import gudhi
from scripts.ssc.persistence_pairings_visualization.utils_definitions import make_plot
from src.datasets.datasets import SwissRoll
if __name__ == "__main__":
dataset_sampler = SwissRoll()
n_points = 2048
seed = 13
samples, color = dataset_sampler.sample(n_points, seed=seed)
tc = gudhi.TangentialComplex(intrisic_dim=1, points=samples)
tc.compute_tangential_complex()
simplex_tree = tc.create_simplex_tree()
print(simplex_tree.get_skeleton(1))
skeleton_sorted = sorted(simplex_tree.get_skeleton(1), key=lambda t: t[1])
pairings = []
for element in skeleton_sorted:
pair = element[0]
if len(pair) == 2 and element[1] == 0:
print(pair)
pairings.append(pair)
make_plot(samples, pairings, color, name='witness_TEST')
r_max=10,
create_simplex_tree=False,
create_metric=True)
for k in ks:
print('{} out of {}'.format(counter, ntot))
landmarks_dist = torch.tensor(witness_complex.landmarks_dist)
sorted, indices = torch.sort(landmarks_dist)
kNN_mask = torch.zeros(
(n_samples, n_samples),
device='cpu').scatter(1, indices[:, 1:(k + 1)], 1)
pairings_i = np.where(kNN_mask.numpy() == 1)
pairings = np.column_stack((pairings_i[0], pairings_i[1]))
name = 'wc{nw}_k{k}_seed{seed}'.format(nw=N_WITNESSES,
k=k,
seed=seed)
make_plot(landmarks,
pairings,
color,
name=name,
path_root=path_to_save,
knn=False,
show=False,
dpi=50)
counter += 1
#
# count_pairings(n_samples, pairs_filtered)
# make_plot(landmarks, pairs_filtered, color, name=name)
n_samples_array = [32,48,64,96,128]
n_witnesses_array = [256,512,1024]
seeds = [10,13,20]
n_samples_array = [64]
n_witnesses_array = [512]
seeds = [27]
for n_witnesses in n_witnesses_array:
for seed in seeds:
for n_samples in n_samples_array:
name = 'witness_ssc_corrected_nl{}_nw{}_seed{}'.format(n_samples, n_witnesses, seed)
dataset_sampler = SwissRoll()
n_landmarks = n_samples
seed = seed
landmarks, color = dataset_sampler.sample(n_landmarks, seed = seed)
witnesses, _ = dataset_sampler.sample(n_witnesses, seed=(seed+17))
distances = wl_table(witnesses,landmarks)
pairs = get_pairs_0(distances)
pairs_filtered = get_persistence_pairs(pairs, n_samples)
count_pairings(n_samples, pairs_filtered)
make_plot(landmarks, pairs_filtered, color, name=name)
from scripts.ssc.persistence_pairings_visualization.utils_definitions import make_plot
from src.datasets.datasets import SwissRoll
from src.topology.witness_complex import WitnessComplex
PATH = '/Users/simons/PycharmProjects/MT-VAEs-TDA/output/SwissRoll_pairings/witness_complex_k/'
if __name__ == "__main__":
n_landmarks = 512
n_witnesses = 2048
seed = 0
dataset_sampler = SwissRoll()
landmarks, color = dataset_sampler.sample(n_landmarks, seed=seed)
witnesses, _ = dataset_sampler.sample(n_witnesses, seed=(seed + 17))
witness_complex = WitnessComplex(landmarks, witnesses)
witness_complex.compute_simplicial_complex(1, True, r_max=7)
for k in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]:
name = 'nl{}_nw{}_k{}_seed{}'.format(n_landmarks, n_witnesses, k, seed)
neigh = NearestNeighbors(n_neighbors=(k + 1),
metric='precomputed').fit(
witness_complex.landmarks_dist)
distances, pairings = neigh.kneighbors(witness_complex.landmarks_dist)
print(distances)
print(pairings)
make_plot(landmarks, pairings, color, name, path_root=PATH, knn=True)
ax.legend((plt_obs, plt_out), ('data', 'outliers'), loc='lower left')
return data
if __name__ == "__main__":
dataset_sampler = SwissRoll()
name = '128_noise4_DTM'
data, color = dataset_sampler.sample(128, noise=0.4)
st = DTMFiltration(data, m=0.01, p=10, dimension_max=1)
st.persistence()
pers_pairs = st.persistence_pairs()
print(pers_pairs)
pairings = np.array([[pers_pairs[0][1][0], pers_pairs[0][1][1]]])
for pair in pers_pairs[1:-1]:
pairings = np.vstack((pairings, np.array([[pair[1][0], pair[1][1]]])))
make_plot(data, pairings, color, name=name)
# name = '128_noise4_reg'
# make_data(data, color, name = name)
#
# path_pairings = '{}pairings_{}.npy'.format(PATH_ROOT, name)
# path_data = '{}data_{}.npy'.format(PATH_ROOT, name)
# path_color = '{}color_{}.npy'.format(PATH_ROOT, name)
# pairings, data, color = np.load(path_pairings), np.load(path_data), np.load(path_color)
# print(type(pairings))
# print(pairings.shape)
# #
# make_plot(data, pairings, color, name = name)
points_tensor = torch.from_numpy(points)
pairwise_distances = torch.norm(points_tensor[:, None] - points_tensor,
dim=2,
p=2)
sorted, indices = torch.sort(pairwise_distances)
for k in ks:
print('{} out of {}'.format(counter, ntot))
kNN_mask = torch.zeros(
(points_tensor.size(0),
points_tensor.size(0))).scatter(1, indices[:, 1:(k + 1)], 1)
pairings_i = np.where(kNN_mask.numpy() == 1)
pairings = np.column_stack((pairings_i[0], pairings_i[1]))
name = 'knn_k{k}_seed{seed}'.format(k=k, seed=seed)
make_plot(points,
pairings,
color,
name=name,
path_root=path_to_save,
knn=False,
show=True,
dpi=400,
cmap=plt.cm.viridis)
counter += 1
if __name__ == "__main__":
alpha_beta = []
for i in range(20):
alpha_beta.append([2**(0.25 * i + 0.0000001), 2**(0.25 * i)])
n_samples_array = [32, 48, 64, 96, 128]
n_witnesses_array = [256, 512]
seeds = [10, 13, 20]
for n_witnesses in n_witnesses_array:
for seed in seeds:
for n_samples in n_samples_array:
name = 'witness_alphabeta_nl{}_nw{}_seed{}'.format(
n_samples, n_witnesses, seed)
dataset_sampler = SwissRoll()
n_landmarks = n_samples
seed = seed
landmarks, color = dataset_sampler.sample(n_landmarks,
seed=seed)
witnesses, _ = dataset_sampler.sample(n_witnesses,
seed=(seed + 17))
nlt = get_nlt(landmarks, witnesses)
pairings = pseudo_alpha_beta_witness_complex(
nlt, alpha_beta, n_landmarks)
count_pairings(n_landmarks, pairings)
make_plot(landmarks, pairings, color, name=name)
