c1 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='r',s=50,marker='+')
elif y_train[i] == 1.0:
c2 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='g',s=50,marker='o')
elif y_train[i] == 2.0:
c3 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='b',s=50,marker='*')
# Now the boundaries
# plt.figure(2)
x_min, x_max = pca_2d[:, 0].min() - 1, pca_2d[:,0].max() + 1
y_min, y_max = pca_2d[:, 1].min() - 1, pca_2d[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .01), np.arange(y_min, y_max, .01))
Z = svmClassifier_2d.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contour(xx, yy, Z)
plt.grid()
plt.title("Projection of Iris data set")
plt.savefig("iris_projection.png",format="png",pad_inches=0.1)
a = math.floor(min(pca_2d[0]))
b = math.ceil(max(pca_2d[0]))
x = np.linspace(a,b,100)
fake_data = np.array([x.T,np.ones(len(x))])
fake_data = fake_data.T
print('Reloading topology!')
m.resetSyncInbox()
time.sleep(5)
m.reloadTopology()
