KNN主要包括训练过程和分类过程。在训练过程上，需要将训练集存储起来。在分类过程中，将测试集和训练集中的每一张图片去比较，选取差别最小的那张图片。

如果数据集多，就把训练集分成两部分，一小部分作为验证集(假的测试集)，剩下的都为训练集(一般来说是70%-90%，具体多少取决于需要调整的超参数的多少，如果超参数多，验证集占比就更大一点)。验证集的好处是用来调节超参数，如果数据集不多，使用交叉验证的方法来调节参数。但是交叉验证的代价比较高，K折交叉验证，K越大越好，但是代价也更高。

决策分类

明确K个邻居中所有数据类别的个数，将测试数据划分给个数最多的那一类。即由输入实例的 K 个最临近的训练实例中的多数类决定输入实例的类别。

常用决策规则：

多数表决法：多数表决法和我们日常生活中的投票表决是一样的，少数服从多数，是最常用的一种方法。

加权表决法：有些情况下会使用到加权表决法，比如投票的时候裁判投票的权重更大，而一般人的权重较小。所以在数据之间有权重的情况下，一般采用加权表决法。

优点：

所选择的邻居都是已经正确分类的对象

KNN算法本身比较简单，分类器不需要使用训练集进行训练，训练时间复杂度为0。本算法分类的复杂度与训练集中数据的个数成正比。

对于类域的交叉或重叠较多的待分类样本，KNN算法比其他方法跟合适。

缺点：

当样本分布不平衡时，很难做到正确分类

计算量较大，因为每次都要计算测试数据到全部数据的距离。

python代码实现：

import numpy as np

class kNearestNeighbor:

def init(self):

pass

def train(self, X, y):

self.Xtr = X

self.ytr = y

def predict(self, X, k=1):

num_test = X.shape[0]

Ypred = np.zeros(num_test, dtype = self.ytr.dtype)

for i in range(num_test):

distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1)

closest_y = y_train[np.argsort(distances)[:k]]

u, indices = np.unique(closest_y, return_inverse=True)

Ypred[i] = u[np.argmax(np.bincount(indices))]

return Ypred

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load_CIFAR_batch()和load_CIFAR10()是用来加载CIFAR-10数据集的

import pickle

def load_CIFAR_batch(filename):

“”" load single batch of cifar “”"

with open(filename, ‘rb’) as f:

datadict = pickle.load(f, encoding=‘latin1’)

X = datadict[‘data’]

Y = datadict[‘labels’]

X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(“float”)

Y = np.array(Y)

return X, Y

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import os

def load_CIFAR10(ROOT):

“”" load all of cifar “”"

xs = []

ys = []

for b in range(1,6):

f = os.path.join(ROOT, ‘data_batch_%d’ %(b))

X, Y = load_CIFAR_batch(f)

xs.append(X)

ys.append(Y)

Xtr = np.concatenate(xs) #使变成行向量

Ytr = np.concatenate(ys)

del X,Y

Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, ‘test_batch’))

return Xtr, Ytr, Xte, Yte

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Xtr, Ytr, Xte, Yte = load_CIFAR10(‘cifar10’)

Xtr_rows = Xtr.reshape(Xtr.shape[0], 32 * 32 * 3)

Xte_rows = Xte.reshape(Xte.shape[0], 32 * 32 * 3)

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#由于数据集稍微有点大，在电脑上跑的很慢，所以取训练集5000个，测试集500个

num_training = 5000

num_test = 500

x_train = Xtr_rows[:num_training, :]

y_train = Ytr[:num_training]

x_test = Xte_rows[:num_test, :]

y_test = Yte[:num_test]

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knn = kNearestNeighbor()

knn.train(x_train, y_train)

y_predict = knn.predict(x_test, k=7)

acc = np.mean(y_predict == y_test)

print(‘accuracy : %f’ %(acc))

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accuracy : 0.302000

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#k值取什么最后的效果会更好呢？可以使用交叉验证的方法，这里使用的是5折交叉验证

num_folds = 5

k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]

x_train_folds = np.array_split(x_train, num_folds)

y_train_folds = np.array_split(y_train, num_folds)

k_to_accuracies = {}

for k_val in k_choices:

print('k = ’ + str(k_val))

k_to_accuracies[k_val] = []

for i in range(num_folds):

x_train_cycle = np.concatenate([f for j,f in enumerate (x_train_folds) if j!=i])

y_train_cycle = np.concatenate([f for j,f in enumerate (y_train_folds) if j!=i])

x_val_cycle = x_train_folds[i]

y_val_cycle = y_train_folds[i]

knn = kNearestNeighbor()

knn.train(x_train_cycle, y_train_cycle)

y_val_pred = knn.predict(x_val_cycle, k_val)

num_correct = np.sum(y_val_cycle == y_val_pred)

k_to_accuracies[k_val].append(float(num_correct) / float(len(y_val_cycle)))

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k = 1

k = 3

k = 5

k = 8

k = 10

k = 12

k = 15

k = 20

k = 50

k = 100

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for k in sorted(k_to_accuracies):

for accuracy in k_to_accuracies[k]:

print(‘k = %d, accuracy = %f’ % (int(k), accuracy))

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k = 1, accuracy = 0.098000

k = 1, accuracy = 0.148000

k = 1, accuracy = 0.205000

k = 1, accuracy = 0.233000

k = 1, accuracy = 0.308000

k = 3, accuracy = 0.089000

k = 3, accuracy = 0.142000

k = 3, accuracy = 0.215000

k = 3, accuracy = 0.251000

k = 3, accuracy = 0.296000

k = 5, accuracy = 0.096000

k = 5, accuracy = 0.176000

k = 5, accuracy = 0.240000

k = 5, accuracy = 0.284000

k = 5, accuracy = 0.309000

k = 8, accuracy = 0.100000

k = 8, accuracy = 0.175000

k = 8, accuracy = 0.263000

k = 8, accuracy = 0.289000

k = 8, accuracy = 0.310000

k = 10, accuracy = 0.099000

k = 10, accuracy = 0.174000

k = 10, accuracy = 0.264000

k = 10, accuracy = 0.318000

k = 10, accuracy = 0.313000

k = 12, accuracy = 0.100000

k = 12, accuracy = 0.192000

k = 12, accuracy = 0.261000

k = 12, accuracy = 0.316000

k = 12, accuracy = 0.318000

k = 15, accuracy = 0.087000

k = 15, accuracy = 0.197000

k = 15, accuracy = 0.255000

k = 15, accuracy = 0.322000

k = 15, accuracy = 0.321000

k = 20, accuracy = 0.089000

k = 20, accuracy = 0.225000

k = 20, accuracy = 0.270000

k = 20, accuracy = 0.319000

k = 20, accuracy = 0.306000

k = 50, accuracy = 0.079000

k = 50, accuracy = 0.248000

k = 50, accuracy = 0.278000

k = 50, accuracy = 0.287000

k = 50, accuracy = 0.293000

k = 100, accuracy = 0.075000

k = 100, accuracy = 0.246000

k = 100, accuracy = 0.275000

k = 100, accuracy = 0.284000

k = 100, accuracy = 0.277000

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可视化交叉验证的结果

import matplotlib.pyplot as plt

plt.rcParams[‘figure.figsize’] = (10.0, 8.0)

plt.rcParams[‘image.interpolation’] = ‘nearest’

plt.rcParams[‘image.cmap’] = ‘gray’

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for k in k_choices:

accuracies = k_to_accuracies[k]

plt.scatter([k] * len(accuracies), accuracies)

accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])

accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])

plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)

plt.title(‘Cross-validation on k’)

plt.xlabel(‘k’)

plt.ylabel(‘Cross-validation accuracy’)

plt.show()

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