kNN（k近邻）算法代码实现
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kNN（k近邻）算法代码实现
⽬标：预测未知数据(或测试数据)X的分类y
批量kNN算法
1.输⼊⼀个待预测的X(⼀维或多维)给训练数据集,计算出训练集X_train中的每⼀个样本与其的距离
2.找到前k个距离该数据最近的样本-->所属的分类y_train
3.将前k近的样本进⾏统计，哪个分类多，则我们将x分类为哪个分类
# 准备阶段：
import numpy as np
# import matplotlib.pyplot as plt
raw_data_X = [[3.393533211, 2.331273381],
[3.110073483, 1.781539638],
[1.343808831, 3.368360954],
[3.582294042, 4.679179110],
[2.280362439, 2.866990263],
[7.423436942, 4.696522875],
[5.745051997, 3.533989803],
[9.172168622, 2.511101045],
[7.792783481, 3.424088941],
[7.939820817, 0.791637231]
]
raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
X_train = np.array(raw_data_X)
y_train = np.array(raw_data_y)
x = np.array([8.093607318, 3.365731514])
核⼼代码：
⽬标：预测未知数据(或测试数据)X的分类y
批量kNN算法 1.输⼊⼀个待预测的X(⼀维或多维)给训练数据集,计算出训练集X_train中的每⼀个样本与其的距离
2.找到前k个距离该数据最近的样本-->所属的分类y_train
3.将前k近的样本进⾏统计，哪个分类多，则我们将x分类为哪个分类
from math import sqrt
from collections import Counter
# 已知X_train，y_train
# 预测x的分类
def predict(x, k=5):
# 计算训练集每个样本与x的距离
distances = [sqrt(np.sum((x-x_train)**2)) for x_train in X_train] # 这⾥⽤了numpy的fancy⽅法，np.sum((x-x_train)**2)
# 获得距离对应的索引，可以通过这些索引找到其所属分类y_train
nearest = np.argsort(distances)
# 得到前k近的分类y
topK_y = [y_train[neighbor] for neighbor in nearest[:k]]
# 投票的⽅式，得到⼀个字典，key是分类，value数个数
votes = Counter(topK_y)
# 取出得票第⼀名的分类
return votes.most_common(1)[0][0] # 得到y_predict
predict(x, k=6)
⾯向对象的⽅式，模仿sklearn中的⽅法实现kNN算法：
import numpy as np
from math import sqrt
from collections import Counter
class kNN_classify:
def __init__(self, n_neighbor=5):
self.k = n_neighbor
self._X_train = None
self._y_train = None
def fit(self, X_train, y_train):
self._X_train = X_train
self._y_train = y_train
return self
def predict(self, X):
'''接收多维数据，返回y_predict也是多维的'''
y_predict = [self._predict(x) for x in X]
# return y_predict
return np.array(y_predict) # 返回array的格式 def _predict(self, x):
'''接收⼀个待预测的x，返回y_predict'''
distances = [sqrt(np.sum((x-x_train)**2)) for x_train in self._X_train]
nearest = np.argsort(distances)
topK_y = [self._y_train[neighbor] for neighbor in nearest[:self.k]]
votes = Counter(topK_y)
return votes.most_common(1)[0][0]
def __repr__(self):
return 'kNN_clf(k=%d)' % self.k
