Iris data

## get iris data

from sklearn import datasets

import numpy as np

iris=datasets.load_iris()

X=iris.data[:, [2,3]]

y=iris.target

#divide data

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3, random_state=1, stratify=y)

##iris data scale modify

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

sc.fit(X_train)

X_train_std=sc.transform(X_train)

X_test_std=sc.transform(X_test)

##decision_regions drawing function

from matplotlib.colors import ListedColormap

import matplotlib.pyplot as plt

def plot_decision_regions(X,y, classifier, test_idx=None, resolution=0.02):

markers=('s','x','o', '^','v')

colors=('red','blue','lightgreen','gray','cyan')

cmap=ListedColormap(colors[:len(np.unique(y))])

x1_min,x1_max=X[:,0].min()-1, X[:,0].max()+1

x2_min,x2_max=X[:,1].min()-1, X[:,1].max()+1

xx1,xx2=np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))

Z=classifier.predict(np.array([xx1.ravel(),xx2.ravel()]).T)

Z=Z.reshape(xx1.shape)

plt.contourf(xx1,xx2,Z,alpha=0.3, cmap=cmap)

plt.xlim(xx1.min(), xx1.max())

plt.ylim(xx2.min(), xx2.max())

for idx, cl in enumerate(np.unique(y)):

plt.scatter(x=X[y==cl, 0],y=X[y==cl,1],alpha=0.8, c=colors[idx], label=cl, edgecolor='black')

### add ###

if test_idx:

X_test, y_test=X[test_idx, :], y[test_idx]

plt.scatter(X_test[:,0],X_test[:,1], facecolors='none', edgecolor='black', alpha=1.0, linewidth=1, marker='o',s=100,label='test set')

## before using plot_decision_regions set data

X_combined_std=np.vstack((X_train_std, X_test_std))

y_combined=np.hstack((y_train, y_test))