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import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics  
from sklearn.model_selection import train_test_split  
from sklearn.model_selection import cross_val_score  

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X1,y1=[],[]
fr = open('./knn.txt')
for line in fr.readlines():
    lineArr = line.strip().split()
    X1.append([int(lineArr[0]),int(lineArr[1])])
    y1.append(int(lineArr[2]))
X=np.array(X1)  
y=np.array(y1)  
X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.16)

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k_range = range(1, 9)
k_error = []  
for k in k_range:  
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X, y, cv=3, scoring='accuracy')
    
    k_error.append(1 - scores.mean())  

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k_error.pop(0)
k_min = min(k_error)
k = k_error.index(k_min)

knn=KNeighborsClassifier(k + 2)
knn.fit(X,y)

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KNeighborsClassifier(n_neighbors=3)

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y_pred=knn.predict(X_test)
print(knn.score(X_test,Y_test))  

print(metrics.accuracy_score(y_true=Y_test,y_pred=y_pred))

print(metrics.confusion_matrix(y_true=Y_test,y_pred=y_pred)) 

from sklearn.metrics import classification_report
target_names = ['labels_1','labels_2','labels_3']
print(classification_report(Y_test,y_pred))

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1.0
1.0
[[2]]
              precision    recall  f1-score   support

           2       1.00      1.00      1.00         2

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2

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label=knn.predict([[7,27],[2,4]])
print(label)  

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[2 1]

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import matplotlib as mpl
N, M = 90, 90  
t1 = np.linspace(0, 25, N)  
t2 = np.linspace(0,12, M)  
x1, x2 = np.meshgrid(t1, t2)  
x_show = np.stack((x1.flat, x2.flat), axis=1)  
y_show_hat = knn.predict(x_show)  
y_show_hat = y_show_hat.reshape(x1.shape)  
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light,alpha=0.3)