and
## 71 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.4 and
## 72 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.5 and
## 73 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.1 and
## 74 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.6 and
## 75 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.5 and
## 76 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.4 and
## 77 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.6 and
## 78 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.6 and
## 79 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.6 and
## 80 IF concave_points_mean is v.1_a.1 and symmetry_mean is v.2_a.6 and
## 81 IF concave_points_mean is v.1_a.7 and symmetry_mean is v.2_a.6 and
## 82 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 83 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.7 and
## 84 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.7 and
## 85 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.7 and
## 86 IF concave_points_mean is v.1_a.8 and symmetry_mean is v.2_a.8 and
## 87 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.8 and
## 88 IF concave_points_mean is v.1_a.10 and symmetry_mean is v.2_a.9 and
## 89 IF concave_points_mean is v.1_a.6 and symmetry_mean is v.2_a.12 and
## 90 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.8 and
## 91 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.5 and
## 92 IF concave_points_mean is v.1_a.14 and symmetry_mean is v.2_a.7 and
## 93 IF concave_points_mean is v.1_a.11 and symmetry_mean is v.2_a.12 and
## 94 IF concave_points_mean is v.1_a.4 and symmetry_mean is v.2_a.8 and
## 95 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.6 and
## 96 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.8 and
## 97 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.2 and
## 98 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.4 and
## 99 IF concave_points_mean is v.1_a.5 and symmetry_mean is v.2_a.8 and
## 100 IF concave_points_mean is v.1_a.3 and symmetry_mean is v.2_a.7 and
## 101 IF concave_points_mean is v.1_a.2 and symmetry_mean is v.2_a.4 and
## V10 V11 V12 V13 V14 V15 V16 V17
## 1 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 2 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.4 and
## 3 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.2 and
## 4 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.2 and
## 5 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 6 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 7 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 8 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.1 and
## 9 fractal_dimension_mean is v.3_a.8 and radius_se is v.4_a.12 and
## 10 fractal_dimension_mean is v.3_a.8 and radius_se is v.4_a.4 and
## 11 fractal_dimension_mean is v.3_a.8 and radius_se is v.4_a.3 and
## 12 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.3 and
## 13 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.3 and
## 14 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.1 and
## 15 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 16 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.4 and
## 17 fractal_dimension_mean is v.3_a.9 and radius_se is v.4_a.5 and
## 18 fractal_dimension_mean is v.3_a.1 and radius_se is v.4_a.5 and
## 19 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.1 and
## 20 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.7 and
## 21 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.15 and
## 22 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 23 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.1 and
## 24 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 25 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.4 and
## 26 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.5 and
## 27 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## 28 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.5 and
## 29 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.4 and
## 30 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.3 and
## 31 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.6 and
## 32 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 33 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.4 and
## 34 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 35 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.13 and
## 36 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.3 and
## 37 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## 38 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.7 and
## 39 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 40 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.2 and
## 41 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.4 and
## 42 fractal_dimension_mean is v.3_a.9 and radius_se is v.4_a.3 and
## 43 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.4 and
## 44 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 45 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.2 and
## 46 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.4 and
## 47 fractal_dimension_mean is v.3_a.12 and radius_se is v.4_a.2 and
## 48 fractal_dimension_mean is v.3_a.15 and radius_se is v.4_a.8 and
## 49 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 50 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 51 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.8 and
## 52 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.5 and
## 53 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 54 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.3 and
## 55 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.5 and
## 56 fractal_dimension_mean is v.3_a.1 and radius_se is v.4_a.10 and
## 57 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.3 and
## 58 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.6 and
## 59 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## 60 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.6 and
## 61 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.11 and
## 62 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.3 and
## 63 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.5 and
## 64 fractal_dimension_mean is v.3_a.8 and radius_se is v.4_a.4 and
## 65 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## 66 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.3 and
## 67 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.2 and
## 68 fractal_dimension_mean is v.3_a.11 and radius_se is v.4_a.4 and
## 69 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 70 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.2 and
## 71 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 72 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.4 and
## 73 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.3 and
## 74 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.4 and
## 75 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.2 and
## 76 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 77 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.2 and
## 78 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.2 and
## 79 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.7 and
## 80 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 81 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.3 and
## 82 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.2 and
## 83 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.6 and
## 84 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.6 and
## 85 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.4 and
## 86 fractal_dimension_mean is v.3_a.10 and radius_se is v.4_a.4 and
## 87 fractal_dimension_mean is v.3_a.7 and radius_se is v.4_a.4 and
## 88 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.7 and
## 89 fractal_dimension_mean is v.3_a.9 and radius_se is v.4_a.3 and
## 90 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 91 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 92 fractal_dimension_mean is v.3_a.3 and radius_se is v.4_a.8 and
## 93 fractal_dimension_mean is v.3_a.9 and radius_se is v.4_a.6 and
## 94 fractal_dimension_mean is v.3_a.4 and radius_se is v.4_a.2 and
## 95 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 96 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## 97 fractal_dimension_mean is v.3_a.8 and radius_se is v.4_a.3 and
## 98 fractal_dimension_mean is v.3_a.2 and radius_se is v.4_a.2 and
## 99 fractal_dimension_mean is v.3_a.1 and radius_se is v.4_a.7 and
## 100 fractal_dimension_mean is v.3_a.5 and radius_se is v.4_a.2 and
## 101 fractal_dimension_mean is v.3_a.6 and radius_se is v.4_a.2 and
## V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28
## 1 texture_se is v.5_a.6 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 2 texture_se is v.5_a.4 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 3 texture_se is v.5_a.4 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 4 texture_se is v.5_a.5 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 5 texture_se is v.5_a.4 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 6 texture_se is v.5_a.5 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 7 texture_se is v.5_a.5 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 8 texture_se is v.5_a.3 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 9 texture_se is v.5_a.5 and perimeter_se is v.6_a.15 THEN area_se is c.11
## 10 texture_se is v.5_a.4 and perimeter_se is v.6_a.4 THEN area_se is c.2
## 11 texture_se is v.5_a.5 and perimeter_se is v.6_a.4 THEN area_se is c.2
## 12 texture_se is v.5_a.4 and perimeter_se is v.6_a.5 THEN area_se is c.2
## 13 texture_se is v.5_a.6 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 14 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 15 texture_se is v.5_a.3 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 16 texture_se is v.5_a.3 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 17 texture_se is v.5_a.3 and perimeter_se is v.6_a.5 THEN area_se is c.4
## 18 texture_se is v.5_a.4 and perimeter_se is v.6_a.5 THEN area_se is c.4
## 19 texture_se is v.5_a.1 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 20 texture_se is v.5_a.5 and perimeter_se is v.6_a.7 THEN area_se is c.6
## 21 texture_se is v.5_a.9 and perimeter_se is v.6_a.15 THEN area_se is c.15
## 22 texture_se is v.5_a.2 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 23 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 24 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 25 texture_se is v.5_a.3 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 26 texture_se is v.5_a.3 and perimeter_se is v.6_a.4 THEN area_se is c.4
## 27 texture_se is v.5_a.2 and perimeter_se is v.6_a.3 THEN area_se is c.1
## 28 texture_se is v.5_a.2 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 29 texture_se is v.5_a.4 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 30 texture_se is v.5_a.2 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 31 texture_se is v.5_a.3 and perimeter_se is v.6_a.5 THEN area_se is c.5
## 32 texture_se is v.5_a.4 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 33 texture_se is v.5_a.8 and perimeter_se is v.6_a.5 THEN area_se is c.3
## 34 texture_se is v.5_a.3 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 35 texture_se is v.5_a.4 and perimeter_se is v.6_a.13 THEN area_se is c.7
## 36 texture_se is v.5_a.4 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 37 texture_se is v.5_a.1 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 38 texture_se is v.5_a.4 and perimeter_se is v.6_a.7 THEN area_se is c.5
## 39 texture_se is v.5_a.6 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 40 texture_se is v.5_a.4 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 41 texture_se is v.5_a.2 and perimeter_se is v.6_a.4 THEN area_se is c.2
## 42 texture_se is v.5_a.4 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 43 texture_se is v.5_a.6 and perimeter_se is v.6_a.5 THEN area_se is c.3
## 44 texture_se is v.5_a.2 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 45 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 46 texture_se is v.5_a.4 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 47 texture_se is v.5_a.6 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 48 texture_se is v.5_a.8 and perimeter_se is v.6_a.6 THEN area_se is c.4
## 49 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 50 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 51 texture_se is v.5_a.4 and perimeter_se is v.6_a.8 THEN area_se is c.6
## 52 texture_se is v.5_a.6 and perimeter_se is v.6_a.5 THEN area_se is c.3
## 53 texture_se is v.5_a.2 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 54 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 55 texture_se is v.5_a.5 and perimeter_se is v.6_a.6 THEN area_se is c.3
## 56 texture_se is v.5_a.2 and perimeter_se is v.6_a.10 THEN area_se is c.8
## 57 texture_se is v.5_a.5 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 58 texture_se is v.5_a.2 and perimeter_se is v.6_a.7 THEN area_se is c.7
## 59 texture_se is v.5_a.1 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 60 texture_se is v.5_a.4 and perimeter_se is v.6_a.6 THEN area_se is c.5
## 61 texture_se is v.5_a.4 and perimeter_se is v.6_a.11 THEN area_se is c.8
## 62 texture_se is v.5_a.4 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 63 texture_se is v.5_a.3 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 64 texture_se is v.5_a.2 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 65 texture_se is v.5_a.5 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 66 texture_se is v.5_a.6 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 67 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 68 texture_se is v.5_a.7 and perimeter_se is v.6_a.4 THEN area_se is c.2
## 69 texture_se is v.5_a.4 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 70 texture_se is v.5_a.3 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 71 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 72 texture_se is v.5_a.3 and perimeter_se is v.6_a.3 THEN area_se is c.3
## 73 texture_se is v.5_a.3 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 74 texture_se is v.5_a.4 and perimeter_se is v.6_a.4 THEN area_se is c.2
## 75 texture_se is v.5_a.1 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 76 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 77 texture_se is v.5_a.6 and perimeter_se is v.6_a.1 THEN area_se is c.1
## 78 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 79 texture_se is v.5_a.5 and perimeter_se is v.6_a.7 THEN area_se is c.4
## 80 texture_se is v.5_a.15 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 81 texture_se is v.5_a.3 and perimeter_se is v.6_a.5 THEN area_se is c.2
## 82 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 83 texture_se is v.5_a.6 and perimeter_se is v.6_a.7 THEN area_se is c.4
## 84 texture_se is v.5_a.4 and perimeter_se is v.6_a.6 THEN area_se is c.5
## 85 texture_se is v.5_a.3 and perimeter_se is v.6_a.6 THEN area_se is c.4
## 86 texture_se is v.5_a.3 and perimeter_se is v.6_a.3 THEN area_se is c.3
## 87 texture_se is v.5_a.5 and perimeter_se is v.6_a.4 THEN area_se is c.3
## 88 texture_se is v.5_a.3 and perimeter_se is v.6_a.7 THEN area_se is c.7
## 89 texture_se is v.5_a.6 and perimeter_se is v.6_a.3 THEN area_se is c.2
## 90 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 91 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 92 texture_se is v.5_a.4 and perimeter_se is v.6_a.9 THEN area_se is c.8
## 93 texture_se is v.5_a.2 and perimeter_se is v.6_a.6 THEN area_se is c.6
## 94 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 95 texture_se is v.5_a.1 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 96 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.2
## 97 texture_se is v.5_a.3 and perimeter_se is v.6_a.5 THEN area_se is c.1
## 98 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 99 texture_se is v.5_a.4 and perimeter_se is v.6_a.8 THEN area_se is c.6
## 100 texture_se is v.5_a.2 and perimeter_se is v.6_a.2 THEN area_se is c.1
## 101 texture_se is v.5_a.3 and perimeter_se is v.6_a.2 THEN area_se 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 [1:369, 1] 15.5 40.9 105 28.3 24.9 ...
str(data_research)
## tibble [369 × 2] (S3: tbl_df/tbl/data.frame)
## $ concave_points_mean: num [1:369] 0.0411 0.0749 0.162 0.0918 0.0382 ...
## $ perimeter_se : num [1:369] 2.31 3.09 4.67 3.91 2.5 ...
data_research$predict <- pred
data_research
## # A tibble: 369 × 3
## concave_points_mean perimeter_se predict[,1]
## <dbl> <dbl> <dbl>
## 1 0.0411 2.31 15.5
## 2 0.0749 3.09 40.9
## 3 0.162 4.67 105.
## 4 0.0918 3.91 28.3
## 5 0.0382 2.50 24.9
## 6 0.0408 1.97 25.0
## 7 0.0195 1.52 9.47
## 8 0.0539 4.11 64.6
## 9 0.0510 1.49 26.1
## 10 0.0316 1.52 25.0
## # … with 359 more rows
data_research$residual <- (data_research$perimeter_se - data_research$predict)
data_research
## # A tibble: 369 × 4
## concave_points_mean perimeter_se predict[,1] residual[,1]
## <dbl> <dbl> <dbl> <dbl>
## 1 0.0411 2.31 15.5 -13.2
## 2 0.0749 3.09 40.9 -37.8
## 3 0.162 4.67 105. -100.
## 4 0.0918 3.91 28.3 -24.4
## 5 0.0382 2.50 24.9 -22.4
## 6 0.0408 1.97 25.0 -23.0
## 7 0.0195 1.52 9.47 -7.95
## 8 0.0539 4.11 64.6 -60.5
## 9 0.0510 1.49 26.1 -24.6
## 10 0.0316 1.52 25.0 -23.4
## # … with 359 more rows
mean(data_research$predict != data_research$perimeter_se)
## [1] 1
accuracy <- table(data_research$predict, data_research$perimeter_se)
sum(diag(accuracy))/sum(accuracy)
## [1] 0.008130081
library(ggplot2)
library(reshape2)
x <- 1:9
real_data <- data_research$`perimeter_se`
predict_data <- data_research$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 = "", x = "") + theme(axis.text.x = element_text(angle = -45))
