f.close()
X = np.array(X)
Z = np.array(Z)
y = np.array(y)
print "x"
print X
print "z"
print Z
print "y"
print y
print "p1"
result = trainGsvm(Z,y)
print result
def k(a,b):
return (2+np.dot(a.T,b))**2
print "p3"
result = kernelSVMwithQP(X,y,k)
print result
for i in range(7):
print X[i],X[5],y[i],k(X[i],X[5])
for _ in xrange(100):
X = []
y = []
random.seed()
# get 80% as train data
for i in random.sample(range(len(trainX)),num):
X.append(trainX[i])
y.append(trainy[i])
X = np.array(X)
y = np.array(y)
result = trainGsvm(X,y)["x"]
b = result[0]
w = result[1:]
margin = 1.0/np.dot(w,w.T)
err = 0.
for i in xrange(len(testX)):
if np.dot(w.T,testX[i]) < 0:
if testy[i] > 0:
err = err + 1
else:
if testy[i] < 0:
err= err + 1
