半監督學習——LabelPropagation

print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import svm
from sklearn.semi_supervised import label_propagation


rng = np.random.RandomState(0)

iris = datasets.load_iris()

X = iris.data[:,:2]
y = iris.target

#step size in the mesh
h = .02

y_30 = np.copy(y)
# len(y_30)
y_30[rng.rand(len(y)) < 0.3]

y_50 = np.copy(y)
y_50[rng.rand(len(y)) < 0.5] 

ls30 = (label_propagation.LabelSpreading().fit(X,y_30),y_30)
ls50 = (label_propagation.LabelSpreading().fit(X,y_50),y_50)
ls100 = (label_propagation.LabelSpreading().fit(X,y),y)
rbf_svc = (svm.SVC(kernel = 'rbf', gamma= .5).fit(X,y),y)

#create a mesh to plot in
x_min,x_max = X[:,0].min() - 1 ,X[:,0].max() + 1

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

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

color_map = {-1:(1,1,1),0:(0,0,.9),1:(1,0,0),2:(.8,.6,0)} #(1,1,1)白色

titles = ['30%','50%','100%','svc']

for i ,(clf,y_train) in enumerate((ls30,ls50,ls100,rbf_svc)):
    plt.subplot(2,2,i+1)
    Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])
    
    Z = Z.reshape(xx.shape)
    plt.contourf(xx,yy,Z,cmap = plt.cm.Paired)
    plt.axis('off')
    
    colors = [color_map[y] for y in y_train]
    plt.scatter(X[:,0],X[:,1],c=colors,edgecolors='black')
    
    plt.title(titles[i])

plt.suptitle('XXXX')
plt.show()

When you’re young, you just believe there will be many people with whom you’ll connect. Later in life, you realize it only happens a few times。
年輕的時候你以爲你會和許多人心靈相通，後來你會發現，這樣的事情一輩子只會發生那麼幾次。 ​

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