Dinfio Playground
Let's play with Dinfio!
Machine Learning: Iris Classification using k-Nearest Neighbours
This is the implementation of k-Nearest Neighbours (kNN) algorithm with Iris (https://archive.ics.uci.edu/ml/datasets/Iris) as dataset.
' ------------------------------------
' Machine Learning Example in Dinfio
' ------------------------------------
' k-Nearest Neighbours classifier
' Dataset: Iris
' By: Faruq
' ------------------------------------
import math
import string, fileio
start
var KNN: knn = KNN(3)
dataset = knn.load_dataset("iris")
test_index = 4
testing = dataset.testing.features[test_index]
knn.train(dataset.training.features, dataset.training.labels)
prediction = knn.predict(testing)
print()
stop
class KNN
field x_train, y_train
field k
function construct(k)
this.k = k
stop
function train(features, labels)
this.x_train = features
this.y_train = labels
stop
function predict(test)
best_distance = this.distance(this.x_train[0], test)
prediction = this.y_train[0]
for i, 1, size(this.x_train) - 1
dist = this.distance(this.x_train[i], test)
if dist < best_distance
best_distance = dist
prediction = this.y_train[i]
endif
endfor
return {label: prediction, distance: best_distance}
stop
function distance(point_1, point_2)
sum = 0
for i, 0, size(point_1) - 1
sum += (point_1[i] - point_2[i]) ^ 2
endfor
return sum ^ 0.5
stop
function load_dataset(dataset)
training_features = []; training_labels = []
testing_features = []; testing_labels = []
' Load training data
path = getfoldername(getcurrentfile()) & "/" & dataset & "-training.data"
var file: f = file(path, file_read)
iif(is_nothing(f), exit("Training data not found"), nothing)
i = -1
while !f.eof()
d = split(f.readln(), ",")
if i == -1
header = d
else
c = size(d) - 1
temp = []
for j, 0, c - 1
temp[j] = getnumber(d[j])
endfor
training_features[i] = temp
training_labels[i] = d[c]
endif
i += 1
endwhile
' Load testing data
path = getfoldername(getcurrentfile()) & "/" & dataset & "-testing.data"
f = file(path, file_read)
iif(is_nothing(f), exit("Testing data not found"), nothing)
i = -1
while !f.eof()
d = split(f.readln(), ",")
if i != -1
c = size(d) - 1
temp = []
for j, 0, c - 1
temp[j] = getnumber(d[j])
endfor
testing_features[i] = temp
testing_labels[i] = d[c]
endif
i += 1
endwhile
return {
header: header,
training: {
features: training_features,
labels: training_labels
},
testing: {
features: testing_features,
labels: testing_labels
}
}
stop
endclass
function print()
writeln("-----------------------")
writeln(" kNN Example in Dinfio")
writeln("-----------------------")
writeln("\nInput:")
for i, 0, size(dataset.header) - 2
writeln(dataset.header[i] & " = " & testing[i])
endfor
writeln()
writeln("Prediction result: " & prediction.label)
writeln("Actual result: " & dataset.testing.labels[test_index])
writeln("Euclidean distance: " & prediction.distance)
writeln("Total training data: " & size(dataset.training.features))
stop
Screenshot:
sepal length,sepal width,petal length,petal width,label
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginicairis-testing.data:
sepal length,sepal width,petal length,petal width,label
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginicaIris dataset dump
writer(dataset):
object(
.header = array(
[0] = "sepal length"
[1] = "sepal width"
[2] = "petal length"
[3] = "petal width"
[4] = "label"
)
.training = object(
.features = array(
[0] = array(
[0] = 5.1
[1] = 3.5
[2] = 1.4
[3] = 0.2
)
[1] = array(
[0] = 4.9
[1] = 3
[2] = 1.4
[3] = 0.2
)
[2] = array(
[0] = 4.7
[1] = 3.2
[2] = 1.3
[3] = 0.2
)
[3] = array(
[0] = 4.6
[1] = 3.1
[2] = 1.5
[3] = 0.2
)
[4] = array(
[0] = 5
[1] = 3.6
[2] = 1.4
[3] = 0.2
)
[5] = array(
[0] = 5.4
[1] = 3.9
[2] = 1.7
[3] = 0.4
)
[6] = array(
[0] = 4.6
[1] = 3.4
[2] = 1.4
[3] = 0.3
)
[7] = array(
[0] = 5
[1] = 3.4
[2] = 1.5
[3] = 0.2
)
[8] = array(
[0] = 4.4
[1] = 2.9
[2] = 1.4
[3] = 0.2
)
[9] = array(
[0] = 4.9
[1] = 3.1
[2] = 1.5
[3] = 0.1
)
[10] = array(
[0] = 5.4
[1] = 3.7
[2] = 1.5
[3] = 0.2
)
[11] = array(
[0] = 4.8
[1] = 3.4
[2] = 1.6
[3] = 0.2
)
[12] = array(
[0] = 4.8
[1] = 3
[2] = 1.4
[3] = 0.1
)
[13] = array(
[0] = 4.3
[1] = 3
[2] = 1.1
[3] = 0.1
)
[14] = array(
[0] = 5.8
[1] = 4
[2] = 1.2
[3] = 0.2
)
[15] = array(
[0] = 5.7
[1] = 4.4
[2] = 1.5
[3] = 0.4
)
[16] = array(
[0] = 5.4
[1] = 3.9
[2] = 1.3
[3] = 0.4
)
[17] = array(
[0] = 5.1
[1] = 3.5
[2] = 1.4
[3] = 0.3
)
[18] = array(
[0] = 5.7
[1] = 3.8
[2] = 1.7
[3] = 0.3
)
[19] = array(
[0] = 5.1
[1] = 3.8
[2] = 1.5
[3] = 0.3
)
[20] = array(
[0] = 5.4
[1] = 3.4
[2] = 1.7
[3] = 0.2
)
[21] = array(
[0] = 5.1
[1] = 3.7
[2] = 1.5
[3] = 0.4
)
[22] = array(
[0] = 4.6
[1] = 3.6
[2] = 1
[3] = 0.2
)
[23] = array(
[0] = 5.1
[1] = 3.3
[2] = 1.7
[3] = 0.5
)
[24] = array(
[0] = 4.8
[1] = 3.4
[2] = 1.9
[3] = 0.2
)
[25] = array(
[0] = 5
[1] = 3
[2] = 1.6
[3] = 0.2
)
[26] = array(
[0] = 5
[1] = 3.4
[2] = 1.6
[3] = 0.4
)
[27] = array(
[0] = 5.2
[1] = 3.5
[2] = 1.5
[3] = 0.2
)
[28] = array(
[0] = 5.2
[1] = 3.4
[2] = 1.4
[3] = 0.2
)
[29] = array(
[0] = 4.7
[1] = 3.2
[2] = 1.6
[3] = 0.2
)
[30] = array(
[0] = 4.8
[1] = 3.1
[2] = 1.6
[3] = 0.2
)
[31] = array(
[0] = 5.4
[1] = 3.4
[2] = 1.5
[3] = 0.4
)
[32] = array(
[0] = 5.2
[1] = 4.1
[2] = 1.5
[3] = 0.1
)
[33] = array(
[0] = 5.5
[1] = 4.2
[2] = 1.4
[3] = 0.2
)
[34] = array(
[0] = 4.9
[1] = 3.1
[2] = 1.5
[3] = 0.1
)
[35] = array(
[0] = 5
[1] = 3.2
[2] = 1.2
[3] = 0.2
)
[36] = array(
[0] = 5.5
[1] = 3.5
[2] = 1.3
[3] = 0.2
)
[37] = array(
[0] = 4.9
[1] = 3.1
[2] = 1.5
[3] = 0.1
)
[38] = array(
[0] = 4.4
[1] = 3
[2] = 1.3
[3] = 0.2
)
[39] = array(
[0] = 5.1
[1] = 3.4
[2] = 1.5
[3] = 0.2
)
[40] = array(
[0] = 5
[1] = 3.5
[2] = 1.3
[3] = 0.3
)
[41] = array(
[0] = 4.5
[1] = 2.3
[2] = 1.3
[3] = 0.3
)
[42] = array(
[0] = 4.4
[1] = 3.2
[2] = 1.3
[3] = 0.2
)
[43] = array(
[0] = 5
[1] = 3.5
[2] = 1.6
[3] = 0.6
)
[44] = array(
[0] = 5.1
[1] = 3.8
[2] = 1.9
[3] = 0.4
)
[45] = array(
[0] = 7
[1] = 3.2
[2] = 4.7
[3] = 1.4
)
[46] = array(
[0] = 6.4
[1] = 3.2
[2] = 4.5
[3] = 1.5
)
[47] = array(
[0] = 6.9
[1] = 3.1
[2] = 4.9
[3] = 1.5
)
[48] = array(
[0] = 5.5
[1] = 2.3
[2] = 4
[3] = 1.3
)
[49] = array(
[0] = 6.5
[1] = 2.8
[2] = 4.6
[3] = 1.5
)
[50] = array(
[0] = 5.7
[1] = 2.8
[2] = 4.5
[3] = 1.3
)
[51] = array(
[0] = 6.3
[1] = 3.3
[2] = 4.7
[3] = 1.6
)
[52] = array(
[0] = 4.9
[1] = 2.4
[2] = 3.3
[3] = 1
)
[53] = array(
[0] = 6.6
[1] = 2.9
[2] = 4.6
[3] = 1.3
)
[54] = array(
[0] = 5.2
[1] = 2.7
[2] = 3.9
[3] = 1.4
)
[55] = array(
[0] = 5
[1] = 2
[2] = 3.5
[3] = 1
)
[56] = array(
[0] = 5.9
[1] = 3
[2] = 4.2
[3] = 1.5
)
[57] = array(
[0] = 6
[1] = 2.2
[2] = 4
[3] = 1
)
[58] = array(
[0] = 6.1
[1] = 2.9
[2] = 4.7
[3] = 1.4
)
[59] = array(
[0] = 5.6
[1] = 2.9
[2] = 3.6
[3] = 1.3
)
[60] = array(
[0] = 6.7
[1] = 3.1
[2] = 4.4
[3] = 1.4
)
[61] = array(
[0] = 5.6
[1] = 3
[2] = 4.5
[3] = 1.5
)
[62] = array(
[0] = 5.8
[1] = 2.7
[2] = 4.1
[3] = 1
)
[63] = array(
[0] = 6.2
[1] = 2.2
[2] = 4.5
[3] = 1.5
)
[64] = array(
[0] = 5.6
[1] = 2.5
[2] = 3.9
[3] = 1.1
)
[65] = array(
[0] = 5.9
[1] = 3.2
[2] = 4.8
[3] = 1.8
)
[66] = array(
[0] = 6.1
[1] = 2.8
[2] = 4
[3] = 1.3
)
[67] = array(
[0] = 6.3
[1] = 2.5
[2] = 4.9
[3] = 1.5
)
[68] = array(
[0] = 6.1
[1] = 2.8
[2] = 4.7
[3] = 1.2
)
[69] = array(
[0] = 6.4
[1] = 2.9
[2] = 4.3
[3] = 1.3
)
[70] = array(
[0] = 6.6
[1] = 3
[2] = 4.4
[3] = 1.4
)
[71] = array(
[0] = 6.8
[1] = 2.8
[2] = 4.8
[3] = 1.4
)
[72] = array(
[0] = 6.7
[1] = 3
[2] = 5
[3] = 1.7
)
[73] = array(
[0] = 6
[1] = 2.9
[2] = 4.5
[3] = 1.5
)
[74] = array(
[0] = 5.7
[1] = 2.6
[2] = 3.5
[3] = 1
)
[75] = array(
[0] = 5.5
[1] = 2.4
[2] = 3.8
[3] = 1.1
)
[76] = array(
[0] = 5.5
[1] = 2.4
[2] = 3.7
[3] = 1
)
[77] = array(
[0] = 5.8
[1] = 2.7
[2] = 3.9
[3] = 1.2
)
[78] = array(
[0] = 6
[1] = 2.7
[2] = 5.1
[3] = 1.6
)
[79] = array(
[0] = 5.4
[1] = 3
[2] = 4.5
[3] = 1.5
)
[80] = array(
[0] = 6
[1] = 3.4
[2] = 4.5
[3] = 1.6
)
[81] = array(
[0] = 6.7
[1] = 3.1
[2] = 4.7
[3] = 1.5
)
[82] = array(
[0] = 6.3
[1] = 2.3
[2] = 4.4
[3] = 1.3
)
[83] = array(
[0] = 5.6
[1] = 3
[2] = 4.1
[3] = 1.3
)
[84] = array(
[0] = 5.5
[1] = 2.5
[2] = 4
[3] = 1.3
)
[85] = array(
[0] = 5.5
[1] = 2.6
[2] = 4.4
[3] = 1.2
)
[86] = array(
[0] = 6.1
[1] = 3
[2] = 4.6
[3] = 1.4
)
[87] = array(
[0] = 5.8
[1] = 2.6
[2] = 4
[3] = 1.2
)
[88] = array(
[0] = 5
[1] = 2.3
[2] = 3.3
[3] = 1
)
[89] = array(
[0] = 5.6
[1] = 2.7
[2] = 4.2
[3] = 1.3
)
[90] = array(
[0] = 6.3
[1] = 3.3
[2] = 6
[3] = 2.5
)
[91] = array(
[0] = 5.8
[1] = 2.7
[2] = 5.1
[3] = 1.9
)
[92] = array(
[0] = 7.1
[1] = 3
[2] = 5.9
[3] = 2.1
)
[93] = array(
[0] = 6.3
[1] = 2.9
[2] = 5.6
[3] = 1.8
)
[94] = array(
[0] = 6.5
[1] = 3
[2] = 5.8
[3] = 2.2
)
[95] = array(
[0] = 7.6
[1] = 3
[2] = 6.6
[3] = 2.1
)
[96] = array(
[0] = 4.9
[1] = 2.5
[2] = 4.5
[3] = 1.7
)
[97] = array(
[0] = 7.3
[1] = 2.9
[2] = 6.3
[3] = 1.8
)
[98] = array(
[0] = 6.7
[1] = 2.5
[2] = 5.8
[3] = 1.8
)
[99] = array(
[0] = 7.2
[1] = 3.6
[2] = 6.1
[3] = 2.5
)
[100] = array(
[0] = 6.5
[1] = 3.2
[2] = 5.1
[3] = 2
)
[101] = array(
[0] = 6.4
[1] = 2.7
[2] = 5.3
[3] = 1.9
)
[102] = array(
[0] = 6.8
[1] = 3
[2] = 5.5
[3] = 2.1
)
[103] = array(
[0] = 5.7
[1] = 2.5
[2] = 5
[3] = 2
)
[104] = array(
[0] = 5.8
[1] = 2.8
[2] = 5.1
[3] = 2.4
)
[105] = array(
[0] = 6.4
[1] = 3.2
[2] = 5.3
[3] = 2.3
)
[106] = array(
[0] = 6.5
[1] = 3
[2] = 5.5
[3] = 1.8
)
[107] = array(
[0] = 7.7
[1] = 3.8
[2] = 6.7
[3] = 2.2
)
[108] = array(
[0] = 7.7
[1] = 2.6
[2] = 6.9
[3] = 2.3
)
[109] = array(
[0] = 6
[1] = 2.2
[2] = 5
[3] = 1.5
)
[110] = array(
[0] = 6.9
[1] = 3.2
[2] = 5.7
[3] = 2.3
)
[111] = array(
[0] = 5.6
[1] = 2.8
[2] = 4.9
[3] = 2
)
[112] = array(
[0] = 7.7
[1] = 2.8
[2] = 6.7
[3] = 2
)
[113] = array(
[0] = 6.3
[1] = 2.7
[2] = 4.9
[3] = 1.8
)
[114] = array(
[0] = 6.7
[1] = 3.3
[2] = 5.7
[3] = 2.1
)
[115] = array(
[0] = 7.2
[1] = 3.2
[2] = 6
[3] = 1.8
)
[116] = array(
[0] = 6.2
[1] = 2.8
[2] = 4.8
[3] = 1.8
)
[117] = array(
[0] = 6.1
[1] = 3
[2] = 4.9
[3] = 1.8
)
[118] = array(
[0] = 6.4
[1] = 2.8
[2] = 5.6
[3] = 2.1
)
[119] = array(
[0] = 7.2
[1] = 3
[2] = 5.8
[3] = 1.6
)
[120] = array(
[0] = 7.4
[1] = 2.8
[2] = 6.1
[3] = 1.9
)
[121] = array(
[0] = 7.9
[1] = 3.8
[2] = 6.4
[3] = 2
)
[122] = array(
[0] = 6.4
[1] = 2.8
[2] = 5.6
[3] = 2.2
)
[123] = array(
[0] = 6.3
[1] = 2.8
[2] = 5.1
[3] = 1.5
)
[124] = array(
[0] = 6.1
[1] = 2.6
[2] = 5.6
[3] = 1.4
)
[125] = array(
[0] = 7.7
[1] = 3
[2] = 6.1
[3] = 2.3
)
[126] = array(
[0] = 6.3
[1] = 3.4
[2] = 5.6
[3] = 2.4
)
[127] = array(
[0] = 6.4
[1] = 3.1
[2] = 5.5
[3] = 1.8
)
[128] = array(
[0] = 6
[1] = 3
[2] = 4.8
[3] = 1.8
)
[129] = array(
[0] = 6.9
[1] = 3.1
[2] = 5.4
[3] = 2.1
)
[130] = array(
[0] = 6.7
[1] = 3.1
[2] = 5.6
[3] = 2.4
)
[131] = array(
[0] = 6.9
[1] = 3.1
[2] = 5.1
[3] = 2.3
)
[132] = array(
[0] = 5.8
[1] = 2.7
[2] = 5.1
[3] = 1.9
)
[133] = array(
[0] = 6.8
[1] = 3.2
[2] = 5.9
[3] = 2.3
)
[134] = array(
[0] = 6.7
[1] = 3.3
[2] = 5.7
[3] = 2.5
)
)
.labels = array(
[0] = "Iris-setosa"
[1] = "Iris-setosa"
[2] = "Iris-setosa"
[3] = "Iris-setosa"
[4] = "Iris-setosa"
[5] = "Iris-setosa"
[6] = "Iris-setosa"
[7] = "Iris-setosa"
[8] = "Iris-setosa"
[9] = "Iris-setosa"
[10] = "Iris-setosa"
[11] = "Iris-setosa"
[12] = "Iris-setosa"
[13] = "Iris-setosa"
[14] = "Iris-setosa"
[15] = "Iris-setosa"
[16] = "Iris-setosa"
[17] = "Iris-setosa"
[18] = "Iris-setosa"
[19] = "Iris-setosa"
[20] = "Iris-setosa"
[21] = "Iris-setosa"
[22] = "Iris-setosa"
[23] = "Iris-setosa"
[24] = "Iris-setosa"
[25] = "Iris-setosa"
[26] = "Iris-setosa"
[27] = "Iris-setosa"
[28] = "Iris-setosa"
[29] = "Iris-setosa"
[30] = "Iris-setosa"
[31] = "Iris-setosa"
[32] = "Iris-setosa"
[33] = "Iris-setosa"
[34] = "Iris-setosa"
[35] = "Iris-setosa"
[36] = "Iris-setosa"
[37] = "Iris-setosa"
[38] = "Iris-setosa"
[39] = "Iris-setosa"
[40] = "Iris-setosa"
[41] = "Iris-setosa"
[42] = "Iris-setosa"
[43] = "Iris-setosa"
[44] = "Iris-setosa"
[45] = "Iris-versicolor"
[46] = "Iris-versicolor"
[47] = "Iris-versicolor"
[48] = "Iris-versicolor"
[49] = "Iris-versicolor"
[50] = "Iris-versicolor"
[51] = "Iris-versicolor"
[52] = "Iris-versicolor"
[53] = "Iris-versicolor"
[54] = "Iris-versicolor"
[55] = "Iris-versicolor"
[56] = "Iris-versicolor"
[57] = "Iris-versicolor"
[58] = "Iris-versicolor"
[59] = "Iris-versicolor"
[60] = "Iris-versicolor"
[61] = "Iris-versicolor"
[62] = "Iris-versicolor"
[63] = "Iris-versicolor"
[64] = "Iris-versicolor"
[65] = "Iris-versicolor"
[66] = "Iris-versicolor"
[67] = "Iris-versicolor"
[68] = "Iris-versicolor"
[69] = "Iris-versicolor"
[70] = "Iris-versicolor"
[71] = "Iris-versicolor"
[72] = "Iris-versicolor"
[73] = "Iris-versicolor"
[74] = "Iris-versicolor"
[75] = "Iris-versicolor"
[76] = "Iris-versicolor"
[77] = "Iris-versicolor"
[78] = "Iris-versicolor"
[79] = "Iris-versicolor"
[80] = "Iris-versicolor"
[81] = "Iris-versicolor"
[82] = "Iris-versicolor"
[83] = "Iris-versicolor"
[84] = "Iris-versicolor"
[85] = "Iris-versicolor"
[86] = "Iris-versicolor"
[87] = "Iris-versicolor"
[88] = "Iris-versicolor"
[89] = "Iris-versicolor"
[90] = "Iris-virginica"
[91] = "Iris-virginica"
[92] = "Iris-virginica"
[93] = "Iris-virginica"
[94] = "Iris-virginica"
[95] = "Iris-virginica"
[96] = "Iris-virginica"
[97] = "Iris-virginica"
[98] = "Iris-virginica"
[99] = "Iris-virginica"
[100] = "Iris-virginica"
[101] = "Iris-virginica"
[102] = "Iris-virginica"
[103] = "Iris-virginica"
[104] = "Iris-virginica"
[105] = "Iris-virginica"
[106] = "Iris-virginica"
[107] = "Iris-virginica"
[108] = "Iris-virginica"
[109] = "Iris-virginica"
[110] = "Iris-virginica"
[111] = "Iris-virginica"
[112] = "Iris-virginica"
[113] = "Iris-virginica"
[114] = "Iris-virginica"
[115] = "Iris-virginica"
[116] = "Iris-virginica"
[117] = "Iris-virginica"
[118] = "Iris-virginica"
[119] = "Iris-virginica"
[120] = "Iris-virginica"
[121] = "Iris-virginica"
[122] = "Iris-virginica"
[123] = "Iris-virginica"
[124] = "Iris-virginica"
[125] = "Iris-virginica"
[126] = "Iris-virginica"
[127] = "Iris-virginica"
[128] = "Iris-virginica"
[129] = "Iris-virginica"
[130] = "Iris-virginica"
[131] = "Iris-virginica"
[132] = "Iris-virginica"
[133] = "Iris-virginica"
[134] = "Iris-virginica"
)
)
.testing = object(
.features = array(
[0] = array(
[0] = 4.8
[1] = 3
[2] = 1.4
[3] = 0.3
[4] = 0
)
[1] = array(
[0] = 5.1
[1] = 3.8
[2] = 1.6
[3] = 0.2
[4] = 0
)
[2] = array(
[0] = 4.6
[1] = 3.2
[2] = 1.4
[3] = 0.2
[4] = 0
)
[3] = array(
[0] = 5.3
[1] = 3.7
[2] = 1.5
[3] = 0.2
[4] = 0
)
[4] = array(
[0] = 5
[1] = 3.3
[2] = 1.4
[3] = 0.2
[4] = 0
)
[5] = array(
[0] = 5.7
[1] = 3
[2] = 4.2
[3] = 1.2
[4] = 0
)
[6] = array(
[0] = 5.7
[1] = 2.9
[2] = 4.2
[3] = 1.3
[4] = 0
)
[7] = array(
[0] = 6.2
[1] = 2.9
[2] = 4.3
[3] = 1.3
[4] = 0
)
[8] = array(
[0] = 5.1
[1] = 2.5
[2] = 3
[3] = 1.1
[4] = 0
)
[9] = array(
[0] = 5.7
[1] = 2.8
[2] = 4.1
[3] = 1.3
[4] = 0
)
[10] = array(
[0] = 6.7
[1] = 3
[2] = 5.2
[3] = 2.3
[4] = 0
)
[11] = array(
[0] = 6.3
[1] = 2.5
[2] = 5
[3] = 1.9
[4] = 0
)
[12] = array(
[0] = 6.5
[1] = 3
[2] = 5.2
[3] = 2
[4] = 0
)
[13] = array(
[0] = 6.2
[1] = 3.4
[2] = 5.4
[3] = 2.3
[4] = 0
)
[14] = array(
[0] = 5.9
[1] = 3
[2] = 5.1
[3] = 1.8
[4] = 0
)
)
.labels = array(
[0] = "Iris-setosa"
[1] = "Iris-setosa"
[2] = "Iris-setosa"
[3] = "Iris-setosa"
[4] = "Iris-setosa"
[5] = "Iris-versicolor"
[6] = "Iris-versicolor"
[7] = "Iris-versicolor"
[8] = "Iris-versicolor"
[9] = "Iris-versicolor"
[10] = "Iris-virginica"
[11] = "Iris-virginica"
[12] = "Iris-virginica"
[13] = "Iris-virginica"
[14] = "Iris-virginica"
)
)
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