#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 18 11:41:39 2021

@author: doug
"""

import numpy as np
import scipy.io as sio
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from matplotlib import pyplot
import pylab as plt
# from sklearn import metrics   # For accuracy_score and confusion_matrix


#Load dataset
mat_contents=sio.loadmat("knnregressdata1.mat")
X=mat_contents['age']
T=mat_contents['bmd']

X_train, X_test, t_train, t_test = train_test_split(X, T, test_size=0.3)

knnR=KNeighborsRegressor(n_neighbors=4)
knnR.fit(X_train,t_train)

t_score=knnR.score(X_test,t_test)    

# New data to plot the full function:
XX=np.linspace(np.max(X),np.min(X),200)
temp=XX.reshape(1,-1)                    
t_out=knnR.predict(temp.T)              # This works, but is a bit awkward

pyplot.plot(temp.T,t_out,color='r')
plt.scatter(X,T,color='k')
plt.show()