Test knn fda.usc $ scikit-fda

knn.py

from skfda.datasets import fetch_growth
from skfda.ml.classification import KNeighborsClassifier

data = fetch_growth()

fd = data['data']
girls = fd[data['target'] == 1]
boys = fd[data['target'] == 0]

labels = [0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,0,0,0,1,1,1,0,0,1,1,0,1,1,1,0,1,1,0,1,1,0,0,0,0,1,1,1,1,0,1]

knn = KNeighborsClassifier(3)
knn.fit(girls, labels)

knn.predict(boys)

# array([0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
#        1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1])

knn.R

library(fda)
library(fda.usc)

# Random data and labels
data("growth")

girls_data <- t(matrix(growth$hgtf, 31, 54))
boys_data <- t(matrix(growth$hgtm, 31, 39))
ages <- growth$age
girls <- fdata(girls_data, ages)
boys <- fdata(boys_data, ages)

# Random labels
label <- factor(c(0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,0,0,0,1,1,1,0,0,1,1,0,1,1,1,0,1,1,0,1,1,0,0,0,0,1,1,1,1,0,1))

# Train knn with k=3
model <- classif.knn(label, girls, knn=3)

# Predict labels
res <- predict.classif(model, boys)
print(res)
# 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1

res_python <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
res == res_python
# True