ml.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from matplotlib import style
style.use("ggplot")

# x and y are seperated features
x = [1,5,1.5,8,1,9]
y = [2,8,1.8,8,0.6,11]

plt.scatter(x,y)
plt.show()

X = np.array([  [1,2],
                [5,8],
                [1.5,1.8],
                [8,8],
                [1,0.6],
                [9,11]
                ])
y = [0,1,0,1,0,1]

clf = svm.SVC(kernel='linear',C = 1.0)
clf.fit(X,y)

print clf.predict([10.58,10.76])

# directional coefficient
w = clf.coef_[0]
print "coef: ", w

# learning rate a
a = -w[0] / w[1]
xx = np.linspace(0,12)
yy = a * xx - clf.intercept_[0] / w[1]

h0 = plt.plot(xx,yy,'k-', label="non weighted div")

plt.scatter(X[:,0],X[:,1], c = y)
plt.show()