def exp_gbrbm(exp_name, T=[]):
dataset = dg.one_exception_dataset(N=6,
n=1500,
T=T,
lam=5000,
exc=1,
noise_k=1)
utils.write_data(dataset, exp_name, "generated_data")
new_data = utils.tsne(dataset, exp_name, "generated_data", T, 2)
utils.write_data(new_data, exp_name, "generated_data_for_tsne")
_, recovery_sample, decode_res = decoder.gbrbm_decoder(dataset,
learning_rate=0.1,
training_epochs=50,
batch_size=1001,
n_hidden=2000,
plot_every=1)
utils.write_data(decode_res, exp_name, "decoded_data")
new_data = utils.tsne(decode_res, exp_name, "decoded_data", T, 2)
utils.write_data(new_data, exp_name, "decoded_tsne_data_for_tsne")
def exp_gbrbm(exp_name, T=[]):
dataset = dg.one_exception_dataset(
N=6,
n=1500,
T=T,
lam=5000,
exc=1,
noise_k=1
)
utils.write_data(dataset, exp_name, "generated_data")
new_data = utils.tsne(dataset, exp_name, "generated_data", T, 2)
utils.write_data(new_data, exp_name, "generated_data_for_tsne")
_, recovery_sample, decode_res = decoder.gbrbm_decoder(
dataset,
learning_rate=0.1,
training_epochs=50,
batch_size=1001,
n_hidden=2000,
plot_every=1
)
utils.write_data(decode_res, exp_name, "decoded_data")
new_data = utils.tsne(decode_res, exp_name, "decoded_data", T, 2)
utils.write_data(new_data, exp_name, "decoded_tsne_data_for_tsne")
def cluster(data, true_labels, n_clusters=3):
km = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
km.fit(data)
km_means_labels = km.labels_
km_means_cluster_centers = km.cluster_centers_
km_means_labels_unique = np.unique(km_means_labels)
colors_ = cycle(colors.cnames.keys())
initial_dim = np.shape(data)[1]
data_2 = tsne(data, 2, initial_dim, 30)
plt.figure(figsize=(12, 6))
plt.scatter(data_2[:, 0], data_2[:, 1], c=true_labels)
plt.title('True Labels')
return km_means_labels
def generate_raw_image_pixels(list_of_demonstrations):
"""
PCA and t-SNE on raw image pixels
"""
# Design matrix of raw image pixels
X = None
for demonstration in list_of_demonstrations:
print "Raw image pixels ", demonstration
PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(constants.CAMERA) + ".p"
start, end = utils.get_start_end_annotations(PATH_TO_ANNOTATION)
for frm in range(start, end + 1):
if ((frm % 6) == 0):
PATH_TO_IMAGE = utils.get_full_image_path(constants.PATH_TO_DATA + constants.NEW_FRAMES_FOLDER + demonstration + "_" + constants.CAMERA + "/", frm)
print demonstration, str(frm)
img = utils.reshape(cv2.imread(PATH_TO_IMAGE).flatten())
X = utils.safe_concatenate(X, img)
X_pca = utils.pca(X, PC = 2)
X_tsne = utils.tsne(X)
data_dimred = [X_pca, X_tsne]
pickle.dump(X_tsne, open("raw_pixel_" + demonstration + "_dimred.p", "wb"))
def tsne():
utils.tsne(app.config['CROPPED_PATH'], app.config['TSNE_PATH'])
files_tsne = os.listdir(app.config['TSNE_PATH'])
print('files:',files_tsne)
return render_template("tsne.html", files_tsne = files_tsne)
def generate_SIFT():
data = pickle.load(open("sift_features/SIFT_plane_9_1.p", "rb"))
X_pca = utils.pca(data, PC = 2)
X_tsne = utils.tsne(data)
data_dimred = [X_pca, X_tsne]
pickle.dump(data_dimred, open("SIFT_plane_9_dimred.p", "wb"))
parser.add_argument("--image", help="Parse image mode", default = None)
args = parser.parse_args()
if args.a_2 and args.PATH_TO_DATA_2 and not args.image:
X1, label_map_1, index_map_1 = parse_annotations_pickle(args.a, args.PATH_TO_DATA, args.layer)
X2, label_map_2, index_map_2 = parse_annotations_pickle(args.a_2, args.PATH_TO_DATA_2, args.layer)
X1_pca = utils.pca(X1)
X2_pca = utils.pca(X2)
plot_annotated_joint(X1_pca, X2_pca, label_map_1, index_map_1, label_map_2, index_map_2, figure_name = args.file_name +".png", title = "PCA " + args.layer)
elif args.image and not args.PATH_TO_DATA_2:
X, label_map, index_map = utils.parse_annotations_images(args.a, args.PATH_TO_DATA)
pickle.dump(X, open(args.file_name + "_allimages.p", "wb"))
pickle.dump(label_map, open(args.file_name + "_labelmap.p", "wb"))
pickle.dump(index_map, open(args.file_name + "_indexmap.p", "wb"))
IPython.embed()
X_pca = utils.pca(X)
X_tsne = utils.tsne(X)
X_tsne_pca = utils.tsne_pca(X)
utils.plot_annotated_embedding(X_pca, label_map, index_map, args.file_name + '_' + args.layer + '_pca.png', 'PCA ' + args.layer)
utils.plot_annotated_embedding(X_tsne, label_map, index_map, args.file_name + '_' + args.layer + '_tsne.png', 't-SNE ' + args.layer)
utils.plot_annotated_embedding(X_tsne_pca, label_map, index_map, args.file_name + '_' + args.layer + '_tsne_pca.png', 't-SNE (PCA Input) ' + args.layer)
else:
if args.a:
X, label_map, index_map = parse_annotations_pickle(args.a, args.PATH_TO_DATA, args.layer)
else:
X, label_map, index_map = parse_annotations(args)
X_pca = utils.pca(X)
X_tsne = utils.tsne(X)
X_tsne_pca = utils.tsne_pca(X)
utils.plot_annotated_embedding(X_pca, label_map, index_map, args.file_name + '_' + args.layer + '_pca.png', 'PCA - C3D ' + args.layer)
utils.plot_annotated_embedding(X_tsne, label_map, index_map, args.file_name + '_' + args.layer + '_tsne.png', 't-SNE - C3D ' + args.layer)