def auxiliary_tot_cost(params_together):
# here x, y are understood as global variables defined above
b2, b3, b4, W2, W3, W4 = nn_potential.split_params(params_together)
return nn_potential.total_cost(nn_potential.x, nn_potential.y, b2, b3, b4,
W2, W3, W4, 10)
def auxiliary_gradient(params_together):
b2, b3, b4, W2, W3, W4 = nn_potential.split_params(params_together)
return nn_potential.grad_cost(nn_potential.x, nn_potential.y, b2, b3, b4,
W2, W3, W4)
coeffi, coeffj = 2, 2
elif i == 22:
coeffi, coeffj = 2, 3
plt.hist(centroids[:, i], bb, density=True, color='darkblue')
plt.title('W4[' + str(coeffi) + ',' + str(coeffj) + ']')
plt.grid(True)
plt.suptitle("1d projections of the appoximated 23d energy landscape;" + \
" x: values, y: probabilities\nPeaks correspond to lower total cost")
plt.show()
# Perform gradient descent on each centroid to identify the modes
print("\nSearching for the modes... ")
modes_list = []
for i in range(ncent):
print("Gradient descent on centroid number", i)
b2, b3, b4, W2, W3, W4 = nn_potential.split_params(centroids[i])
if (nn_potential.performGradientDescent(nn_potential.x,
nn_potential.y,
b2,
b3,
b4,
W2,
W3,
W4,
eta=0.5)[0]):
print("MODE FOUND: centroid number ", i)
modes_list.append(i)
# Now, plot all the energies for the modes found
print("A total of " + str(len(modes_list)) + " modes have been found")
d = len(modes_list)
