*Let's play with Dinfio!*

This is the implementation of simple k-Nearest Neighbours (kNN) classifier algorithm with Euclidean distance as the distance metric.

```
' ------------------------------------
' Machine Learning Example in Dinfio
' ------------------------------------
' k-Nearest Neighbours classifier
' By: Faruq
' ------------------------------------
import math
start
var KNN: knn = KNN(3)
training_features = [
[140, 1],
[130, 1],
[150, 0],
[170, 0]
]
training_labels = ["orange", "orange", "apple", "apple"]
testing = [160, 1]
knn.train(training_features, training_labels)
prediction = knn.predict(testing)
writeln("Input: [" & testing[0] & ", " & testing[1] & "]")
writeln("Prediction result: " & prediction.label)
writeln("Distance: " & prediction.distance)
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 sqrt(sum)
stop
endclass
```

Screenshot:

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