data_list = []
resolution = 100
colors=('b', 'k', 'r')
fig, ax = plt.subplots()
x_min, x_max = data_set[:, 0].min() - 1, data_set[:, 0].max() + 1
y_min, y_max = data_set[:, 1].min() - 1, data_set[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
Z = adaClassify(np.ndarray.tolist(np.c_[xx.ravel(), yy.ravel()]),classifierArray)
Z = Z.reshape(xx.shape)
#ax.contour(xx, yy, Z, colors='k')
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
# Plot decision contours using grid and
# make a scatter plot of training data
#ax.contour(xrange, yrange, grid, colors='k')
plt.scatter(sample_0_data_set[:,0], sample_0_data_set[:,1], s=15, c="blue")
plt.scatter(sample_1_data_set[:,0], sample_1_data_set[:,1], s=15, c="red")
#ax.scatter(data_set[:,0], data_set[:,1],c=data_set[:,2], cmap=plt.cm.viridis, lw=0, alpha=0.5)
plt.title("Adabost Decision Boundary")
plt.savefig("adabost_2949.png")
plt.show()
