import numpy as np

trainingDataFile = 'data/t10k-images.idx3-ubyte'
trainingLabelFile = 'data/t10k-labels.idx1-ubyte'


# def euclidean_distance(img1, img2):
#     return np.sqrt(np.sum((img1 - img2) ** 2))

def getData():
    with open(trainingDataFile, mode='rb') as file:
        magicNumber = file.read(4)
        numOfImages = int.from_bytes(file.read(4), 'big')
        height, width = int.from_bytes(file.read(4), 'big'), int.from_bytes(file.read(4), 'big')
        print(height, width)
        data = []

        for i in range(numOfImages):
            image = []
            for j in range(width):
                for k in range(height):
                    pixel = int.from_bytes(file.read(1), 'big')
                    image.append(pixel)

            data.append(np.array(image))

        return data


def getLabels():
    with open(trainingLabelFile, mode='rb') as file:
        magicNumber = file.read(4)
        numOfImages = int.from_bytes(file.read(4), 'big')

        data = []

        for i in range(numOfImages):
            data.append(int.from_bytes(file.read(1), 'big'))

        return data


#np.lin.alg
def euclidean_distance2(img1, img2):
    distance = 0
    for i in range(img1.__len__()):
        distance += (img1[i] - img2[i])**2
    return distance

def nearestNeighbor(image, reference, referenceTags, k=11):
    nearestDistance = []
    for i in range(reference.__len__()):
        distance = euclidean_distance2(image, reference[i])
        nearestDistance.append((distance, referenceTags[i]))
        nearestDistance.sort(key=lambda x: x[0])
        if nearestDistance.__len__() > k:
            nearestDistance.pop()

    labels = {}
    for i in nearestDistance:
        if labels.get(i[1]) is None:
            labels[i[1]] = 1
        else:
            labels[i[1]] += 1

    sorted(labels.items(), key=lambda item: item[1])
    return list(labels.keys())[0]




labels = getLabels()
data = getData()

baseVectorsCount = 1000

realLabels = labels[:baseVectorsCount]
realData = data[:baseVectorsCount]

trainingLabels = labels[7000:]
trainingData = data[7000:]

wrong = 0
for i in range(baseVectorsCount):
    nearestDistance = nearestNeighbor(realData[i], trainingData, trainingLabels)
    if nearestDistance != realLabels[i]:
        print("Identified as " + str(nearestDistance) + ", in reality: " + str(realLabels[i]))
        wrong += 1
    if i % 100 == 0:
        if i == 0:
            continue
        print("Step: " + str(i) + " Error rate: " + str((wrong/i)*100) + "%")

print("Final error rate: " + str((wrong/baseVectorsCount)*100) + "%")