def isposdef(a):
"""
% ISPOSDEF Test for positive definite matrix.
% ISPOSDEF(A) returns 1 if A is positive definite, 0 otherwise.
% Using chol is much more efficient than computing eigenvectors.
% From Tom Minka's lightspeed toolbox
"""
try:
cholesky(a)
return True
except LinAlgError:
return False
def isposdef(a):
'''
% ISPOSDEF Test for positive definite matrix.
% ISPOSDEF(A) returns 1 if A is positive definite, 0 otherwise.
% Using chol is much more efficient than computing eigenvectors.
% From Tom Minka's lightspeed toolbox
'''
try:
cholesky(a)
return True
except LinAlgError:
return False
def logdet(A):
'''
% log(det(A)) where A is positive-definite.
% This is faster and more stable than using log(det(A)).
% From Tom Minka's lightspeed toolbox
'''
U = cholesky(A).T;
y = 2*np.sum(np.log(np.diag(U)),0)
return y
def logdet(A):
'''
% log(det(A)) where A is positive-definite.
% This is faster and more stable than using log(det(A)).
% From Tom Minka's lightspeed toolbox
'''
U = cholesky(A).T
y = 2 * np.sum(np.log(np.diag(U)), 0)
return y
