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#数据
# 特征
raw_data_x= [[3.393533211,2.331273381],
[2.110073483,1.781539638],
[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]
]
# 所属类别
raw_data_y = [0,0,0,0,0,1,1,1,1,1]
np.array(raw_data_x)#将数据转化成numpy类型
X_train=np.array(raw_data_x)
y_train=np.array(raw_data_y)
plt.scatter(X_train[y_train==0, 0],X_train[y_train==0, 1], color ='r')
plt.scatter(X_train[y_train==1, 0],X_train[y_train==1, 1], color ='g')
plt.show()
from math import sqrt
封装一下上面的代码
from collections import Counter
from math import sqrt
import numpy as np
class KNNClassifier():
"""封装自己KNN算法"""
def __init__(self, k):
assert k >= 1, "k 值必须大于1"
self.k = k
self._X_train = None # 类似private
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_predict):
return np.array([self._predict(x) for x in X_predict])
# _predict私有方法
def _predict(self, x_single):
distances = [sqrt(np.sum((x_train - x_single) ** 2)) for x_train in self._X_train]
nearst = np.argsort(distances)
topK_y = [self._y_train[i] for i in nearst[:self.k]]
votes = Counter(topK_y)
predict_y = votes.most_common(1)[0][0]
return predict_y
def __repr__(self):
return "KNNClassifier()"
调用试试：
还是用库吧嘿嘿嘿
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