c_values = np.logspace(-4, 8, num=13, base=2.0)
gamma_values = np.arange(48,97,4)
param_grid = dict(gamma=gamma_values, C=c_values)
grid = GridSearchCV(svm, param_grid=param_grid)
grid.fit(X, Y)
C = grid.best_params_['C']
gamma = grid.best_params_['gamma']

clf = SVC(C=C,kernel='rbf',gamma=gamma,class_weight={1:2,2:1})
clf.fit(X,Y)

plt.figure(figsize=(8,6))
xx, yy = np.meshgrid(np.linspace(0, 1, 200), np.linspace(0, 1, 200))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
plt.contour(xx, yy, Z, colors='g', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
ax.pcolormesh(xx, yy, -Z, cmap=plt.cm.RdBu, shading='auto')
plt.scatter(svm.support_vectors_[:, 0], svm.support_vectors_[:, 1], s=100, linewidth=1, facecolors='none', edgecolors='k')
plt.scatter(t1_train,x1_train,c='w', edgecolors='b')
plt.scatter(t2_train,x2_train,c='r', marker='x')
plt.show()