$3.$ Implement K$-$Nearest Neighborhood with R$/$Python

$($a$)$ Given a dataset Xn$\backslash$times p $($n observations, p features$),$ Yn$\backslash$times $1($class labels, yi $=0,1),$ write

a function to classify one new point xnew $($i$.$e$.$ to predict ynew$).($Please do NOT use

any packages in R$/$Python$,$ write your own code.$)$

Hint:

$2$

i$.$ Find all the distances between xnew and Xn$\backslash$times p

ii$.$ Pick up class labels of the nearest K points

iii. Predict ynew by majority vote

$($b$)$ Run your function using dataset $$HW$1$Q$3$data.Rdata$$ in R or $$HW$1$Q$3$data.npy$$

in Python $($use data$=$numpy.load$($HW$1$Q$3$data.npy$,$allow

pickle$=$True$).$item$()$

after installing package $$numpy$$ to load the data $,$ which will return a dict object$)$

to classify xnew, with K $=5.$