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Using the SVC algorithm implemented by the Python Scikit-learn, classify the three types of flowers (Setosa, Versicolor, Virgin) in Iris dataset according to the Petal length and width

May I know how to modify my Python programming as refer to the attached file -

# To Get iris dataset

from sklearn import datasets    

# To fit the svm classifier

from sklearn import svm             

import numpy as np

import matplotlib.pyplot as plt 



iris_dataset = datasets.load_iris()



def visuvalise_petal_data():

    iris = datasets.load_iris()

    # Only take the first two features

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

    y = iris.target



visuvalise_petal_data()

iris = datasets.load_iris()

# Only take the Sepal two features

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

y = iris.target

# SVM regularization parameter



# SVC with rbf kernel

rbf_svc = svm.SVC(kernel='rbf', gamma=0.01, C=1).fit(X, y)

rbf_svc = svm.SVC(kernel='rbf', gamma=0.01, C=10).fit(X, y)

# step size in the mesh

h = 0.02  

# create a mesh to plot in

def plotSVC(title):

    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1

    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

    h = (x_max / x_min)/100

    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),

             np.arange(y_min, y_max, h))

    plt.subplot(1, 1, 1)

    Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])

    Z = Z.reshape(xx.shape)



C = [1, 10] 

for c in cs:

    svc = svm.SVC(kernel='rbf', C=1).fit(X, y)

    svc = svm.SVC(kernel='rbf', C=10).fit(X, y)

    plotSVC('C=' + str(c))



from sklearn.svm import SVC 

from sklearn.preprocessing import StandardScaler

from sklearn.cross_validation import train_test_split



X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 100, random_state = 0)



sc = StandardScaler()

sc.fit(X_train)

X_train_std = sc.transform(X_train)

X_test_std = sc.transform(X_test)



linear_svm1 = SVC(kernel = 'rbf', C = 1, random_state = 0)

linear_svm1.fit(X_train_std, y_train)

y_predict1 = linear_svm1.predict(X_test_std)

print('Gamma=0.01,C=1')



linear_svm2 = SVC(kernel = 'rbf', C = 10, random_state = 0)

linear_svm2.fit(X_train_std, y_train)

y_predict2 = linear_svm2.predict(X_test_std)

print('Gamma=0.01,C=10')



svm = SVC(kernel='linear', C=1.0, random_state=0)

svm.fit(X_train_std, y_train)

plot_decision_regions(X, y, classifier=svm, test_idx=range(105,150))

plt.xlabel('petal length [standardized]')

plt.ylabel('petal width [standardized]')

plt.legend(loc='upper left')

plt.show()

The error message is -

runfile('C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')

Traceback (most recent call last):



  File "<ipython-input-85-761bed922ac3>", line 1, in <module>

    runfile('C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py', wdir='C:/Users/HSIPL/Desktop')



  File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 668, in runfile

    execfile(filename, namespace)



  File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 108, in execfile

    exec(compile(f.read(), filename, 'exec'), namespace)



  File "C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py", line 44, in <module>

    plotSVC('C=' + str(c))



  File "C:/Users/HSIPL/Desktop/Homework 6 Solution draft.py", line 32, in plotSVC

    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1



IndexError: index 1 is out of bounds for axis 1 with size 1

Please help so that I can improve my computing skills