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doc-sources/changelog.rst

@@ -4,7 +4,7 @@
 Changelog
 =========
 
-Versions 0.9.0 to 0.9.1
+Versions 0.9.0 to 0.9.2-dev
 =======================
 
 - Pre-process the learning set before all learning algorithms.

doc-sources/get-started.rst

@@ -83,7 +83,7 @@ Generate a classification problem with 4 criteria and 3 categories (@todo(Docume
 
 The generated ``problem.yml`` should look like::
 
-    # Reproduction command (with lincs version 0.9.1): lincs generate classification-problem 4 3 --random-seed 40
+    # Reproduction command (with lincs version 0.9.2-dev): lincs generate classification-problem 4 3 --random-seed 40
     kind: classification-problem
     format_version: 1
     criteria:
@@ -137,7 +137,7 @@ Then generate an NCS classification model (@todo(Documentation, soon) Link to co
 
 It should look like::
 
-    # Reproduction command (with lincs version 0.9.1): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
+    # Reproduction command (with lincs version 0.9.2-dev): lincs generate classification-model problem.yml --random-seed 41 --model-type mrsort
     kind: ncs-classification-model
     format_version: 1
     boundaries:
@@ -196,7 +196,7 @@ 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 0.9.1): lincs generate classified-alternatives problem.yml model.yml 1000 --random-seed 42 --misclassified-count 0
+    # Reproduction command (with lincs version 0.9.2-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"
@@ -254,7 +254,7 @@ 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 0.9.1): 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 0.9.2-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

@@ -130,7 +130,7 @@ If those conditions are verified, you can tweak the "weights, profiles, breed" l
 This should output a similar model, with slight numerical differences.
 
 .. START other-learnings/expected-gpu+alglib-trained-model.yml
-    # Reproduction command (with lincs version 0.9.1): 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 gpu --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 0.9.2-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 gpu --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
@@ -166,7 +166,7 @@ You can also use an entirely different approach using SAT and max-SAT solvers::
 
 It should produce a different kind of model, with the sufficient coalitions specified explicitly by their roots::
 
-    # Reproduction command (with lincs version 0.9.1): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.approach sat-by-coalitions
+    # Reproduction command (with lincs version 0.9.2-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.approach sat-by-coalitions
     kind: ncs-classification-model
     format_version: 1
     boundaries:
@@ -181,7 +181,7 @@ It should produce a different kind of model, with the sufficient coalitions spec
 .. STOP
 
 .. START other-learnings/expected-minisat-separation-trained-model.yml
-    # Reproduction command (with lincs version 0.9.1): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.approach sat-by-separation
+    # Reproduction command (with lincs version 0.9.2-dev): lincs learn classification-model problem.yml learning-set.csv --model-type ucncs --ucncs.approach sat-by-separation
     kind: ncs-classification-model
     format_version: 1
     boundaries:

lincs/__init__.py

@@ -1,6 +1,6 @@
 # Copyright 2023 Vincent Jacques
 
-__version__ = "0.9.1"
+__version__ = "0.9.2-dev"
 
 # I/O
 from liblincs import Criterion, Category, Problem