#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
import random
import time


"""
from sklearn.datasets import fetch_openml 
mnist = fetch_openml('mnist_784') 
"""

"""
---Ex 1---
images = mnist.data.values.reshape((-1, 28, 28)) 
plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation="nearest") 
plt.show()

print(mnist.target[0])
"""

"""
---Ex 2---
"""
indices = [i for i in range(len(mnist.data))]
random.shuffle(indices)
indices = indices[:5000]

data = [mnist.data.values[i] for i in indices]
target = [mnist.target[i] for i in indices]

bestClf = None
bestScore = 0
bestK = 0
scores = []

kvalues = [i for i in range(2,16)]
train_sizes = [0.05*i for i in range(1,20)]
pvalues = [i for i in range(1,11)]

k = 3
t = 0.9

start = time.time()
xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data, target, train_size=t)
clf = KNeighborsClassifier(3, n_jobs = 1)
clf.fit(xtrain, ytrain)
score = clf.score(xtest, ytest)
end = time.time()
print(f"n_jobs = 1, training + evaluating time : {end - start}")

start = time.time()
xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data, target, train_size=t)
clf = KNeighborsClassifier(3, n_jobs = -1)
clf.fit(xtrain, ytrain)
score = clf.score(xtest, ytest)
end = time.time()
print(f"n_jobs = -1, training + evaluating time : {end - start}")


"""
#for k in kvalues:    
#for t in train_sizes:
for p in pvalues:
    print(p)
    xtrain,  xtest,  ytrain,  ytest  =  train_test_split(data, target, train_size=t)
    clf = KNeighborsClassifier(k, p = p)
    clf.fit(xtrain, ytrain)
    score = clf.score(xtest, ytest)
    scores += [score]
    if score > bestScore:
        bestClf = clf
        #bestK = k
        #bestSize = t
        bestP = p
        bestScore = score


print(target[4])
print(bestClf.predict(data[4].reshape(1,-1)))
plt.imshow(data[4].reshape(28, 28), cmap=plt.cm.gray_r, interpolation="nearest")
plt.show()

#plt.plot(kvalues, scores)
#plt.plot(train_sizes, scores)
plt.plot(pvalues, scores)

print(bestScore)
#print(bestSize)
"""