markdown formatting

README.txt

@@ -9,29 +9,29 @@ Please cite the above paper in scientific research using this model or the inclu
 Files
 =====
 
-* ```description.tex``` -- a description of the model, also see the downloadable file ```description.pdf```.
-* ```generate.py``` -- generates the physics of a 2-D world and some episodes
-* ```util.py``` -- some geophysical utility functions
-* ```solve.py``` -- a sample solver that learns the physics and solves the episodes
-* ```pysolve.py``` -- a python-based solver
-* ```csolve.c``` -- a C-based solver, identical to pysolve.py but much faster
-* ```evaluate.py``` -- evaluates a solution versus a reference
-* ```mwmatching.py``` -- utility script for max-weight max cardinality matching
-* ```short_data/``` or ```large_data/``` -- data directories created by uncompressing the downloadable file ```data.tar.gz```. These directories contain the following files:
-
-  * ```physics.data``` -- physics for the training and test episodes
-  * ```training.data``` -- 100 (or 10K) episodes for training
-  * ```test.data``` -- another 100 (or 10K) episodes for testing
-  * ```test.blind``` -- the test data with the event-to-detection mapping omitted
-  * ```test.solution``` -- a sample solution on the test data
+* ``description.tex`` -- a description of the model, also see the downloadable file ``description.pdf``.
+* ``generate.py`` -- generates the physics of a 2-D world and some episodes
+* ``util.py`` -- some geophysical utility functions
+* ``solve.py`` -- a sample solver that learns the physics and solves the episodes
+* ``pysolve.py`` -- a python-based solver
+* ``csolve.c`` -- a C-based solver, identical to pysolve.py but much faster
+* ``evaluate.py`` -- evaluates a solution versus a reference
+* ``mwmatching.py`` -- utility script for max-weight max cardinality matching
+* ``short_data/`` or ``large_data/`` -- data directories created by uncompressing the downloadable file ``data.tar.gz``. These directories contain the following files:
+
+  * ``physics.data`` -- physics for the training and test episodes
+  * ``training.data`` -- 100 (or 10K) episodes for training
+  * ``test.data`` -- another 100 (or 10K) episodes for testing
+  * ``test.blind`` -- the test data with the event-to-detection mapping omitted
+  * ``test.solution`` -- a sample solution on the test data
 
 
 Overview
 ========
 
-The simplified model is completely described in [```description.tex```](https://bitbucket.org/nimar/seismic-2d/downloads/description.pdf) and this should be translated in the Probabilistic Programming Language of your choosing. Your model can be tested on the included data files in the *Downloads* section of this repository. The unlabeled test data in ```test.blind``` and the labeled training data in ```training.data``` comprises the observations to the model. The query of interest is the seismic bulletin for each of the observed episodes in the test data.
+The simplified model is completely described in [``description.tex``](https://bitbucket.org/nimar/seismic-2d/downloads/description.pdf) and this should be translated in the Probabilistic Programming Language of your choosing. Your model can be tested on the included data files in the *Downloads* section of this repository. The unlabeled test data in ``test.blind`` and the labeled training data in ``training.data`` comprises the observations to the model. The query of interest is the seismic bulletin for each of the observed episodes in the test data.
 
-Once all the bulletins have been produced, the script ```evaluate.py``` can be used to produce the reports on the accuracy versus the reference script ```test.data```. One can also compare the results versus the baseline in ```test.solution```. For example to check the accuracy of the sample solution:
+Once all the bulletins have been produced, the script ``evaluate.py`` can be used to produce the reports on the accuracy versus the reference script ``test.data``. One can also compare the results versus the baseline in ``test.solution``. For example to check the accuracy of the sample solution:
 
     $> python evaluate.py short_data/test.data short_data/test.solution 
 
@@ -45,41 +45,42 @@ Once all the bulletins have been produced, the script ```evaluate.py``` can be u
 
     Mag Errors mean 0.3 std 0.2
 
-The files ```generate.py``` and ```solve.py``` have only been provided for convenience. These files shouldn't normally be used. However, if you want to check the performance of your model on more than just the provided data you may generate more as needed. The sample solver is based loosely on the published greedy algorithm, and may be used as a very simple baseline.
+The files ``generate.py`` and ``solve.py`` have only been provided for convenience. These files shouldn't normally be used. However, if you want to check the performance of your model on more than just the provided data you may generate more as needed. The sample solver is based loosely on the published greedy algorithm, and may be used as a very simple baseline.
 
 The sample solver first tries to run the C-based solver, and if that is not found it attempts the python version. Both versions of the solver are identical except that the C one is a lot faster. Note that the output of the solvers is already included with the provided data, so it is only necessary to run the solver for additional data that you might generate.
 
-Note that the sample solver cheats a little bit by directly looking at the underlying physics rather than learning it from the training data. Your solver should *not* be reading any of the following files -- ```physics.data```, ```test.data```, or ```test.solution```.
+Note that the sample solver cheats a little bit by directly looking at the underlying physics rather than learning it from the training data. Your solver should *not* be reading any of the following files -- ``physics.data``, ``test.data``, or ``test.solution``.
 
 Compilation Instructions For C-Based Solver
 ===========================================
-After building the C-based solver please run the script ```solve.py``` as usual, and it will automatically use the newly built extension ```csolve```.
+After building the C-based solver please run the script ``solve.py`` as usual, and it will automatically use the newly built extension ``csolve``.
 
 In order to build the C-based solver the following command must be executed:
 
-```python setup.py build_ext --inplace```
+``python setup.py build_ext --inplace``
+
 This will generate a file csolve.so (or csolve.pyd on Windows). Compiling Python C extensions on Windows usually involves additional steps. We strongly recommend using a 32-bit Python even on 64-bit Windows, as the steps are a lot easier.
 
 * Compiling on 32-bit Python using MinGW:
 
   * Install MinGW
   * Compile using the --compiler flag:
 
-    ```python setup.py build_ext --inplace --compiler=mingw32```
+    ``python setup.py build_ext --inplace --compiler=mingw32``
 
 * Compiling on 64-bit Python:
 
   * [Install Visual Studio 10.0 Express](http://www.microsoft.com/visualstudio/eng/downloads#d-2010-express)
   * [Install Microsoft Windows SDK for Windows 7 and .NET Framework 4](http://www.microsoft.com/en-us/download/details.aspx?id=8279)
   * Create the following file:  
 
-    ```C:\Program Files (x86)\Microsoft Visual Studio 10.0\vc\bin\amd64\vcvars64.bat```
+    ``C:\Program Files (x86)\Microsoft Visual Studio 10.0\vc\bin\amd64\vcvars64.bat``
     with the single-line content:   
 
-    ```CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64```
+    ``CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64``
   * Compile as usual
 
-    ```python setup.py build_ext --inplace```
+    ``python setup.py build_ext --inplace``
 
 Authors
 =======