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
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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, 9.601689710329435],
[1.4510380954215019, 2.881774408258535],
[4.277818457749411, 1.678280532735491],
[2.1730411343389577, 6.154624297864412],
[0.8391759616325278, 9.132874053979851],
[6.393201283524858, 2.258211666421289],
[4.326858835755829, 5.00722654285687],
[6.770471728413304, 1.4186352187024065],
[8.593796545147972, 1.348713650347162],
[0.3923396433228221, 5.8951809979295975],
[6.053889120490908, 1.9032404693747784],
[5.442359561024564, 7.254818610413771e-05],
[2.0481614873726195, 8.911904883715446],
[3.5923127825504473, 2.5606800390693385],
[5.465262150805565, 3.9696137383807315],
[7.313728061887289, 3.821064172530245],
[5.267545019269595, 4.771096736570247],
[1.6622591232423345, 1.9812396564596757],
[8.978282313329288, 1.9847529903012695],
[4.309645043193985, 3.8518756917464203],
[1.1071399060295883, 9.852770738683134],
[0.6619825077146058, 0.23270968975591444],
[5.5038852940787875, 0.8740136137506194],
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[0.08468097510209827, 3.0463798333072347],
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[3.1821572471381474, 1.657841082230268],
[4.856941034647599, 5.778704970490008],
[9.855183953513382, 2.270022637722784],
[5.570131384590193, 6.180077614271764],
[0.2429361525491469, 0.7946281684749323],
[4.041292217781188, 3.2098341506594554],
[0.2433594161145869, 7.479287952173301],
[4.369847041218856, 9.349686935936909],
[5.953217058268093, 3.3075927826953078],
[8.110835671169985, 0.18656745616397075],
[9.921301262000796, 6.480230608117875],
[3.24569047375989, 4.516259772281243],
[4.5907509107764195, 9.913116098195903],
[1.673460055654712, 6.01641239046764],
[8.120073259134143, 0.5846918052152372],
[1.8168619431831468, 2.1357412753323146],
[9.248808039372529, 4.552480524526413],
[7.620050769585728, 2.6874701281315794],
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[2.1499006716792635, 4.398858835545739]],Y=[0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0])
#调用Knn_Print方法
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
