svc.py

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
from plot_decision_regions import plot_decision_regions 
import pandas as pd, numpy as np, matplotlib.pyplot as plt

from sklearn.svm import SVC

iris = datasets.load_iris()
X = iris.data[:, [2,3]]
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

svm = SVC(kernel='linear', C=1.0, random_state=0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std, y_combined, classifier=svm, test_idx=range(105,150))
plt.xlabel('Długość płatka [standaryzowana]')
plt.ylabel('Szerokość płatka [standaryzowana]')
plt.legend(loc='upper left')
plt.show()