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104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
import numpy as np
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class Covobs:
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def __init__(self, mean, cov, name, pos=None, grad=None):
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""" Initialize Covobs object.
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Parameters
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----------
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mean : float
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Mean value of the new Obs
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cov : list or array
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2d Covariance matrix or 1d diagonal entries
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name : str
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identifier for the covariance matrix
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pos : int
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Position of the variance belonging to mean in cov.
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Is taken to be 1 if cov is 0-dimensional
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grad : list or array
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Gradient of the Covobs wrt. the means belonging to cov.
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"""
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self._set_cov(cov)
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if '|' in name:
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raise Exception("Covobs name must not contain replica separator '|'.")
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self.name = name
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if grad is None:
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if pos is None:
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if self.N == 1:
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pos = 0
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else:
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raise Exception('Have to specify position of cov-element belonging to mean!')
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else:
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if pos > self.N:
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raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
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self._grad = np.zeros((self.N, 1))
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self._grad[pos] = 1.
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else:
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self._set_grad(grad)
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self.value = mean
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def errsq(self):
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""" Return the variance (= square of the error) of the Covobs
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"""
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return float(np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)))
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def _set_cov(self, cov):
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""" Set the covariance matrix of the covobs
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Parameters
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----------
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cov : list or array
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Has to be either of:
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0 dimensional number: variance of a single covobs,
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1 dimensional list or array of lenght N: variances of multiple covobs
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2 dimensional list or array (N x N): Symmetric, positive-semidefinite covariance matrix
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"""
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self._cov = np.array(cov)
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if self._cov.ndim == 0:
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self.N = 1
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self._cov = np.diag([self._cov])
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elif self._cov.ndim == 1:
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self.N = len(self._cov)
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self._cov = np.diag(self._cov)
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elif self._cov.ndim == 2:
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self.N = self._cov.shape[0]
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if self._cov.shape[1] != self.N:
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raise Exception('Covariance matrix has to be a square matrix!')
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else:
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raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
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for i in range(self.N):
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for j in range(i):
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if not self._cov[i][j] == self._cov[j][i]:
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raise Exception('Covariance matrix is non-symmetric for (%d, %d' % (i, j))
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evals = np.linalg.eigvalsh(self._cov)
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for ev in evals:
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if ev < 0:
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raise Exception('Covariance matrix is not positive-semidefinite!')
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def _set_grad(self, grad):
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""" Set the gradient of the covobs
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Parameters
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----------
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grad : list or array
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Has to be either of:
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0 dimensional number: gradient w.r.t. a single covobs,
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1 dimensional list or array of lenght N: gradient w.r.t. multiple covobs
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"""
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self._grad = np.array(grad)
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if self._grad.ndim in [0, 1]:
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self._grad = np.reshape(self._grad, (self.N, 1))
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elif self._grad.ndim != 2:
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raise Exception('Invalid dimension of grad!')
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@property
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def cov(self):
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return self._cov
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@property
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def grad(self):
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return self._grad
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