import pandas as pd import numpy as np import matplotlib.pylab as plt import scipy as sp import scipy.stats plt.close("all") dfLoad=pd.read_csv('https://sites.google.com/site/vlsicir/ClassificationSample2.txt', sep='\s+') samples=np.array(dfLoad) x=samples[:,0] y=samples[:,1] N=len(x) numK=2 #Initialize categorial distribution pi=np.ones([1, numK])*(1/numK) mx=np.mean(x) my=np.mean(y) sx=np.std(..

from sklearn.datasets import load_iris iris=load_iris() from sklearn.model_selection import train_test_split X=iris.data y=iris.target X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.4, random_state=42) from sklearn.neighbors import KNeighborsClassifier from sklearn import metrics knn=KNeighborsClassifier(n_neighbors=5) knn.fit(X_train, y_train) y_pred=knn.predict(X_test) sco..