import sys
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


class KNNBase:
    
    def __init__(self, k):
        self.k = k

    def fit(self, x, y):
        self.x = x
        self.y = y
    
    def __str__(self):
        return "KNN Classifier"

class KNNClassifier(KNNBase):
    
    def __str__(self):
        return "KNNClassifier({!s})".format(self.k)
    
    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        
        return sorted_class_count[0][0]
    
class KNNRegressor(KNNBase):
    
    def __str__(self):
        return "KNNRegressor({!s})".format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
            
        return total_sum / self.k

def demo(k, dir="", row=5000):
    try:
        data = pd.read_csv(dir + "train.csv")
        print("Data loaded successfully.")
    except:
        print("Can not find train.csv in the directory specified.")
        print("To download, please visit https://www.kaggle.com/c/digit-recognizer/data.")
    x = data.values[0:row, 1:]
    y = data.values[0:row, 0]
    train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, random_state=0)
    print("Data splitted for validation.")
    classifier = KNNClassifier(k)
    classifier.fit(train_x, train_y)
    size = test_x.shape[0]
    predictions = []
    for i in range(size):
        result = classifier.predict(test_x[i])
        predictions.append(result)
    print("Prediction completes.")
    print("Validation score: {}".format(accuracy_score(test_y, predictions)))
    print("To learn more about K-Nearest Neighbor Algorithm: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm")
e9e6429fa5ee547009efa652f8a9c774b9aadd83
<mask token>


class KNNClassifier(KNNBase):

    def __str__(self):
        return 'KNNClassifier({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        return sorted_class_count[0][0]


class KNNRegressor(KNNBase):

    def __str__(self):
        return 'KNNRegressor({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
        return total_sum / self.k


<mask token>

<mask token>


class KNNBase:
    <mask token>
    <mask token>

    def __str__(self):
        return 'KNN Classifier'


class KNNClassifier(KNNBase):

    def __str__(self):
        return 'KNNClassifier({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        return sorted_class_count[0][0]


class KNNRegressor(KNNBase):

    def __str__(self):
        return 'KNNRegressor({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
        return total_sum / self.k


<mask token>

<mask token>


class KNNBase:
    <mask token>

    def fit(self, x, y):
        self.x = x
        self.y = y

    def __str__(self):
        return 'KNN Classifier'


class KNNClassifier(KNNBase):

    def __str__(self):
        return 'KNNClassifier({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        return sorted_class_count[0][0]


class KNNRegressor(KNNBase):

    def __str__(self):
        return 'KNNRegressor({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
        return total_sum / self.k


<mask token>

<mask token>


class KNNBase:

    def __init__(self, k):
        self.k = k

    def fit(self, x, y):
        self.x = x
        self.y = y

    def __str__(self):
        return 'KNN Classifier'


class KNNClassifier(KNNBase):

    def __str__(self):
        return 'KNNClassifier({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        return sorted_class_count[0][0]


class KNNRegressor(KNNBase):

    def __str__(self):
        return 'KNNRegressor({!s})'.format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
        return total_sum / self.k


def demo(k, dir='', row=5000):
    try:
        data = pd.read_csv(dir + 'train.csv')
        print('Data loaded successfully.')
    except:
        print('Can not find train.csv in the directory specified.')
        print(
            'To download, please visit https://www.kaggle.com/c/digit-recognizer/data.'
            )
    x = data.values[0:row, 1:]
    y = data.values[0:row, 0]
    train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2,
        random_state=0)
    print('Data splitted for validation.')
    classifier = KNNClassifier(k)
    classifier.fit(train_x, train_y)
    size = test_x.shape[0]
    predictions = []
    for i in range(size):
        result = classifier.predict(test_x[i])
        predictions.append(result)
    print('Prediction completes.')
    print('Validation score: {}'.format(accuracy_score(test_y, predictions)))
    print(
        'To learn more about K-Nearest Neighbor Algorithm: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm'
        )

import sys
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


class KNNBase:
    
    def __init__(self, k):
        self.k = k

    def fit(self, x, y):
        self.x = x
        self.y = y
    
    def __str__(self):
        return "KNN Classifier"

class KNNClassifier(KNNBase):
    
    def __str__(self):
        return "KNNClassifier({!s})".format(self.k)
    
    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        
        class_count = {}
        for i in range(self.k):
            vote = self.y[sorted_indicies[i]]
            class_count[vote] = class_count.get(vote, 0) + 1
        sorted_class_count = sorted(class_count.items(), reverse=True)
        
        return sorted_class_count[0][0]
    
class KNNRegressor(KNNBase):
    
    def __str__(self):
        return "KNNRegressor({!s})".format(self.k)

    def predict(self, data):
        size = self.x.shape[0]
        diff_matrix = np.tile(data, (size, 1)) - self.x
        sq_matrix = diff_matrix ** 2
        sum_matrix = sq_matrix.sum(axis=1)
        distances = sum_matrix ** 0.5
        sorted_indicies = distances.argsort()
        
        total_sum = 0
        for i in range(self.k):
            total_sum += self.y[sorted_indicies[i]]
            
        return total_sum / self.k

def demo(k, dir="", row=5000):
    try:
        data = pd.read_csv(dir + "train.csv")
        print("Data loaded successfully.")
    except:
        print("Can not find train.csv in the directory specified.")
        print("To download, please visit https://www.kaggle.com/c/digit-recognizer/data.")
    x = data.values[0:row, 1:]
    y = data.values[0:row, 0]
    train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, random_state=0)
    print("Data splitted for validation.")
    classifier = KNNClassifier(k)
    classifier.fit(train_x, train_y)
    size = test_x.shape[0]
    predictions = []
    for i in range(size):
        result = classifier.predict(test_x[i])
        predictions.append(result)
    print("Prediction completes.")
    print("Validation score: {}".format(accuracy_score(test_y, predictions)))
    print("To learn more about K-Nearest Neighbor Algorithm: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm")