Start working on next version

doc-sources/conceptual-overview.rst

@@ -362,7 +362,7 @@ If yes, they get a partial scholarship.
 .. STOP
 
 .. START concept-example/expected-classified-alternatives.csv
-    # Reproduction command (with lincs version 0.10.2): lincs classify problem.yml model.yml alternatives.csv
+    # Reproduction command (with lincs version 0.10.3-dev): lincs classify problem.yml model.yml alternatives.csv
     name,Math,Physics,Literature,History,category
     A,1,1,1,1,"Full scholarship"
     B,1,1,1,0,"Full scholarship"

doc-sources/get-started.rst

@@ -86,7 +86,7 @@ So, start by generating a classification problem with 4 criteria and 3 categorie
 
 The generated ``problem.yml`` should look like::
 
-    # Reproduction command (with lincs version 0.10.2): lincs generate classification-problem 4 3 --random-seed 40
+    # Reproduction command (with lincs version 0.10.3-dev): lincs generate classification-problem 4 3 --random-seed 40
     kind: classification-problem
     format_version: 1
     criteria:
@@ -142,7 +142,7 @@ Then generate an NCS classification model::
 
 It should look like::
 
-    # Reproduction command (with lincs version 0.10.2): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
+    # Reproduction command (with lincs version 0.10.3-dev): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -212,7 +212,7 @@ Then we'll need to think about the how the ``--max-imbalance`` option interacts
 
 It should start with something like this, and contain 1000 alternatives::
 
-    # Reproduction command (with lincs version 0.10.2): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
+    # Reproduction command (with lincs version 0.10.3-dev): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
     name,"Criterion 1","Criterion 2","Criterion 3","Criterion 4",category
     "Alternative 1",0.37454012,0.796543002,0.95071429,0.183434784,"Category 3"
     "Alternative 2",0.731993914,0.779690981,0.598658502,0.596850157,"Category 2"
@@ -268,7 +268,7 @@ The learning set doesn't contain all the information from the original model,
 and the trained model was reconstituted from this partial information,
 so it is numerically different::
 
-    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
+    # Reproduction command (with lincs version 0.10.3-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver glop --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
     # Termination condition: target accuracy reached
     # Number of iterations: 22
     kind: ncs-classification-model

doc-sources/user-guide.rst

@@ -475,7 +475,7 @@ Here is an example using the `Alglib <https://www.alglib.net/>`_ solver::
 It should produce a very similar model, with slight numerical differences.
 
 .. START alglib-learning/expected-alglib-trained-model.yml
-    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver alglib --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
+    # Reproduction command (with lincs version 0.10.3-dev): lincs learn classification-model problem.yml learning-set.csv --model-type mrsort --mrsort.strategy weights-profiles-breed --mrsort.weights-profiles-breed.models-count 9 --mrsort.weights-profiles-breed.accuracy-heuristic.random-seed 43 --mrsort.weights-profiles-breed.initialization-strategy maximize-discrimination-per-criterion --mrsort.weights-profiles-breed.weights-strategy linear-program --mrsort.weights-profiles-breed.linear-program.solver alglib --mrsort.weights-profiles-breed.profiles-strategy accuracy-heuristic --mrsort.weights-profiles-breed.accuracy-heuristic.processor cpu --mrsort.weights-profiles-breed.breed-strategy reinitialize-least-accurate --mrsort.weights-profiles-breed.reinitialize-least-accurate.portion 0.5 --mrsort.weights-profiles-breed.target-accuracy 1.0
     # Termination condition: target accuracy reached
     # Number of iterations: 9
     kind: ncs-classification-model
@@ -591,7 +591,7 @@ And::
 
 They should produce a different kind of model, with the sufficient coalitions specified explicitly by their roots::
 
-    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
+    # Reproduction command (with lincs version 0.10.3-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy sat-by-coalitions
     kind: ncs-classification-model
     format_version: 1
     accepted_values:
@@ -613,7 +613,7 @@ They should produce a different kind of model, with the sufficient coalitions sp
 .. STOP
 
 .. START sat-learnings/expected-max-sat-by-separation-trained-model.yml
-    # Reproduction command (with lincs version 0.10.2): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
+    # Reproduction command (with lincs version 0.10.3-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.strategy max-sat-by-separation
     kind: ncs-classification-model
     format_version: 1
     accepted_values:

lincs/__init__.py

@@ -1,6 +1,6 @@
 # Copyright 2023 Vincent Jacques
 
-__version__ = "0.10.2"
+__version__ = "0.10.3-dev"
 
 # I/O
 from liblincs import DataValidationException