07.k近邻算法kNN

1、将数据分为测试数据和预测数据

2、数据分为data和target，data是矩阵，target是向量

3、将每条data（向量）绘制在坐标系中，就得到了一系列的点

4、根据每条data的target的不同，给点赋予不同的颜色

5、当新数据来到时，比如只有一条数据，将新数据绘制在坐标系中，就得到了新的一个点

6、确定k值，比如k=3

7、计算所有数据和新数据点的距离，找出最近的3个

8、根据结果判断新数据应该归属的分类，即为预测数据确定target

生成测试数据

import numpy as np
import matplotlib.pyplot as plt

raw_data_a = np.random.random((10,2))
raw_data_b = np.random.randint(0,10,size=(10,2))
X_train = raw_data_a + raw_data_b
X_train

array([[8.28164975, 4.19315143],
       [2.92775657, 0.81556491],
       [4.50761169, 2.84927016],
       [6.53083961, 4.26093009],
       [2.27051172, 4.05065263],
       [7.7954489 , 8.68019714],
       [4.43708588, 6.72986275],
       [4.65529575, 4.7985332 ],
       [3.52301327, 4.19730249],
       [2.2773095 , 0.07817849]])

构造target

y_train = np.array([0,0,0,0,0,1,1,1,1,1])
y_train

array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])

绘制散点图

plt.scatter(X_train[y_train == 0,0], X_train[y_train == 0,1],color="b")
plt.scatter(X_train[y_train == 1,0], X_train[y_train == 1,1],color="r")

构造新数据

new_data_a = np.random.random((10,2))
new_data_b = np.random.randint(0,10,size=(10,2))
new_data = new_data_a + new_data_b
new_data

array([[7.66654552, 3.43737304],
       [9.00627805, 8.35944151],
       [4.35322638, 5.29260333],
       [2.9064343 , 0.05002835],
       [8.68350808, 1.50262447],
       [0.23152764, 9.688442  ],
       [9.2139265 , 7.96068869],
       [5.14763436, 2.40288244],
       [9.52077384, 9.4833882 ],
       [5.04330854, 4.96045193]])

预测数据（拿一条数据举例）

X_predict = new_data[0]
X_predict

array([3.05595894, 6.89591993])

原始数据和预测数据共同绘制在一个坐标系中

plt.scatter(X_train[y_train == 0,0], X_train[y_train == 0,1])
plt.scatter(X_train[y_train == 1,0], X_train[y_train == 1,1])
plt.scatter(X_predict[0], X_predict[1], color="g")

 计算与测试点的距离

from math import sqrt

distances = [sqrt(np.sum((x - X_predict)**2)) for x in X_train]
distances

[5.883264572139944,
 6.0817064237981535,
 4.299147591752391,
 4.360959428669816,
 2.951689913705071,
 5.064228496651553,
 1.3910739045173681,
 2.637595368153982,
 2.7387361757860287,
 6.862061882958762]

K = 6，找出距离最近的6个点

k = 6
nearest = np.argsort(distances)
topk_y = [y_train[i] for i in nearest[:k]]
topk_y

[1, 1, 1, 1, 1, 0]

结果

from collections import Counter
Counter(topk_y)

Counter({1: 5, 0: 1})

votes = Counter(topk_y)
votes.most_common(1)

[(1, 5)]

votes.most_common(1)[0][0]

1

predic = votes.most_common(1)[0][0]
predic

1

使用scikit-learn中的kNN

from sklearn.neighbors import KNeighborsClassifier


kNN_classifier = KNeighborsClassifier()
kNN_classifier.fit(X_train,y_train)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,
                     weights='uniform')

kNN_classifier.predict(new_data_1.reshape(1,-1))

array([1])

重新整理knn代码

import numpy as np
from math import sqrt
from collections import Counter


class KNNClassifier:

    def __init__(self, k):
        # 初始化KNN分类器
        self.k = k
        self._X_train = None
        self._y_train = None

    def fit(self, X_train, y_train):
        # 根据训练集X_train, Y_train训练分类器
        self._X_train = X_train
        self._y_train = y_train
        return self

    def predict(self, X_predict):
        # 给定待遇测的数据集X_predict,返回表示X_predict的结果向量
        y_predict = [self._predict(x) for x in X_predict]
        return np.array(y_predict)

    def _predict(self, x):
        # 给定单个待遇测数据x,返回x的预测结果值
        distances = [sqrt(np.sum((x_train - x) ** 2)) for x_train in self._X_train]
        nearest = np.argsort(distances)
        topK_y = [self._y_train[i] for i in nearest[:self.k]]
        votes = Counter(topK_y)
        return votes.most_common(1)[0][0]

    def __repr__(self):
        return "KNN=(%d)" % self.k

knn_clf = KNNClassifier(k=6)
knn_clf.fit(X_train, y_train)

KNN=(6)

y_predict = knn_clf.predict(X_predict)
y_predict

array([0, 1])