import numpy as np
from sklearn import tree
import matplotlib.pyplot as plt



greyhounds = 500
labs = 500

labels = [0,1]
grey_height = 28 + 4 * np.random.randn(greyhounds)
lab_height = 24 + 4* np.random.randn(labs)

grey_weight = 90 + 12* np.random.randn(greyhounds)
lab_weight = 120 + 12* np.random.randn(labs)
grey_height1 = grey_height[0]
grey_height2 = grey_height[1]
grey_weight1 = grey_weight[0]
grey_weight2 = grey_weight[1]

lab_height1 = lab_height[0]
lab_height2 = lab_height[1]
lab_weight1 = lab_weight[0]
lab_weight2 = lab_weight[1]

features = [[grey_height1,grey_weight1],[grey_height2,grey_weight2],[lab_height1,lab_weight1],[lab_height2,lab_weight2]]
labels = [0,0,1,1]

classifier  = tree.DecisionTreeClassifier()
classifier  = classifier.fit(features, labels)
print (classifier.predict([[31.2345,96.236754653]]))
#print ([grey_height[0], grey_weight[0]], [grey_height[1],grey_weight[1]])

#plt.hist([grey_height, lab_height], stacked = False, color = ['r', 'b'])
#plt.show()