Merge pull request #1506 from pints-team/682-no-discrete-parameters

Add note to readme on discrete parameters
pints-team · Nov 14, 2023 · 220e49d · 220e49d
2 parents 107af2f + 569b957
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,6 +6,7 @@ All notable changes to this project will be documented in this file.
 
 ### Added
 - [#1499](https://github.com/pints-team/pints/pull/1499) Added a log-uniform prior class.
+- [#1505](https://github.com/pints-team/pints/pull/1505) Added notes to `ErrorMeasure` and `LogPDF` to say parameters must be real and continuous.
 ### Changed
 ### Deprecated
 ### Removed

diff --git a/README.md b/README.md
@@ -13,7 +13,6 @@ PINTS (Probabilistic Inference on Noisy Time-Series) is a framework for optimisa
 PINTS is described in [this publication in JORS](http://doi.org/10.5334/jors.252), and can be cited using the information given in our [CITATION file](https://github.com/pints-team/pints/blob/main/CITATION).
 More information about PINTS papers can be found in the [papers directory](https://github.com/pints-team/pints/tree/main/papers).
 
-
 ## Using PINTS
 
 PINTS can work with any model that implements the [pints.ForwardModel](http://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model) interface.
@@ -35,6 +34,8 @@ _(Left)_ A noisy experimental time series and a computational forward model.
 _(Right)_ Example code for an optimisation problem.
 The full code can be [viewed here](https://github.com/pints-team/pints/blob/main/examples/sampling/readme-example.ipynb) but a friendlier, more elaborate, introduction can be found on the [examples page](https://github.com/pints-team/pints/blob/main/examples/README.md).
 
+Beyond time-series models, PINTS can be used on any error function or log-likelihood that takes real-valued, continuous parameters.
+
 A graphical overview of the methods included in PINTS can be [viewed here](https://pints-team.github.io/pints-methods-overview/).
 
 ### Examples and documentation

diff --git a/pints/_error_measures.py b/pints/_error_measures.py
@@ -17,7 +17,8 @@ class ErrorMeasure(object):
 
     ErrorMeasures are callable objects: If ``e`` is an instance of an
     :class:`ErrorMeasure` class you can calculate the error by calling ``e(p)``
-    where ``p`` is a point in parameter space.
+    where ``p`` is a point in parameter space. In PINTS, all parameters must be
+    continuous and real.
     """
     def __call__(self, x):
         raise NotImplementedError

diff --git a/pints/_log_pdfs.py b/pints/_log_pdfs.py
@@ -16,7 +16,8 @@ class LogPDF(object):
     All :class:`LogPDF` types are callable: when called with a vector argument
     ``p`` they return some value ``log(f(p))`` where ``f(p)`` is an
     unnormalised PDF. The size of the argument ``p`` is given by
-    :meth:`n_parameters()`.
+    :meth:`n_parameters()`. In PINTS, all parameters must be continuous and
+    real.
     """
     def __call__(self, x):
         raise NotImplementedError