def plot_results():
    data = load_data("val", independent=False)
    data = make_hierarchical_data(data, lateral=False, latent=True)
    logger = SaveLogger("test_latent_2.0001.pickle", save_every=100)
    ssvm = logger.load()
    plot_results_hierarchy(data, ssvm.predict(data.X),
                           folder="latent_results_val_50_states_no_lateral")
def plot_init():
    data = load_data("train", independent=False)
    data = make_hierarchical_data(data, lateral=False, latent=True)
    #X, Y = discard_void(data.X, data.Y, 21)
    #data.X, data.Y = X, Y
    H = kmeans_init(data.X, data.Y, n_labels=22, n_hidden_states=22)
    plot_results_hierarchy(data, H)
Example #3
0
def plot_init():
    data = load_data("train", independent=False)
    data = make_hierarchical_data(data, lateral=False, latent=True)
    #X, Y = discard_void(data.X, data.Y, 21)
    #data.X, data.Y = X, Y
    H = kmeans_init(data.X, data.Y, n_labels=22, n_hidden_states=22)
    plot_results_hierarchy(data, H)
Example #4
0
def plot_results():
    data = load_data("val", independent=False)
    data = make_hierarchical_data(data, lateral=False, latent=True)
    logger = SaveLogger("test_latent_2.0001.pickle", save_every=100)
    ssvm = logger.load()
    plot_results_hierarchy(data,
                           ssvm.predict(data.X),
                           folder="latent_results_val_50_states_no_lateral")