lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-03-15 14:50:25 +01:00
Code Issues Projects Releases Packages Wiki Activity

Added tests when changing cov and grad in covobs

Browse source
This commit is contained in:
Simon Kuberski 2021-12-07 17:33:40 +01:00
parent 3190140023
commit 7c5465d828
1 changed files with 34 additions and 18 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
pyerrors/covobs.py
View file

@ -20,19 +20,7 @@ class Covobs:
grad : list or array
Gradient of the Covobs wrt. the means belonging to cov.
"""
self.cov = np.array(cov)
if self.cov.ndim == 0:
self.N = 1
self.cov = np.diag([self.cov])
elif self.cov.ndim == 1:
self.N = len(self.cov)
self.cov = np.diag(self.cov)
elif self.cov.ndim == 2:
self.N = self.cov.shape[0]
if self.cov.shape[1] != self.N:
raise Exception('Covariance matrix has to be a square matrix!')
else:
raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
self.set_cov(cov)
if '|' in name:
raise Exception("Covobs name must not contain replica separator '|'.")
self.name = name
@ -45,15 +33,43 @@ class Covobs:
else:
if pos > self.N:
raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
self.grad = np.zeros((self.N, 1))
self.grad[pos] = 1.
self._grad = np.zeros((self.N, 1))
self._grad[pos] = 1.
else:
self.grad = np.array(grad)
if self.grad.ndim == 1:
self.grad = np.reshape(self.grad, (self.N, 1))
self.set_grad(grad)
self.value = mean
def errsq(self):
""" Return the variance (= square of the error) of the Covobs
"""
return float(np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)))
def set_cov(self, cov):
self._cov = np.array(cov)
if self._cov.ndim == 0:
self.N = 1
self._cov = np.diag([self._cov])
elif self._cov.ndim == 1:
self.N = len(self._cov)
self._cov = np.diag(self._cov)
elif self._cov.ndim == 2:
self.N = self._cov.shape[0]
if self._cov.shape[1] != self.N:
raise Exception('Covariance matrix has to be a square matrix!')
else:
raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
def set_grad(self, grad):
self._grad = np.array(grad)
if self._grad.ndim in [0, 1]:
self._grad = np.reshape(self._grad, (self.N, 1))
elif self._grad.ndim != 2:
raise Exception('Invalid dimension of grad!')
@property
def cov(self):
return self._cov
@property
def grad(self):
return self._grad