Esempio n. 1
0
t = np.linspace(0,1,N)
R = 10
noise = 1/1.25

# Generate dataset

x = R*sin(theta)*np.exp(-t) + noise*np.random.random(len(t))
y = R*cos(theta)*np.exp(-t) + noise*np.random.random(len(t))
shuffle = np.random.permutation(np.arange(len(x)))
data = np.vstack((x[shuffle],y[shuffle])).T

x = R*sin(-theta)*np.exp(-t) + noise*np.random.random(len(t))
y = R*cos(-theta)*np.exp(-t) + noise*np.random.random(len(t))
data = np.vstack((data,np.vstack((x,y)).T))

dm = DiffusionMap(data, kernel=gauss_kernel, kernel_params={'eps': eps},
                  cache_filename=None)

w,v = dm.map(ndim=2)

plt.title('Time step %s' % T)

plt.subplot(311)
plt.plot(data[:,0],data[:,1],'x')
plt.axis('equal')

plt.subplot(312)
plt.imshow(dm.H, cmap='viridis')

plt.subplot(313)
for i,(x,y) in enumerate(zip(v[:,0].flat,v[:,1].flat)):
    plt.plot([x],[y],'.b')
Esempio n. 2
0
    template = io.imread('template.png', as_grey=True)
    shape = np.array(template.shape)
    x_off,y_off = (shape-1)/2

    data = np.empty((len(angles),np.prod(template.shape)),float)
    dx,dy = data.shape
    for i,a in enumerate(angles):
        print("Rotating %i" % i)
        data[i] = rotate_around_centre(template,angle=a,cval=255).flat

    # EXPERIMENT: Simulate colour image
    old_data = data.copy()
    data = data.repeat(3, axis=1)

    # Compute diffusion map on dataset
    dm = DiffusionMap(data, kernel=gauss_kernel, kernel_params={'eps':1e9})
    w,v = dm.map()

    # Plot the raw data as seen by the diffusion map
    plt.figure()
    plt.imshow(data,aspect=float(dy)/dx,cmap=plt.cm.gray)
    plt.xticks([])

    # Plot the unsorted images on a circle on the x-y plane
    plot_images(np.cos(angles_ord),np.sin(angles_ord),old_data,template.shape)
    #plt.savefig('images_unordered.png')

    # Plot the images on the diffusion coordinates, which should be sorted
    plot_images(v[:,0],v[:,1],old_data,template.shape)
    #plt.savefig('images_ordered.png')