def simplified_algo(R, iterations, warp=0):
S = best_embedding_on_sphere(R, ndim=3)
R_order = scale_score(R).astype('int16')
for i in range(iterations): # @UnusedVariable
C = np.dot(S.T, S)
C_sorted = np.sort(C.flat)
R = C_sorted[R_order]
S = best_embedding_on_sphere(R, ndim=3)
if warp > 0:
base_D = distances_from_directions(S)
diameter = base_D.max()
min_ratio = 0.1
max_ratio = np.pi / diameter
ratios = np.exp(np.linspace(np.log(min_ratio),
np.log(max_ratio), warp))
scores = []
guesses = []
for ratio in ratios:
Cwarp = cosines_from_distances(base_D * ratio)
Sw = best_embedding_on_sphere(Cwarp, ndim=3)
Cw_order = scale_score(cosines_from_directions(Sw))
scores.append(correlation_coefficient(Cw_order, R_order))
guesses.append(Sw)
best = np.argmax(scores)
print('Best warp: %d (%f)' % (best, ratios[best]))
S = guesses[best]
return S
def simplified_algo(R, iterations, warp=0):
S = best_embedding_on_sphere(R, ndim=3)
R_order = scale_score(R).astype('int16')
for i in range(iterations): # @UnusedVariable
C = np.dot(S.T, S)
C_sorted = np.sort(C.flat)
R = C_sorted[R_order]
S = best_embedding_on_sphere(R, ndim=3)
if warp > 0:
base_D = distances_from_directions(S)
diameter = base_D.max()
min_ratio = 0.1
max_ratio = np.pi / diameter
ratios = np.exp(np.linspace(np.log(min_ratio), np.log(max_ratio),
warp))
scores = []
guesses = []
for ratio in ratios:
Cwarp = cosines_from_distances(base_D * ratio)
Sw = best_embedding_on_sphere(Cwarp, ndim=3)
Cw_order = scale_score(cosines_from_directions(Sw))
scores.append(correlation_coefficient(Cw_order, R_order))
guesses.append(Sw)
best = np.argmax(scores)
print('Best warp: %d (%f)' % (best, ratios[best]))
S = guesses[best]
return S
def artificial(Y, true_S):
if true_S is None:
return None
print('Computing correlation')
true_D = distances_from_directions(true_S)
def exponential_kernel(D, alpha):
return np.exp(-D / alpha)
R = exponential_kernel(true_D, alpha=0.52)
return R.astype('float32')
def artificial(Y, true_S):
if true_S is None:
return None
print('Computing correlation')
true_D = distances_from_directions(true_S)
def exponential_kernel(D, alpha):
return np.exp(-D / alpha)
R = exponential_kernel(true_D, alpha=0.52)
return R.astype('float32')
