通过函数与欧拉距离公式实现Knn算法，再调用我们的py文件实现模拟
1.首先设置好欧拉距离公式计算个点之间距离的函数，收集它们的距离。
import numpy as np
import math
from collections import Counter
class Knn:
def __init__(self,k):
self.k=k
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):
return np.array([self._predict(x) for x in X])
def _predict(self,x):
distance = [math.sqrt(np.sum((x3 - x) ** 2)) for x3 in self.x_train]
knn_ = np.argsort(distance)
knn__ = knn_[:self.k]
knn__1 = [i for i in knn__]
vnest = self.y_train[knn__1]
y_sco = [i for i in vnest]
votos=Counter(y_sco)
fin_predict=votos.most_common()[0][0] #获取投票数最多的选择
return fin_predict
第一步这里的文件夹名称设置为:Knn_neighbor，此py文件名为:Knn_return
这是为了第二步导入第一步的py文件模块
2.设置训练集和测试集函数并打乱。
from Knn_neighbor import Knn_return as Knn
import numpy as np
class Knn_neighbor:
def __init__(self,X,Y):
self.x_ = X
self.y = Y
self.x_1 = np.array(X)
self.y_1 = np.array(Y)
assert len(self.x_1)>=5
assert len(self.x_1)==len(self.y_1)
def Knn_train(self,test_sor,k):
a_sor=np.random.permutation(len(self.x_)) #随机打乱所有集长度的下标
test_sor_size=int(len(self.x_)*test_sor)
train_=a_sor[test_sor_size:]
test_=a_sor[:test_sor_size]
X_train=self.x_1[train_]
Y_train=self.y_1[train_]
X_test=self.x_1[test_]
Y_test=self.y_1[test_]
knn=Knn.Knn(k)
knn.fit(x_train=X_train,y_train=Y_train) #传入训练集
predict_=knn.predict(X=X_test) #传入测试集
return Knn_neighbor.Knn_Run(self,predict_,Y_test,X_test)
def Knn_Run(self,predict_,Y_test,X_test):
finnally=np.sum(np.array(predict_==Y_test),dtype='int')/len(X_test)
return f"机器学习(Knn算法) 正确判断:{np.sum(np.array(predict_==Y_test),dtype='int')}",f"错误判断:{len(Y_test)-np.sum(np.array(predict_==Y_test),dtype='int')}",f'准确率:',finnally
def Knn_Print(self,test_sor,k): #test_sor:测试占总数的比例
print(Knn_neighbor.Knn_train(self,test_sor,k)) #k:Knn算法考虑个数设置
第二步的py文件名为:Knn_train，为了第三步导入第二步的py文件模块
3.调用我们自己写好的Knn算法，并传入参数（X,Y数据集 + test_sor：测试集占的比例 + k值：算法考虑的点个数）
此处的数据集是随机生成的1000个
from Knn_neighbor import Knn_train knn=Knn_train.Knn_neighbor(X=[[0.8658851593524342, 1.3008071123615783], [1.1191479114066571, 6.711049510980138], [2.4603235088363884, 0.5209909397075674], [4.923422062208561, 0.5509096018206827], [5.98433791114897, 6.620140676055923], [5.5064573818254345, 4.511000601156851], [4.287828101255311, 6.372596982784051], [7.238778437637823, 1.7393226638886916], [3.824348052031911, 5.777450851610036], [2.8004911290799237, 9.001560844429415], [4.872548947808035, 6.706923949363636], [1.4043656941060378, 4.31507410947945], [8.976326008388824, 7.086568909062122], [7.124475876849164, 3.507684361376792], [4.957028622673205, 4.8471692286513965], [1.3423176076713095, 7.7781329606732195], [4.329874244736862, 6.816326228159521], [4.606105004610125, 8.350473869147075], [8.114195201068837, 8.42607294216927], [9.314268080411033, 0.30347152857599147], [4.948498126452096, 5.146922199304833], [9.234998936228743, 7.888274726370189], [8.154331031514909, 7.988017953344197], [8.919677233015728, 7.43194661011664], [0.6069722419407508, 8.047638652557113], [5.730630352692378, 7.214881458670785], [2.015683957526011, 6.122105495728983], [5.743483681049373, 2.792799771487781], [5.377248938656253, 9.482936599568115], [6.674956763710039, 1.6701003064161468], [7.459856583579311, 5.158363520314895], [4.222102049444997, 8.622777144531495], [3.2671681137954645, 0.34466286120081513], [2.5404378420767437, 1.5708052726132726], [9.101444680806356, 5.039260532838482], [8.510783784330565, 9.288756382735064], [8.828703822418296, 6.465458689648587], [6.169476614617679, 5.9903777761416075], [6.478149723691269, 4.974925102349798], [0.6400790149410407, 4.709776540362112], [1.5676990912801658, 2.629935803680201], [7.21609254909842, 6.779141541544329], [9.448044034939175, 9.554672193922599], [8.160716633873738, 0.4054498030339171], [4.1382332971549864, 2.4347300200934106], [5.19732581659782, 2.4303144007508726], [8.926682503923844, 1.9259820422808283], [7.997062954998279, 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knn.Knn_Print(test_sor=0.2,k=3)
此处设置的 test_sor=0.2,k=3
4.Knn算法实现展示
