Merge pull request #67 from ayrna/fix_links

Fix links

README.md

@@ -4,20 +4,20 @@
 ![ORCA logo](doc/orca_small.png)
 <!-- TOC depthFrom:1 depthTo:6 withLinks:1 updateOnSave:1 orderedList:1 -->
 
-1. [ORCA](#orca)
-2. [Cite ORCA](#cite-orca)
-3. [Installation, tutorials and documentation](#installation-tutorials-and-documentation)
-4. [Methods](#methods)
-	1. [Ordinal regression algorithms](#ordinal-regression-algorithms)
-	2. [Partial order methods](#partial-order-methods)
-	3. [Nominal methods](#nominal-methods)
-5. [Performance metrics](#performance-metrics)
-6. [Utilities, classes and scripts](#utilities-classes-and-scripts)
-7. [Datasets](#datasets)
-8. [Experiments parallelization with HTCondor](#experiments-parallelization-with-htcondor)
-9. [External software](#external-software)
-10. [Other contributors](#other-contributors)
-11. [References](#references)
+- [ORCA](#orca)
+- [Cite ORCA](#cite-orca)
+- [Installation, tutorials and documentation](#installation-tutorials-and-documentation)
+- [Methods included](#methods-included)
+  - [Ordinal regression algorithms](#ordinal-regression-algorithms)
+  - [Partial order methods](#partial-order-methods)
+  - [Nominal methods](#nominal-methods)
+- [Performance metrics](#performance-metrics)
+- [Utilities, classes and scripts](#utilities-classes-and-scripts)
+- [Datasets](#datasets)
+- [Experiments parallelization with HTCondor](#experiments-parallelization-with-htcondor)
+- [External software](#external-software)
+- [Other contributors](#other-contributors)
+- [References](#references)
 
 <!-- /TOC -->
 
@@ -67,7 +67,7 @@ The documentation can be found in the [doc](doc) folder and includes:
 	  1. A first *'how to' tutorial* ([Jupyter Notebook](doc/orca_tutorial_1.ipynb), [MD](doc/orca_tutorial_1.md)) to get started with ORCA.
   	1. A specific *tutorial for naive approaches and decomposition methods* ([Jupyter Notebook](doc/orca_tutorial_2.ipynb), [MD](doc/orca_tutorial_2.md)) covering the different considerations needed for these methods.
   	1. A *tutorial for threshold models* ([Jupyter Notebook](doc/orca_tutorial_3.ipynb), [MD](doc/orca_tutorial_3.md)) examining the differences of these models.
-  - A guide about how to [paralelize ORCA experiments](doc/orca_parallel.md).
+  - A guide about how to [parallelize ORCA experiments](doc/orca_parallel.md).
   - Some notes about the [use of ORCA with HTCondor](doc/orca_condor.md).
   - An example about how to [add a new method to ORCA](doc/orca_addmethod.md).
   - An additional branch that includes other methods ready to use in ORCA. Visit [orca-extra-methods branch](https://github.com/ayrna/orca/tree/orca-extra-methods).
@@ -125,7 +125,7 @@ The [measures](src/Measures) folder contains the MATLAB classes for the metrics
 - [Experiment.m](src/Utils/Experiment.m): Class that runs the different experiments.
 - [Utilities.m](src/Utils/Utilities.m): Class that pre-process the experiment files, run the different algorithms and produces the results.
 - [runtests_single.m](src/runtests_single.m): Script to run all the methods using the ORCA API. Reference performance is compared with `toy` dataset in order to check that the installation is correct.
-- [runtestscv.m](src/runtestscv.m): This script runs full experiment tests using the ORCA configuration files to describe experiments.
+- [runtests_cv.m](src/runtests_cv.m): This script runs full experiment tests using the ORCA configuration files to describe experiments.
 
 # Datasets
 

doc/orca_quick_install.md

@@ -59,7 +59,7 @@ If you prefer to build `mex` files from the Linux shell, you can use standard `m
 
 ## Installation testing
 
-We provide a set of basic tests to for checking that all the algorithms work, both using ORCA's API and experiment scripts (see [tutorial](orca_tutorial_1_md) for more information).
+We provide a set of basic tests to for checking that all the algorithms work, both using ORCA's API and experiment scripts (see [tutorial](orca_tutorial_1.md) for more information).
 
 The way to run the tests checking the API (see [single test scripts](../src/tests/singletests/)) is the following (running time is ~12 seconds):
 
@@ -82,8 +82,7 @@ All tests ended successfully
 To run the tests checking the experiment scripts (running time is ~123 seconds):
 
 ```MATLAB
->> cd src/
->> runtestscv
+>> runtests_cv
 ...
 Running experiment exp-svr-real1-toy-1
 Processing Experiments/exp-2017-11-16-13-59-1/exp-svr-real1-toy-1
@@ -98,7 +97,7 @@ If any of these tests fail, please read the [detailed installation guide](orca_i
 
 ## Use ORCA as a toolbox
 
-The first of three tutorials cvers the basic use of ORCA as a toolbox but also as an experimental framework: *'how to' tutorial* ([HTML](doc/orca_tutorial_1.html), [Jupyter Notebook](doc/orca_tutorial_1.ipynb), [MD](doc/orca_tutorial_1.md)).
+The first of three tutorials covers the basic use of ORCA as a toolbox but also as an experimental framework: *'how to' tutorial* ([Jupyter Notebook](orca_tutorial_1.ipynb), [MD](orca_tutorial_1.md)).
 
 For instance, to use ORCA as a toolbox:
 

doc/orca_tutorial_1.ipynb

@@ -558,7 +558,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The whole example is available at [exampleProjections.m](../code-examples/exampleProjections.m)."
+    "The whole example is available at [exampleProjections.m](../src/code-examples/exampleProjections.m)."
    ]
   },
   {
@@ -600,7 +600,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The whole example is available at [exampleProjectionsThresholds.m](../code-examples/exampleProjectionsThresholds.m)."
+    "The whole example is available at [exampleProjectionsThresholds.m](../src/code-examples/exampleProjectionsThresholds.m)."
    ]
   },
   {
@@ -757,7 +757,7 @@
     "\n",
     "---\n",
     "\n",
-    "***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca-tutorial.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?\n",
+    "***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca_tutorial_1.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?\n",
     "\n",
     "---\n",
     "\n",
@@ -796,12 +796,25 @@
     },
     {
      "text": "MetaKernel Magics",
-     "url": "https://metakernel.readthedocs.io/en/latest/source/README.html"
+     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
     }
    ],
    "mimetype": "text/x-octave",
    "name": "octave",
-   "version": "4.4.1"
+   "version": "4.2.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

doc/orca_tutorial_1.md

@@ -168,13 +168,13 @@ Utilities.runExperiments('tutorial/config-files/pom.ini')
     Running experiment exp-pom-tutorial-toy-8.ini
     Running experiment exp-pom-tutorial-toy-9.ini
     Calculating results...
-    Experiments/exp-2019-5-6-18-34-3/Results/pasture-pom-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-3/Results/tae-pom-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-3/Results/toy-pom-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-3/Results/pasture-pom-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-3/Results/tae-pom-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-3/Results/toy-pom-tutorial/dataset
-    ans = Experiments/exp-2019-5-6-18-34-3
+    Experiments/exp-2019-7-23-10-53-40/Results/pasture-pom-tutorial/dataset
+    Experiments/exp-2019-7-23-10-53-40/Results/tae-pom-tutorial/dataset
+    Experiments/exp-2019-7-23-10-53-40/Results/toy-pom-tutorial/dataset
+    Experiments/exp-2019-7-23-10-53-40/Results/pasture-pom-tutorial/dataset
+    Experiments/exp-2019-7-23-10-53-40/Results/tae-pom-tutorial/dataset
+    Experiments/exp-2019-7-23-10-53-40/Results/toy-pom-tutorial/dataset
+    ans = Experiments/exp-2019-7-23-10-53-40
 
 
 As can be observed, ORCA analyses all the files included in the folder of the dataset, where training and test partitions are included (a pair of files `train_dataset.X` and `test_dataset.X` for each dataset, where `X` is the number of partition). For each partition, a model is trained on training data and tested on test data.
@@ -199,13 +199,13 @@ Utilities.runExperiments('tutorial/config-files/svc1v1-3holdout.ini')
     Running experiment exp-svorim-mae-tutorial-toy-2.ini
     Running experiment exp-svorim-mae-tutorial-toy-3.ini
     Calculating results...
-    Experiments/exp-2019-5-6-18-34-21/Results/pasture-svorim-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-21/Results/tae-svorim-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-21/Results/toy-svorim-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-21/Results/pasture-svorim-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-21/Results/tae-svorim-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-21/Results/toy-svorim-mae-tutorial/dataset
-    ans = Experiments/exp-2019-5-6-18-34-21
+    Experiments/exp-2019-7-23-10-54-2/Results/pasture-svorim-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-2/Results/tae-svorim-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-2/Results/toy-svorim-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-2/Results/pasture-svorim-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-2/Results/tae-svorim-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-2/Results/toy-svorim-mae-tutorial/dataset
+    ans = Experiments/exp-2019-7-23-10-54-2
     Setting up experiments...
     Running experiment exp-svc1v1-mae-tutorial-pasture-1.ini
     Running experiment exp-svc1v1-mae-tutorial-pasture-2.ini
@@ -217,13 +217,13 @@ Utilities.runExperiments('tutorial/config-files/svc1v1-3holdout.ini')
     Running experiment exp-svc1v1-mae-tutorial-toy-2.ini
     Running experiment exp-svc1v1-mae-tutorial-toy-3.ini
     Calculating results...
-    Experiments/exp-2019-5-6-18-34-27/Results/pasture-svc1v1-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-27/Results/tae-svc1v1-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-27/Results/toy-svc1v1-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-27/Results/pasture-svc1v1-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-27/Results/tae-svc1v1-mae-tutorial/dataset
-    Experiments/exp-2019-5-6-18-34-27/Results/toy-svc1v1-mae-tutorial/dataset
-    ans = Experiments/exp-2019-5-6-18-34-27
+    Experiments/exp-2019-7-23-10-54-11/Results/pasture-svc1v1-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-11/Results/tae-svc1v1-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-11/Results/toy-svc1v1-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-11/Results/pasture-svc1v1-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-11/Results/tae-svc1v1-mae-tutorial/dataset
+    Experiments/exp-2019-7-23-10-54-11/Results/toy-svc1v1-mae-tutorial/dataset
+    ans = Experiments/exp-2019-7-23-10-54-11
 
 
 Once the experiments are finished, the corresponding results can be found in the `Experiments` subfolder, as described in the [corresponding section](#Experimental-results-and-reports) of this tutorial.
@@ -633,7 +633,7 @@ amae2 = AMAE.calculateMetric(test.targets,info2.predictedTest)
     amae2 =  0.11670
 
 
-The whole example is available at [exampleProjections.m](../code-examples/exampleProjections.m).
+The whole example is available at [exampleProjections.m](../src/code-examples/exampleProjections.m).
 
 ### Visualizing projections and decision thresholds
 
@@ -666,7 +666,7 @@ hold off;
 ![png](orca_tutorial_1_files/orca_tutorial_1_38_0.png)
 
 
-The whole example is available at [exampleProjectionsThresholds.m](../code-examples/exampleProjectionsThresholds.m).
+The whole example is available at [exampleProjectionsThresholds.m](../src/code-examples/exampleProjectionsThresholds.m).
 
 ## Using ORCA with your own datasets
 
@@ -842,7 +842,7 @@ The source code of this example is in [exampleERAHHoldout.m](../src/code-example
 
 ---
 
-***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca-tutorial.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?
+***Exercise 4***: train classifiers for both `ERA` and `ESL` datasets, using the same experimental design you used in the [experiment section](orca_tutorial_1.md#launch-experiments-through-ini-files). Compare the results obtained for both datasets. Generate bar plots for comparing accuracy and AMAE. Which one is better classified? Which one is better ordered?
 
 ---