Hi Jean-Patrick,

Y is the known corresponding digit identity. The function to "jitter" the digit images around a bit just takes digits.target as Y and concatenates it with itself five times, so the expanded dataset has known identities to compare against.

Regards,

Josh

On Wednesday, February 25, 2015 at 7:09:39 AM UTC-6, Jean-Patrick Pommier wrote:

On Wednesday, February 25, 2015 at 7:09:39 AM UTC-6, Jean-Patrick Pommier wrote:

Thanks you for the links.

Regarding the rbm classifier in the following example. At first sight I don't understand what is Y array (X array seems to be the set of images).

Jean-Patrick

Le mardi 24 février 2015 17:21:10 UTC+1, Jean-Patrick Pommier a écrit :Dear All,

I am trying to make pairs of images from the following set of images (chromosomes sorted by size after rotation). The idea is to make a feature vector for unsupervised classification (kmeans with 19 clusters)

From each chromosome an integral image was calculated:plt.figure(figsize = (15,15))

gs1 = gridspec.GridSpec(6,8)

gs1.update(wspace=0.0, hspace=0.0) # set the spacing between axes.

for i in range(38):

# i = i + 1 # grid spec indexes from 0

ax1 = plt.subplot(gs1[i])

plt.axis('off')

ax1.set_xticklabels([])

ax1.set_yticklabels([])

ax1.set_aspect('equal')

image = sk.transform.integral_image(reallysorted[i][:,:,2])

imshow(image , interpolation='nearest')

Then each integral image was flatten and combined with the others:Features =[]The X array contains

for i in range(38):

Feat = np.ndarray.flatten(sk.transform.integral_image( reallysorted[i][:,:,2]))

Features.append(Feat)

X = np.asarray(Features)

print X.shape38lines and 9718 features, which is not good. However, I trried to submit these raw features to kmeans classification with sklearn using a direct example :from sklearn.neighbors import NearestNeighborsPloting the connection graph shows that a chromosomes is similar to more than one ...

nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(X)9

distances, indices = nbrs.kneighbors(X)

connection = nbrs.kneighbors_graph(X).toarray() Thanks for your advices.

- Do you think that integral images can be used to discriminate the chromosomes pairs?
- If so, how to reduce the number of features to 10~20? (to get a better discrimination)

Jean-Patrick