Hi Troels,
This is not the correct approach. These higher dimensions must be
missing from the gradient, Hessian, Jacobian, and covariance matrix.
If N is the number of parameters and M is the number of input data
sets (R2eff/R1rho/R1), then these structures must absolutely have the
following dimensions:
- function = 1,
- gradient = N,
- Hessian = NxN,
- Jacobian = NxM,
- covariance matrix = NxN.
If they don't have these exact dimensions, then they cannot be called
by those names. You have no choice. Their dimensionality matches
that of the input parameter vector! You have to loose all of the NE,
NS, NM, NO, and ND dimensions in all of these structures.
Regards,
Edward
On 2 September 2014 10:29, <tlinnet@xxxxxxxxxxxxx> wrote:
Author: tlinnet
Date: Tue Sep 2 10:29:48 2014
New Revision: 25530
URL: http://svn.gna.org/viewcvs/relax?rev=25530&view=rev
Log:
Added comments to co-variance module, for explanation of data
dimensionality.
task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR
estimation from Jacobian and Co-variance matrix of dispersion models.
Modified:
branches/est_par_error/lib/statistics.py
Modified: branches/est_par_error/lib/statistics.py
URL:
http://svn.gna.org/viewcvs/relax/branches/est_par_error/lib/statistics.py?rev=25530&r1=25529&r2=25530&view=diff
==============================================================================
--- branches/est_par_error/lib/statistics.py (original)
+++ branches/est_par_error/lib/statistics.py Tue Sep 2 10:29:48 2014
@@ -229,6 +229,11 @@
# Get the expected shape of the higher dimensional column numpy array.
if len(weights.shape) == 2:
# Extract shapes from data.
+ # NE: Number of experiments.
+ # NS: Number of spins.
+ # NM: Number of spectrometer frequencies.
+ # NO: Maximum number of offsets.
+ # ND: Number of dispersion(data) points.
NE, NS, NM, NO, ND = 1, 1, 1, 1, weights.shape[-1]
# Make a eye matrix, with Shape [ND][ND]
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Re: r25530 - /branches/est_par_error/lib/statistics.py