Skip to content

KCSD correction in L-Curve #122

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: npx_test
Choose a base branch
from
Open

KCSD correction in L-Curve #122

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
304 changes: 0 additions & 304 deletions figures/npx/dan_kCSD_from_npx.py
View file
Open in desktop

This file was deleted.

4 changes: 3 additions & 1 deletion kcsd/KCSD.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -373,8 +373,9 @@ def suggest_lambda(self):

"""
u, s, v = svd(self.k_pot)
self.diag_std = np.std(np.diag(self.k_pot))
print('min lambda', 10**np.round(np.log10(s[-1]), decimals=0))
print('max lambda', str.format('{0:.4f}', np.std(np.diag(self.k_pot))))
print('max lambda', "%.2E" % self.diag_std)
return np.logspace(np.log10(s[-1]), np.log10(np.std(np.diag(self.k_pot))), 20)

def L_curve(self, lambdas=None, Rs=None, n_jobs=1):
Expand Down Expand Up @@ -403,6 +404,7 @@ def L_curve(self, lambdas=None, Rs=None, n_jobs=1):
else:
lambdas = lambdas.flatten()
if Rs is None:
# self.R = np.array(ele_pos[])
Rs = np.array((self.R)).flatten()
else:
Rs = np.array((Rs)).flatten()
Expand Down
5 changes: 3 additions & 2 deletions kcsd/utility_functions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -309,8 +309,9 @@ def L_model_fast(k_pot, pots, lamb, i):
residual : float

"""
k_inv = np.linalg.inv(k_pot + lamb*np.identity(k_pot.shape[0]))
beta_new = np.dot(k_inv, pots)
kernel = k_pot + lamb*np.identity(k_pot.shape[0])
beta_new = np.linalg.solve(kernel, pots)
# beta_new = np.dot(k_inv, pots)
V_est = np.dot(k_pot, beta_new)
modelnorm = np.einsum('ij,ji->i', beta_new.T, V_est)
residual = np.linalg.norm(V_est - pots)
Expand Down