def start(p = 12,num_clusters =100,max_iter=50, batch_size = 500,init = 'random' ):
data_loader = DataLoader()
cifar_data = data_loader.load_cifar_data()
images = cifar_data['data'].reshape((-1,3,32,32)).astype('float32')
# img_test = images[2,:,:,:]
# img_test = np.rollaxis(img_test,0,3)
# img_test = img_test[:,:,::-1]
# plt.imshow(img_test)
# plt.show()
images = np.rollaxis(images,1,4)
images = images[:,:,:,::-1]
num_patches = images.shape[0]
patch_size = [p,p]
#
kmeans = MiniBatchKMeans(num_clusters,max_iter,batch_size,init)
patches_img = kmeans.generate_patches(images, patch_size)
# plt.imshow(patches_img[0,:,:,:])
# plt.show();
# Convert to matrix form rows X cols
patches=patches_img.reshape(patches_img.shape[0],-1)
# i=display(patches[0,:], patch_size)
# plt.imshow(i)
# plt.show()
#pre-processing
centers,counts = kmeans.fit(patches)
fig = plt.figure()
disp_row_size = np.ceil(np.sqrt(kmeans.num_clusters))
for i in xrange(kmeans.num_clusters):
subplot = fig.add_subplot(disp_row_size, disp_row_size, i+1)
subplot.get_xaxis().set_visible(False)
subplot.get_yaxis().set_visible(False)
img = display(centers[:,i], patch_size)
subplot.imshow(img, interpolation='none')
#plt.show()
directory=check_create_observations_dir()
plt.savefig(directory+'/repFields.png')
# patch_test=patches[0,:,:,:]
# plt.imshow(patch_test)
# plt.show()
display_bar(counts,directory+'/clusterCount.png')
print "THE END"
def fit_cifar():
print "Computing for CIFAR 10"
data_loader = DataLoader()
cifar_data=data_loader.load_cifar_data()
train_set_x=cifar_data['data']
train_set_y=cifar_data['labels']
plt=get_pairwise_plot(train_set_x, train_set_y)
obs_dir=check_create_observations_dir("PCA")
target_path = os.path.join(obs_dir,"scatterplotCIFAR.png")
plt.savefig(target_path)
print "THE END"
def display(data_row, patch_size):
data_row = data_row - data_row.min()
data_row = data_row / data_row.max()
img = data_row.reshape(3, patch_size[0], patch_size[1]).astype("float32")
img = np.rollaxis(img, 0, 3)
return img
# plt.imshow(img)
# plt.show()
if __name__ == "__main__":
data_loader = DataLoader()
cifar_data = data_loader.load_cifar_data()
images = cifar_data["data"].reshape((-1, 3, 32, 32)).astype("float32")
# img_test = images[2,:,:,:]
# img_test = np.rollaxis(img_test,0,3)
# img_test = img_test[:,:,::-1]
# plt.imshow(img_test)
# plt.show()
images = np.rollaxis(images, 1, 4)
images = images[:, :, :, ::-1]
num_patches = images.shape[0]
patch_size = [12, 12]
#
kmeans = KMeans()
