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")
Exemplo n.º 3
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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
Exemplo n.º 4
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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"))
Exemplo n.º 5
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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)
Exemplo n.º 6
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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"))
Exemplo n.º 7
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	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)