def get_diag(embbeded, diagramas):
homology_dimensions = (0, 1)
VR = hl.VietorisRipsPersistence(homology_dimensions=[0, 1], n_jobs=1)
sliding_window = ts.SlidingWindow(embbeded.shape[1] - 1, 4)
embedded_window = sliding_window.fit_transform(embbeded.T)
#sliding_window3 = ts.SlidingWindow(embbeded3.shape[1] - 1, 4)
#embedded_window3 = sliding_window3.fit_transform(embbeded3.T)
diagrams = VR.fit_transform(embedded_window)
#diagrams3 = VR.fit_transform(embedded_window3)
diagrams = diagrams[:, :, :-1]
#diagrams3 = diagrams3[:, :, :-1]
diagrams = [clean_ceros(item) for item in diagrams]
#diagrams3 = [clean_ceros(item) for item in diagrams3]
entropy = persistent_entropy.persistent_entropy(diagrams,
keep_inf=False,
val_inf=None,
normalize=False)
#z = np.zeros((len(diagrams[0]), 1), dtype=int)
#diagrams[0] = np.append(diagrams[0], z, 1)
#diagrams = [list(diagrams[0][item]) for item in range(len(diagrams[0]))]
diagramas.append(diagrams)
#entropy3 = persistent_entropy.persistent_entropy(diagrams3, keep_inf=False, val_inf=None, normalize=False)
entropy = pd.DataFrame(entropy)
#entropy3 = pd.DataFrame(entropy3)
return entropy, diagramas
def get_diag(embbeded, diagramas):
homology_dimensions = (0, 1)
#Vietoris Rips filter applied
VR = hl.VietorisRipsPersistence(homology_dimensions = [0,1], n_jobs=1)
sliding_window = ts.SlidingWindow(embbeded.shape[1]-1, 4)
embedded_window = sliding_window.fit_transform(embbeded.T)
diagrams = VR.fit_transform(embedded_window)
diagrams = diagrams[:, :, :-1]
#clean ceros elements
diagrams = [clean_ceros(item) for item in diagrams]
entropy = persistent_entropy.persistent_entropy(diagrams, keep_inf=False, val_inf=None, normalize=False)
diagramas.append(diagrams)
entropy = pd.DataFrame(entropy)
return entropy, diagramas
def test_one_diagram(self):
dgm = np.array([[0,1],[0,3],[2,4]])
p = pe.persistent_entropy(dgm)
# An upper bound of persistent entropy is the logarithm of the number of bars.
assert p < np.log(len(dgm))
def test_diagram_one_bar(self):
dgm = np.array([[-1,2]])
p = pe.persistent_entropy(dgm)
assert all(p==0)
def test_diagrams_inf(self):
dgms = [np.array([[0,1],[0,3],[2,4]]),np.array([[2,5],[3,8]]),np.array([[0,np.inf]])]
p = pe.persistent_entropy(dgms, keep_inf = True, val_inf = 10)
# An upper bound of persistent entropy is the logarithm of the number of bars.
assert all(p < np.log(3))
def test_diagrams(self):
dgms = [np.array([[0,1],[0,3],[2,4]]),np.array([[2,5],[3,8]]),np.array([[0,10]])]
p = pe.persistent_entropy(dgms)
# An upper bound of persistent entropy is the logarithm of the number of bars.
assert all(p < np.log(3))