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from sklearn import datasets

#load data
iris=datasets.load_iris()
X=iris.data[:,[2,3]]
y=iris.target
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)
#feature scaling
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)
# set outlier
X_train_std[0]=(1,-1)
#define plot_decision_regions
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
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=0.4, cmap=cmap)

    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    print(np.unique(y))
   
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y==cl, 0], y=X[y==cl, 1],
                    alpha=0.8, c=cmap(idx),
                    marker = markers[idx],label = cl,edgecolor='black')
#SVM
from sklearn.svm import SVC
C=[]
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
#draw figure

fig=plt.subplots(nrows=2, ncols=2,figsize=(10,5))   
for c in np.arange(-1,3):
    svm=SVC(kernel='linear',C=10.**c, 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('petal lengh[standardized]')
    plt.ylabel('petal width[standardized]')
    plt.title('C='+str(10.**c))
    plt.legend(loc='upper left')
    plt.tight_layout() 
    plt.show()
        

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