KNN

import numpy as np
import os
from PIL import Image

def load_data(path, index):
    path = path + str(index)
    counter = 0
    images = np.array([[]])

    for root,dirs,files in os.walk(path):
        for f in files:
            abs_path = os.path.join(root,f)
            image = Image.open(abs_path)
            image_array = np.array(image)
            # flatten to 1-D vector; image_f: 784
            image_f = image_array.flatten()
            # add the new flattened image to our images set for training
            if counter == 0:
                images = np.concatenate((images, np.array([image_f])),axis=1)
            else:
                images = np.concatenate((images, np.array([image_f])))
            # images' shape: (num_img, 784) # (1, num_img*784)
            counter += 1
        print(images.shape)

    return images, counter                            # images: [num_image, 784]

def max_label(array):
    u, counter = np.unique(array, return_counts=True)
    index = np.argmax(counter)
    return u[index]

def KNN(test_file_name, k):
    train_images = np.array([[]])
    train_label = np.array([])
    for i in range(0,10):
        label = str(i)
        images, counter = load_data("D:\\HW2\\dataset\\train\\",i)
        if i == 0:
            train_images = images
        else:
            train_images = np.concatenate((train_images, images))
            # train_images' shape: (total_train_img, 784)
        for j in range(0, counter):
            train_label = np.append(train_label, i)
        print("label %d has been loaded"%(i))

    test_images = np.array([[]])
    test_label = np.array([])
    for i in range(0,10):
        label = str(i)
        images, counter = load_data(test_file_name,i)
        if i == 0:
            test_images = images
        else:
            test_images = np.concatenate((test_images, images))
            # test_images' shape: (total_test_img, 784)
        for j in range(0, counter):
            test_label = np.append(test_label, i)

    print("Data loaded successfully")
    print(train_images.shape)  # output: (60000, 784)
    print(test_images.shape)   # output: (10000, 784)

    counter = 0
    error_times = 0

    for test_image in test_images:  # test_label's shape: [total_test_img, 784]
                                    # test_image's shape: [784]
        # E distance between this test_image and all train images
        distance = np.array([])
        for train_image in train_images:
            d = np.linalg.norm(test_image - train_image)
            distance = np.append(distance, d)
        sort_index = distance.argsort()

        top_k = np.array([])
        for m in range(0, k):
            top_k = np.append(top_k, train_label[sort_index[m]])
        predict_label = max_label(top_k)
        print(predict_label)

        if predict_label != test_label[counter]:
            error_times += 1

        counter += 1
        print(str(counter)+" test(s) completed!")

    error_rate = error_times/counter
    return error_rate


error_rate = KNN("D:\\HW2\\dataset\\test\\",10)
print("The error rate is %f"%(error_rate))