From 0f1846abad07b8c38847b935d3e2a623ae1c92bd Mon Sep 17 00:00:00 2001
From: Evan Bolyen <ebolyen@gmail.com>
Date: Fri, 1 May 2020 07:39:41 -0700
Subject: [PATCH] Add gitignore, remove build artifacts

---
 .DS_Store                                     | Bin 10244 -> 0 bytes
 .gitignore                                    | 229 +++++
 .../inspectionProfiles/profiles_settings.xml  |   6 -
 .idea/misc.xml                                |   4 -
 .idea/modules.xml                             |   8 -
 .idea/q2-classo.iml                           |  12 -
 .idea/vcs.xml                                 |   6 -
 .idea/workspace.xml                           |  56 --
 build/lib/q2_classo/CLasso/__init__.py        |   5 -
 build/lib/q2_classo/CLasso/compact_func.py    | 124 ---
 .../lib/q2_classo/CLasso/cross_validation.py  |  98 ---
 build/lib/q2_classo/CLasso/misc_functions.py  | 399 ---------
 build/lib/q2_classo/CLasso/path_alg.py        | 454 ----------
 build/lib/q2_classo/CLasso/solve_R1.py        | 251 ------
 build/lib/q2_classo/CLasso/solve_R2.py        | 233 -----
 build/lib/q2_classo/CLasso/solve_R3.py        | 243 ------
 build/lib/q2_classo/CLasso/solve_R4.py        | 242 ------
 build/lib/q2_classo/CLasso/solver 2.py        | 820 -----------------
 build/lib/q2_classo/CLasso/solver.py          | 822 ------------------
 .../q2_classo/CLasso/stability_selection 2.py | 134 ---
 .../q2_classo/CLasso/stability_selection.py   | 134 ---
 build/lib/q2_classo/__init__.py               |   5 -
 build/lib/q2_classo/_dict.py                  | 102 ---
 build/lib/q2_classo/_dictionaries.py          | 100 ---
 build/lib/q2_classo/_formats.py               |  20 -
 build/lib/q2_classo/_func.py                  | 131 ---
 build/lib/q2_classo/_summarize/__init__.py    |   1 -
 build/lib/q2_classo/_summarize/_visualizer.py | 241 -----
 build/lib/q2_classo/_summarize/assets/cv.html |  42 -
 .../q2_classo/_summarize/assets/index.html    |   1 -
 .../_summarize/assets/lam-fixed.html          |  41 -
 .../q2_classo/_summarize/assets/overview.html |  28 -
 .../lib/q2_classo/_summarize/assets/path.html |  45 -
 .../q2_classo/_summarize/assets/stabsel.html  |  45 -
 build/lib/q2_classo/_summarize/form/R1.png    | Bin 1345 -> 0 bytes
 build/lib/q2_classo/_summarize/form/R2.png    | Bin 1376 -> 0 bytes
 build/lib/q2_classo/_summarize/form/R3.png    | Bin 1642 -> 0 bytes
 build/lib/q2_classo/_summarize/form/R4.png    | Bin 2501 -> 0 bytes
 build/lib/q2_classo/_transformer.py           |  18 -
 build/lib/q2_classo/_types.py                 |  38 -
 build/lib/q2_classo/_visualizer.py            | 303 -------
 build/lib/q2_classo/citations.bib             |   0
 build/lib/q2_classo/dictionaries.py           | 100 ---
 build/lib/q2_classo/functions.py              | 123 ---
 build/lib/q2_classo/plugin_setup.py           | 140 ---
 dist/q2_classo-0.0.0.dev0-py3.6.egg           | Bin 154922 -> 0 bytes
 q2_classo.egg-info/PKG-INFO                   |  10 -
 q2_classo.egg-info/SOURCES.txt                |  39 -
 q2_classo.egg-info/dependency_links.txt       |   1 -
 q2_classo.egg-info/entry_points.txt           |   3 -
 q2_classo.egg-info/not-zip-safe               |   1 -
 q2_classo.egg-info/top_level.txt              |   1 -
 q2_classo/.DS_Store                           | Bin 14340 -> 0 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 527 -> 0 bytes
 .../__pycache__/__init__.cpython-37.pyc       | Bin 518 -> 0 bytes
 .../CLasso/__pycache__/algos.cpython-37.pyc   | Bin 10532 -> 0 bytes
 .../__pycache__/class_of_param.cpython-37.pyc | Bin 1447 -> 0 bytes
 .../class_of_problem.cpython-37.pyc           | Bin 5684 -> 0 bytes
 .../classification.cpython-36 2.pyc           | Bin 4129 -> 0 bytes
 .../__pycache__/classification.cpython-36.pyc | Bin 4129 -> 0 bytes
 .../__pycache__/classification.cpython-37.pyc | Bin 4133 -> 0 bytes
 .../__pycache__/compact_func.cpython-36.pyc   | Bin 3005 -> 0 bytes
 .../__pycache__/compact_func.cpython-37.pyc   | Bin 2963 -> 0 bytes
 .../constrained_lasso.cpython-37.pyc          | Bin 14064 -> 0 bytes
 .../cross_validation.cpython-36.pyc           | Bin 3797 -> 0 bytes
 .../cross_validation.cpython-37.pyc           | Bin 3783 -> 0 bytes
 .../functions_4algos.cpython-37.pyc           | Bin 2766 -> 0 bytes
 .../huber_classification.cpython-36.pyc       | Bin 4986 -> 0 bytes
 .../huber_classification.cpython-37.pyc       | Bin 4980 -> 0 bytes
 .../__pycache__/huber_path.cpython-37.pyc     | Bin 4691 -> 0 bytes
 .../little_functions.cpython-36.pyc           | Bin 7476 -> 0 bytes
 .../little_functions.cpython-37.pyc           | Bin 7469 -> 0 bytes
 .../__pycache__/misc_functions.cpython-36.pyc | Bin 10936 -> 0 bytes
 .../__pycache__/misc_functions.cpython-37.pyc | Bin 10913 -> 0 bytes
 .../model_selection.cpython-37 2.pyc          | Bin 672 -> 0 bytes
 .../model_selection.cpython-37.pyc            | Bin 672 -> 0 bytes
 .../__pycache__/path_alg.cpython-36.pyc       | Bin 13284 -> 0 bytes
 .../__pycache__/path_alg.cpython-37.pyc       | Bin 13145 -> 0 bytes
 .../__pycache__/path_algorithm.cpython-36.pyc | Bin 4410 -> 0 bytes
 .../__pycache__/path_algorithm.cpython-37.pyc | Bin 4414 -> 0 bytes
 .../path_algorithm_huber.cpython-36.pyc       | Bin 4284 -> 0 bytes
 .../path_algorithm_huber.cpython-37.pyc       | Bin 4288 -> 0 bytes
 .../path_algorithm_nw.cpython-37.pyc          | Bin 3956 -> 0 bytes
 .../__pycache__/performance.cpython-36.pyc    | Bin 1187 -> 0 bytes
 .../__pycache__/performance.cpython-37.pyc    | Bin 1185 -> 0 bytes
 .../__pycache__/solve_R1.cpython-36.pyc       | Bin 5314 -> 0 bytes
 .../__pycache__/solve_R1.cpython-37.pyc       | Bin 5288 -> 0 bytes
 .../__pycache__/solve_R2.cpython-36.pyc       | Bin 5434 -> 0 bytes
 .../__pycache__/solve_R2.cpython-37.pyc       | Bin 5410 -> 0 bytes
 .../__pycache__/solve_R3.cpython-36.pyc       | Bin 6116 -> 0 bytes
 .../__pycache__/solve_R3.cpython-37.pyc       | Bin 6089 -> 0 bytes
 .../__pycache__/solve_R4.cpython-36.pyc       | Bin 6658 -> 0 bytes
 .../__pycache__/solve_R4.cpython-37.pyc       | Bin 6636 -> 0 bytes
 .../CLasso/__pycache__/solver.cpython-36.pyc  | Bin 33141 -> 0 bytes
 .../CLasso/__pycache__/solver.cpython-37.pyc  | Bin 33028 -> 0 bytes
 .../stability_selection.cpython-36 2.pyc      | Bin 2857 -> 0 bytes
 .../stability_selection.cpython-36.pyc        | Bin 2796 -> 0 bytes
 .../stability_selection.cpython-37.pyc        | Bin 2789 -> 0 bytes
 q2_classo/__pycache__/__init__.cpython-36.pyc | Bin 434 -> 0 bytes
 q2_classo/__pycache__/_dict.cpython-36.pyc    | Bin 4759 -> 0 bytes
 .../__pycache__/_dictionaries.cpython-36.pyc  | Bin 4767 -> 0 bytes
 q2_classo/__pycache__/_types.cpython-36.pyc   | Bin 1002 -> 0 bytes
 .../__pycache__/dictionaries.cpython-36.pyc   | Bin 4766 -> 0 bytes
 q2_classo/__pycache__/foncon.cpython-36.pyc   | Bin 249 -> 0 bytes
 .../__pycache__/functions.cpython-36.pyc      | Bin 3256 -> 0 bytes
 .../__pycache__/plugin_setup.cpython-36.pyc   | Bin 1882 -> 0 bytes
 106 files changed, 229 insertions(+), 5630 deletions(-)
 delete mode 100644 .DS_Store
 create mode 100644 .gitignore
 delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml
 delete mode 100644 .idea/misc.xml
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/q2-classo.iml
 delete mode 100644 .idea/vcs.xml
 delete mode 100644 .idea/workspace.xml
 delete mode 100644 build/lib/q2_classo/CLasso/__init__.py
 delete mode 100644 build/lib/q2_classo/CLasso/compact_func.py
 delete mode 100644 build/lib/q2_classo/CLasso/cross_validation.py
 delete mode 100644 build/lib/q2_classo/CLasso/misc_functions.py
 delete mode 100644 build/lib/q2_classo/CLasso/path_alg.py
 delete mode 100644 build/lib/q2_classo/CLasso/solve_R1.py
 delete mode 100644 build/lib/q2_classo/CLasso/solve_R2.py
 delete mode 100644 build/lib/q2_classo/CLasso/solve_R3.py
 delete mode 100644 build/lib/q2_classo/CLasso/solve_R4.py
 delete mode 100644 build/lib/q2_classo/CLasso/solver 2.py
 delete mode 100644 build/lib/q2_classo/CLasso/solver.py
 delete mode 100644 build/lib/q2_classo/CLasso/stability_selection 2.py
 delete mode 100644 build/lib/q2_classo/CLasso/stability_selection.py
 delete mode 100644 build/lib/q2_classo/__init__.py
 delete mode 100644 build/lib/q2_classo/_dict.py
 delete mode 100644 build/lib/q2_classo/_dictionaries.py
 delete mode 100644 build/lib/q2_classo/_formats.py
 delete mode 100644 build/lib/q2_classo/_func.py
 delete mode 100644 build/lib/q2_classo/_summarize/__init__.py
 delete mode 100644 build/lib/q2_classo/_summarize/_visualizer.py
 delete mode 100644 build/lib/q2_classo/_summarize/assets/cv.html
 delete mode 100644 build/lib/q2_classo/_summarize/assets/index.html
 delete mode 100644 build/lib/q2_classo/_summarize/assets/lam-fixed.html
 delete mode 100644 build/lib/q2_classo/_summarize/assets/overview.html
 delete mode 100644 build/lib/q2_classo/_summarize/assets/path.html
 delete mode 100644 build/lib/q2_classo/_summarize/assets/stabsel.html
 delete mode 100644 build/lib/q2_classo/_summarize/form/R1.png
 delete mode 100644 build/lib/q2_classo/_summarize/form/R2.png
 delete mode 100644 build/lib/q2_classo/_summarize/form/R3.png
 delete mode 100644 build/lib/q2_classo/_summarize/form/R4.png
 delete mode 100644 build/lib/q2_classo/_transformer.py
 delete mode 100644 build/lib/q2_classo/_types.py
 delete mode 100644 build/lib/q2_classo/_visualizer.py
 delete mode 100644 build/lib/q2_classo/citations.bib
 delete mode 100644 build/lib/q2_classo/dictionaries.py
 delete mode 100644 build/lib/q2_classo/functions.py
 delete mode 100644 build/lib/q2_classo/plugin_setup.py
 delete mode 100644 dist/q2_classo-0.0.0.dev0-py3.6.egg
 delete mode 100644 q2_classo.egg-info/PKG-INFO
 delete mode 100644 q2_classo.egg-info/SOURCES.txt
 delete mode 100644 q2_classo.egg-info/dependency_links.txt
 delete mode 100644 q2_classo.egg-info/entry_points.txt
 delete mode 100644 q2_classo.egg-info/not-zip-safe
 delete mode 100644 q2_classo.egg-info/top_level.txt
 delete mode 100644 q2_classo/.DS_Store
 delete mode 100644 q2_classo/CLasso/__pycache__/__init__.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/__init__.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/algos.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/class_of_param.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/class_of_problem.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/classification.cpython-36 2.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/classification.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/classification.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/compact_func.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/compact_func.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/constrained_lasso.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/cross_validation.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/cross_validation.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/functions_4algos.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/huber_classification.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/huber_classification.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/huber_path.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/little_functions.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/little_functions.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/misc_functions.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/misc_functions.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/model_selection.cpython-37 2.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/model_selection.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_alg.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_alg.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_algorithm.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_algorithm.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_algorithm_huber.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_algorithm_huber.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/path_algorithm_nw.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/performance.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/performance.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R1.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R1.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R2.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R2.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R3.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R3.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R4.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solve_R4.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solver.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/solver.cpython-37.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/stability_selection.cpython-36 2.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/stability_selection.cpython-36.pyc
 delete mode 100644 q2_classo/CLasso/__pycache__/stability_selection.cpython-37.pyc
 delete mode 100644 q2_classo/__pycache__/__init__.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/_dict.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/_dictionaries.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/_types.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/dictionaries.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/foncon.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/functions.cpython-36.pyc
 delete mode 100644 q2_classo/__pycache__/plugin_setup.cpython-36.pyc

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diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..230c737
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,229 @@
+# Created by https://www.gitignore.io/api/vim,code,python,intellij
+# Edit at https://www.gitignore.io/?templates=vim,code,python,intellij
+
+### Code ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+
+### Intellij ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### Intellij Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator/
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+### Vim ###
+# Swap
+[._]*.s[a-v][a-z]
+[._]*.sw[a-p]
+[._]s[a-rt-v][a-z]
+[._]ss[a-gi-z]
+[._]sw[a-p]
+
+# Session
+Session.vim
+Sessionx.vim
+
+# Temporary
+.netrwhist
+*~
+
+# Auto-generated tag files
+tags
+
+# Persistent undo
+[._]*.un~
+
+# Coc configuration directory
+.vim
+
+# End of https://www.gitignore.io/api/vim,code,python,intellij
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2d..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index a2e120d..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index 7085915..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/q2-classo.iml" filepath="$PROJECT_DIR$/.idea/q2-classo.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/q2-classo.iml b/.idea/q2-classo.iml
deleted file mode 100644
index 8b8c395..0000000
--- a/.idea/q2-classo.iml
+++ /dev/null
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="PLAIN" />
-    <option name="myDocStringFormat" value="Plain" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 94a25f7..0000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index 05bd270..0000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,56 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ChangeListManager">
-    <list default="true" id="94bcdad1-84c6-4955-9593-b8c4c0208d54" name="Default Changelist" comment="">
-      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-    </list>
-    <option name="SHOW_DIALOG" value="false" />
-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
-  </component>
-  <component name="Git.Settings">
-    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
-  </component>
-  <component name="ProjectId" id="1a7p4wKVXKnuoOaA8uAmw1lW89u" />
-  <component name="ProjectViewState">
-    <option name="hideEmptyMiddlePackages" value="true" />
-    <option name="showExcludedFiles" value="true" />
-    <option name="showLibraryContents" value="true" />
-  </component>
-  <component name="PropertiesComponent">
-    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
-    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
-  </component>
-  <component name="ServiceViewManager">
-    <option name="viewStates">
-      <list>
-        <serviceView>
-          <treeState>
-            <expand />
-            <select />
-          </treeState>
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-      </list>
-    </option>
-  </component>
-  <component name="SvnConfiguration">
-    <configuration />
-  </component>
-  <component name="TaskManager">
-    <task active="true" id="Default" summary="Default task">
-      <changelist id="94bcdad1-84c6-4955-9593-b8c4c0208d54" name="Default Changelist" comment="" />
-      <created>1586096298181</created>
-      <option name="number" value="Default" />
-      <option name="presentableId" value="Default" />
-      <updated>1586096298181</updated>
-    </task>
-    <servers />
-  </component>
-  <component name="WindowStateProjectService">
-    <state x="399" y="282" key="com.intellij.ide.util.TipDialog" timestamp="1586355922481">
-      <screen x="0" y="0" width="1440" height="900" />
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-    <state x="399" y="282" key="com.intellij.ide.util.TipDialog/0.0.1440.900@0.0.1440.900" timestamp="1586355922481" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/__init__.py b/build/lib/q2_classo/CLasso/__init__.py
deleted file mode 100644
index 6a2a9f5..0000000
--- a/build/lib/q2_classo/CLasso/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .misc_functions import random_data, csv_to_mat, mat_to_np, rescale,clr, theoretical_lam, to_zarr
-from .compact_func import Classo, pathlasso
-from .cross_validation import CV
-from .stability_selection import stability
-from .solver import classo_problem, classo_data
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/compact_func.py b/build/lib/q2_classo/CLasso/compact_func.py
deleted file mode 100644
index a42c557..0000000
--- a/build/lib/q2_classo/CLasso/compact_func.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import numpy as np
-import numpy.linalg as LA
-
-from .solve_R1 import problem_R1, Classo_R1, pathlasso_R1
-from .solve_R2 import problem_R2, Classo_R2, pathlasso_R2
-from .solve_R3 import problem_R3, Classo_R3, pathlasso_R3
-from .solve_R4 import problem_R4, Classo_R4, pathlasso_R4
-from .path_alg import solve_path, pathalgo_general, h_lambdamax
-
-'''
-Classo and pathlasso are the main functions, they can call every algorithm acording to the method and formulation required
-'''
-
-# can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-
-def Classo(matrix,lam,typ = 'R1', meth='DR', rho = 1.345, get_lambdamax = False, true_lam=False, e=1., rho_classification=-1.):
-    if(typ=='R3'):
-        if not meth in ['Path-Alg', 'DR']: meth='DR'
-        pb = problem_R3(matrix,meth,e=e)
-        if (true_lam): beta,s = Classo_R3(pb,lam/pb.lambdamax)
-        else : beta, s = Classo_R3(pb, lam)
-        s = s/np.sqrt(e)
-
-    elif(typ=='R4'):
-        if not meth in ['Path-Alg', 'DR']: meth='DR'
-        pb  = problem_R4(matrix,meth,rho,e=e)
-        if (true_lam): beta,s = Classo_R4(pb,lam/pb.lambdamax,e=e)
-        else : beta, s = Classo_R4(pb, lam,e=e)
-
-
-    elif(typ=='R2'):
-        if not meth in ['Path-Alg', 'P-PDS' , 'PF-PDS' , 'DR']: meth = 'ODE'
-        pb = problem_R2(matrix,meth,rho)
-        if (true_lam): beta = Classo_R2(pb,lam/pb.lambdamax)
-        else : beta = Classo_R2(pb, lam)
-
-    elif (typ == 'C2'):
-        if (true_lam):  BETA = solve_path(matrix, lamin, False, rho_classification, 'huber_cl')[0] #TO DO HERE !!!!!!!!!
-        else :    BETA = solve_path(matrix, lamin, False, rho_classification, 'huber_cl')[0]
-        beta = BETA[0]
-
-    elif (typ == 'C1'):
-        if(true_lam) : BETA = solve_path(matrix,lamin, False,0, 'cl')[0] # TO DO HERE !!!!!!!!
-        else : BETA = solve_path(matrix,lamin, False,0, 'cl')[0]
-        beta = BETA[0]
-
-
-    else: # LS
-        if not meth in ['Path-Alg', 'P-PDS' , 'PF-PDS' , 'DR']: meth='DR'
-        pb = problem_R1(matrix,meth)
-        if (true_lam) : beta = Classo_R1(pb,lam/pb.lambdamax)
-        else : beta = Classo_R1(pb,lam)
-
-    if (typ  in ['R3','R4']):
-        if (get_lambdamax): return(pb.lambdamax,beta,s)
-        else              : return(beta,s)
-    if (get_lambdamax): return(pb.lambdamax,beta)
-    else              : return(beta)
-
-
-def pathlasso(matrix,lambdas=False,n_active=0,lamin=1e-2,typ='LS',meth='Path-Alg',rho = 1.345, true_lam = False, e= 1.,return_sigm= False,rho_classification=-1):
-    Nactive = n_active
-    if(Nactive==0):Nactive=False
-    if (type(lambdas)!= bool):
-        if (lambdas[0]<lambdas[-1]): lambdas = [lambdas[i] for i in range(len(lambdas)-1,-1,-1)]  # reverse the list if needed
-    else: lambdas = np.linspace(1.,lamin,100)
-
-    if(typ=='R2'):
-        pb = problem_R2(matrix,meth,rho)
-        lambdamax = pb.lambdamax
-        #if (true_lam): lambdas=[lamb/lambdamax for lamb in lambdas]
-        BETA  = pathlasso_R2(pb,lambdas,n_active=Nactive)
-
-    elif(typ=='R3'):
-        pb = problem_R3(matrix,meth,e=e)
-        lambdamax = pb.lambdamax
-        #if (true_lam): lambdas=[lamb/lambdamax for lamb in lambdas]
-        BETA,S = pathlasso_R3(pb,lambdas,n_active=Nactive)
-        S=np.array(S)/np.sqrt(e)
-
-    elif(typ=='R4'):
-        meth='DR'
-        pb = problem_R4(matrix,meth,rho)
-        lambdamax = pb.lambdamax
-        #if (true_lam): lambdas=[lamb/lambdamax for lamb in lambdas]
-        BETA,S = pathlasso_R4(pb,lambdas,n_active=Nactive)
-        
-    elif(typ == 'C2'):
-        lambdamax = h_lambdamax(matrix[0],matrix[2],rho)
-        #if (true_lam): lambdas=[lamb/lambdamax for lamb in lambdas]
-        BETA = pathalgo_general(matrix, lambdas, 'huber_cl', n_active=Nactive, rho=rho_classification)
-
-    elif (typ == 'C1'):
-        lambdamax = 2*LA.norm((matrix[0].T).dot(matrix[2]),np.infty)
-        #if (true_lam): lambdas = [lamb / lambdamax for lamb in lambdas]
-        BETA = pathalgo_general(matrix, lambdas, 'cl', n_active=Nactive)
-
-    else:
-        pb = problem_R1(matrix,meth)
-        lambdamax = pb.lambdamax
-        #if (true_lam): lambdas=[lamb/lambdamax for lamb in lambdas]
-        BETA = pathlasso_R1(pb,lambdas,n_active=n_active)
-
-    real_path = [lam*lambdamax for lam in lambdas]
-    if(typ in ['R3','R4'] and return_sigm): return(BETA,real_path,S)
-    return(BETA,real_path)
- 
-    
-
-
-'''
-# Cost fucntions for the three 'easiest' problems. Useful for test, to compare two solutions slightly different
-def L_LS(A,y,lamb,x): return(LA.norm( A.dot(x) - y )**2 + lamb * LA.norm(x,1))
-def L_conc(A,y,lamb,x): return(LA.norm( A.dot(x) - y ) + np.sqrt(2)*lamb * LA.norm(y,1))
-def L_H(A,y,lamb,x,rho): return(hub( A.dot(x) - y , rho) + lamb * LA.norm(x,1))
-
-def hub(r,rho) : 
-    h=0
-    for j in range(len(r)):
-        if(abs(r[j])<rho): h+=r[j]**2
-        elif(r[j]>0)     : h+= (2*r[j]-rho)*rho
-        else             : h+= (-2*r[j]-rho)*rho
-    return(h)
-'''
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/cross_validation.py b/build/lib/q2_classo/CLasso/cross_validation.py
deleted file mode 100644
index f7232ab..0000000
--- a/build/lib/q2_classo/CLasso/cross_validation.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-import numpy.random as rd
-import numpy.linalg as LA
-from .compact_func import Classo,pathlasso
-n_lam = 200
-
-def compute_1SE(mse_max,MSE,i): 
-    j=i
-    while(MSE[j]<mse_max): j-=1
-    return max(j,0)
-
-
-def train_test_CV(n,k,test_pourcent):
-    idx, training_size = rd.permutation(n), int(n-n*test_pourcent)
-    idx_train, idx_test = idx[:training_size], idx[training_size:]
-    SUBLIST,end = [],0 
-    for i in range(k):
-        begin,end = end,end+training_size//k
-        if(i<training_size%k): end+=1
-        SUBLIST.append(idx[begin:end])
-    return(SUBLIST, idx_train,idx_test) 
-
-
-def train_test_i (SUBLIST,i):
-    training_set,test_set = np.array([],dtype=int),SUBLIST[i]
-    for j in range(len(SUBLIST)):
-        if (j != i): training_set = np.concatenate((training_set,SUBLIST[j]))
-    return(training_set,test_set)
-            
-
-def training(matrices,typ,num_meth, training_set, rho, rho_classification, e,lambdas):
-    (A,C,y)   = matrices
-    mat       = (A[training_set],C,y[training_set]) 
-    sol       = pathlasso(mat,lambdas = lambdas,typ=typ,meth=num_meth,
-                            rho = rho, e=e,rho_classification=rho_classification)[0]
-    return(sol)
-
-
-def test_i (matrices,typ,num_meth, SUBLIST,i, rho, rho_classification, e,lambdas):
-    training_set,test_set = train_test_i (SUBLIST,i)
-    BETA                  = training(matrices,typ,num_meth, training_set, rho, rho_classification, e, lambdas)
-    n_lam = len(lambdas)
-    residual = np.zeros(n_lam)
-    for j in range(n_lam):
-        residual[j] = accuracy_func(matrices[0][test_set],matrices[2][test_set],BETA[j],typ)/len(test_set)
-    return(residual)
-
-def average_test(matrices,typ,num_meth, SUBLIST, rho, rho_classification, e,lambdas):
-    k = len(SUBLIST)
-    n_lam = len(lambdas)
-    RESIDUAL = np.zeros((k,n_lam))
-    for i in range(k):
-        RESIDUAL[i,:] = test_i (matrices,typ,num_meth, SUBLIST,i, rho, rho_classification, e, lambdas)
-    MSE = np.mean(RESIDUAL,axis = 0)
-    SE = np.std(RESIDUAL,axis = 0) # official standard error should be divided by sqrt(k) ... 
-    return(MSE,SE)
-
-def CV(matrices,k,typ='LS',num_meth="Path-Alg",test=0., seed = 1, rho = 1.345, rho_classification=-1., e= 1.,lambdas = np.linspace(1.,1e-3,n_lam),oneSE = True):
-    
-    rd.seed(seed)
-    (A,C,y) = matrices
-    SUBLIST, idx_train, idx_test = train_test_CV(len(y),k,test)
-    MSE,SE  = average_test(matrices,typ,num_meth, SUBLIST, rho, rho_classification, e, lambdas)
-    i       = np.argmin(MSE)
-    i_1SE   = compute_1SE(MSE[i]+SE[i],MSE,i)
-    if oneSE : lam = lambdas[i_1SE]
-    else     : lam = lambdas[i]
-    out = Classo((A[idx_train],C,y[idx_train]),lam,typ=typ,meth=num_meth,rho=rho, e=e,rho_classification=rho_classification)
-    return(out,MSE,SE,i,i_1SE)
-    
-        
-# Computation of the residual, for huber, LS, huber_classification and classification
-def hub(r,rho) : 
-    h=0
-    for j in range(len(r)):
-        if(abs(r[j])<rho): h+=r[j]**2
-        elif(r[j]>0)     : h+= (2*r[j]-rho)*rho
-        else             : h+= (-2*r[j]-rho)*rho
-    return(h)
-
-
-def hinge(A,y,beta):
-    return(sum(np.max(0,1 - y * A.dot(beta))**2))
-
-def huber_hinge(A,y,beta, rho):
-    h = np.max(0,1 - y * A.dot(beta))
-    s=0
-    for i in range(len(h)):
-        if h[i]<rho : s+=2*h[i]*rho - rho**2
-        else :        s+=h[i]**2
-    return s
-
-
-def accuracy_func(A,y,beta, typ='LS',rho = 1.345, rho_classification=-1.):
-    if (typ == 'Huber'):                   return(hub( A.dot(beta) - y , rho ) )
-    elif (typ == 'Classification') :       return(hinge(A,y,beta))
-    elif (typ == 'Huber_Classification') : return(huber_hinge(A,y,beta,rho_classification))
-    else :                                 return(LA.norm( A.dot(beta) - y )**2)
diff --git a/build/lib/q2_classo/CLasso/misc_functions.py b/build/lib/q2_classo/CLasso/misc_functions.py
deleted file mode 100644
index c6695ee..0000000
--- a/build/lib/q2_classo/CLasso/misc_functions.py
+++ /dev/null
@@ -1,399 +0,0 @@
-colo = ['red','orange','g','b','pink','y','c','m','purple',
-        'yellowgreen','silver','coral','plum','lime',
-        'hotpink','palegreen', 'tan', 'firebrick','darksalmon',
-        'sienna', 'sandybrown','olive', 'cadetblue','lawngreen',
-        'palevioletred','papayawhip','turquoise', 'teal',
-        'khaki','peru','indianred','brown', 'slategrey']
-colo = colo*100
-import numpy as np
-import numpy.linalg as LA
-import matplotlib.pyplot as plt
-import pandas as pd
-import h5py
-from scipy.special import erfinv
-
-def random_data(n,d,d_nonzero,k,sigma,zerosum=False,seed=False, classification = False, exp = False):
-    ''' Generation of random matrices as data such that y = X.sol + sigma. noise
-
-    The data X is generated as a normal matrix
-    The vector sol is generated randomly with a random support of size d_nonzero,
-    and componants are projected random intergers between -10  and 10 on the kernel of C restricted to the support
-    The vector y is then generated with X.dot(sol)+ sigma*noise , with noise a normal vector
-
-
-    Args:
-        n (int): Number of sample, dimension of y
-        d (int): Number of variables, dimension of sol
-        d_nonzero (int): Number of non null componant of sol
-        k (int) : Number of constraints, number of rows of C
-        sigma (float) : size of standard error
-        zerosum (bool, optional) : If True, then C is the all-one matrix with 1 row, independently of k
-        seed (bool or int, optional) : Seed for random values, for an equal seed, the result will be the same. If set to False: pseudo-random vectors
-        classification (bool, optional) : if True, then it returns sign(y) instead of y
-
-    Returns:
-        tuple : tuple of three ndarray that corresponds to the data :  (X,C,y)
-        ndarray : array corresponding to sol which is the real solution of the problem y = Xbeta + noise s.t. beta sparse and Cbeta = 0
-    '''
-    if (type(seed) == int): np.random.seed(seed)
-    else : np.random.seed()
-    X= np.random.randn(n,d)
-    if (zerosum): C,k = np.ones((1,d)),1
-    else :
-        if (k==0):
-            sol, sol_reduc,list_i = np.zeros(d), np.random.randint(-10,11,d_nonzero),np.random.randint(d, size=d_nonzero)
-            sol[list_i]=sol_reduc
-            y = X.dot(sol)+np.random.randn(m)*sigma
-            return((X,np.zeros((0,d)),y),sol)
-        while True :        
-            C = np.random.randint(low=-1,high=1, size=(k, d))
-            if (LA.matrix_rank(C)==k): break
-    while True:
-        sol, sol_reduc = np.zeros(d), np.random.randint(-10,11,d_nonzero)
-        list_i = np.random.randint(d, size=d_nonzero)
-        C_reduc = np.array([C.T[i] for i in list_i]).T
-        if (LA.matrix_rank(C_reduc)<k): continue
-        proj = proj_c(C_reduc,d_nonzero).dot(sol_reduc)
-        for i in range(len(list_i)):
-            sol[list_i[i]]=proj[i]
-        break
-    y = X.dot(sol)+np.random.randn(n)*sigma
-    if classification : y = np.sign(y)
-    if exp : return (np.exp(X), C, y), sol
-    return (X,C,y),sol 
-
-
-def check_size(X,y,C,A=None):
-    samples, n_features = min(len(y),len(X)), len(X[0])
-    X2,y2 = X[:samples] , y[:samples]
-    if len(y)   >samples   : print("More outputs than features ! ")
-    elif len(X) > samples  : print("More features than outputs !")
-
-    if C is None : C2 = np.ones((1, n_features ))
-    else : 
-        k = len(C)
-        if len(C[0])==n_features : C2 = C
-        elif len(C)>n_features : 
-            print("Too many colomns in constraint matrix !")
-            C2 = C[:,:n_features]
-        else : 
-            print("Too few colomns in constraint matrix !")
-            C2 = np.zeros((k,n_features))
-            C2[:,:len(C)] = C
-
-    if not A is None and len(A)==n_features : 
-        return X2.dot(A),y2,C2.dot(A)
-    else : 
-        return X2,y2,C2
-
-
-
-
-
-
-def influence(BETAS,ntop):
-    means = np.mean(abs(BETAS),axis=0)
-    top = np.argpartition(means, -ntop)[-ntop:]
-    return(np.sort(top))
-
-def plot_betai(labels,l_index,path,BETAS):
-    j=0
-    for i in range(len(BETAS[0])) :
-        if(j<len(l_index) and i==l_index[j]):
-            if not (type(labels)==bool): leg = 'Coefficient '+str(labels[i])
-            else : leg = 'Coefficient '+str(i)
-            plt.plot(path,BETAS[:,i],label=leg,color=colo[j])
-            j+=1
-        else:
-            plt.plot(path, BETAS[:, i], color=colo[i+j])
-
-def affichage(LISTE_BETA, path, title=' ', labels=False, pix=False, xlabel=" ", ylabel=" ", naffichage=10):
-    BETAS = np.array(LISTE_BETA)
-    l_index = influence(BETAS, naffichage)
-    plt.figure(figsize=(10, 3), dpi=80)
-    if (pix == 'path'): plt.plot(path, [0] * len(path), 'r+')
-    plot_betai(labels, l_index, path, BETAS)
-    plt.title(title), plt.legend(loc=4, borderaxespad=0.)
-    plt.xlabel(xlabel), plt.ylabel(ylabel)
-    if (type(pix) == bool and pix == True):
-        plt.matshow([[(abs(LISTE_BETA[i][j]) > 1e-2) for i in range(len(LISTE_BETA))] for j in
-                     range(len(LISTE_BETA[0]))]),plt.show()
-
-
-def normalize(lb,lna,ly):
-    for j in range(len(lb[0])):
-        lb[:,j] =  lb[:,j]*ly/lna[j]
-    return(lb)     
-
-def denorm(B,lna,ly): return(np.array([ly*B[j]/(np.sqrt(len(B))*lna[j]) for j in range(len(B))]) ) 
-
-
-
-
-
-def csv_to_mat(file,begin = 1, header=None):
-    ''' Function to read a csv file and to create an ndarray with this
-
-    Args:
-        file (str): Name of csv file
-        begin (int, optional): First colomn where it should read the matrix
-        header (None or int, optional): Same parameter as in the function :func:`pandas.read_csv`
-
-    Returns:
-        ndarray : matrix of the csv file
-    '''
-    tab1=pd.read_csv(file,header=header)
-    return(np.array(tab1)[:,begin:])
-
-def rescale(matrices):
-    ''' Function that rescale the matrix and returns its scale
-
-    Substract the mean of y, then divides by its norm. Also divide each colomn of X by its norm.
-    This will change the solution, not only by scaling it, because then the L1 norm will affect every component equally (and not only the variables with big size)
-
-    Args:
-        matrices (tuple) : tuple of three ndarray matrices corresponding to (X,C,y)
-
-    Returns:
-        tuple : tuple of the three corresponding matrices after normalization
-        tuple : tuple of the three information one need to recover the initial data : lX (list of initial colomn-norms of X), ly (initial norm of y), my (initial mean of y)
-
-    '''
-    (X,C,y)=matrices
-    my = sum(y)/len(y)
-    lX = [LA.norm(X[:,j]) for j in range(len(X[0]))]
-    ly = LA.norm(y-my*np.ones(len(y)))
-    Xn = np.array([[X[i,j]/(lX[j])  for j in range(len(X[0]))] for i in range(len(X))])
-    yn = np.array([ (y[j]-my)/ly for j in range(len(y)) ])
-    Cn = np.array([[C[i,j]*ly/lX[j] for j in range(len(X[0]))] for i in range(len(C))])
-    return((Xn,Cn,yn),(lX,ly,my))
-
-
-def hub(r,rho) : 
-    h=0
-    for j in range(len(r)):
-        if(abs(r[j])<rho): h+=r[j]**2
-        elif(r[j]>0)     : h+= (2*r[j]-rho)*rho
-        else             : h+= (-2*r[j]-rho)*rho
-    return(h)
-def L_LS(A,y,lamb,x): return(LA.norm( A.dot(x) - y )**2 + lamb * LA.norm(x,1))
-def L_conc(A,y,lamb,x): return(LA.norm( A.dot(x) - y ) + np.sqrt(2)*lamb * LA.norm(y,1))
-def L_H(A,y,lamb,x,rho): return(hub( A.dot(x) - y , rho) + lamb * LA.norm(x,1))
-
-
-# Compute I - C^t (C.C^t)^-1 . C : the projection on Ker(C)
-def proj_c(M,d):
-    if (LA.matrix_rank(M)==0):  return(np.eye(d))
-    return(np.eye(d)-LA.multi_dot([M.T,np.linalg.inv(M.dot(M.T) ),M]) )
-
-
-def theoretical_lam(n,d):
-    ''' Theoretical lambda as a function of the dimension of the problem
-
-    This function returns (with :math:`\phi = erf`) :
-
-    :math:`4/ \sqrt{n}  \phi^{-1}(1 - 2x)` such that  :math:`x = 4/d ( \phi^{-1}(1-2x)4 + \phi^{-1}(1-2x)^2 )`
-
-    Which is the same (thanks to formula : :math:`norm^{-1}(1-t) = \sqrt{2}\phi^{-1}(1-2t)` ) as :
-
-    :math:`\sqrt{2/n} * norm^{-1}(1-k/p)` such that  :math:`k = norm^{-1}(1 - k/p)^4 + 2norm^{-1}(1 - k/p)^2`
-
-    Args:
-        n (int) : number of sample
-        d (int)  : number of variables
-
-    Returns:
-        float : theoretical lambda
-
-    '''
-    x=0.
-    dx = 0.1
-    for i in range(10):
-        bo = True
-        while bo :
-            x    += dx
-            f     = erfinv(1-2*x)
-            xd    = 4/d * (f**4+f**2)
-            bo    = (xd>x)           
-        x = x-dx
-        dx = dx/10
-    return(2*f/np.sqrt(n))
-
-# function to do LS : return  X (X^t X)^-1  X^t y
-def min_LS(matrices,selected):
-    X,C,y = matrices
-    Xr, Cr = X[:,selected],C.T[selected]
-    proj = np.eye(len(Cr)) - Cr.dot(LA.pinv(Cr))
-    ls = LA.multi_dot([ proj, LA.pinv(Xr.dot(proj)),y]) 
-    beta = np.zeros(len(X[0]))
-    beta[selected] = ls
-    return(beta)
-
-def clr(array, coef=0.5):
-    ''' Centered-Log-Ratio transformation
-
-    Set all negaitve or null entry to a constant coef. Then compute the log of each component. Then substract the mean of each colomn on each colomn.
-
-    Args:
-        array (ndarray) : matrix nxd
-        coef (float, optional)  : Value to replace the zero values
-
-    Returns:
-        ndarray : clr transformed matrix nxd
-
-    '''
-    M = np.copy(array)
-    null_set = (M <= 0.)
-    M[null_set] = np.ones(M[null_set].shape)*coef
-    M = np.log(M)
-    return(M - np.mean(M, axis=0))
-
-def mat_to_np(file):
-    ''' Function to read a mat file and to create an ndarray with this
-
-    Args:
-        file (str): Name of mat file
-
-    Returns:
-         ndarray : matrix of the mat file
-    '''
-    arrays = {}
-    f = h5py.File(file)
-    for k,v in f.items():
-        arrays[k]=np.array(v)
-    return arrays
-
-
-def to_zarr(obj,name,root, first=True):
-
-    if type(obj)==dict:
-        if first : 
-            zz = root 
-        else : 
-            zz = root.create_group(name)
-
-        for key,value in obj.items() :
-            to_zarr(value,key,zz,first=False) 
-            
-    elif type(obj) == np.ndarray:
-         root.create_dataset(name,data=obj,shape=obj.shape)
-
-    elif type(obj)==np.float64 :
-        root.attrs[name] = float(obj)
-
-    elif type(obj)==np.int64 :
-         root.attrs[name] = int(obj)
-         
-    elif type(obj)== list : 
-        to_zarr(np.array(obj),name,root,first=False)
-    
-    elif obj is None or type(obj) in [str,bool,float,int]:
-        root.attrs[name] = obj
-
-    else :
-        to_zarr(obj.__dict__,name,root,first=first)
-
-
-
-
-
-
-
-
-
-
-'''
-def verify(obj,beta,sigma):
-    epsilon = 0.01
-    N = 1000
-    booleans = np.array([True]*N)
-    opt = obj(beta,sigma)
-    for i in range(N):
-        beta_prime = beta[:]
-        for j in range(len(beta_prime)):
-            beta_prime[j]+=epsilon*np.random.random()
-        sigma_prime = sigma + epsilon*np.random.random()
-        booleans[i] = (obj(beta_prime,sigma_prime) > opt)
-    return(np.all(booleans))
-
-
-def plot_influence(labels,l_influence):
-    print("influent variables  : \n \n")
-    for beta in l_influence:
-        string=''
-        for colo in range(1,7):
-            string+= str(labels.loc[beta,colo])
-        print(string + '\n')
-
-def support_dist(x,y):
-    s = 0 
-    n = len(x)
-    for k in range(n):
-        x_null, y_null = (abs(x[k])<1e-4), (abs(y[k])<1e-4)
-        if (x_null and not y_null) or (y_null and not x_null): s+=1
-    return(s)
-        
-def compare(L,show,lam):
-    str1 = 'Execution time : \n \n'
-    str2 = '\n Iterations : \n \n'
-    str3 = '\n L2-difference between : \n \n'
-    str4 = '\n For concomitant algorithms : \n \n'
-    for i in range(len(L)) :
-        x,niter,dt = L[i][0][:3]
-        name = L[i][1]
-        if (show=='sep' or show==True):
-            plt.title('lam = '+str(lam))
-            plt.bar(range(len(x)),x,label=name,color=colo[i])
-            plt.legend()
-            if (show=='sep'): plt.show()
-        if (niter != 0):
-            str1+= name+' :'+str(round(dt,5)) + '\n'
-            str2+= name+' :'+str(niter)+ '\n'
-        for j in range (i+1,len(L)): str3+= name + ' and '+L[j][1] +'             : ' + str(round(LA.norm((L[j][0][0]-x)),4)) + '\n'
-    plt.show()
-    printsigma =False
-    for i in range(len(L)) :
-        if (len(L[i][0])==4): 
-            printsigma = True
-            str4+=' Sigma '+L[i][1]+'  :'+str(round(L[i][0][3],3))
- 
-    print(str1)
-    print(str2)
-    if (len(L)>1): print(str3)
-    if (printsigma): print(str4)
-    
-    
-def old_normalize(lb,lna,ly):
-    lbetaprime = []
-    for beta in lb:
-        lbetaprime.append( np.array([ly*beta[j]/(lna[j]) for j in range(len(beta))]) )
-    return(lbetaprime)
-    
-def name(tol,string): return(string+'  tol={:.0e}'.format(float(tol)))   
-
-def aff(M):
-    (m,n)= M.shape
-    for i in range(m):
-        string =  ''
-        for j in range(n):
-            to_ad=str(round(abs(M[i,j]),3))
-            string+=to_ad+' '*(5-len(to_ad))+' |'
-        print(string + '//')   
-        
-def influence_old(sol,top):
-    l_influence = []
-    for j in range(top):
-        maxi = 0.
-        for i in range(len(sol)): 
-            if ( not (i in l_influence) and (abs(sol[i])>=maxi)): maxi,argmaxi = abs(sol[i]), i
-        l_influence.append(argmaxi)
-    return(l_influence)
-    
-    
-def plot_betai_old(labels,l_index,path,BETA):
-    for i in range(len(l_index)) :
-        leg = 'index = '+str(l_index[i])
-        if not (type(labels)==bool): leg+='  '+ str(labels[l_index[i]])
-        if (i>10): plt.plot(path,[BETA[ilam][l_index[i]] for ilam in range(len(path))],color=colo[l_index[i]])
-        else:     plt.plot(path,[BETA[ilam][l_index[i]] for ilam in range(len(path))],label=leg,color=colo[l_index[i]])
-'''
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/path_alg.py b/build/lib/q2_classo/CLasso/path_alg.py
deleted file mode 100644
index 6e7fed5..0000000
--- a/build/lib/q2_classo/CLasso/path_alg.py
+++ /dev/null
@@ -1,454 +0,0 @@
-N=100000
-import numpy as np
-import numpy.linalg as LA
-
-
-class parameters_for_update:
-    '''Object that has parameters needed at each breaking point during path algorithm
-
-    Attributes :
-        number_act  (int) : number of active parameter
-        idr         : saves the independant rows of the matrix C resctricted to the actives parameters
-        Xt          :  inverse of M
-        activity    :  list of boolean, activity[i] is True when variable i is active
-        beta        : current solution beta
-        s           : current subgradient
-        lam         : current lam
-        lambdamax   : lambdamax
-        M           : matrix to invert
-        y           : output
-        r           : residual
-        F           : F is the set where r<1 and if huber, then it is the set where rho<r<1
-        rho         : only use when doing huber path algo
-
-
-    '''
-    def __init__(self,matrices,lamin, rho,typ):
-
-        (self.A, self.C, self.y) = matrices
-        self.lamin = lamin
-        self.rho = rho
-        self.formulation = typ
-        n, d, k = len(self.A), len(self.A[0]), len(self.C)
-        self. number_act = 0
-        self.idr         = [False] * k
-        self.activity    = [False] * d
-        self.beta        = np.zeros(d)
-        s                = 2*self.A.T.dot(h_prime(self.y,rho))
-        self.lambdamax   = LA.norm(s, np.inf)
-        self.s           = s/self.lambdamax
-        self.lam         = 1.
-        self.r           = -self.y
-        self.F           = [True] * n
-        if (rho>0):
-            for j in range(n):
-                if( abs(self.y[j]) > self.rho ): self.F[j] = False
-        elif (rho<0):
-            for j in range(n):
-                if( self.y[j] < self.rho ): self.F[j] = False
-        AtA = self.A[self.F].T.dot(self.A[self.F])
-        for i in range(d):
-            if (self.s[i] == 1. or self.s[i] == -1.):
-                self.activity[i] = True
-                self.number_act += 1
-                if (k > 0):
-                    to_ad = next_idr1(self.idr, self.C[:, self.activity])
-                    if (type(to_ad) == int): self.idr[to_ad] = True
-
-
-        if(k==0):  self.M = 2 * AtA
-        else    :  self.M  = np.concatenate((np.concatenate((2 * AtA, self.C.T), axis=1), np.concatenate((self.C, np.zeros((k, k))), axis=1)),axis=0)
-
-        self.Xt          = LA.inv(self.M[self.activity + self.idr, :][:, self.activity + self.idr])
-
-
-# iteration of the function up to solve the path at each breaking points.
-def solve_path(matrices, lamin, n_active, rho,typ):
-    param = parameters_for_update(matrices, lamin, rho,typ)
-    BETA, LAM = [param.beta], [param.lam]
-    if param.lam < lamin : return BETA,LAM
-    for i in range(N):
-        no = up(param)
-        BETA.append(param.beta), LAM.append(param.lam)
-        if (type(n_active) == int and param.number_act >= n_active) or param.lam == lamin or no is not None : return (BETA, LAM)
-
-    print('no conv')
-    return (BETA, LAM)
-
-def solve_path_Conc(matrices, stop, n_active=False, lassopath=True):
-    (A, C, y) = matrices
-    n, d, k = len(A), len(A[0]), len(C)
-    # to compute r = (A beta - y)/||y|| more efficientely ; and we set reduclam=lam/stop to 2 so that if stop = 0, the condition reduclam < ||r|| is never furfilled
-    A_over_NORMy, y_over_NORMy, reduclam = A / (LA.norm(y)), y / (LA.norm(y)), 2.
-    if (lassopath):
-        lamin, R = 0, [-y_over_NORMy]
-    else:
-        lamin, beta_old, reduclam_old, r_old = 0, np.zeros(d), 1., -y_over_NORMy
-
-    param = parameters_for_update(matrices, lamin, 0, "Conc")
-    BETA, LAM = [param.beta], [param.lam]
-    for i in range(N):
-
-        up(param)
-        BETA.append(param.beta), LAM.append(param.lam)
-        param.r = A_over_NORMy.dot(param.beta) - y_over_NORMy
-        if (stop != 0): reduclam = param.lam / stop
-        if (lassopath):
-            R.append(param.r)
-            if (reduclam <= LA.norm(param.r)) or (param.number_act >= n - k) or (
-                    type(n_active) == int and param.number_act >= n_active): return (BETA, LAM, R)
-        else:
-            if reduclam <= LA.norm(param.r): return ((beta_old, param.beta), (reduclam_old, reduclam), (r_old, param.r))
-            beta_old, reduclam_old, r_old = param.beta, reduclam, param.r
-
-    print('no conv')
-    return (BETA, LAM,R)
-
-
-
-
-# Functions to interpolate the solution path between the breaking points
-def pathalgo_general(matrix,path,typ,n_active=False,rho=0):
-    BETA, i = [], 0
-    X,sp_path = solve_path(matrix,path[-1],n_active,rho,typ)
-    sp_path.append(path[-1]),X.append(X[-1])
-    for lam in path:
-        while (lam<sp_path[i+1]): i+=1
-        teta = (sp_path[i]-lam)/(sp_path[i]-sp_path[i+1])
-        BETA.append(X[i]*(1-teta)+X[i+1]*teta)
-    return(BETA)
-
-def pathalgo_huber_cl(matrix, path, rho, n_active=False):
-    return( pathalgo_general(matrix, path, 'huber_cl', n_active=n_active, rho=rho) )
-
-def pathalgo_cl(matrix,path,n_active=False):
-    return(pathalgo_general(matrix,path,'cl',n_active))
-
-
-
-
-def up(param):
-    formulation = param.formulation
-    if (formulation in ['LS','Conc']  ):
-        return (up_LS(param))
-    elif (formulation == 'huber'):
-        return (up_huber(param))
-    elif (formulation == 'cl'):
-        return (up_cl(param))
-    elif (formulation == 'huber_cl'):
-        return (up_huber_cl(param))
-
-# function that search the next lambda where something happen, and update the solution Beta
-def up_LS(param):
-    lambdamax, lamin, M, C = param.lambdamax, param.lamin, param.M, param.C
-    number_act, idr, Xt, activity, beta, s, lam = param.number_act, param.idr, param.Xt, param.activity, param.beta, param.s, param.lam
-
-    d = len(activity)
-    L = [lam] * d
-    D, E = direction(activity, s, M[:len(activity), :][:, :len(activity)], M[d:, :][:, :d], Xt, idr, number_act)
-    for i in range(d):
-        bi, di, e, s0 = beta[i], D[i], E[i], s[i]
-        if (activity[i]):
-            if (abs(bi * di) > 1e-10 and bi * di < 0):
-                L[i] = -bi / (di * lambdamax)
-        else:
-            if (abs(e - s0) < 1e-10): continue
-            if (e > s0):
-                dl = (1 + s0) / (1 + e)
-            else:
-                dl = (1 - s0) / (1 - e)
-            L[i] = dl * lam
-    dlamb = min(min(L), lam - lamin)
-    # Update matrix inverse, list of rows in C and activity
-    for i in range(d):
-        if (L[i] < dlamb + 1e-10):
-            if (activity[i]):
-                activity[i], number_act = False, number_act - 1
-                if (len(M) > d):
-                    to_ad = next_idr2(idr, C[:, activity])
-                    if (type(to_ad) == int): idr[to_ad] = False
-            else:
-                x = M[:, activity + idr][i]
-                al = M[i, i] - np.vdot(x, Xt.dot(x))
-                if (abs(al) < 1e-10): break
-                activity[i], number_act = True, number_act + 1
-                if (len(M) > d):
-                    to_ad = next_idr1(idr, C[:, activity])
-                    if (type(to_ad) == int): idr[to_ad] = True
-
-            Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-
-    beta = beta + lambdamax * D * dlamb
-    if not (lam == dlamb): s = E + lam / (lam - dlamb) * (s - E)
-    lam -= dlamb
-
-    param.number_act, param.idr, param.Xt, param.activity, param.beta, param.s, param.lam = number_act, idr, Xt, activity, beta, s, lam
-
-def up_huber(param):
-    lambdamax, lamin, A, y, C, rho = param.lambdamax, param.lamin, param.A, param.y, param.C, param.rho
-    number_act, idr, Xt, activity, F, beta, s, lam, M, r = param.number_act, param.idr, param.Xt, param.activity, param.F, param.beta, param.s, param.lam, param.M, param.r
-    d = len(activity)
-    L = [lam] * d
-    D, E = direction(activity, s, M[:len(activity), :][:, :len(activity)], M[d:, :][:, :d], Xt, idr, number_act)
-    for i in range(d):
-        bi, di, e, s0 = beta[i], D[i], E[i], s[i]
-        if (activity[i]):
-            if (abs(bi * di) > 1e-10 and bi * di < 0):
-                L[i] = -bi / (di * lambdamax)
-        else:
-            if (abs(e - s0) < 1e-10 or abs(s0) > 1): continue
-            if (e > s0):
-                dl = (1 + s0) / (1 + e)
-            else:
-                dl = (1 - s0) / (1 - e)
-            L[i] = dl * lam
-
-    dlamb = min(min(L), lam, lam - lamin)
-    huber_up, ADl = False, A.dot(D) * lambdamax
-    j_switch = None
-    for j in range(len(r)):
-        #     find if there is a  0< dl < dlamb such that F[j] and |r[j]+ADl[j]*dl|>rho
-        # or  find if there is a  0< dl < dlamb such that not F[j] |r[j]+ADl[j]*dl|<rho
-        if (abs(ADl[j]) < 1e-6 or abs(r[j]) < 1e-6 or abs(abs(r[j]) - rho) < 1e-6): continue
-        c = -r[j] / ADl[j]
-        dl = (1 - rho / abs(r[j])) * c
-        if (dl < 0):
-            if (c > 0):
-                dl = (1 + rho / abs(r[j])) * c
-            else:
-                dl = dlamb
-        if (dl < dlamb):
-            huber_up, j_switch, dlamb = True, j, dl
-    beta, s, r, lam = beta + lambdamax * D * dlamb, E + lam / (lam - dlamb) * (s - E), r + ADl * dlamb, lam - dlamb
-
-    if (huber_up):
-        F[j_switch] = not F[j_switch]
-        M[:d, :][:, :d] = 2 * A[F].T.dot(A[F])
-    else:
-        # Update matrix inverse, list of rows in C and activity
-        for i in range(d):
-            if (L[i] < dlamb + 1e-10):
-                if (activity[i]):
-                    activity[i], number_act = False, number_act - 1
-                    if (len(M) > d):
-                        to_ad = next_idr2(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = False
-                else:
-                    activity[i], number_act = True, number_act + 1
-                    if (len(M) > d):
-                        to_ad = next_idr1(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = True
-    try : Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-    except LA.LinAlgError :
-        print("matrix is not invertible, can't do a path algorithm... (huber case)")
-        return False
-    param.number_act, param.idr, param.Xt, param.activity, param.F, param.beta, param.s, param.lam, param.M, param.r = number_act, idr, Xt, activity, F, beta, s, lam, M, r
-
-def up_cl(param):
-    lambdamax,lamin,A, y  = param.lambdamax,param.lamin,param.A, param.y
-    number_act,idr,Xt,activity,F,beta,s, lam, M,r =\
-        param.number_act,param.idr,param.Xt,param.activity,param.F,param.beta,param.s, param.lam, param.M,param.r
-
-    d = len(activity)
-    L = [lam] * d
-    D, E = direction(activity, s, M[:len(activity), :][:, :len(activity)], M[d:, :][:, :d], Xt, idr, number_act)
-    for i in range(d):
-        bi, di, e, s0 = beta[i], D[i], E[i], s[i]
-        if (activity[i]):
-            if (abs(bi * di) > 1e-10 and bi * di < 0):
-                L[i] = -bi / (di * lambdamax)
-        else:
-            if (abs(e - s0) < 1e-10 or abs(s0) > 1): continue
-            if (e > s0):
-                dl = (1 + s0) / (1 + e)
-            else:
-                dl = (1 - s0) / (1 - e)
-            L[i] = dl * lam
-
-    dlamb = min(min(L), lam, lam - lamin)
-    max_up, yADl = False, y*A.dot(D) * lambdamax
-    j_switch = None
-    for j in range(len(r)):
-        #     find if there is a  0< dl < dlamb such that F[j] and r[j]+yADl[j]*dl > 1
-        # or  find if there is a  0< dl < dlamb such that not F[j] r[j]+yADl[j]*dl < 1
-        if (abs(r[j]-1)<1e-4): continue
-        if (yADl[j] != 0.): dl = (1-r[j]) / yADl[j]
-        else : dl = -1
-        if (dl < dlamb and dl >0):
-            max_up, j_switch, dlamb = True, j, dl
-
-    beta, s, r, lam = beta + lambdamax * D * dlamb, E + lam / (lam - dlamb) * (s - E), r + yADl * dlamb, lam - dlamb
-
-    if (max_up):
-        F[j_switch] = not F[j_switch]
-        M[:d, :][:, :d] = 2 * A[F].T.dot(A[F])
-        Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-    else:
-        # Update matrix inverse, list of rows in C and activity
-        for i in range(d):
-            if (L[i] < dlamb + 1e-10):
-                if (activity[i]):
-                    activity[i], number_act = False, number_act - 1
-                    if (len(M) > d):
-                        to_ad = next_idr2(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = False
-                else:
-                    # x = M[:,activity+idr][i]
-                    # al = M[i,i]-np.vdot(x,Xt.dot(x))
-                    # if (abs(al)<1e-10): break
-                    activity[i], number_act = True, number_act + 1
-                    if (len(M) > d):
-                        to_ad = next_idr1(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = True
-                Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-
-    param.number_act, param.idr, param.Xt, param.activity, param.F, param.beta, param.s, param.lam, param.M, param.r = \
-        number_act, idr, Xt, activity, F, beta, s, lam, M, r
-
-def up_huber_cl(param):
-    lambdamax, lamin, A, y, rho = param.lambdamax, param.lamin, param.A, param.y, param.rho
-    number_act, idr, Xt, activity, F, beta, s, lam, M, r = param.number_act, param.idr, param.Xt, param.activity, param.F, param.beta, param.s, param.lam, param.M, param.r
-    d = len(activity)
-    L = [lam] * d
-    D, E = direction(activity, s, M[:len(activity), :][:, :len(activity)], M[d:, :][:, :d], Xt, idr, number_act)
-    for i in range(d):
-        bi, di, e, s0 = beta[i], D[i], E[i], s[i]
-        if (activity[i]):
-            if (abs(bi * di) > 1e-10 and bi * di < 0):
-                L[i] = -bi / (di * lambdamax)
-        else:
-            if (abs(e - s0) < 1e-10 or abs(s0) > 1): continue
-            if (e > s0):
-                dl = (1 + s0) / (1 + e)
-            else:
-                dl = (1 - s0) / (1 - e)
-            L[i] = dl * lam
-
-    dlamb = min(min(L), lam, lam - lamin)
-    max_up, yADl = False, y * A.dot(D) * lambdamax
-    j_switch = None
-    for j in range(len(r)):
-        #     find if there is a  0< dl < dlamb such that F[j] and r[j]+yADl[j]*dl > 1
-        # or  find if there is a  0< dl < dlamb such that not F[j] r[j]+yADl[j]*dl < 1
-        #     find if there is a  0< dlhuber < dlamb such that F[j] and r[j]+yADl[j]*dl < rho
-        # or  find if there is a  0< dlhuber < dlamb such that not F[j] r[j]+yADl[j]*dl > rho
-
-
-        if (yADl[j] != 0.):
-            dlmax      = (1 - r[j]) / yADl[j]
-            dlhuber = (rho - r[j]) / yADl[j]
-        else: dlmax,dlhuber = dlamb+1,dlamb+1
-        if(dlmax<=0.):     dlmax   = dlamb+1
-        if(dlhuber <= 0.): dlhuber = dlamb+1
-
-        dl = min ( dlhuber , dlmax)
-
-        if (dl < dlamb) : max_up, j_switch, dlamb = True, j, dl
-
-    beta, s, r, lam = beta + lambdamax * D * dlamb, E + lam / (lam - dlamb) * (s - E), r + yADl * dlamb, lam - dlamb
-
-    if (max_up):
-        F[j_switch] = not F[j_switch]
-        M[:d, :][:, :d] = 2 * A[F].T.dot(A[F])
-        Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-    else:
-        # Update matrix inverse, list of rows in C and activity
-        for i in range(d):
-            if (L[i] < dlamb + 1e-10):
-                if (activity[i]):
-                    activity[i], number_act = False, number_act - 1
-                    if (len(M) > d):
-                        to_ad = next_idr2(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = False
-                else:
-                    # x = M[:,activity+idr][i]
-                    # al = M[i,i]-np.vdot(x,Xt.dot(x))
-                    # if (abs(al)<1e-10): break
-                    activity[i], number_act = True, number_act + 1
-                    if (len(M) > d):
-                        to_ad = next_idr1(idr, M[d:, :][:, :d][:, activity])
-                        if (type(to_ad) == int): idr[to_ad] = True
-
-                Xt = LA.inv(M[activity + idr, :][:, activity + idr])
-    param.number_act, param.idr, param.Xt, param.activity, param.F, param.beta, param.s, param.lam, param.M, param.r = number_act, idr, Xt, activity, F, beta, s, lam, M, r
-
-
-
-
-
-
-
-
-
-
-# Compute the derivatives of the solution Beta and the derivative of lambda*subgradient thanks to the ODE
-def direction(activity,s,Mat,C,Xt,idr,number_act):
-    if (len(C)==0):
-        D,product =np.zeros(len(activity)), Xt[:,:number_act].dot(s[activity])
-        D[activity]= product
-        return(D,Mat.dot(D))
-    D,Dadj=np.zeros(len(activity)),np.zeros(len(C))
-    product = Xt[:,:number_act].dot(s[activity])
-    D[activity],Dadj[idr]=product[:number_act],product[number_act:]
-    E = (Mat.dot(D)+C.T.dot(Dadj))   #D and D2 in Rd with zeros in inactives and E. D is - derivatives
-    return(D,E)
-
-#Upddate a list of constraints which are independant if we restrict the matrix C to the acrive set (C_A has to have independant rows)
-#When we ad an active parameter
-def next_idr1(liste,mat):
-    if(sum(liste)==len(mat)): return(False)
-    if (sum(liste)==0):
-        for i in range(len(mat)):
-            for j in range(len(mat[0])):
-                if not (mat[i,j]==0): return(i)
-        return(False)
-    Q = LA.qr(mat[liste,:].T)[0]
-    for j in range(len(mat)):
-        if (not liste[j]) and (  LA.norm(mat[j]-LA.multi_dot([Q,Q.T,mat[j]]))>1e-10 ): return(j)
-    return(False)
-
-# When we remove an active parameter
-def next_idr2(liste, mat):
-    if (sum(liste) == 0): return (False)
-    R = LA.qr(mat[liste, :].T)[1]
-    for i in range(len(R)):
-        if (abs(R[i, i]) < 1e-10):  # looking for the i-th True element of liste
-            j, somme = 0, liste[0]
-            while (somme <= i):
-                j, somme = j + 1, somme + liste[j + 1]
-            return (j)
-    return (False)
-
-# Update the invers of a matrix whom we add a line, which is useul to compute the derivatives
-def next_inv(Xt, B, al, ligne):
-    n = len(Xt)
-    Yt = np.zeros((n + 1, n + 1))
-    alpha = 1 / al
-    B = np.array([B])
-    b1 = Xt[:ligne, :][:, :ligne] + alpha * B[:, :ligne].T.dot(B[:, :ligne])
-    b2 = Xt[ligne:, :][:, :ligne] + alpha * B[:, ligne:].T.dot(B[:, :ligne])
-    b4 = Xt[ligne:, :][:, ligne:] + alpha * B[:, ligne:].T.dot(B[:, ligne:])
-    col1 = np.concatenate((b1, -alpha * B[:, :ligne], b2), axis=0)
-    col2 = np.concatenate((b2.T, -alpha * B[:, ligne:], b4), axis=0)
-    col = np.concatenate((-alpha * B[0, :ligne], [alpha], -alpha * B[0, ligne:]), axis=0)
-    return (np.concatenate((col1, np.array([col]).T, col2), axis=1))
-
-def h_lambdamax(X, y, rho):
-    return 2 * LA.norm(X.T.dot(h_prime(y, rho)), np.infty)
-
-# Compute the derivative of the huber function, particulary useful for the computing of lambdamax
-def h_prime(y,rho):
-    if (rho==0): return(y)
-    m = len(y)
-    lrho = rho*np.ones(m)
-    return(np.maximum(lrho,-y)+ np.minimum(y-lrho,0))
-
-
-
-'''
-Function that solve the concomitant problem for every lambda thanks to the previous function : we firstly compute all the non concomitant Least square problems, then we use it to find sigma and so the solution, using the equation for sigma: 
-
-sigma = || A*B(lambda*sigma) - y ||_2       (where B(lambda) is found thanks to solve_path)
-
-            teta = (sp_path[i]-lam)/(sp_path[i]-sp_path[i+1])
-'''
diff --git a/build/lib/q2_classo/CLasso/solve_R1.py b/build/lib/q2_classo/CLasso/solve_R1.py
deleted file mode 100644
index 2cd8dde..0000000
--- a/build/lib/q2_classo/CLasso/solve_R1.py
+++ /dev/null
@@ -1,251 +0,0 @@
-from .path_alg import solve_path
-import numpy as np
-import numpy.linalg as LA
-    
-'''    
-Problem    :   min ||Ab - y||^2 + lambda ||b||1 with C.b= 0
-
-Dimensions :   A : m*d  ;  y : m  ;  b : d   ; C : k*d
-
-The first function compute a solution of a Lasso problem for a given lambda. 
-The parameters are lam (lambda/lambdamax, in [0,1]) and pb, which has to be a 'problem_LS type'
- which is defined bellow in order to contain all the important parameters of the problem. 
-'''    
-
-
-
-def Classo_R1(pb,lam):
-    pb_type = pb.type  # can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-
-    # ODE
-    # here we compute the path algo until our lambda, and just take the last beta
-    if(pb_type == 'Path-Alg'):
-        BETA = solve_path(pb.matrix, lam, False, 0, 'LS')[0]
-        return(BETA[-1])
-    
-    (m,d,k),(A,C,y)  = pb.dim,pb.matrix            
-    lamb = lam * pb.lambdamax
-    tol = pb.tol * LA.norm(y)/LA.norm(A,'fro')  # tolerance rescaled
-    regpath = pb.regpath 
-
-    #cvx
-    # call to the cvx function of minimization
-    if (pb_type == 'cvx'):
-        import cvxpy as cp
-        lamb = lam*2*LA.norm(A.T.dot(y),np.infty)
-        x = cp.Variable(d)
-        objective, constraints = cp.Minimize(cp.sum_squares(A*x-y)+ lamb*cp.norm(x, 1)), [C*x == 0]
-        prob = cp.Problem(objective, constraints)
-        result = prob.solve(warm_start=regpath,eps_abs= tol)
-        if (regpath): return(x.value,True) 
-        return(x.value)
-
-    if(not regpath): pb.compute_param()   # this is a way to compute costful matrices computation like A^tA only once when we do pathcomputation with warm starts.
-    Proj,AtA, Aty = proj_c(C,d), pb.AtA, pb.Aty     # Save some matrix products already computed in problem.compute_param()
-    gamma, tau    = pb.gam / (2*pb.AtAnorm),    pb.tauN
-    w,zerod       = lamb *gamma*pb.weights, np.zeros(d) # two vectors usefull to compute the prox of f(b)= sum(wi |bi|)
-
-
-    # NO PROJ
-    
-    if (pb_type == 'PF-PDS'):     # y1 --> S ; p1 --> p . ; p2 --> y2
-        (x,v) = pb.init
-        for i in range(pb.N):    
-            
-            S = x - gamma*(AtA.dot(x)-Aty)*2 - (C.T).dot(v)
-            p = prox(S,w,zerod)
-            
-            y2 = v + tau*C.dot(x)
-            v  = v + tau*C.dot(p)
-            
-            eps = p - gamma*(AtA.dot(p)-Aty)*2 - C.T.dot(y2)-S
-            x = x + eps
-            
-            if (i>0 and LA.norm(eps)<tol): 
-                if (regpath): return(x,(x,v)) 
-                else :        return(x) 
-            
-            if (LA.norm(x)+LA.norm(p)+LA.norm(v)>1e6): 
-                print('DIVERGENCE')
-                return(x)
-        print('NO CONVERGENCE')
-        return(x)
-    
-
-    #FORARD BACKWARD
-    
-    if (pb_type=='P-PDS'):
-        xbar,x,v = pb.init
-        for i in range(pb.N):                   
-            grad = (AtA.dot(x)-Aty)
-            v = v + tau*C.dot(xbar)            
-            s = x - 2*gamma*grad - (C.T).dot(v) 
-            p = prox(s,w,zerod)              
-            nw_x = Proj.dot(p)
-            
-            eps = nw_x - x                       
-            xbar= p + eps                        
-            
-            
-            if (i%10==2 and LA.norm(eps)<tol):      # 0.6
-                if (regpath): return(x,(xbar,x,v)) 
-                else :        return(x) 
-            x= nw_x 
-            if (LA.norm(x)>1e10): 
-                print('DIVERGENCE')
-                return(x)
-        print('NO CONVERGENCE')
-        return(x)
-
-    gamma             = gamma/(2*lam)
-    w                 = w /(2*lam)
-    mu,ls, c, root    = pb.mu,[], pb.c, 0.
-     # 2 PROX  
-    if (pb_type=='DR'):
-        
-        Q1,Q2  = QQ(2*gamma/(mu-1),A)
-        QA,qy = Q1.dot(A), Q1.dot(y)
-        
-        qy_mult = qy*(mu-1)
-        
-        b,xbar,x = pb.init
-        for i in range(pb.N):
-            xbar= xbar + mu*(prox(2*b-xbar,w,zerod)-b)
-            x   = x    + mu*(Proj.dot(2*b-x)       -b)
-
-            nv_b = (2-mu)*b
-            nv_b = nv_b + qy_mult + Q2.dot(x+xbar- 2*nv_b)
-            if (i%2==1 and LA.norm(b-nv_b)<tol): 
-                if (regpath):
-                    return(b,(b,xbar,x))
-                else :       return(b)  
-    
-            b = nv_b
-
-        print('NO CONVERGENCE')
-        return(b)  
- 
-
-    
-'''
-This function compute the the solution for a given path of lam : by calling the function 'algo' for each lambda with warm start, or with the method ODE, by computing the whole path thanks to the ODE that rules Beta and the subgradient s, and then to evaluate it in the given finite path.
-'''
-    
-def pathlasso_R1(pb,path,n_active=False,return_sp_path=False):
-    n = pb.dim[0]
-    BETA,tol = [],pb.tol
-    if(pb.type == 'Path-Alg'):
-        beta,sp_path = solve_path(pb.matrix,path[-1],n_active, 0, 'LS')
-        if (return_sp_path): return(beta,sp_path) # in the method ODE, we only compute the solution for breaking points. We can stop here if return_sp_path=True
-        else : # else, we do a little manipulation to interpolated the value of beta between those points, as we know beta is affine between those breaking points.
-            sp_path.append(path[-1]),beta.append(beta[-1])
-            i=0
-            for lam in path:
-                while (lam<sp_path[i+1]): i+=1
-                teta = (sp_path[i]-lam)/(sp_path[i]-sp_path[i+1])
-                BETA.append(beta[i]*(1-teta)+beta[i+1]*teta)
-            return(BETA)
-    
-    
-    # Now we are in the case where we have to do warm starts.
-    save_init = pb.init   
-    pb.regpath = True
-    pb.compute_param()
-    for lam in path:
-        X = Classo_R1(pb,lam)
-        BETA.append(X[0])
-        pb.init = X[1]
-        if (type(n_active)==int) : n_act = n_active
-        else : n_act = n
-        if(sum([ (abs(X[0][i])>1e-4) for i in range(len(X[0])) ])>=n_act):
-                pb.init = save_init
-                BETA = BETA + [BETA[-1]]*(len(path)-len(BETA))
-                pb.regpath = False
-                return(BETA)
-            
-    pb.init = save_init
-    pb.regpath = False
-    return(BETA)
-
-
-
-
-
-
-
-
-'''
-Class of problem : we define a type, which will contain as attributes all the parameters we need for a given problem.
-'''
-
-
-class problem_R1 :
-    
-    def __init__(self,data,algo):
-        self.N = 500000
-        
-        if(len(data)==3):self.matrix, self.dim = data, (data[0].shape[0],data[0].shape[1],data[1].shape[0])
-        
-        elif(len(data)==5):
-            (A,C,sol), self.dim = generate_random(data), data[:3]
-            self.sol,y = sol, A.dot(sol)+np.random.randn(data[0])*data[-1]
-            self.matrix = (A,C,y)
-        
-        (m,d,k) = self.dim
-        
-        if(algo=='P-PDS') : self.init = np.zeros(d), np.zeros(d), np.zeros(k)
-        elif (algo=='PF-PDS')         : self.init = np.zeros(d), np.zeros(k)
-        else                        : self.init = np.zeros(d), np.zeros(d), np.zeros(d)
-        self.tol = 1e-6 
-         
-        self.weights = np.ones(d)  
-        self.regpath = False
-        self.name = algo + ' LS'
-        self.type = algo          # type of algorithm used
-        self.mu = 1.95
-        self.Aty= (self.matrix[0].T).dot(self.matrix[2])
-        self.lambdamax = 2*LA.norm(self.Aty,np.infty)
-        self.gam = 1.
-        self.tau = 0.5         # equation for the convergence of 'PF-PDS' and LS algorithms : gam + tau < 1
-        if (algo =='DR'): self.gam = self.dim[1]
-
-
-    #this is a method of the class pb that is used to computed the expensive multiplications only once. (espacially usefull for warm start. )
-
-    def compute_param(self):
-        (A,C,y) = self.matrix
-        m,d,k = self.dim
-        self.c = (d/LA.norm(A,2))**2  # parameter for Concomitant problem : the matrix is scaled as c*A^2 
-
-        self.AtA        =(A.T).dot(A)
-        self.Cnorm      = LA.norm(C,2)**2
-        self.tauN       = self.tau/self.Cnorm
-        self.AtAnorm    = LA.norm(self.AtA,2)
-
-        
-        
-        
-        
-
-
-
-'''
-Functions used in the algorithms, modules needed : 
-import numpy as np
-import numpy.linalg as LA
-from .../class_of_problem import problem
-'''
-
-
-# compute the prox of the function : f(b)= sum (wi * |bi| )
-def prox(b,w,zeros): return(np.minimum(b+w,zeros)+np.maximum(b-w,zeros)) 
-
-# Compute I - C^t (C.C^t)^-1 . C : the projection on Ker(C)
-def proj_c(M,d):
-    if (LA.matrix_rank(M)==0):  return(np.eye(d))
-    return(np.eye(d)-LA.multi_dot([M.T,np.linalg.inv(M.dot(M.T) ),M]) )
-
-
-
-def QQ(coef,A): return(coef*(A.T).dot(LA.inv(2*np.eye(A.shape[0])+coef*A.dot(A.T))),LA.inv(2*np.eye(A.shape[1])+coef*(A.T).dot(A)))    
-
diff --git a/build/lib/q2_classo/CLasso/solve_R2.py b/build/lib/q2_classo/CLasso/solve_R2.py
deleted file mode 100644
index f039ec8..0000000
--- a/build/lib/q2_classo/CLasso/solve_R2.py
+++ /dev/null
@@ -1,233 +0,0 @@
-from .path_alg import solve_path
-import numpy as np
-import numpy.linalg as LA
-from .solve_R1 import problem_R1, Classo_R1
-    
-'''    
-Problem    :   min h_rho(Ab - y) + lambda ||b||1 with C.b= 0 <=>   min ||Ab - y - r*o||^2 + lambda ||b,o||1 with C.b= 0, o in R^m
-                                                                                        r = lambda / 2rho
-Dimensions :   A : m*d  ;  y : m  ;  b : d   ; C : k*d
-
-The first function compute a solution of a Lasso problem for a given lambda. The parameters are lam (lambda/lambdamax, \in [0,1]) and pb, which has to be a 'problem_LS type', which is defined bellow in order to contain all the important parameters of the problem. One can initialise it this way : pb = class_of_problem.problem(data=(A,C,y),type_of_algo). We solve the problem without normalizing anything. 
-'''    
-
-
-def Classo_R2(pb,lam, compute=True):
-    
-    pb_type = pb.type    # can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-    
-    
-    (m,d,k),(A,C,y)  = pb.dim,pb.matrix        
-    lamb, rho = lam * pb.lambdamax,  pb.rho
-    
-    
-    #ODE
-    # here we compute the path algo until our lambda, and just take the last beta
-    
-    if(pb_type == 'Path-Alg'):
-        beta = solve_path((A,C,y), lam,False,rho,'huber')[0]
-        return(beta[-1])
-
-    # 2 prox :
-    regpath = pb.regpath
-    r = lamb / (2 * rho)
-    if (pb_type == 'DR'):
-        Ahuber = np.concatenate((A, r * np.eye(len(A))), axis=1)
-        Chuber = np.concatenate((C, np.zeros((len(C), len(y)))), axis=1)
-        matrices_huber = (Ahuber, Chuber, y)
-        prob = problem_R1(matrices_huber, 'DR')
-        prob.regpath = regpath
-        if (len(pb.init) == 3): prob.init = pb.init
-        if not (regpath): return (Classo_R1(prob, lamb / prob.lambdamax)[:d])
-        x, warm_start = Classo_R1(prob, lamb / prob.lambdamax)
-        return (x[:d], warm_start)
-
-
-    tol = pb.tol * LA.norm(y)/LA.norm(A,'fro')  # tolerance rescaled
-    
-    #cvx 
-    # call to the cvx function of minimization
-    if (pb_type == 'cvx'):
-        import cvxpy as cp
-        x = cp.Variable(d)
-        objective, constraints = cp.Minimize(cp.sum(cp.huber(A*x - y, rho))+lamb*cp.norm(x, 1)), [C*x == 0]
-        prob = cp.Problem(objective, constraints)
-        result = prob.solve(warm_start=regpath,eps_abs= tol)
-        if (regpath): return(x.value,True) 
-        return(x.value)
-     
-    if(compute): pb.compute_param()
-    tau,Proj, AtA, Aty = pb.tauN,  proj_c(C,d), pb.AtA, pb.Aty
-    gamma                 = pb.gam / (2*(pb.AtAnorm+r**2))   
-    t                     = lamb * gamma
-    w,tm,zerom,zerod      = t*pb.weights,t*np.ones(m), np.zeros(m), np.zeros(d) 
-    o,xbar,x,v = pb.init
-    # vectors usefull to compute the prox of f(b)= sum(wi |bi|)
-    
-    #FORWARD BACKWARD
-    if (pb_type=='P-PDS'):
-        
-        for i in range(pb.N):                        
-            grad = (AtA.dot(x)-Aty)                  
-            v = v + tau*C.dot(xbar)             
-            S = x - 2*gamma*grad - 2*gamma*r*(A.T).dot(o) - (C.T).dot(v) 
-            o = prox(o*(1-2*gamma*r**2) + 2*gamma*r*(y-A.dot(x)),tm,zerom)
-            p = prox(S,w,zerod)             
-            nw_x = Proj.dot(p)                   
-            eps = nw_x - x                      
-            xbar= p + eps                     
-
-            if (i%10==2 and LA.norm(eps)<tol):      # 0.6
-                if (regpath): return(x,(o,xbar,x,v)) 
-                else :        return(x) 
-            x= nw_x 
-            if (LA.norm(x)>1e10): 
-                print('DIVERGENCE')
-                return(x)
-        print('NO CONVERGENCE')
-        return(x)
-     
-        
-    # NO PROJ
-    
-    if (pb_type == 'PF-PDS'):     # y1 --> S ; p1 --> p . ; p2 --> y2
-        for i in range(pb.N):    
-            grad = (AtA.dot(x)-Aty)
-            
-            S1 = x - 2*gamma*grad - 2*gamma*r*(A.T).dot(o) - (C.T).dot(v)
-            S2 = o*(1-2*gamma*r**2) + 2*gamma*r*(y-A.dot(x))
-            
-            p1 = prox(S1,w,zerod)
-            p2 = prox(S2,tm,zerom)
-            
-            v   = v + tau*C.dot(p1)
-            v2  = v + tau*C.dot(x)
-            
-            eps1 = p1 + 2*gamma*(Aty-AtA.dot(p1)-r*A.T.dot(o)) - C.T.dot(v2)-S1
-            eps2 = p2 + 2*r*gamma*(y-r*p2-A.dot(x)) -S2
-            
-            x = x + eps1
-            o = o + eps2
-            
-            if (i>0 and LA.norm(eps1)+LA.norm(eps2)<tol): 
-                if (regpath): return(x,(o,xbar,x,v)) 
-                else :        return(x) 
-            
-            if (LA.norm(x)+LA.norm(o)+LA.norm(v)>1e6): 
-                print('DIVERGENCE')
-                return(x)
-        print('NO CONVERGENCE')
-        return(x)
-    
-    
-    
-    
-
-    
-'''
-This function compute the the solution for a given path of lam : by calling the function 'algo' for each lambda with warm start, or wuth the method ODE, by computing the whole path thanks to the ODE that rules Beta and the subgradient s, and then to evaluate it in the given finite path.  
-'''
-    
-    
-def pathlasso_R2(pb,path,n_active=False):
-    n = pb.dim[0]
-    BETA,tol = [],pb.tol
-    if(pb.type == 'Path-Alg'):
-        X,sp_path = solve_path(pb.matrix,path[-1],n_active,pb.rho, 'huber')
-        # we do a little manipulation to interpolated the value of beta between breaking points, as we know beta is affine between those those points.
-        i=0
-        sp_path.append(path[-1]),X.append(X[-1])
-        for lam in path:
-            while (lam<sp_path[i+1]): i+=1
-            teta = (sp_path[i]-lam)/(sp_path[i]-sp_path[i+1])
-            BETA.append(X[i]*(1-teta)+X[i+1]*teta)
-        return(BETA)
-
-    # Now we are in the case where we have to do warm starts.
-    save_init = pb.init   
-    pb.regpath = True
-    pb.compute_param()
-    if (type(n_active)==int) : n_act = n_active
-    else : n_act = n
-    for lam in path:
-        X = Classo_R2(pb,lam,compute=False)
-        BETA.append(X[0])
-        pb.init = X[1]
-        if(sum([ (abs(X[0][i])>1e-2) for i in range(len(X[0])) ])>=n_act):
-                pb.init = save_init
-                BETA = BETA + [BETA[-1]]*(len(path)-len(BETA))
-                pb.regpath = False
-                return(BETA)
-            
-    pb.init = save_init
-    pb.regpath = False
-    return(BETA)
-
-
-
-
-
-
-
-
-'''
-Class of problem : we define a type, which will contain as keys, all the parameters we need for a given problem.
-'''
-
-
-class problem_R2 :
-    
-    def __init__(self,data,algo,rho):
-        self.N = 500000
-        
-        if(len(data)==3):self.matrix, self.dim = data, (data[0].shape[0],data[0].shape[1],data[1].shape[0])
-        
-        elif(len(data)==5):
-            (A,C,sol), self.dim = generate_random(data), data[:3]
-            self.sol,y = sol, A.dot(sol)+np.random.randn(data[0])*data[-1]
-            self.matrix = (A,C,y)
-        
-        (m,d,k) = self.dim
-        self.weights = np.ones(d)
-        self.init = np.zeros(m), np.zeros(d), np.zeros(d), np.zeros(k)
-        self.tol = 1e-6
-        self.regpath = False
-        self.name = algo + ' Huber'
-        self.type = algo        # type of algo used
-        self.rho = rho
-        self.gam = 1.
-        self.tau = 0.5         # equation for the convergence of Noproj and LS algorithms : gam + tau < 1
-        self.lambdamax = 2*LA.norm(self.matrix[0].T.dot(h_prime(self.matrix[2],rho)),np.infty)
-
-
-    '''
-    this is a method of the class pb that is used to computed the expensive multiplications only once. (espacially usefull for warm start. )
-    '''
-    def compute_param(self):
-        (A,C,y) = self.matrix
-        m,d,k = self.dim
-        self.c = (d/LA.norm(A,2))**2  # parameter for Concomitant problem : the matrix is scaled as c*A^2
-
-        self.AtA        =(A.T).dot(A)
-        self.Aty        = (A.T).dot(y)
-        self.Cnorm      = LA.norm(C,2)**2
-        self.tauN       = self.tau/self.Cnorm
-        self.AtAnorm    = LA.norm(self.AtA,2)
-        
-
-
-# compute the prox of the function : f(b)= sum (wi * |bi| )
-def prox(b,w,zeros): return(np.minimum(b+w,zeros)+np.maximum(b-w,zeros)) 
-
-# Compute I - C^t (C.C^t)^-1 . C : the projection on Ker(C)
-def proj_c(M,d):
-    if (LA.matrix_rank(M)==0):  return(np.eye(d))
-    return(np.eye(d)-LA.multi_dot([M.T,np.linalg.inv(M.dot(M.T) ),M]) )
-
-# Compute the derivative of the huber function, particulary useful for the computing of lambdamax 
-def h_prime(y,rho):
-    m = len(y)
-    lrho = rho*np.ones(m)
-    return(np.maximum(lrho,-y)+ np.minimum(y-lrho,0))
-
-
diff --git a/build/lib/q2_classo/CLasso/solve_R3.py b/build/lib/q2_classo/CLasso/solve_R3.py
deleted file mode 100644
index b74a387..0000000
--- a/build/lib/q2_classo/CLasso/solve_R3.py
+++ /dev/null
@@ -1,243 +0,0 @@
-from .path_alg import solve_path_Conc
-import numpy as np
-import numpy.linalg as LA
-    
-'''    
-Problem    :   min ||Ab - y||^2/sigma + e sigma + lambda ||b||1 with C.b= 0 and sigma > 0
-
-Dimensions :   A : m*d  ;  y : m  ;  b : d   ; C : k*d
-
-The first function compute a solution of a Lasso problem for a given lambda. The parameters are lam (lambda/lambdamax, \in [0,1]) and pb, which has to be a 'problem_LS type', which is defined bellow in order to contain all the important parameters of the problem. One can initialise it this way : pb = class_of_problem.problem(data=(A,C,y),type_of_algo). We solve the problem without normalizing anything. 
-'''    
-
-
-
-def Classo_R3(pb,lam):
-    pb_type = pb.type     # can be 'Path-Alg' or 'DR'
-    (m,d,k),(A,C,y)  = pb.dim,pb.matrix
-    sigmax = LA.norm(y)*np.sqrt(2)
-    #Path algorithm
-    # here we compute the path algo until our lambda, and just take the last beta
-
-    # here we use our path algorithm for concomitant problem, and then only takes the last beta.
-    # Actually, the function solve_path_Conc has the argument concomitant= 'fix_lam' so it means it will directly stop when it has to.
-    # Then we only have to finc the solution between the last beta computed and the one before.
-    if(pb_type == 'Path-Alg'):
-        (beta1,beta2), (s1,s2), (r1,r2) = solve_path_Conc((A,C,y),lam,lassopath=False)
-        dr,ds = r1-r2,s1-s2
-        teta  = root_2(LA.norm(dr)**2-ds**2 ,np.vdot(dr,r2)-s2*ds,LA.norm(r2)**2-s2**2)
-        sigma = (s1    *teta + s2    *(1-teta) )*sigmax
-        beta  = beta1 *teta + beta2 *(1-teta)
-        return(beta,sigma)
-    
-
-    if(not pb.regpath): pb.compute_param()
-
-    lamb          = lam * LA.norm(pb.Aty, np.infty) / LA.norm(y) * np.sqrt(2)
-    Anorm         = LA.norm(A,'fro')
-    tol,regpath   = pb.tol * LA.norm(y)/Anorm,pb.regpath   # tolerance rescaled
-    Proj          = proj_c(C,d)   # Proj = I - C^t . (C . C^t )^-1 . C
-    QA, Q1, Q2    = pb.QA, pb.Q1, pb.Q2     # Save some matrix products already computed in problem.compute_param()
-    gamma         = pb.gam / (pb.Anorm2*lam)   # Normalize gamma
-    w,zerod       = lamb *gamma*pb.weights, np.zeros(d) # two vectors usefull to compute the prox of f(b)= sum(wi |bi|)
-    mu, c, root   = pb.mu, pb.c, 0.
-    xs,nu,o,xbar,x= pb.init
-
-    #2prox
-    if (pb_type == 'DR' ):
-        for i in range(pb.N):
-            nv_b, nv_s = x + Q1.dot(o) - QA.dot(x) - Q2.dot(x-xbar), (xs+nu)/2
-            if (i>0 and LA.norm(b-nv_b)+LA.norm(s-nv_s)/Anorm <2*tol):
-                if (regpath):return(b,(xs,nu,o,xbar,x),s) 
-                else : return(b,s)
-                        
-            s,b = nv_s, nv_b
-            Ab = A.dot(b)
-            p1,p2,root = prox_phi_1(xs,2*Ab-o-y,gamma/c,root)
-            sup = [ max(0,nu)-s , p1-s , p2 + y - Ab , prox(2*b-xbar,w,zerod)-b, Proj.dot(2*b-x)-b]
-            xs,nu,o,xbar,x = xs+mu*sup[0] ,  nu+mu*sup[1] ,  o+mu*sup[2] ,  xbar+mu*sup[3] ,  x+mu*sup[4]
-
-            if (LA.norm(b)+LA.norm(s)>1e6):
-                print('DIVERGENCE')
-                return(b,s)
-        print('NO CONVERGENCE')
-        return(b,'slow',s)
-    print('none of the cases ! ')
-
-
-'''
-This function compute the the solution for a given path of lam : by calling the function 'algo' for each lambda with warm start, or wuth the method ODE, by computing the whole path thanks to the ODE that rules Beta and the subgradient s, and then to evaluate it in the given finite path.  
-'''
-def pathlasso_R3(pb,path,n_active=False,e=1.):
-    n,d,k  = pb.dim
-    BETA,SIGMA,tol = [],[],pb.tol
-
-    if(pb.type=='Path-Alg'):
-        y=pb.matrix[2]
-        sigmax= LA.norm(y)
-        X,LAM,R = solve_path_Conc(pb.matrix,path[-1],n_active=n_active)
-        LAM.append(path[-1]),X.append(X[-1]),R.append(R[-1])
-        beta2,l2,r2,j = X[0],path[0]+0.1,-y/LA.norm(y),0
-        for lam in path: 
-            if (lam==0): lam=1e-4
-            while (LA.norm(r2)<l2/lam) and (j < len(LAM)): beta1,l1,r1,beta2,l2,r2,j = beta2,l2,r2,X[j],LAM[j],R[j],j+1
-            s1,s2 = l1/lam,l2/lam
-            dr,ds = r1-r2,s1-s2
-            teta  = root_2(LA.norm(dr)**2-ds**2 ,np.vdot(dr,r2)-s2*ds,LA.norm(r2)**2-s2**2)
-            SIGMA.append((s1*teta + s2*(1-teta))*sigmax)
-            BETA.append(beta1 *teta + beta2 *(1-teta))
-        return(BETA,SIGMA)
-
-    save_init = pb.init   
-    pb.regpath = True
-    pb.compute_param()
-    for lam in path:
-        X = Classo_R3(pb,lam)
-        BETA.append(X[0])
-        SIGMA.append(X[-1])
-        pb.init = X[1]
-        if (type(n_active)==int) : n_act = n_active
-        else : n_act = n
-        if(sum([ (abs(X[0][i])>1e-2) for i in range(len(X[0])) ])>=n_act):
-                pb.init = save_init
-                BETA = BETA + [BETA[-1]]*(len(path)-len(BETA))
-                pb.regpath = False
-                return(BETA,SIGMA)
-            
-    pb.init = save_init
-    pb.regpath = False
-    return(BETA,SIGMA)
-
-'''
-Class of problem : we define a type, which will contain as keys, all the parameters we need for a given problem.
-'''
-class problem_R3 :
-    
-    def __init__(self,data,algo,e=1.):
-        self.N = 500000
-
-        (A,C,y) = data
-        self.dim = (A.shape[0],A.shape[1],C.shape[0])
-        self.matrix = (A,C,y)
-
-        (m,d,k) = self.dim
-        self.weights = np.ones(d)
-        
-        self.tol = 1e-6
-          
-        self.regpath = False
-        self.name = algo + ' Concomitant'
-        self.type = algo          # type of algorithm used
-        
-        self.proj_sigm = lambda x : max(x,0)
-        self.mu = 1.95
-
-        self.gam        = np.sqrt(self.dim[1])
-        self.Aty        = (A.T).dot(y)
-        self.sigmax     = LA.norm(y)/np.sqrt(e)
-        self.lambdamax = 2 * LA.norm(self.Aty, np.infty) / self.sigmax
-        self.init       = 1.,1.,np.zeros(m), np.zeros(d), np.zeros(d)    
-
-
-    # Here we compute the costful matrices products and inverts in order to compute it only once, which is especially helpful for warmstarts.
-    def compute_param(self):
-        (A,C,y) = self.matrix
-        m,d,k = self.dim
-        self.Anorm2 = LA.norm(A, 2) ** 2
-        c = d**2/self.Anorm2  # parameter for Concomitant problem : the matrix is scaled as c*A^2
-        self.c = c
-        self.Q1, self.Q2 = QQ(c, A)
-        self.QA = self.Q1.dot(A)
-
-
-        
-
-
-
-'''
-Functions used in the algorithms, modules needed : 
-import numpy as np
-import numpy.linalg as LA
-from .../class_of_problem import problem
-'''
-
-
-# compute the prox of the function : f(b)= sum (wi * |bi| )
-def prox(b,w,zeros): return(np.minimum(b+w,zeros)+np.maximum(b-w,zeros)) 
-
-# Compute I - C^t (C.C^t)^-1 . C : the projection on Ker(C)
-def proj_c(M,d):
-    if (LA.matrix_rank(M)==0):  return(np.eye(d))
-    return(np.eye(d)-LA.multi_dot([M.T,np.linalg.inv(M.dot(M.T) ),M]) )
-
-# Compute the real positive root of a polynomial of degree 3 in the form : X^3 + a*X - b with Newton method and a warm start (for Comcomitant problem)
-def calc_Newton(a,b,root):
-    er = -b
-    while (abs(er)>1e-6): 
-        root= root-er/(3*root**2+a)
-        er = root**3 + a*root-b
-    return(root)
-
-def QQ(coef,A): return(coef*(A.T).dot(LA.inv(2*np.eye(A.shape[0])+coef*A.dot(A.T))),LA.inv(2*np.eye(A.shape[1])+coef*(A.T).dot(A)))    
-
-# Return the cost function of some Beta for a given Lasso problem : L_LS = ||y-Ab||2^2  + lambda* ||b||1
-def L_LS(pb,lam,sol): return(LA.norm( pb.matrix[0].dot(sol) - pb.matrix[2] )**2 + lam*pb.lambdamax * LA.norm(sol,1))
-
-# Return the prox operator for the function phi = ||(y-Ab)||2^2 /sigma + 1/2 * sigma with the warm start on the computation of the root    
-def prox_phi_1(sig,u,gamma,warm_start):
-    l2u = LA.norm(u)
-    Po0 = 4*gamma*sig + l2u**2
-    if (Po0 < 2*gamma**2): return(0.,0.,0.)
-    else : 
-        root=calc_Newton((4*sig/gamma+6.),8*l2u/gamma,warm_start)
-        return(sig+0.5*gamma*(0.5*root**2-1.),u * (1-gamma*root/l2u),root)
-
-
-    
-# Return the positive root in [0,1] of the polynomial aX^2 + 2bX + c if it exists, 1. if not
-def root_2(a,b,c):
-    
-    if (a==0.) : return(c/(2*b))
-    root=   (-np.sqrt(b**2-a*c)-b)/a
-    if (root > 1) : return(1.)
-    return(root)
-
-
-
-# The aim : 
-    # solve the equation : 
-    # |dr|^2 teta^2 + 2 (dr.r2) teta  + |r2|^2 =
-    # |ds|^2 teta^2 + 2 (ds.s2) teta  + |s2|^2
-
-    # Which comes from the equation : 
-    # | teta r1 + (1-teta) r2 | = | teta s1 + (1-teta) s2 |
-    
-    #  Which comes from the equation : 
-    # lam * | teta r1 + (1-teta) r2 | = | teta l1 + (1-teta) l2 |
-    
-    # lam * | y - X.( teta beta1 + (1-teta)beta2 ) | = | teta l1 + (1-teta) l2 |
-    
-    # in other word : $lam.|y-X.beta(gamma)| = gamma $
-    
-    # so find a gamma such that sigma(gamma) * lam = gamma 
-
-
-
-''' 
-def courbes_gamma(lG,pb,lam):
-    nG, C = len(lG), pb.matrix[1]
-    T,F,D = np.zeros(nG), np.zeros(nG), np.zeros(nG)
-    for j in range(nG) :
-        pb.gam = lG[j]
-        X = algo(pb,lam)
-        if (X[1]==False): break
-        T[j], F[j], D[j] = X[2], L_LS(pb,lam,X[0]), LA.norm(C.dot(X[0]))
-    plt.figure(figsize=(14,7), dpi=80),plt.plot(lG,T, label = pb.name)
-    plt.xlabel("gamma"),plt.ylabel("Running time"), plt.legend(), plt.show()
-    
-    plt.figure(figsize=(14,7), dpi=80),plt.plot(lG,F, label = pb.name)
-    plt.xlabel("gamma"),plt.ylabel("Value of minimizing f"), plt.legend(), plt.show()    
-    
-    plt.figure(figsize=(14,7), dpi=80),plt.plot(lG,D, label = pb.name)
-    plt.xlabel("gamma"),plt.ylabel("Violation of constraints"), plt.legend(), plt.show() 
-'''
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/solve_R4.py b/build/lib/q2_classo/CLasso/solve_R4.py
deleted file mode 100644
index bbcbecc..0000000
--- a/build/lib/q2_classo/CLasso/solve_R4.py
+++ /dev/null
@@ -1,242 +0,0 @@
-import numpy as np
-import numpy.linalg as LA
-from .solve_R3 import problem_R3, Classo_R3
-    
-'''    
-Problem    :   min h_rho((Ab - y)/sigma)sigma + simga + lambda ||b||1 with C.b= 0, sigma>0 
-
-Dimensions :   A : m*d  ;  y : m  ;  b : d   ; C : k*d
-
-The first function compute a solution of a Lasso problem for a given lambda. The parameters are lam (lambda/lambdamax, \in [0,1]) and pb, which has to be a 'problem_LS type', which is defined bellow in order to contain all the important parameters of the problem. One can initialise it this way : pb = class_of_problem.problem(data=(A,C,y),type_of_algo). We solve the problem without normalizing anything. 
-'''    
-
-
-def Classo_R4(pb,lam,e=1.):
-    pb_type = pb.type      # can be 'Path-Alg' or 'DR'
-    (m,d,k),(A,C,y)  = pb.dim,pb.matrix
-    lamb,rho  = lam * pb.lambdamax, pb.rho
-    regpath = pb.regpath
-    # Only alternative to 2prox : one can use the other formulation of the problem which shows that we can augment the data and then simply solve a concomitant problem
-    # (we do that with the method ODE for example becasue it is pretty efficient). Unfortunately we can do that only for fixed lambda and not for any path algorithms
-    # because the augmentation of the data required depends on lambda.
-    if pb_type=='Path-Alg' and not regpath:
-        matrix_aug = (np.concatenate((A,lamb/(2*rho)*np.eye(m)),axis=1),np.concatenate((C,np.zeros((k,m))),axis=1),y)
-        pb_aug = problem_R3(matrix_aug, 'Path-Alg', e=e)
-        beta,s = Classo_R3(pb_aug, lamb / pb_aug.lambdamax)
-        s = s / np.sqrt(e)
-        beta= beta[:d]
-        return beta,s
-
-    # Else, we do simply doulgas rachford. Hence, the prox is not so easy to compute because there is a root of polynomial of degree 3 to compute.
-    # We do that in the function prox_phi_2 which use the function prox_phi_i (prox of one componant), and it uses calc_Newton which uses newton's method with good initialization.
-
-    if (not regpath): pb.compute_param()
-    proj_sigm, QA, Q1, Q2, Proj, Anorm = pb.proj_sigm, pb.QA, pb.Q1, pb.Q2, pb.Proj, pb.Anorm
-    tol     = pb.tol * LA.norm(y) # tolerance rescaled
-    gamma = LA.norm(y) * pb.gam / (Anorm ** 2)
-    w,zerod = lamb *gamma*pb.weights, np.zeros(d) # two vectors usefull to compute the prox of f(b)= sum(wi |bi|)
-    mu, c   = pb.mu, pb.c
-    root    = [0.]*len(y)
-    xs,nu,o,xbar,x = pb.init
-
-
-    #2prox
-    if (pb_type == 'DR'):
-        for i in range(pb.N):
-            nv_b, nv_s = x + Q1.dot(o) - QA.dot(x) - Q2.dot(x-xbar), (xs+nu)/2
-            if (i>0 and LA.norm(b-nv_b)*Anorm +LA.norm(s-nv_s)<2*tol):
-                if (regpath):
-                            return(b,(xs,nu,o,xbar,x),sum(s)/len(s)/pb.sigmax)
-                else :      return(b,sum(s)/len(s))
-                        
-            s,b = nv_s, nv_b
-            Ab = A.dot(b)
-            p1,p2,root = prox_phi_2(xs,2*Ab-o-y,gamma/c,root,rho)
-            sup = [ proj_sigm(nu)-s , p1-s , p2 + y - Ab , prox(2*b-xbar,w,zerod)-b, Proj.dot(2*b-x)-b]
-            xs,nu,o,xbar,x = xs+mu*sup[0] ,  nu+mu*sup[1] ,  o+mu*sup[2] ,  xbar+mu*sup[3] ,  x+mu*sup[4]
-            if (LA.norm(b)+LA.norm(s)>1e6): 
-                print('DIVERGENCE')
-                return(b,np.sqrt(m)*sum(s)/len(s))
-        print('NO CONVERGENCE')
-        return(b,sum(s)/len(s))
-
-    print('none of the cases ! ')        
-    
-    
-
-    
-'''
-This function compute the the solution for a given path of lam : by calling the function 'algo' for each lambda with warm start, or wuth the method ODE, by computing the whole path thanks to the ODE that rules Beta and the subgradient s, and then to evaluate it in the given finite path.  
-'''
-    
-def pathlasso_R4(pb,path,n_active=False):
-    n = pb.dim[0]
-    BETA,SIGMA,tol = [],[],pb.tol
-
-    save_init = pb.init   
-    pb.regpath = True
-    pb.compute_param()
-    for lam in path:
-        X = Classo_R4(pb,lam)
-        BETA.append(X[0]), SIGMA.append(X[2])
-        pb.init = X[1]
-        if (type(n_active)==int) : n_act = n_active
-        else : n_act = n
-        if(sum([ (abs(X[0][i])>1e-1) for i in range(len(X[0])) ])>=n_act):
-                pb.init, BETA, SIGMA = save_init, BETA + [BETA[-1]]*(len(path)-len(BETA)),SIGMA + [SIGMA[-1]]*(len(path)-len(SIGMA))
-                return(BETA,SIGMA)
-            
-    pb.init = save_init
-    pb.regpath = False
-    return(BETA,SIGMA)
-
-
-
-
-
-
-
-
-'''
-Class of problem : we define a type, which will contain as keys, all the parameters we need for a given problem.
-'''
-
-
-class problem_R4 :
-    
-    def __init__(self,data,algo,rho,e=1.):
-        self.N = 500000
-        
-        (A,C,y), self.dim = data, (data[0].shape[0],data[0].shape[1],data[1].shape[0])
-        self.matrix = (A,C,y)
-        
-        (m,d,k) = self.dim
-        self.weights = np.ones(d)
-        self.tol = 1e-3
-
-        self.regpath = False
-        self.name = algo + ' Concomitant Huber'
-        self.type = algo          # type of algorithm used
-        rho_max = LA.norm(y,np.inf)
-        self.rho = rho
-        self.mu  = 1.95
-         
-        self.c = (d/LA.norm(A,2))**2  # parameter for Concomitant problem : the matrix is scaled as c*A^2 
-        self.gam = np.sqrt(d)
-        e = m
-        sigmax = find_sigmax(y,rho,e)
-        #sigmax = LA.norm(y)/np.sqrt(m)
-        self.sigmax = sigmax
-        self.lambdamax = 2 * LA.norm(A.T.dot(y), np.infty) / LA.norm(y) * np.sqrt(e)
-        self.lambdamax = 2*LA.norm((A.T).dot(h_prime(y/sigmax,rho)),np.infty)
-        self.init = sigmax*np.ones(m),sigmax*np.ones(m),np.zeros(m), np.zeros(d), np.zeros(d)
-
-    def compute_param(self):
-        (A, C, y) = self.matrix
-        m, d, k = self.dim
-        self.Anorm = LA.norm(A, 'fro')
-        self.Proj = proj_c(C, d)  # Proj = I - C^t . (C . C^t )^-1 . C
-        self.Q1, self.Q2 = QQ(self.c, A)
-        self.QA = self.Q1.dot(A)
-        self.proj_sigm = lambda vect: ([max(0,sum(vect))/len(vect)]*len(vect)) # here,
-                                # compared to the Muller&Combettes paper, there is a projection more on sigma =0,
-                                # for numerical issues...
-        
-
-
-
-'''
-Functions used in the algorithms, modules needed : 
-import numpy as np
-import numpy.linalg as LA
-from .../class_of_problem import problem
-'''
-
-
-# compute the prox of the function : f(b)= sum (wi * |bi| )
-def prox(b,w,zeros): return(np.minimum(b+w,zeros)+np.maximum(b-w,zeros)) 
-
-# Compute I - C^t (C.C^t)^-1 . C : the projection on Ker(C)
-def proj_c(M,d):
-    if (LA.matrix_rank(M)==0):  return(np.eye(d))
-    return(np.eye(d)-LA.multi_dot([M.T,np.linalg.inv(M.dot(M.T) ),M]) )
-
-
-
-def QQ(coef,A): return(coef*(A.T).dot(LA.inv(2*np.eye(A.shape[0])+coef*A.dot(A.T))),LA.inv(2*np.eye(A.shape[1])+coef*(A.T).dot(A)))    
-
-
-
-# Compute the real positive root of a polynomial of degree 3 in the form : X^3 + a*X - b with Newton method and a warm start (for Comcomitant problem)
-def calc_Newton(a,b,root):
-    er = root**3 + a*root-b
-    i=0
-    bound = min(np.cbrt(b), b/a)
-    while (abs(er)>1e-6 and i < 20):
-        if(root<0.): root, er =0., -b
-        elif(root>bound):root, er =bound, bound**3 + a*bound-b
-        root= root-er/(3*root**2+a)
-        er = root**3 + a*root-b
-        i+=1
-    if(i==20):
-        r = np.roots([1.,0.,a,-b])
-        root = np.amax((r[np.isreal(r)]).real)
-    return(root)
-
-    
-def prox_phi_2(sig,u,gamma,warm_start,rho):
-    p,q, ws = np.zeros(len(u)), np.zeros(len(u)),  np.zeros(len(u))
-    for i in range(len(u)):
-        p[i],q[i],ws[i]=prox_phi_i(sig[i],u[i],2*gamma,warm_start[i],rho)
-    return(p,q,ws)
-
-
-
-
-
-# Each componant of the prox. Explicit formula were given in the Combettes&Muller paper.
-# It is the prox of the function (u,s) --> (0.5*h_rho(u/sigma) + 0.5)*sigma
-def prox_phi_i(s,u,gamma,root,rho):
-    if (u==0.): return(0,0,root)
-    frac = gamma*rho/abs(u)
-    term = s+gamma*(rho**2-1)/2
-    bool1 = (frac>=1) 
-    bool2 = (abs(u)**2<=gamma*(gamma-2*s))
-    bool3 = (term<=0)
-    bool4 = (abs(u)>=rho*s + gamma*rho*(1+rho**2)/2)
-    
-    
-    if   (bool1  and bool2 ): return(0.,0.,root)
-    elif (bool3 and not bool1): return(0.,u*(1-frac),root) 
-    elif (not bool3 and bool4):  return(term,u*(1-frac),root)
-    root = calc_Newton(2*s/gamma+1,2*abs(u)/gamma,root)
-    return(s+gamma*(root**2-1)/2,u-gamma*root*np.sign(u),root)
-
-
-# Compute the derivative of the huber function, particulary useful for the computing of lambdamax 
-def h_prime(y,rho):
-    m = len(y)
-    lrho = rho*np.ones(m)
-    return(np.maximum(lrho,-y)+ np.minimum(y-lrho,0))
-
-
-
-#useful for computing lambdamax.
-def find_sigmax(y,rho,e):
-    m,evol = len(y), True
-    F = [True]*m
-    if (rho > 1):
-        while(evol):
-            evol = False
-            s = LA.norm(y[F])/np.sqrt(e-(m-sum(F))*rho**2)
-            cste= rho*s
-            for j in range(m):
-                if (F[j] and y[j]>cste): F[j],evol= False , True
-                elif (not F[j] and not y[j]>cste) : F[j],evol= True , True
-        return(s)
-    else:
-        print('rho too little ==> sigma is always 0')
-
-
-
-
diff --git a/build/lib/q2_classo/CLasso/solver 2.py b/build/lib/q2_classo/CLasso/solver 2.py
deleted file mode 100644
index 0ffcc18..0000000
--- a/build/lib/q2_classo/CLasso/solver 2.py	
+++ /dev/null
@@ -1,820 +0,0 @@
-from time import time
-import numpy as np
-import matplotlib.pyplot as plt
-
-from .misc_functions import rescale, theoretical_lam, min_LS, affichage
-from .compact_func import Classo, pathlasso
-from .cross_validation import CV
-from .stability_selection import stability, selected_param
-import matplotlib.patches as mpatches
-
-
-class classo_data:
-    ''' Class containing the data of the problem
-
-    Args:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or array, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem
-
-    Attributes:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or array, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem
-
-    '''
-    def __init__(self, X, y, C, rescale=False):
-        self.rescale = rescale  # booleen to know if we rescale the matrices
-        self.X,self.y,self.C = y = X, y,C
-        if type(C) == str: self.C = np.ones((1, len(X[0])))
-
-class classo_formulation:
-    ''' Class containing the data of the problem
-
-    Attributes:
-        huber (bool) : True if the formulation of the problem should be robust
-            Default value = False
-
-        concomitant (bool) : True if the formulation of the problem should be with an M-estimation of sigma.
-            Default value = True
-
-        classification (bool) : True if the formulation of the problem should be classification (if yes, then it will not be concomitant)
-            Default value = False
-
-        rho (float) : Value of rho for robust problem.
-            Default value = 1.345
-
-        rho_classification (float) : value of rho for huberized hinge loss function for classification (this parameter has to be negative).
-            Default value = -1.
-
-        e (float or string)  : value of e in concomitant formulation.
-            If 'n/2' then it becomes n/2 during the method solve(), same for 'n'.
-            Default value : 'n' if huber formulation ; 'n/2' else
-
-
-    '''
-    def __init__(self):
-        self.huber = False
-        self.concomitant = True
-        self.classification = False
-        self.rho = 1.345
-        self.rho_classification = -1.
-        self.e = 'not specified'
-
-    def name(self):
-        if self.classification:
-            if self.huber:
-                return "C2"
-            else : return "C1"
-        if self.concomitant:
-            if self.huber:
-                return "R4"
-            else : return "R3"
-        if self.huber:
-            return "R2"
-        else : return "R1"
-
-
-    def __repr__(self):
-        return (self.name())
-
-class classo_model_selection:
-    ''' Class containing the data of the problem
-
-    Attributes:
-        PATH (bool): True if path should be computed.
-            Default Value = False
-
-        PATHparameters (PATHparameters): object parameters to compute the lasso-path.
-
-
-        CV (bool):  True if Cross Validation should be computed.
-            Default Value = False
-
-        CVparameters (CVparameters):  object parameters to compute the cross-validation.
-
-
-        StabSel (boolean):  True if Stability Selection should be computed.
-            Default Value = True
-
-        StabSelparameters (StabSelparameters):  object parameters to compute the stability selection.
-
-        LAMfixed (boolean):  True if solution for a fixed lambda should be computed.
-            Default Value = False
-
-        LAMfixedparameters (LAMparameters):  object parameters to compute the lasso for a fixed lambda
-
-    '''
-    def __init__(self):
-
-        # Model selection variables
-
-        self.PATH = False
-        self.PATHparameters = PATHparameters()
-
-        self.CV = False
-        self.CVparameters = CVparameters()
-
-        self.StabSel = True            # Only model selection that is used by default
-        self.StabSelparameters = StabSelparameters()
-
-        self.LAMfixed = False
-        self.LAMfixedparameters = LAMfixedparameters()
-
-    def __repr__(self):
-        string = ''
-        if self.PATH: string +=     '\n     Path'
-        if self.CV: string +=       '\n     Cross Validation'
-        if self.StabSel: string +=  '\n     Stability selection'
-        if self.LAMfixed: string += '\n     Lambda fixed'
-        return string
-
-class PATHparameters:
-    '''object parameters to compute the lasso-path.
-
-    Attributes:
-        numerical_method (str) : name of the numerical method that is used, it can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        n_active (int): if it is higher than 0, then the algo stop computing the path when n_active variables are actives. then the solution does not change from this point.
-            Dafault value : 0
-
-        lambdas (numpy.ndarray) : list of lambdas for computinf lasso-path for cross validation on lambda.
-            Default value : np.array([10**(-delta * float(i) / nlam) for i in range(0,nlam) ] ) with delta=2. and nlam = 40
-
-        plot_sigma (bool) : if True then the print method of the solution will also show sigma if it is computed (formulation R3 or R4)
-            Default value : True
-
-        label (numpy.ndarray of str) : labels on each coefficients
-    '''
-    def __init__(self):
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-        self.n_active = False
-        lamin= 1e-2
-        nlam = 40
-        self.lambdas = np.array([10**(np.log10(lamin) * float(i) / nlam) for i in range(0,nlam) ] )
-        self.plot_sigma = True
-
-    def __repr__(self): return (  '\n     Npath = ' + str(len(self.lambdas))
-                                + '\n     n_active = ' + str(self.n_active)
-                                + '\n     lamin = ' + str(self.lambdas[-1])
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class CVparameters:
-    '''object parameters to compute the cross-validation.
-
-    Attributes:
-        seed (bool or int, optional) : Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors
-            Default value : None
-
-        numerical_method (str) : name of the numerical method that is used, can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        lambdas (numpy.ndarray) : list of lambdas for computinf lasso-path for cross validation on lambda.
-            Default value : np.linspace(1., 1e-3, 500)
-
-        oneSE (bool) : if set to True, the selected lambda if computed with method 'one-standard-error'
-            Default value : True
-
-        Nsubsets (int): number of subset in the cross validation method
-            Dafault value : 5
-
-    '''
-    def __init__(self):
-        self.seed = None
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-
-        self.Nsubset = 5  # Number of subsets used
-        self.lambdas = np.linspace(1., 1e-3, 500)
-        self.oneSE = True
-
-    def __repr__(self): return (  '\n     Nsubset = ' + str(self.Nsubset)
-                                + '\n     lamin = ' + str(self.lambdas[-1])
-                                + '\n     n_lam = ' + str(len(self.lambdas))
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class StabSelparameters:
-    '''object parameters to compute the stability selection.
-
-    Attributes:
-
-        seed (bool or int, optional) : Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors
-            Default value : None
-
-        numerical_method (str) : name of the numerical method that is used, can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        lam (float or str) : (only used if :obj:`method` = 'lam') lam for which the lasso should be computed.
-            Default value : 'theoretical' which mean it will be equal to :obj:`theoretical_lam` once it is computed
-
-        true_lam (bool) : (only used if :obj:`method` = 'lam') True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1.
-            If True and lam = 'theoretical' , then it will takes the  value n*theoretical_lam.
-            Default value : True
-
-
-        theoretical_lam (float) : (only used if :obj:`method` = 'lam') Theoretical lam.
-            Default value : 0.0 (once it is not computed yet, it is computed thanks to the function :func:`theoretical_lam` used in :meth:`classo_problem.solve`)
-
-
-        method (str) : 'first', 'lam' or 'max' depending on the type of stability selection we do.
-            Default value : 'first'
-
-        B (int) : number of subsample considered.
-            Default value : 50
-
-        q (int) : number of selected variable per subsample.
-            Default value : 10
-
-        percent_nS (float) : size of subsample relatively to the total amount of sample
-            Default value : 0.5
-
-        lamin (float) : lamin when computing the lasso-path for method 'max'
-            Default value : 1e-2
-
-        hd (bool) : if set to True, then the 'max' will stop when it reaches n-k actives variables
-            Default value : False
-
-        threshold (float) : threhold for stability selection
-            Default value : 0.7
-
-        threshold_label (float) : threshold to know when the label should be plot on the graph.
-            Default value : 0.4
-
-    '''
-    def __init__(self):
-        self.seed = None
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-
-        self.method = 'first'  # Can be 'first' ; 'max' or 'lam'
-        self.B = 50
-        self.q = 10
-        self.percent_nS = 0.5
-        self.lamin = 1e-2  # the lambda where one stop for 'max' method
-        self.hd = False  # if set to True, then the 'max' will stop when it reaches n-k actives variables
-        self.lam = 'theoretical'  # can also be a float, for the 'lam' method
-        self.true_lam = True
-        self.threshold = 0.7
-        self.threshold_label = 0.4
-        self.theoretical_lam = 0.0
-
-    def __repr__(self): return (  '\n     method = ' + str(self.method)
-                                + '\n     lamin = ' + str(self.lamin)
-                                + '\n     lam = ' + str(self.lam)
-                                + '\n     B = ' + str(self.B)
-                                + '\n     q = ' + str(self.q)
-                                + '\n     percent_nS = ' + str(self.percent_nS)
-                                + '\n     threshold = ' + str(self.threshold)
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class LAMfixedparameters:
-            '''object parameters to compute the lasso for a fixed lambda
-
-            Attributes:
-                numerical_method (str) : name of the numerical method that is used, can be :
-                    'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-                    Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-                lam (float or str) : lam for which the lasso should be computed.
-                    Default value : 'theoretical' which mean it will be equal to :obj:`theoretical_lam` once it is computed
-
-                true_lam (bool) : True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1.
-                    If True and lam = 'theoretical' , then it will takes the  value n*theoretical_lam.
-                    Default value : True
-
-
-                theoretical_lam (float) : Theoretical lam
-                    Default value : 0.0 (once it is not computed yet, it is computed thanks to the function :func:`theoretical_lam` used in :meth:`classo_problem.solve`)
-            '''
-            def __init__(self):
-                self.lam = 'theoretical'
-                self.formulation = 'not specified'
-                self.numerical_method = 'choose'
-                self.true_lam = True
-                self.theoretical_lam = 0.0
-
-            def __repr__(self): return (  '\n     lam = ' + str(self.lam)
-                                        + '\n     theoretical_lam = ' + str(round(self.theoretical_lam, 4))
-                                        + '\n     numerical_method = ' + str(self.numerical_method))
-
-class classo_problem:
-    ''' Class that contains all the information about the problem
-
-    Args:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or ndarray, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-               Default value to 'zero-sum'
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem.
-               Default value is 'False'
-
-    Attributes:
-        label (list or bool) : list of the labels of each variable. If set to False then there is no label
-        data (classo_data) :  object containing the data of the problem.
-        formulation (classo_formulation) : object containing the info about the formulation of the minimization problem we solve.
-        model_selection (classo_model_selection) : object giving the parameters we need to do variable selection.
-        solution (classo_solution) : object giving caracteristics of the solution of the model_selection that is asked.
-                                      Before using the method solve() , its componant are empty/null.
-
-    '''
-    def __init__(self, X, y, C='zero-sum', label=False):  # zero sum constraint by default, but it can be any matrix
-        if (type(label)==bool): self.label = np.array([str(i) for i in range(len(X[0]))])
-        else : self.label = label
-        self.data = classo_data(X, y, C)
-        self.formulation = classo_formulation()
-        self.model_selection = classo_model_selection()
-        self.solution = classo_solution()
-
-
-    # This method is the way to solve the model selections contained in the object model_selection, with the formulation of 'formulation' and the data.
-    def solve(self):
-        ''' Method that solve every model required in the attributes of the problem and update the attribute :obj:`problem.solution` with the characteristics of the solution.
-        '''
-        data = self.data
-        matrices = (data.X, data.C, data.y)
-        solution = classo_solution()
-        n, d = len(data.X), len(data.X[0])
-        if (self.formulation.e == 'n/2'): self.formulation.e = n/2  #useful to be able to write e='n/2' as it is in the default parameters
-        elif(self.formulation.e == 'n'): self.formulation.e = n     # same
-        elif(self.formulation.e == 'not specified'):
-            if (self.formulation.huber): self.formulation.e = n
-            else                       : self.formulation.e = n / 2
-
-        if data.rescale:
-            matrices, data.scaling = rescale(matrices)  # SCALING contains  :
-            # (list of initial norms of A-colomns,
-            #         initial norm of centered y,
-            #          mean of initial y )
-
-        # Compute the path thanks to the class solution_path which contains directely the computation in the initialisation
-        if self.model_selection.PATH:
-            solution.PATH = solution_PATH(matrices, self.model_selection.PATHparameters, self.formulation, self.label)
-
-        # Compute the cross validation thanks to the class solution_CV which contains directely the computation in the initialisation
-        if self.model_selection.CV:
-            solution.CV = solution_CV(matrices, self.model_selection.CVparameters, self.formulation, self.label)
-
-        # Compute the Stability Selection thanks to the class solution_SS which contains directely the computation in the initialisation
-        if self.model_selection.StabSel:
-            param = self.model_selection.StabSelparameters
-            param.theoretical_lam = theoretical_lam(int(n * param.percent_nS), d)
-            if(param.true_lam): param.theoretical_lam = param.theoretical_lam*int(n * param.percent_nS)
-
-            solution.StabSel = solution_StabSel(matrices, param, self.formulation, self.label)
-
-        # Compute the c-lasso problem at a fixed lam thanks to the class solution_LAMfixed which contains directely the computation in the initialisation
-        if self.model_selection.LAMfixed:
-            param = self.model_selection.LAMfixedparameters
-            param.theoretical_lam = theoretical_lam(n, d)
-            if(param.true_lam): param.theoretical_lam = param.theoretical_lam*n
-            solution.LAMfixed = solution_LAMfixed(matrices, param, self.formulation, self.label)
-
-        self.solution = solution
-
-    def __repr__(self):
-        print_parameters = ''
-        if (self.model_selection.CV):
-            print_parameters += '\n \nCROSS VALIDATION PARAMETERS: ' + self.model_selection.CVparameters.__repr__()
-        if (self.model_selection.StabSel):
-            print_parameters += '\n \nSTABILITY SELECTION PARAMETERS: ' + self.model_selection.StabSelparameters.__repr__()
-        if (self.model_selection.LAMfixed):
-            print_parameters += '\n \nLAMBDA FIXED PARAMETERS: ' + self.model_selection.LAMfixedparameters.__repr__()
-
-        if (self.model_selection.PATH):
-            print_parameters += '\n \nPATH PARAMETERS: ' + self.model_selection.PATHparameters.__repr__()
-
-        return (' \n \nFORMULATION: ' + self.formulation.__repr__()
-                + '\n \n' +
-                'MODEL SELECTION COMPUTED:  ' + self.model_selection.__repr__()
-                + print_parameters + '\n'
-                )
-
-
-class classo_solution:
-    ''' Class giving characteristics of the solution of the model_selection that is asked.
-                                      Before using the method solve() , its componant are empty/null.
-
-
-    Attributes:
-        PATH (solution_PATH): Solution components of the model PATH
-        CV (solution_CV):  Solution components of the model CV
-        StabelSel (solution_StabSel): Solution components of the model StabSel
-        LAMfixed (solution_LAMfixed): Solution components of the model LAMfixed
-
-    '''
-    def __init__(self):
-        self.PATH = 'not computed' #this will be filled with an object of the class 'solution_PATH' when the method solve() will be used.
-        self.CV = 'not computed'  # will be an object of the class 'solution_PATH'
-        self.StabSel = 'not computed' # will be an object of the class 'solution_StabSel'
-        self.LAMfixed = 'not computed'
-
-    def __repr__(self):
-        return ("Running time : " + '\n'
-                                 'Running time for Path computation    : ' + self.PATH.__repr__() + '\n'
-                + 'Running time for Cross Validation    : ' + self.CV.__repr__() + '\n'
-                + 'Running time for Stability Selection : ' + self.StabSel.__repr__() + '\n'
-                + 'Running time for Fixed LAM           : ' + self.LAMfixed.__repr__()
-                )
-
-
-#Here, the main function used is pathlasso ; from the file compact_func
-class solution_PATH:
-    ''' Class giving characteristics of the lasso-path computed,
-    which also contains a method _repr_ that plot the graphic of this lasso-path
-
-
-    Attributes:
-        BETAS (numpy.ndarray) : array of size Npath x d with the solution beta for each lambda on each row
-        SIGMAS (numpy.ndarray) : array of size Npath with the solution sigma for each lambda when the formulation of the problem is R2 or R4
-        LAMBDAS (numpy.ndarray) : array of size Npath with the lambdas (real lambdas, not divided by lambda_max) for which the solution is computed
-        method (str) : name of the numerical method that has been used. It can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-        save (bool or str) : if it is a str, then it gives the name of the file where the graphics has been/will be saved (after using print(solution) )
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'PATH', param.formulation)
-        param.numerical_method = numerical_method
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-
-        out = pathlasso(matrices, lambdas=param.lambdas, n_active=param.n_active,
-                                                typ=name_formulation, meth=numerical_method, return_sigm=True,
-                                                rho=rho, e=e,rho_classification=rho_classification)
-        if(formulation.concomitant): self.BETAS, self.LAMBDAS, self.SIGMAS = out
-        else :
-            self.BETAS, self.LAMBDAS = out
-            self.SIGMAS = 'not computed'
-
-        self.formulation = formulation
-        self.plot_sigma = param.plot_sigma
-        self.method = numerical_method
-        self.save = False
-        self.label = label
-        self.time = time() - t0
-
-    def __repr__(self):
-        affichage(self.BETAS, self.LAMBDAS, labels=self.label, naffichage=5,
-                  title=PATH_beta_path["title"] + self.formulation.name(),xlabel=PATH_beta_path["xlabel"],ylabel=PATH_beta_path["ylabel"])
-        if (type(self.save) == str): plt.savefig(self.save + 'Beta-path')
-        plt.show()
-        if(type(self.SIGMAS)!=str and self.plot_sigma):
-            plt.plot(self.LAMBDAS, self.SIGMAS), plt.ylabel(PATH_sigma_path["ylabel"]), plt.xlabel(PATH_sigma_path["xlabel"])
-            plt.title(PATH_sigma_path["title"] + self.formulation.name())
-            if (type(self.save)==str) : plt.savefig(self.save + 'Sigma-path')
-            plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-#Here, the main function used is CV ; from the file cross_validation
-class solution_CV:
-    ''' Class giving characteristics of the cross validation computed,
-    which also contains a method _repr_() that plot the selected parameters and the solution of the not-sparse problem on the selected variables set
-    It also contains a method gaphic(self, mse_max=1.,save=False) that computes the curve of validation error as a function of lambda
-
-
-    Attributes:
-        xGraph (numpy.ndarray) : array of size Nlambdas of the lambdas / lambda_max
-        yGraph (numpy.ndarray) : array of size Nlambdas of the average validation residual (over the K subsets)
-        standard_error (numpy.ndarray) : array of size Nlambdas of the standard error of the validation residual (over the K subsets)
-        index_min (int) : index on xGraph of the selected lambda without 1-standard-error method
-        index_1SE (int) : index on xGraph of the selected lambda with 1-standard-error method
-        lambda_min (float) : selected lambda without 1-standard-error method
-        lambda_oneSE (float) : selected lambda with 1-standard-error method
-        beta (numpy.ndarray) : solution beta of classo at lambda_oneSE
-        sigma (float) : solution sigma of classo at lambda_oneSE when formulation is 'R2' or 'R4'
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'CV', param.formulation)
-        param.numerical_method = numerical_method
-
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-        out, self.yGraph, self.standard_error, self.index_min, self.index_1SE = CV(matrices, param.Nsubset,
-                                                                                   typ=name_formulation,
-                                                                                   num_meth=numerical_method,
-                                                                                   lambdas=param.lambdas,
-                                                                                   seed=param.seed, rho=rho,
-                                                                                   rho_classification=rho_classification,
-                                                                                   oneSE=param.oneSE, e=e)
-
-        self.xGraph = param.lambdas
-        self.lambda_1SE = param.lambdas[self.index_1SE]
-        self.lambda_min = param.lambdas[self.index_min]
-
-        if param.formulation.concomitant:
-            self.beta, self.sigma = out
-        else:
-            self.beta = out
-
-        self.selected_param = abs(self.beta) > 1e-3  # boolean array, false iff beta_i =0
-        self.refit = min_LS(matrices, self.selected_param)
-        self.time = time() - t0
-        self.save=False
-        self.label = label
-
-    def __repr__(self):
-        plt.bar(range(len(self.refit)), self.refit), plt.title(CV_beta["title"]), plt.xlabel(CV_beta["xlabel"]),plt.ylabel(CV_beta["ylabel"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if(type(self.save)==str): plt.savefig(self.save)
-        plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-    def graphic(self, mse_max=1.,save=False):
-        i_min, i_1SE = self.index_min, self.index_1SE
-        for j in range(len(self.xGraph)):
-            if (self.yGraph[j] < mse_max): break
-
-        plt.errorbar(self.xGraph[j:], self.yGraph[j:], self.standard_error[j:], label='mean over the k groups of data', errorevery = 10 )
-        plt.axvline(x=self.xGraph[i_min], color='k', label=r'$\lambda$ (min MSE)')
-        plt.axvline(x=self.xGraph[i_1SE],color='r',label=r'$\lambda$ (1SE) ')
-        plt.title(CV_graph["title"]), plt.xlabel(CV_graph["xlabel"]),plt.ylabel(CV_graph["ylabel"])
-        plt.legend()
-        if(type(save)==str) : plt.savefig(save)
-        plt.show()
-
-#Here, the main function used is stability ; from the file stability selection
-class solution_StabSel:
-    ''' Class giving characteristics of the stability selection computed,
-    which also contains a method _repr_() that plot the selected parameters,
-    the solution of the not-sparse problem on the selected variables set, the stability plot with the evolution of it with lambda if the used method is 'first'
-
-
-
-    Attributes:
-        distribution (array) : d array of stability rations.
-        lambdas_path (array or string) : for 'first' method : N_lambdas array of the lambdas used. Other cases : 'not used'
-        distribution_path (array or string) : for 'first' method :  N_lambdas x d array with stability ratios as a function of lambda. Other cases : 'not computed'
-        threshold (float) : threshold for StabSel, ie for a variable i, stability ratio that is needed to get selected
-        save1,save2,save3 (bool or string) : if a string is given, the corresponding graph will be saved with the given name of the file (save1 is for stability plot ; save2 for path-stability plot; and save3 for refit beta-solution)
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected
-        to_label (list of int) : list that indicates the index of the True-componants of selected_param (useful to find the right label in _repr_() method)
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Compute the theoretical lam if necessary
-        if param.lam == 'theoretical':
-            lam = param.theoretical_lam
-        else:
-            lam = param.lam
-
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'StabSel', param.formulation,
-                                                   StabSelmethod=param.method, lam=lam)
-        param.numerical_method = numerical_method
-
-        # Compute the distribution
-        output = stability(matrices, StabSelmethod=param.method, numerical_method=numerical_method,
-                           lam=lam, q=param.q, B=param.B, percent_nS=param.percent_nS,
-                           formulation=name_formulation, seed=param.seed, rho=rho,
-                           rho_classification=rho_classification,
-                           true_lam=param.true_lam, e=e)
-
-        if (param.method == 'first'):
-            distribution, distribution_path, lambdas = output
-        else:
-            distribution, distribution_path, lambdas = output, 'not computed', 'not used'
-
-        self.distribution = distribution
-        self.distribution_path = distribution_path
-        self.lambdas_path = lambdas
-        self.selected_param,self.to_label = selected_param(self.distribution, param.threshold,param.threshold_label)
-        self.threshold = param.threshold
-        self.refit = min_LS(matrices, self.selected_param)
-        self.save1 = False
-        self.save2 = False
-        self.save3 = False
-        self.method = param.method
-        self.formulation = name_formulation
-        self.label = label
-        self.time = time() - t0
-
-
-    def __repr__(self):
-
-        D, Dpath, selected = self.distribution, self.distribution_path, self.selected_param
-        unselected = [not i for i in selected]
-        Dselected, Dunselected  = np.zeros(len(D)), np.zeros(len(D))
-        Dselected[selected], Dunselected[unselected] = D[selected], D[unselected]
-        plt.bar(range(len(Dselected)), Dselected, color='r', label='selected coefficients')
-        plt.bar(range(len(Dunselected)), Dunselected, color='b', label='unselected coefficients')
-        plt.axhline(y=self.threshold, color='g',label='Threshold : thresh = '+ str(self.threshold))
-        plt.xticks(self.to_label, self.label[self.to_label], rotation=30)
-        plt.xlabel(StabSel_graph["xlabel"]), plt.ylabel(StabSel_graph["ylabel"]), plt.title(StabSel_graph["title"] + self.method + " using " + self.formulation), plt.legend()
-        if (type(self.save1) == str): plt.savefig(self.save1)
-        plt.show()
-        print("SELECTED VARIABLES : ")
-        for i in np.where(selected)[0] :
-            print(self.label[i])
-
-        if (type(Dpath) != str):
-            lambdas = self.lambdas_path
-            N = len(lambdas)
-            for i in range(len(selected)):
-                if selected[i]: c='r'
-                else :          c='b'
-                plt.plot(lambdas, [Dpath[j][i] for j in range(N)], c)
-            p1 = mpatches.Patch(color='red', label='selected coefficients')
-            p2 = mpatches.Patch(color='blue',label='unselected coefficients')
-            p3 = mpatches.Patch(color='green',label='Threshold : thresh = '+ str(self.threshold))
-            plt.legend(handles=[p1, p2, p3], loc=1)
-            plt.axhline(y=self.threshold,color='g')
-            plt.xlabel(StabSel_path["xlabel"]), plt.ylabel(StabSel_path["ylabel"]), plt.title(StabSel_path["title"] +  self.method + " using " + self.formulation)
-            if (type(self.save2)==str):plt.savefig(self.save2)
-            plt.show()
-
-        plt.bar(range(len(self.refit)), self.refit)
-        plt.xlabel(StabSel_beta["xlabel"]), plt.ylabel(StabSel_beta["ylabel"]), plt.title(StabSel_beta["title"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if (type(self.save3) == str): plt.savefig(self.save3)
-        plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-#Here, the main function used is Classo ; from the file compact_func
-class solution_LAMfixed:
-    ''' Class giving characteristics of the lasso computed
-    which also contains a method _repr_() that plot this solution.
-
-
-    Attributes:
-        lambdamax (float) : lambda maximum for which the solution is non-null
-        true_lam (bool) : if False, it means that the problem had been computed for lambda*lambdamax (so lambda should be between 0 and 1)
-        lambda (float) : lambda for which the problem is solved
-        beta (numpy.ndarray) : solution beta of classo
-        sigma (float) : solution sigma of classo when formulation is 'R2' or 'R4'
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected (which is the case when the i-th component solution of the classo is non-null)
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-        self.formulation = formulation
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Compute the theoretical lam if necessary
-        if param.lam == 'theoretical':
-            self.lam = param.theoretical_lam
-        else:
-            self.lam = param.lam
-
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'LAM', param.formulation, lam=self.lam)
-        param.numerical_method = numerical_method
-        self.true_lam = param.true_lam
-
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-        out = Classo(
-            matrices, self.lam, typ=name_formulation, meth=numerical_method, rho=rho,
-            get_lambdamax=True, true_lam=self.true_lam, e=e, rho_classification=rho_classification)
-
-        if param.formulation.concomitant: self.lambdamax, self.beta, self.sigma = out
-        else: self.lambdamax, self.beta = out
-        avg_beta = np.mean(abs(self.beta))
-        self.selected_param = abs(self.beta) > avg_beta
-        self.refit = min_LS(matrices, self.selected_param)
-        self.label = label
-        self.time = time() - t0
-        self.save = False
-
-    def __repr__(self):
-        plt.bar(range(len(self.beta)), self.beta), plt.title(LAM_beta["title"] + str(round(self.lam,3) ) ), plt.xlabel(LAM_beta["xlabel"]),plt.ylabel(LAM_beta["ylabel"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if(type(self.save)==str): plt.savefig(self.save)
-        plt.show()
-        if(self.formulation.concomitant) : print("SIGMA FOR LAMFIXED  : ", self.sigma )
-        return (str(round(self.time, 3)) + "s")
-
-
-
-
-
-
-
-def choose_numerical_method(method, model, formulation, StabSelmethod=None, lam=None):
-    ''' Annex function in order to choose the right numerical method, if the given one is invalid
-
-    Args:
-        method (str) :
-        model (str) :
-        formulation (classo_formulation) :
-        StabSelmethod (str, optional) :
-        lam (float, optional) :
-
-    Returns :
-        str : method that should be used.
-
-    '''
-
-    if (formulation.classification): return ('Path-Alg')
-
-    # cases where we use classo at a fixed lambda    
-    elif (model == 'LAM') or (model == 'StabSel' and StabSelmethod == 'lam'):
-
-        if formulation.concomitant:
-            if not method in ['Path-Alg', 'DR']:
-                if (lam > 0.05):
-                    return 'Path-Alg'
-                else:
-                    return 'DR'
-
-        else:
-            if not method in ['Path-Alg', 'DR', 'P-PDS', 'PF-PDS']:
-                if (lam > 0.1):
-                    return 'Path-Alg'
-                else:
-                    return 'DR'
-
-
-
-    # cases where we use pathlasso
-    else:
-        if formulation.classification:
-            if not method in ['Path-Alg', 'DR', 'P-PDS']: return 'Path-Alg'
-
-        elif formulation.concomitant:
-            if not method in ['Path-Alg', 'DR']: return 'Path-Alg'
-
-        else:
-            if not method in ['Path-Alg', 'DR', 'P-PDS', 'PF-PDS']: return 'Path-Alg'
-
-    return method
-
-
-CV_beta             = {
-                            "title"  : r"Refitted coefficients after CV model selection" ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-CV_graph            = {
-                            "title"  : r" " ,
-                            "xlabel" : r"$\lambda / \lambda_{max}$" ,
-                            "ylabel" : r"Mean-Squared Error (MSE) "}
-LAM_beta            = {
-                            "title"  : r"Coefficients at theoretical $\lambda$ = " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-PATH_beta_path      = {
-                            "title"  : r"Coefficients across $\lambda$-path using " ,
-                            "xlabel" : r"$\lambda$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-PATH_sigma_path     = {
-                            "title"  : r"Scale estimate across $\lambda$-path using " ,
-                            "xlabel" : r"$\lambda$" ,
-                            "ylabel" : r"Scale $\sigma$ "}
-StabSel_graph       = {
-                            "title"  : r"Stability selection profile of type " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Selection probability "}
-StabSel_path        = {
-                            "title"  : r"Stability selection profile across $\lambda$-path using " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-StabSel_beta        = {
-                            "title"  : r"Refitted coefficients after stability selection" ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-
diff --git a/build/lib/q2_classo/CLasso/solver.py b/build/lib/q2_classo/CLasso/solver.py
deleted file mode 100644
index 4240090..0000000
--- a/build/lib/q2_classo/CLasso/solver.py
+++ /dev/null
@@ -1,822 +0,0 @@
-from time import time
-import numpy as np
-import matplotlib.pyplot as plt
-
-from .misc_functions import rescale, theoretical_lam, min_LS, affichage, check_size
-from .compact_func import Classo, pathlasso
-from .cross_validation import CV
-from .stability_selection import stability, selected_param
-import matplotlib.patches as mpatches
-
-
-class classo_data:
-    ''' Class containing the data of the problem
-
-    Args:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or array, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem
-
-    Attributes:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or array, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem
-
-    '''
-
-    def __init__(self, X, y, C, A=None, rescale=False):
-        self.rescale = rescale  # booleen to know if we rescale the matrices
-        self.X,self.y,self.C = check_size(X,y,C,A=A)
-        if not A is None : self.A = A
-        
-
-class classo_formulation:
-    ''' Class containing the data of the problem
-
-    Attributes:
-        huber (bool) : True if the formulation of the problem should be robust
-            Default value = False
-
-        concomitant (bool) : True if the formulation of the problem should be with an M-estimation of sigma.
-            Default value = True
-
-        classification (bool) : True if the formulation of the problem should be classification (if yes, then it will not be concomitant)
-            Default value = False
-
-        rho (float) : Value of rho for robust problem.
-            Default value = 1.345
-
-        rho_classification (float) : value of rho for huberized hinge loss function for classification (this parameter has to be negative).
-            Default value = -1.
-
-        e (float or string)  : value of e in concomitant formulation.
-            If 'n/2' then it becomes n/2 during the method solve(), same for 'n'.
-            Default value : 'n' if huber formulation ; 'n/2' else
-
-
-    '''
-    def __init__(self):
-        self.huber = False
-        self.concomitant = True
-        self.classification = False
-        self.rho = 1.345
-        self.rho_classification = -1.
-        self.e = 'not specified'
-
-    def name(self):
-        if self.classification:
-            if self.huber:
-                return "C2"
-            else : return "C1"
-        if self.concomitant:
-            if self.huber:
-                return "R4"
-            else : return "R3"
-        if self.huber:
-            return "R2"
-        else : return "R1"
-
-
-    def __repr__(self):
-        return (self.name())
-
-class classo_model_selection:
-    ''' Class containing the data of the problem
-
-    Attributes:
-        PATH (bool): True if path should be computed.
-            Default Value = False
-
-        PATHparameters (PATHparameters): object parameters to compute the lasso-path.
-
-
-        CV (bool):  True if Cross Validation should be computed.
-            Default Value = False
-
-        CVparameters (CVparameters):  object parameters to compute the cross-validation.
-
-
-        StabSel (boolean):  True if Stability Selection should be computed.
-            Default Value = True
-
-        StabSelparameters (StabSelparameters):  object parameters to compute the stability selection.
-
-        LAMfixed (boolean):  True if solution for a fixed lambda should be computed.
-            Default Value = False
-
-        LAMfixedparameters (LAMparameters):  object parameters to compute the lasso for a fixed lambda
-
-    '''
-    def __init__(self):
-
-        # Model selection variables
-
-        self.PATH = False
-        self.PATHparameters = PATHparameters()
-
-        self.CV = False
-        self.CVparameters = CVparameters()
-
-        self.StabSel = True            # Only model selection that is used by default
-        self.StabSelparameters = StabSelparameters()
-
-        self.LAMfixed = False
-        self.LAMfixedparameters = LAMfixedparameters()
-
-    def __repr__(self):
-        string = ''
-        if self.PATH: string +=     '\n     Path'
-        if self.CV: string +=       '\n     Cross Validation'
-        if self.StabSel: string +=  '\n     Stability selection'
-        if self.LAMfixed: string += '\n     Lambda fixed'
-        return string
-
-class PATHparameters:
-    '''object parameters to compute the lasso-path.
-
-    Attributes:
-        numerical_method (str) : name of the numerical method that is used, it can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        n_active (int): if it is higher than 0, then the algo stop computing the path when n_active variables are actives. then the solution does not change from this point.
-            Dafault value : 0
-
-        lambdas (numpy.ndarray) : list of lambdas for computinf lasso-path for cross validation on lambda.
-            Default value : np.array([10**(-delta * float(i) / nlam) for i in range(0,nlam) ] ) with delta=2. and nlam = 40
-
-        plot_sigma (bool) : if True then the print method of the solution will also show sigma if it is computed (formulation R3 or R4)
-            Default value : True
-
-        label (numpy.ndarray of str) : labels on each coefficients
-    '''
-    def __init__(self):
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-        self.n_active = False
-        lamin= 1e-2
-        nlam = 40
-        self.lambdas = np.array([10**(np.log10(lamin) * float(i) / nlam) for i in range(0,nlam) ] )
-        self.plot_sigma = True
-
-    def __repr__(self): return (  '\n     Npath = ' + str(len(self.lambdas))
-                                + '\n     n_active = ' + str(self.n_active)
-                                + '\n     lamin = ' + str(self.lambdas[-1])
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class CVparameters:
-    '''object parameters to compute the cross-validation.
-
-    Attributes:
-        seed (bool or int, optional) : Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors
-            Default value : None
-
-        numerical_method (str) : name of the numerical method that is used, can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        lambdas (numpy.ndarray) : list of lambdas for computinf lasso-path for cross validation on lambda.
-            Default value : np.linspace(1., 1e-3, 500)
-
-        oneSE (bool) : if set to True, the selected lambda if computed with method 'one-standard-error'
-            Default value : True
-
-        Nsubset (int): number of subset in the cross validation method
-            Dafault value : 5
-
-    '''
-    def __init__(self):
-        self.seed = None
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-
-        self.Nsubset = 5  # Number of subsets used
-        self.lambdas = np.linspace(1., 1e-3, 500)
-        self.oneSE = True
-
-    def __repr__(self): return (  '\n     Nsubset = ' + str(self.Nsubset)
-                                + '\n     lamin = ' + str(self.lambdas[-1])
-                                + '\n     n_lam = ' + str(len(self.lambdas))
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class StabSelparameters:
-    '''object parameters to compute the stability selection.
-
-    Attributes:
-
-        seed (bool or int, optional) : Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors
-            Default value : None
-
-        numerical_method (str) : name of the numerical method that is used, can be :
-            'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-            Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-        lam (float or str) : (only used if :obj:`method` = 'lam') lam for which the lasso should be computed.
-            Default value : 'theoretical' which mean it will be equal to :obj:`theoretical_lam` once it is computed
-
-        true_lam (bool) : (only used if :obj:`method` = 'lam') True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1.
-            If True and lam = 'theoretical' , then it will takes the  value n*theoretical_lam.
-            Default value : True
-
-
-        theoretical_lam (float) : (only used if :obj:`method` = 'lam') Theoretical lam.
-            Default value : 0.0 (once it is not computed yet, it is computed thanks to the function :func:`theoretical_lam` used in :meth:`classo_problem.solve`)
-
-
-        method (str) : 'first', 'lam' or 'max' depending on the type of stability selection we do.
-            Default value : 'first'
-
-        B (int) : number of subsample considered.
-            Default value : 50
-
-        q (int) : number of selected variable per subsample.
-            Default value : 10
-
-        percent_nS (float) : size of subsample relatively to the total amount of sample
-            Default value : 0.5
-
-        lamin (float) : lamin when computing the lasso-path for method 'max'
-            Default value : 1e-2
-
-        hd (bool) : if set to True, then the 'max' will stop when it reaches n-k actives variables
-            Default value : False
-
-        threshold (float) : threhold for stability selection
-            Default value : 0.7
-
-        threshold_label (float) : threshold to know when the label should be plot on the graph.
-            Default value : 0.4
-
-    '''
-    def __init__(self):
-        self.seed = None
-        self.formulation = 'not specified'
-        self.numerical_method = 'choose'
-
-        self.method = 'first'  # Can be 'first' ; 'max' or 'lam'
-        self.B = 50
-        self.q = 10
-        self.percent_nS = 0.5
-        self.lamin = 1e-2  # the lambda where one stop for 'max' method
-        self.hd = False  # if set to True, then the 'max' will stop when it reaches n-k actives variables
-        self.lam = 'theoretical'  # can also be a float, for the 'lam' method
-        self.true_lam = True
-        self.threshold = 0.7
-        self.threshold_label = 0.4
-        self.theoretical_lam = 0.0
-
-    def __repr__(self): return (  '\n     method = ' + str(self.method)
-                                + '\n     lamin = ' + str(self.lamin)
-                                + '\n     lam = ' + str(self.lam)
-                                + '\n     B = ' + str(self.B)
-                                + '\n     q = ' + str(self.q)
-                                + '\n     percent_nS = ' + str(self.percent_nS)
-                                + '\n     threshold = ' + str(self.threshold)
-                                + '\n     numerical_method = ' + str(self.numerical_method))
-class LAMfixedparameters:
-            '''object parameters to compute the lasso for a fixed lambda
-
-            Attributes:
-                numerical_method (str) : name of the numerical method that is used, can be :
-                    'Path-Alg' (path algorithm) , 'P-PDS' (Projected primal-dual splitting method) , 'PF-PDS' (Projection-free primal-dual splitting method) or 'DR' (Douglas-Rachford-type splitting method)
-                    Default value : 'choose', which means that the function :func:`choose_numerical_method` will choose it accordingly to the formulation
-
-                lam (float or str) : lam for which the lasso should be computed.
-                    Default value : 'theoretical' which mean it will be equal to :obj:`theoretical_lam` once it is computed
-
-                true_lam (bool) : True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1.
-                    If True and lam = 'theoretical' , then it will takes the  value n*theoretical_lam.
-                    Default value : True
-
-
-                theoretical_lam (float) : Theoretical lam
-                    Default value : 0.0 (once it is not computed yet, it is computed thanks to the function :func:`theoretical_lam` used in :meth:`classo_problem.solve`)
-            '''
-            def __init__(self):
-                self.lam = 'theoretical'
-                self.formulation = 'not specified'
-                self.numerical_method = 'choose'
-                self.true_lam = True
-                self.theoretical_lam = 0.0
-
-            def __repr__(self): return (  '\n     lam = ' + str(self.lam)
-                                        + '\n     theoretical_lam = ' + str(round(self.theoretical_lam, 4))
-                                        + '\n     numerical_method = ' + str(self.numerical_method))
-
-class classo_problem:
-    ''' Class that contains all the information about the problem
-
-    Args:
-        X (ndarray): Matrix representing the data of the problem
-        y (ndarray): Vector representing the output of the problem
-        C (str or ndarray, optional ): Matrix of constraints to the problem. If it is 'zero-sum' then the corresponding attribute will be all-one matrix.
-               Default value to 'zero-sum'
-        rescale (bool, optional): if True, then the function :func:`rescale` will be applied to data when solving the problem.
-               Default value is 'False'
-
-    Attributes:
-        label (list or bool) : list of the labels of each variable. If set to False then there is no label
-        data (classo_data) :  object containing the data of the problem.
-        formulation (classo_formulation) : object containing the info about the formulation of the minimization problem we solve.
-        model_selection (classo_model_selection) : object giving the parameters we need to do variable selection.
-        solution (classo_solution) : object giving caracteristics of the solution of the model_selection that is asked.
-                                      Before using the method solve() , its componant are empty/null.
-
-    '''
-    def __init__(self, X, y, C=None,A = None, label=False, rescale=False):  # zero sum constraint by default, but it can be any matrix
-        if (type(label)==bool): self.label = np.array([str(i) for i in range(len(X[0]))])
-        else : self.label = label
-        self.data = classo_data(X, y, C,A=A, rescale=rescale)
-        self.formulation = classo_formulation()
-        self.model_selection = classo_model_selection()
-        self.solution = classo_solution()
-
-
-    # This method is the way to solve the model selections contained in the object model_selection, with the formulation of 'formulation' and the data.
-    def solve(self):
-        ''' Method that solve every model required in the attributes of the problem and update the attribute :obj:`problem.solution` with the characteristics of the solution.
-        '''
-        data = self.data
-        matrices = (data.X, data.C, data.y)
-        solution = classo_solution()
-        n, d = len(data.X), len(data.X[0])
-        if (self.formulation.e == 'n/2'): self.formulation.e = n/2  #useful to be able to write e='n/2' as it is in the default parameters
-        elif(self.formulation.e == 'n'): self.formulation.e = n     # same
-        elif(self.formulation.e == 'not specified'):
-            if (self.formulation.huber): self.formulation.e = n
-            else                       : self.formulation.e = n / 2
-
-        if data.rescale:
-            matrices, data.scaling = rescale(matrices)  # SCALING contains  :
-            # (list of initial norms of A-colomns,
-            #         initial norm of centered y,
-            #          mean of initial y )
-
-        # Compute the path thanks to the class solution_path which contains directely the computation in the initialisation
-        if self.model_selection.PATH:
-            solution.PATH = solution_PATH(matrices, self.model_selection.PATHparameters, self.formulation, self.label)
-
-        # Compute the cross validation thanks to the class solution_CV which contains directely the computation in the initialisation
-        if self.model_selection.CV:
-            solution.CV = solution_CV(matrices, self.model_selection.CVparameters, self.formulation, self.label)
-
-        # Compute the Stability Selection thanks to the class solution_SS which contains directely the computation in the initialisation
-        if self.model_selection.StabSel:
-            param = self.model_selection.StabSelparameters
-            param.theoretical_lam = theoretical_lam(int(n * param.percent_nS), d)
-            if(param.true_lam): param.theoretical_lam = param.theoretical_lam*int(n * param.percent_nS)
-
-            solution.StabSel = solution_StabSel(matrices, param, self.formulation, self.label)
-
-        # Compute the c-lasso problem at a fixed lam thanks to the class solution_LAMfixed which contains directely the computation in the initialisation
-        if self.model_selection.LAMfixed:
-            param = self.model_selection.LAMfixedparameters
-            param.theoretical_lam = theoretical_lam(n, d)
-            if(param.true_lam): param.theoretical_lam = param.theoretical_lam*n
-            solution.LAMfixed = solution_LAMfixed(matrices, param, self.formulation, self.label)
-
-        self.solution = solution
-
-    def __repr__(self):
-        print_parameters = ''
-        if (self.model_selection.CV):
-            print_parameters += '\n \nCROSS VALIDATION PARAMETERS: ' + self.model_selection.CVparameters.__repr__()
-        if (self.model_selection.StabSel):
-            print_parameters += '\n \nSTABILITY SELECTION PARAMETERS: ' + self.model_selection.StabSelparameters.__repr__()
-        if (self.model_selection.LAMfixed):
-            print_parameters += '\n \nLAMBDA FIXED PARAMETERS: ' + self.model_selection.LAMfixedparameters.__repr__()
-
-        if (self.model_selection.PATH):
-            print_parameters += '\n \nPATH PARAMETERS: ' + self.model_selection.PATHparameters.__repr__()
-
-        return (' \n \nFORMULATION: ' + self.formulation.__repr__()
-                + '\n \n' +
-                'MODEL SELECTION COMPUTED:  ' + self.model_selection.__repr__()
-                + print_parameters + '\n'
-                )
-
-
-class classo_solution:
-    ''' Class giving characteristics of the solution of the model_selection that is asked.
-                                      Before using the method solve() , its componant are empty/null.
-
-
-    Attributes:
-        PATH (solution_PATH): Solution components of the model PATH
-        CV (solution_CV):  Solution components of the model CV
-        StabelSel (solution_StabSel): Solution components of the model StabSel
-        LAMfixed (solution_LAMfixed): Solution components of the model LAMfixed
-
-    '''
-    def __init__(self):
-        self.PATH = 'not computed' #this will be filled with an object of the class 'solution_PATH' when the method solve() will be used.
-        self.CV = 'not computed'  # will be an object of the class 'solution_PATH'
-        self.StabSel = 'not computed' # will be an object of the class 'solution_StabSel'
-        self.LAMfixed = 'not computed'
-
-    def __repr__(self):
-        return ("Running time : " + '\n'
-                                 'Running time for Path computation    : ' + self.PATH.__repr__() + '\n'
-                + 'Running time for Cross Validation    : ' + self.CV.__repr__() + '\n'
-                + 'Running time for Stability Selection : ' + self.StabSel.__repr__() + '\n'
-                + 'Running time for Fixed LAM           : ' + self.LAMfixed.__repr__()
-                )
-
-
-#Here, the main function used is pathlasso ; from the file compact_func
-class solution_PATH:
-    ''' Class giving characteristics of the lasso-path computed,
-    which also contains a method _repr_ that plot the graphic of this lasso-path
-
-
-    Attributes:
-        BETAS (numpy.ndarray) : array of size Npath x d with the solution beta for each lambda on each row
-        SIGMAS (numpy.ndarray) : array of size Npath with the solution sigma for each lambda when the formulation of the problem is R2 or R4
-        LAMBDAS (numpy.ndarray) : array of size Npath with the lambdas (real lambdas, not divided by lambda_max) for which the solution is computed
-        method (str) : name of the numerical method that has been used. It can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-        save (bool or str) : if it is a str, then it gives the name of the file where the graphics has been/will be saved (after using print(solution) )
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'PATH', param.formulation)
-        param.numerical_method = numerical_method
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-
-        out = pathlasso(matrices, lambdas=param.lambdas, n_active=param.n_active,
-                                                typ=name_formulation, meth=numerical_method, return_sigm=True,
-                                                rho=rho, e=e,rho_classification=rho_classification)
-        if(formulation.concomitant): self.BETAS, self.LAMBDAS, self.SIGMAS = out
-        else :
-            self.BETAS, self.LAMBDAS = out
-            self.SIGMAS = 'not computed'
-
-        self.formulation = formulation
-        self.plot_sigma = param.plot_sigma
-        self.method = numerical_method
-        self.save = False
-        self.label = label
-        self.time = time() - t0
-
-    def __repr__(self):
-        affichage(self.BETAS, self.LAMBDAS, labels=self.label, naffichage=5,
-                  title=PATH_beta_path["title"] + self.formulation.name(),xlabel=PATH_beta_path["xlabel"],ylabel=PATH_beta_path["ylabel"])
-        if (type(self.save) == str): plt.savefig(self.save + 'Beta-path')
-        plt.show()
-        if(type(self.SIGMAS)!=str and self.plot_sigma):
-            plt.plot(self.LAMBDAS, self.SIGMAS), plt.ylabel(PATH_sigma_path["ylabel"]), plt.xlabel(PATH_sigma_path["xlabel"])
-            plt.title(PATH_sigma_path["title"] + self.formulation.name())
-            if (type(self.save)==str) : plt.savefig(self.save + 'Sigma-path')
-            plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-#Here, the main function used is CV ; from the file cross_validation
-class solution_CV:
-    ''' Class giving characteristics of the cross validation computed,
-    which also contains a method _repr_() that plot the selected parameters and the solution of the not-sparse problem on the selected variables set
-    It also contains a method gaphic(self, mse_max=1.,save=False) that computes the curve of validation error as a function of lambda
-
-
-    Attributes:
-        xGraph (numpy.ndarray) : array of size Nlambdas of the lambdas / lambda_max
-        yGraph (numpy.ndarray) : array of size Nlambdas of the average validation residual (over the K subsets)
-        standard_error (numpy.ndarray) : array of size Nlambdas of the standard error of the validation residual (over the K subsets)
-        index_min (int) : index on xGraph of the selected lambda without 1-standard-error method
-        index_1SE (int) : index on xGraph of the selected lambda with 1-standard-error method
-        lambda_min (float) : selected lambda without 1-standard-error method
-        lambda_oneSE (float) : selected lambda with 1-standard-error method
-        beta (numpy.ndarray) : solution beta of classo at lambda_oneSE
-        sigma (float) : solution sigma of classo at lambda_oneSE when formulation is 'R2' or 'R4'
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'CV', param.formulation)
-        param.numerical_method = numerical_method
-
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-        out, self.yGraph, self.standard_error, self.index_min, self.index_1SE = CV(matrices, param.Nsubset,
-                                                                                   typ=name_formulation,
-                                                                                   num_meth=numerical_method,
-                                                                                   lambdas=param.lambdas,
-                                                                                   seed=param.seed, rho=rho,
-                                                                                   rho_classification=rho_classification,
-                                                                                   oneSE=param.oneSE, e=e)
-
-        self.xGraph = param.lambdas
-        self.lambda_1SE = param.lambdas[self.index_1SE]
-        self.lambda_min = param.lambdas[self.index_min]
-
-        if param.formulation.concomitant:
-            self.beta, self.sigma = out
-        else:
-            self.beta = out
-
-        self.selected_param = abs(self.beta) > 1e-3  # boolean array, false iff beta_i =0
-        self.refit = min_LS(matrices, self.selected_param)
-        self.time = time() - t0
-        self.save=False
-        self.label = label
-
-    def __repr__(self):
-        plt.bar(range(len(self.refit)), self.refit), plt.title(CV_beta["title"]), plt.xlabel(CV_beta["xlabel"]),plt.ylabel(CV_beta["ylabel"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if(type(self.save)==str): plt.savefig(self.save)
-        plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-    def graphic(self, mse_max=1.,save=False):
-        i_min, i_1SE = self.index_min, self.index_1SE
-        for j in range(len(self.xGraph)):
-            if (self.yGraph[j] < mse_max): break
-
-        plt.errorbar(self.xGraph[j:], self.yGraph[j:], self.standard_error[j:], label='mean over the k groups of data', errorevery = 10 )
-        plt.axvline(x=self.xGraph[i_min], color='k', label=r'$\lambda$ (min MSE)')
-        plt.axvline(x=self.xGraph[i_1SE],color='r',label=r'$\lambda$ (1SE) ')
-        plt.title(CV_graph["title"]), plt.xlabel(CV_graph["xlabel"]),plt.ylabel(CV_graph["ylabel"])
-        plt.legend()
-        if(type(save)==str) : plt.savefig(save)
-        plt.show()
-
-#Here, the main function used is stability ; from the file stability selection
-class solution_StabSel:
-    ''' Class giving characteristics of the stability selection computed,
-    which also contains a method _repr_() that plot the selected parameters,
-    the solution of the not-sparse problem on the selected variables set, the stability plot with the evolution of it with lambda if the used method is 'first'
-
-
-
-    Attributes:
-        distribution (array) : d array of stability rations.
-        lambdas_path (array or string) : for 'first' method : N_lambdas array of the lambdas used. Other cases : 'not used'
-        distribution_path (array or string) : for 'first' method :  N_lambdas x d array with stability ratios as a function of lambda. Other cases : 'not computed'
-        threshold (float) : threshold for StabSel, ie for a variable i, stability ratio that is needed to get selected
-        save1,save2,save3 (bool or string) : if a string is given, the corresponding graph will be saved with the given name of the file (save1 is for stability plot ; save2 for path-stability plot; and save3 for refit beta-solution)
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected
-        to_label (list of int) : list that indicates the index of the True-componants of selected_param (useful to find the right label in _repr_() method)
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Compute the theoretical lam if necessary
-        if param.lam == 'theoretical':
-            lam = param.theoretical_lam
-        else:
-            lam = param.lam
-
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'StabSel', param.formulation,
-                                                   StabSelmethod=param.method, lam=lam)
-        param.numerical_method = numerical_method
-
-        # Compute the distribution
-        output = stability(matrices, StabSelmethod=param.method, numerical_method=numerical_method,
-                           lam=lam, q=param.q, B=param.B, percent_nS=param.percent_nS,
-                           formulation=name_formulation, seed=param.seed, rho=rho,
-                           rho_classification=rho_classification,
-                           true_lam=param.true_lam, e=e)
-
-        if (param.method == 'first'):
-            distribution, distribution_path, lambdas = output
-        else:
-            distribution, distribution_path, lambdas = output, 'not computed', 'not used'
-
-        self.distribution = distribution
-        self.distribution_path = distribution_path
-        self.lambdas_path = lambdas
-        self.selected_param,self.to_label = selected_param(self.distribution, param.threshold,param.threshold_label)
-        self.threshold = param.threshold
-        self.refit = min_LS(matrices, self.selected_param)
-        self.save1 = False
-        self.save2 = False
-        self.save3 = False
-        self.method = param.method
-        self.formulation = name_formulation
-        self.label = label
-        self.time = time() - t0
-
-
-    def __repr__(self):
-
-        D, Dpath, selected = self.distribution, self.distribution_path, self.selected_param
-        unselected = [not i for i in selected]
-        Dselected, Dunselected  = np.zeros(len(D)), np.zeros(len(D))
-        Dselected[selected], Dunselected[unselected] = D[selected], D[unselected]
-        plt.bar(range(len(Dselected)), Dselected, color='r', label='selected coefficients')
-        plt.bar(range(len(Dunselected)), Dunselected, color='b', label='unselected coefficients')
-        plt.axhline(y=self.threshold, color='g',label='Threshold : thresh = '+ str(self.threshold))
-        plt.xticks(self.to_label, self.label[self.to_label], rotation=30)
-        plt.xlabel(StabSel_graph["xlabel"]), plt.ylabel(StabSel_graph["ylabel"]), plt.title(StabSel_graph["title"] + self.method + " using " + self.formulation), plt.legend()
-        if (type(self.save1) == str): plt.savefig(self.save1)
-        plt.show()
-        print("SELECTED VARIABLES : ")
-        for i in np.where(selected)[0] :
-            print(self.label[i])
-
-        if (type(Dpath) != str):
-            lambdas = self.lambdas_path
-            N = len(lambdas)
-            for i in range(len(selected)):
-                if selected[i]: c='r'
-                else :          c='b'
-                plt.plot(lambdas, [Dpath[j][i] for j in range(N)], c)
-            p1 = mpatches.Patch(color='red', label='selected coefficients')
-            p2 = mpatches.Patch(color='blue',label='unselected coefficients')
-            p3 = mpatches.Patch(color='green',label='Threshold : thresh = '+ str(self.threshold))
-            plt.legend(handles=[p1, p2, p3], loc=1)
-            plt.axhline(y=self.threshold,color='g')
-            plt.xlabel(StabSel_path["xlabel"]), plt.ylabel(StabSel_path["ylabel"]), plt.title(StabSel_path["title"] +  self.method + " using " + self.formulation)
-            if (type(self.save2)==str):plt.savefig(self.save2)
-            plt.show()
-
-        plt.bar(range(len(self.refit)), self.refit)
-        plt.xlabel(StabSel_beta["xlabel"]), plt.ylabel(StabSel_beta["ylabel"]), plt.title(StabSel_beta["title"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if (type(self.save3) == str): plt.savefig(self.save3)
-        plt.show()
-        return (str(round(self.time, 3)) + "s")
-
-#Here, the main function used is Classo ; from the file compact_func
-class solution_LAMfixed:
-    ''' Class giving characteristics of the lasso computed
-    which also contains a method _repr_() that plot this solution.
-
-
-    Attributes:
-        lambdamax (float) : lambda maximum for which the solution is non-null
-        true_lam (bool) : if False, it means that the problem had been computed for lambda*lambdamax (so lambda should be between 0 and 1)
-        lambda (float) : lambda for which the problem is solved
-        beta (numpy.ndarray) : solution beta of classo
-        sigma (float) : solution sigma of classo when formulation is 'R2' or 'R4'
-        selected_param (numpy.ndarray) : boolean arrays of size d with True when the variable is selected (which is the case when the i-th component solution of the classo is non-null)
-        refit (numpy.ndarray) : solution beta after solving unsparse problem over the set of selected variables.
-        formulation (str) : can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-        time (float) : running time of this action
-
-    '''
-    def __init__(self, matrices, param, formulation, label):
-        t0 = time()
-        self.formulation = formulation
-        # Formulation choosing
-        if param.formulation == 'not specified': param.formulation = formulation
-        name_formulation = param.formulation.name()
-
-        rho = param.formulation.rho
-        rho_classification = param.formulation.rho_classification
-        e = param.formulation.e
-        # Compute the theoretical lam if necessary
-        if param.lam == 'theoretical':
-            self.lam = param.theoretical_lam
-        else:
-            self.lam = param.lam
-
-        # Algorithmic method choosing
-        numerical_method = choose_numerical_method(param.numerical_method, 'LAM', param.formulation, lam=self.lam)
-        param.numerical_method = numerical_method
-        self.true_lam = param.true_lam
-
-        # Compute the solution and is the formulation is concomitant, it also compute sigma
-        out = Classo(
-            matrices, self.lam, typ=name_formulation, meth=numerical_method, rho=rho,
-            get_lambdamax=True, true_lam=self.true_lam, e=e, rho_classification=rho_classification)
-
-        if param.formulation.concomitant: self.lambdamax, self.beta, self.sigma = out
-        else: self.lambdamax, self.beta = out
-        avg_beta = np.mean(abs(self.beta))
-        self.selected_param = abs(self.beta) > avg_beta
-        self.refit = min_LS(matrices, self.selected_param)
-        self.label = label
-        self.time = time() - t0
-        self.save = False
-
-    def __repr__(self):
-        plt.bar(range(len(self.beta)), self.beta), plt.title(LAM_beta["title"] + str(round(self.lam,3) ) ), plt.xlabel(LAM_beta["xlabel"]),plt.ylabel(LAM_beta["ylabel"])
-        plt.xticks(np.where(self.selected_param)[0],self.label[self.selected_param], rotation=30)
-        if(type(self.save)==str): plt.savefig(self.save)
-        plt.show()
-        if(self.formulation.concomitant) : print("SIGMA FOR LAMFIXED  : ", self.sigma )
-        return (str(round(self.time, 3)) + "s")
-
-
-
-
-
-
-
-def choose_numerical_method(method, model, formulation, StabSelmethod=None, lam=None):
-    ''' Annex function in order to choose the right numerical method, if the given one is invalid
-
-    Args:
-        method (str) :
-        model (str) :
-        formulation (classo_formulation) :
-        StabSelmethod (str, optional) :
-        lam (float, optional) :
-
-    Returns :
-        str : method that should be used.
-
-    '''
-
-    if (formulation.classification): return ('Path-Alg')
-
-    # cases where we use classo at a fixed lambda    
-    elif (model == 'LAM') or (model == 'StabSel' and StabSelmethod == 'lam'):
-
-        if formulation.concomitant:
-            if not method in ['Path-Alg', 'DR']:
-                if (lam > 0.05):
-                    return 'Path-Alg'
-                else:
-                    return 'DR'
-
-        else:
-            if not method in ['Path-Alg', 'DR', 'P-PDS', 'PF-PDS']:
-                if (lam > 0.1):
-                    return 'Path-Alg'
-                else:
-                    return 'DR'
-
-
-
-    # cases where we use pathlasso
-    else:
-        if formulation.classification:
-            if not method in ['Path-Alg', 'DR', 'P-PDS']: return 'Path-Alg'
-
-        elif formulation.concomitant:
-            if not method in ['Path-Alg', 'DR']: return 'Path-Alg'
-
-        else:
-            if not method in ['Path-Alg', 'DR', 'P-PDS', 'PF-PDS']: return 'Path-Alg'
-
-    return method
-
-
-CV_beta             = {
-                            "title"  : r"Refitted coefficients after CV model selection" ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-CV_graph            = {
-                            "title"  : r" " ,
-                            "xlabel" : r"$\lambda / \lambda_{max}$" ,
-                            "ylabel" : r"Mean-Squared Error (MSE) "}
-LAM_beta            = {
-                            "title"  : r"Coefficients at theoretical $\lambda$ = " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-PATH_beta_path      = {
-                            "title"  : r"Coefficients across $\lambda$-path using " ,
-                            "xlabel" : r"$\lambda$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-PATH_sigma_path     = {
-                            "title"  : r"Scale estimate across $\lambda$-path using " ,
-                            "xlabel" : r"$\lambda$" ,
-                            "ylabel" : r"Scale $\sigma$ "}
-StabSel_graph       = {
-                            "title"  : r"Stability selection profile of type " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Selection probability "}
-StabSel_path        = {
-                            "title"  : r"Stability selection profile across $\lambda$-path using " ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-StabSel_beta        = {
-                            "title"  : r"Refitted coefficients after stability selection" ,
-                            "xlabel" : r"Coefficient index $i$" ,
-                            "ylabel" : r"Coefficients $\beta_i$ "}
-
diff --git a/build/lib/q2_classo/CLasso/stability_selection 2.py b/build/lib/q2_classo/CLasso/stability_selection 2.py
deleted file mode 100644
index 564977e..0000000
--- a/build/lib/q2_classo/CLasso/stability_selection 2.py	
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import numpy.random as rd
-from .compact_func import Classo,pathlasso
-n_lam = 100
-
-'''
-Here is the function that does stability selection. It returns the distribution as an d-array. 
-
-There is three different stability selection methods implemented here : 'first' ; 'max' ; 'lam'
-
-    - 'first' will compute the whole path until q parameters pop.  
-               It will then look at those paremeters, and repeat it for each subset of sample. 
-               It this case it will also return distr_path wich is an n_lam x d - array . It is usefull to have it is one want to plot it.
-               
-   - 'max' will do the same but it will stop at a certain lamin that is set at 1e-2 * lambdamax here, 
-              then will look at the q parameters for which the  max_lam (|beta_i(lam)|) is the highest. 
-              
-  - 'lam' will, for each subset of sample, compute the classo solution at a fixed lambda. That it will look at the q highest value of |beta_i(lam)|.
-              
-'''
-
-
-
-def stability(matrix,StabSelmethod = 'first',numerical_method = "Path-Alg",
-              lam = 0.1, q = 10 ,B = 50, percent_nS = 0.5 ,
-              formulation = 'LS', seed = 1, rho=1.345,
-              rho_classification=-1.,
-              true_lam = False, e=1.):
-    
-    rd.seed(seed)
-    n, d = len(matrix[2]), len(matrix[0][0])
-    nS = int(percent_nS*n)
-    distribution=np.zeros(d)
-
-
-
-
-    if (StabSelmethod == 'first') :
-    
-        NN = 500
-        lambdas= np.linspace(1.,0.,NN)
-        distr_path = np.zeros((NN,d))
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            # compute the path until n_active = q.
-            BETA = np.array(pathlasso(submatrix,lambdas=lambdas,n_active=q+1,lamin=0,
-                             typ=formulation, meth = numerical_method,
-                             rho = rho, rho_classification=rho_classification,e=e )[0])
-
-            distr_path = distr_path + (BETA != 0.)
-        distribution = distr_path[-1]
-        return(distribution * 1./B, distr_path * 1./B,lambdas)
-    
-    elif (StabSelmethod == 'lam') :
-    
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            regress = Classo(submatrix,lam,typ = formulation,
-                             meth=numerical_method, rho = rho, rho_classification=rho_classification, e=e, true_lam = true_lam)
-            if (type(regress)==tuple): beta =regress[0]
-            else : beta = regress
-            qbiggest = biggest_indexes(abs(beta),q)
-            for i in qbiggest:
-                distribution[i]+=1
-                
-    
-    
-    
-    elif (StabSelmethod == 'max') :
-        
-       
-        
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            # compute the path until n_active = q, and only take the last Beta
-            BETA = pathlasso(submatrix,n_active=False,lamin=1e-2,
-                             typ=formulation,meth = numerical_method,
-                             rho = rho, rho_classification=rho_classification, e=e )[0]
-            betamax = np.amax( abs(np.array(BETA)), axis = 0 )
-            qmax = biggest_indexes(betamax,q)
-            for i in qmax:
-                distribution[i]+=1           
-                
-
-    return(distribution * 1./B)
-
-
-'''
-Auxilaries functions that are used in the main function which is stability
-
-'''
-
-
-# returns the list of the q highest componants of an array, using the fact that it is probably sparse.
-def biggest_indexes(array,q):
-    qbiggest = []
-    nonnul = non_nul_indices(array)
-    reduc_array = array[nonnul]
-    for i1 in range(q):
-        if not np.any(nonnul): break
-        reduc_index = np.argmax(reduc_array)
-        index = nonnul[reduc_index]
-        if (reduc_array[reduc_index]==0.): break
-        reduc_array[reduc_index]=0.
-        qbiggest.append(index)
-    return(qbiggest)
-
-# return the list of indices where the componant of the array is null
-def non_nul_indices(array):
-    L = []
-    for i in range(len(array)):
-        if not (array[i]==0.):L.append(i)
-    return(L)
-
-# for a certain threshold, it returns the features that should be selected
-def selected_param(distribution,threshold,threshold_label):
-    selected, to_label  = [False]*len(distribution), []
-    for i in range(len(distribution)):
-        if (distribution[i] > threshold): selected[i]=True
-        if (distribution[i] > threshold_label): to_label.append(i)
-    return(selected, to_label)
-
-
-# submatrices associated to this subset
-def build_submatrix(matrix,subset):
-    (A,C,y) = matrix
-    subA,suby = A[subset],y[subset]
-    return((subA,C,suby))
-
-# random subset of [1,n] of size nS
-def build_subset(n,nS): return(rd.permutation(n)[:nS])
\ No newline at end of file
diff --git a/build/lib/q2_classo/CLasso/stability_selection.py b/build/lib/q2_classo/CLasso/stability_selection.py
deleted file mode 100644
index 564977e..0000000
--- a/build/lib/q2_classo/CLasso/stability_selection.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import numpy.random as rd
-from .compact_func import Classo,pathlasso
-n_lam = 100
-
-'''
-Here is the function that does stability selection. It returns the distribution as an d-array. 
-
-There is three different stability selection methods implemented here : 'first' ; 'max' ; 'lam'
-
-    - 'first' will compute the whole path until q parameters pop.  
-               It will then look at those paremeters, and repeat it for each subset of sample. 
-               It this case it will also return distr_path wich is an n_lam x d - array . It is usefull to have it is one want to plot it.
-               
-   - 'max' will do the same but it will stop at a certain lamin that is set at 1e-2 * lambdamax here, 
-              then will look at the q parameters for which the  max_lam (|beta_i(lam)|) is the highest. 
-              
-  - 'lam' will, for each subset of sample, compute the classo solution at a fixed lambda. That it will look at the q highest value of |beta_i(lam)|.
-              
-'''
-
-
-
-def stability(matrix,StabSelmethod = 'first',numerical_method = "Path-Alg",
-              lam = 0.1, q = 10 ,B = 50, percent_nS = 0.5 ,
-              formulation = 'LS', seed = 1, rho=1.345,
-              rho_classification=-1.,
-              true_lam = False, e=1.):
-    
-    rd.seed(seed)
-    n, d = len(matrix[2]), len(matrix[0][0])
-    nS = int(percent_nS*n)
-    distribution=np.zeros(d)
-
-
-
-
-    if (StabSelmethod == 'first') :
-    
-        NN = 500
-        lambdas= np.linspace(1.,0.,NN)
-        distr_path = np.zeros((NN,d))
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            # compute the path until n_active = q.
-            BETA = np.array(pathlasso(submatrix,lambdas=lambdas,n_active=q+1,lamin=0,
-                             typ=formulation, meth = numerical_method,
-                             rho = rho, rho_classification=rho_classification,e=e )[0])
-
-            distr_path = distr_path + (BETA != 0.)
-        distribution = distr_path[-1]
-        return(distribution * 1./B, distr_path * 1./B,lambdas)
-    
-    elif (StabSelmethod == 'lam') :
-    
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            regress = Classo(submatrix,lam,typ = formulation,
-                             meth=numerical_method, rho = rho, rho_classification=rho_classification, e=e, true_lam = true_lam)
-            if (type(regress)==tuple): beta =regress[0]
-            else : beta = regress
-            qbiggest = biggest_indexes(abs(beta),q)
-            for i in qbiggest:
-                distribution[i]+=1
-                
-    
-    
-    
-    elif (StabSelmethod == 'max') :
-        
-       
-        
-        for i in range(B):
-            subset = build_subset(n,nS)
-            submatrix = build_submatrix(matrix,subset)
-            # compute the path until n_active = q, and only take the last Beta
-            BETA = pathlasso(submatrix,n_active=False,lamin=1e-2,
-                             typ=formulation,meth = numerical_method,
-                             rho = rho, rho_classification=rho_classification, e=e )[0]
-            betamax = np.amax( abs(np.array(BETA)), axis = 0 )
-            qmax = biggest_indexes(betamax,q)
-            for i in qmax:
-                distribution[i]+=1           
-                
-
-    return(distribution * 1./B)
-
-
-'''
-Auxilaries functions that are used in the main function which is stability
-
-'''
-
-
-# returns the list of the q highest componants of an array, using the fact that it is probably sparse.
-def biggest_indexes(array,q):
-    qbiggest = []
-    nonnul = non_nul_indices(array)
-    reduc_array = array[nonnul]
-    for i1 in range(q):
-        if not np.any(nonnul): break
-        reduc_index = np.argmax(reduc_array)
-        index = nonnul[reduc_index]
-        if (reduc_array[reduc_index]==0.): break
-        reduc_array[reduc_index]=0.
-        qbiggest.append(index)
-    return(qbiggest)
-
-# return the list of indices where the componant of the array is null
-def non_nul_indices(array):
-    L = []
-    for i in range(len(array)):
-        if not (array[i]==0.):L.append(i)
-    return(L)
-
-# for a certain threshold, it returns the features that should be selected
-def selected_param(distribution,threshold,threshold_label):
-    selected, to_label  = [False]*len(distribution), []
-    for i in range(len(distribution)):
-        if (distribution[i] > threshold): selected[i]=True
-        if (distribution[i] > threshold_label): to_label.append(i)
-    return(selected, to_label)
-
-
-# submatrices associated to this subset
-def build_submatrix(matrix,subset):
-    (A,C,y) = matrix
-    subA,suby = A[subset],y[subset]
-    return((subA,C,suby))
-
-# random subset of [1,n] of size nS
-def build_subset(n,nS): return(rd.permutation(n)[:nS])
\ No newline at end of file
diff --git a/build/lib/q2_classo/__init__.py b/build/lib/q2_classo/__init__.py
deleted file mode 100644
index c1c43aa..0000000
--- a/build/lib/q2_classo/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from ._dict import regress_parameters, regress_parameter_descriptions
-from ._func import  regress, generate_data
-from ._formats import CLASSOProblemDirectoryFormat, ZarrProblemFormat, ConstraintDirectoryFormat, ConstraintFormat
-from ._summarize import summarize
-from .CLasso import *
diff --git a/build/lib/q2_classo/_dict.py b/build/lib/q2_classo/_dict.py
deleted file mode 100644
index e95dc26..0000000
--- a/build/lib/q2_classo/_dict.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from qiime2.plugin import (SemanticType,Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric)
-from q2_types.feature_table import FeatureTable, Composition
-from q2_types.feature_data import FeatureData
-
-
-regress_parameters={
-    'y': MetadataColumn[Numeric],
-    'do_clr' : Bool,
-    #Formulation parameters
-    'concomitant': Bool,
-    'huber'      : Bool,
-    'rho'        : Float,
-    'rescale'    : Bool,
-
-
-    #PATH parameters :
-    'path' : Bool,
-    'path_numerical_method' : Str,
-    'path_n_active'         : Int, # do something here ! for now it can be a bool !
-    'path_lambdas'          : List[Float],
-    'path_nlam_log'         : Int,
-    'path_lamin_log'        : Float,
-
-
-    #CV parameters :
-    'cv' : Bool,
-    'cv_numerical_method' : Str,
-    'cv_seed'             : Int, # do something here ! for now it can be a bool !
-    'cv_lambdas'          : List[Float],
-    'cv_one_se'            : Bool,
-    'cv_subsets'          : Int,
-
-    #StabSel parameters :
-    'stabsel' : Bool,
-    'stabsel_numerical_method' : Str,
-    'stabsel_seed'             : Int, # do something here ! for now it can be a bool !
-    'stabsel_lam'              : Float, # can be str as well for now !
-    'stabsel_true_lam'         : Bool,
-    'stabsel_method'           : Str,
-    'stabsel_b'                : Int,
-    'stabsel_q'                : Int,
-    'stabsel_percent_ns'       : Float,
-    'stabsel_lamin'            : Float,
-    'stabsel_threshold'        : Float,
-    'stabsel_threshold_label'  : Float, # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'lamfixed' : Bool,
-    'lamfixed_numerical_method' : Str,
-    'lamfixed_lam'              : Float, # can be str as well for now !
-    'lamfixed_true_lam'         : Bool
-}
-regress_parameter_descriptions={
-    'y': 'Vector representing the output of the problem',
-    'do_clr' : 'if set to true, then features will be centered-log-ration transformed and y will be centered',
-    #Formulation parameters
-    'concomitant': 'True if the formulation of the problem should be with an M_estimation of sigma. Default value = True',
-    'huber'      : 'True if the formulation of the problem should be robust Default value = False',
-    'rho'        : 'Value of rho for robust problem. Default value = 1.345',
-    'rescale'    : 'if True, then the function rescale() will be applied to data when solving the problem',
-
-
-    #PATH parameters :
-    'path' : 'True if path should be computed. Default Value = False',
-    'path_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'path_n_active'         : 'if it is an integer, then the algo stop computing the path when n_active variables are actives. then the solution does not change from this point. Dafault value : False',
-    'path_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.array([10**(-delta * float(i) / nlam) for i in range(0,nlam) ] ) with delta=2. and nlam = 40',
-    'path_nlam_log'         : ' number of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-    'path_lamin_log'        : 'minimum of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-
-    #CV parameters :
-    'cv' : 'True if Cross Validation should be computed. Default Value = False',
-    'cv_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'cv_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'cv_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.linspace(1., 1e-3, 500)',
-    'cv_one_se'            : 'if set to True, the selected lambda if computed with method ‘one-standard-error’ Default value : True',
-    'cv_subsets'          : 'number of subset in the cross validation method Dafault value : 5',
-    #StabSel parameters :
-    'stabsel' : 'True if Stability Selection should be computed. Default Value = True',
-    'stabsel_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'stabsel_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'stabsel_lam'              : '(only used if method = ‘lam’) lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed',
-    'stabsel_true_lam'         : '(only used if method = ‘lam’) True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True',
-    'stabsel_method'           : '‘first’, ‘lam’ or ‘max’ depending on the type of stability selection we do. Default value : ‘first’',
-    'stabsel_b'                : 'number of subsample considered. Default value : 50',
-    'stabsel_q'                : 'number of selected variable per subsample. Default value : 10',
-    'stabsel_percent_ns'       : 'size of subsample relatively to the total amount of sample Default value : 0.5',
-    'stabsel_lamin'            : 'lamin when computing the lasso-path for method ‘max’ Default value : 1e-2',
-    'stabsel_threshold'        : 'threhold for stability selection Default value : 0.7',
-    'stabsel_threshold_label'  : 'threshold to know when the label should be plot on the graph. Default value : 0.4', # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'lamfixed' : 'True if solution for a fixed lambda should be computed. Default Value = False',
-    'lamfixed_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'lamfixed_lam'              : 'lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed', # can be str as well for now !
-    'lamfixed_true_lam'         : 'True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True'
-}
-
-
-
diff --git a/build/lib/q2_classo/_dictionaries.py b/build/lib/q2_classo/_dictionaries.py
deleted file mode 100644
index 15a0dd4..0000000
--- a/build/lib/q2_classo/_dictionaries.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from qiime2.plugin import (SemanticType,Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric)
-from q2_types.feature_table import FeatureTable, Composition
-from q2_types.feature_data import FeatureData
-
-
-regress_parameters={
-     'y': MetadataColumn[Numeric],
-    #Formulation parameters
-    'concomitant': Bool,
-    'huber'      : Bool,
-    'rho'        : Float,
-    'rescale'    : Bool,
-
-
-    #PATH parameters :
-    'PATH' : Bool,
-    'PATH_numerical_method' : Str,
-    'PATH_n_active'         : Int, # do something here ! for now it can be a bool !
-    'PATH_lambdas'          : List[Float],
-    'PATH_nlam_log'         : Int,
-    'PATH_lamin_log'        : Float,
-
-
-    #CV parameters :
-    'CV' : Bool,
-    'CV_numerical_method' : Str,
-    'CV_seed'             : Int, # do something here ! for now it can be a bool !
-    'CV_lambdas'          : List[Float],
-    'CV_oneSE'            : Bool,
-    'CV_subsets'          : Int,
-
-    #StabSel parameters :
-    'StabSel' : Bool,
-    'StabSel_numerical_method' : Str,
-    'StabSel_seed'             : Int, # do something here ! for now it can be a bool !
-    'StabSel_lam'              : Float, # can be str as well for now !
-    'StabSel_true_lam'         : Bool,
-    'StabSel_method'           : Str,
-    'StabSel_B'                : Int,
-    'StabSel_q'                : Int,
-    'StabSel_percent_nS'       : Float,
-    'StabSel_lamin'            : Float,
-    'StabSel_threshold'        : Float,
-    'StabSel_threshold_label'  : Float, # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'LAMfixed' : Bool,
-    'LAMfixed_numerical_method' : Str,
-    'LAMfixed_lam'              : Float, # can be str as well for now !
-    'LAMfixed_true_lam'         : Bool
-}
-regress_parameter_descriptions={
-    'y': 'Vector representing the output of the problem',
-    #Formulation parameters
-    'concomitant': 'True if the formulation of the problem should be with an M_estimation of sigma. Default value = True',
-    'huber'      : 'True if the formulation of the problem should be robust Default value = False',
-    'rho'        : 'Value of rho for robust problem. Default value = 1.345',
-    'rescale'    : 'if True, then the function rescale() will be applied to data when solving the problem',
-
-
-    #PATH parameters :
-    'PATH' : 'True if path should be computed. Default Value = False',
-    'PATH_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'PATH_n_active'         : 'if it is an integer, then the algo stop computing the path when n_active variables are actives. then the solution does not change from this point. Dafault value : False',
-    'PATH_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.array([10**(-delta * float(i) / nlam) for i in range(0,nlam) ] ) with delta=2. and nlam = 40',
-    'PATH_nlam_log'         : ' number of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-    'PATH_lamin_log'        : 'minimum of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-
-    #CV parameters :
-    'CV' : 'True if Cross Validation should be computed. Default Value = False',
-    'CV_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'CV_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'CV_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.linspace(1., 1e-3, 500)',
-    'CV_oneSE'            : 'if set to True, the selected lambda if computed with method ‘one-standard-error’ Default value : True',
-    'CV_subsets'          : 'number of subset in the cross validation method Dafault value : 5',
-    #StabSel parameters :
-    'StabSel' : 'True if Stability Selection should be computed. Default Value = True',
-    'StabSel_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'StabSel_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'StabSel_lam'              : '(only used if method = ‘lam’) lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed',
-    'StabSel_true_lam'         : '(only used if method = ‘lam’) True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True',
-    'StabSel_method'           : '‘first’, ‘lam’ or ‘max’ depending on the type of stability selection we do. Default value : ‘first’',
-    'StabSel_B'                : 'number of subsample considered. Default value : 50',
-    'StabSel_q'                : 'number of selected variable per subsample. Default value : 10',
-    'StabSel_percent_nS'       : 'size of subsample relatively to the total amount of sample Default value : 0.5',
-    'StabSel_lamin'            : 'lamin when computing the lasso-path for method ‘max’ Default value : 1e-2',
-    'StabSel_threshold'        : 'threhold for stability selection Default value : 0.7',
-    'StabSel_threshold_label'  : 'threshold to know when the label should be plot on the graph. Default value : 0.4', # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'LAMfixed' : 'True if solution for a fixed lambda should be computed. Default Value = False',
-    'LAMfixed_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'LAMfixed_lam'              : 'lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed', # can be str as well for now !
-    'LAMfixed_true_lam'         : 'True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True'
-}
-
-
-
diff --git a/build/lib/q2_classo/_formats.py b/build/lib/q2_classo/_formats.py
deleted file mode 100644
index 7817f1a..0000000
--- a/build/lib/q2_classo/_formats.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import qiime2.plugin.model as model
-import zarr
-import numpy as np
-
-class ZarrProblemFormat(model.BinaryFileFormat):
-    def validate(self,level):
-        pass
-
-
-CLASSOProblemDirectoryFormat = model.SingleFileDirectoryFormat(
-    'CLASSOProblemDirectoryFormat', 'problem.zip', format=ZarrProblemFormat
-)
-
-class ConstraintFormat(model.BinaryFileFormat):
-    def validate(self,level):
-        pass
-
-ConstraintDirectoryFormat = model.SingleFileDirectoryFormat(
-    'ConstraintDirectoryFormat', 'cmatrix.zip', format=ConstraintFormat
-)
\ No newline at end of file
diff --git a/build/lib/q2_classo/_func.py b/build/lib/q2_classo/_func.py
deleted file mode 100644
index 750666f..0000000
--- a/build/lib/q2_classo/_func.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import numpy as np
-from q2_classo.CLasso import *
-from qiime2.plugin import (SemanticType,Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric)
-from q2_types.feature_table import FeatureTable, Composition
-from q2_types.feature_data import FeatureData
-import qiime2
-import pandas as pd
-
-
-
-def regress(
-            features : np.ndarray,
-            y : qiime2.NumericMetadataColumn,
-            c : np.ndarray  = None,
-            do_clr : bool = True,
-            #phylogenetic_tree : np.ndarray = None,
-            #PATH parameters :
-            path : bool = True,
-            path_numerical_method : str         = 'not specified',
-            path_n_active         : int         = 0,
-            path_lambdas          : list  = None,
-            path_nlam_log         : int         = 40,
-            path_lamin_log        : float       = 1e-2,
-
-            #CV parameters :
-            cv : bool                         = True,
-            cv_numerical_method : str         = 'not specified',
-            cv_seed             : int         = 1,
-            cv_lambdas          : list  = None, # to do 
-            cv_one_se            : bool        = True,
-            cv_subsets          : int         = 5,
-
-            #StabSel parameters :
-            stabsel : bool = True,
-            stabsel_numerical_method : str    = 'not specified',
-            stabsel_seed             : int    = None, # do something here ! for now it can be a bool !
-            stabsel_lam              : float  = -1.0, # if negative, then it means 'theoretical'
-            stabsel_true_lam         : bool   = True,
-            stabsel_method           : str    = 'first',
-            stabsel_b                : int    = 50, 
-            stabsel_q                : int    = 10,
-            stabsel_percent_ns       : float  = 0.5,
-            stabsel_lamin            : float  = 1e-2,
-            stabsel_threshold        : float  = 0.7,
-            stabsel_threshold_label  : float  = 0.4, # might unneeded here, but needed for visualisation
-
-            #LAMfixed parameters :
-            lamfixed : bool = True,
-            lamfixed_numerical_method : str  = 'not specified',
-            lamfixed_lam              : float = -1.0, # if negative, then it means 'theoretical'
-            lamfixed_true_lam         : bool  = True,
-            
-            #Formulation parameters
-            concomitant: bool      = True,
-            huber      : bool      = False,
-            rho        : float     = 1.345,
-            rescale    : bool      = False) -> classo_problem :
-
-
-    Y = y.to_series().to_numpy()
-    if do_clr : 
-        Features = clr(features.T).T
-        Y = Y - np.mean(Y)
-    else : 
-        Features = features
-
-
-    problem = classo_problem(Features, Y , C = c, rescale=rescale)
-    problem.formulation.huber       = huber
-    problem.formulation.concomitant = concomitant
-    problem.formulation.rho         = rho
-
-    problem.model_selection.PATH= path
-    if path:
-        param = problem.model_selection.PATHparameters
-        param.numerical_method = path_numerical_method
-        param.n_active         = path_n_active
-        if path_lambdas is None: 
-            param.lambdas = np.array([10**(np.log10(path_lamin_log) * float(i) / path_nlam_log) for i in range(0,path_nlam_log) ] )
-        else : param.lambdas=  path_lambdas
-
-    problem.model_selection.CV = cv
-    if cv :
-        param = problem.model_selection.CVparameters
-        param.numerical_method = cv_numerical_method
-        param.seed = cv_seed    
-        param.oneSE = cv_one_se  
-        param.Nsubsets = cv_subsets  
-        if cv_lambdas is None: param.lambdas =  np.linspace(1., 1e-3, 500)
-        else                 : param.lambdas =  cv_lambdas
-
-    problem.model_selection.StabSel = stabsel
-    if stabsel : 
-        param = problem.model_selection.StabSelparameters
-        param.numerical_method = stabsel_numerical_method
-        param.seed = stabsel_seed
-        param.true_lam = stabsel_true_lam
-        param.method = stabsel_method 
-        param.B = stabsel_b
-        param.q = stabsel_q
-        param.percent_nS = stabsel_percent_ns
-        param.lamin = stabsel_lamin
-        param.threshold = stabsel_threshold
-        param.threshold_label = stabsel_threshold_label
-        if (stabsel_lam>0.): param.lam = stabsel_lam
-        else               : param.lam = 'theoretical'
-
-    problem.model_selection.LAMfixed = lamfixed
-    if lamfixed: 
-        param = problem.model_selection.LAMfixedparameters
-        param.numerical_method = lamfixed_numerical_method
-        param.true_lam = lamfixed_true_lam
-        if (lamfixed_lam>0.): param.lam = lamfixed_lam
-        else                : param.lam = 'theoretical'
-
-    problem.solve()
-
-    return problem
-    
-
-def generate_data(n : int = 100,
-                  d : int = 150,
-                  d_nonzero : int = 5
-                    ) -> (np.ndarray, np.ndarray) :
-
-    (X,C,y),sol = random_data(n,d,d_nonzero,0,0.5,zerosum=True,seed= 4, exp = True)
-    pd.DataFrame(data={'id':range(len(y)),'col':y}).to_csv("randomy.tsv",sep='\t',index=False)
-    return X, C
-
diff --git a/build/lib/q2_classo/_summarize/__init__.py b/build/lib/q2_classo/_summarize/__init__.py
deleted file mode 100644
index 7e7424d..0000000
--- a/build/lib/q2_classo/_summarize/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ._visualizer import summarize
\ No newline at end of file
diff --git a/build/lib/q2_classo/_summarize/_visualizer.py b/build/lib/q2_classo/_summarize/_visualizer.py
deleted file mode 100644
index bf21441..0000000
--- a/build/lib/q2_classo/_summarize/_visualizer.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import os
-import json
-import zarr
-import pandas as pd
-import numpy as np
-import q2templates
-import shutil
-import matplotlib.pyplot as plt
-from ..CLasso.misc_functions import influence, colo
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-assets = os.path.join(dir_path, 'assets')
-dir_form = os.path.join(dir_path, 'form')
-
-
-
-
-def summarize(output_dir: str, problem : zarr.hierarchy.Group):
-
-    context = build_context(output_dir,problem)
-
-    index = os.path.join(assets, 'index.html')
-    overview_template = os.path.join(assets, 'overview.html')
-    path_template = os.path.join(assets, 'path.html')
-    cv_template = os.path.join(assets, 'cv.html')
-    stabsel_template = os.path.join(assets, 'stabsel.html')
-    lam_fixed_template = os.path.join(assets, 'lam-fixed.html')
-    templates = [index, overview_template, path_template, cv_template, stabsel_template, lam_fixed_template]
-    q2templates.render(templates, output_dir, context=context)
-    
-
-
-def build_context(output_dir,problem):
-
-    labels = problem['label']
-    features = pd.DataFrame(problem['data/X'],columns=labels)
-    y = pd.DataFrame({'y':problem['data/y']})
-    c = pd.DataFrame(problem['data/C'], columns = labels)
-    features.to_csv(os.path.join(output_dir, 'features.csv'),header=True, index=False)
-    y.to_csv(os.path.join(output_dir, 'samples.csv'),header=True, index=False)
-    c.to_csv(os.path.join(output_dir, 'constraints.csv'),header=True, index=False)
-
-    context = {
-        'path': False,'cv': False,'stabsel':False,'lam' : False,
-        'labels':labels,
-        'tabs': [{'title': 'Overview','url': 'overview.html'}]
-    }
-    dico = {
-        'formulation':name_formulation(problem['formulation'].attrs.asdict(),output_dir),
-        'concomitant': problem['formulation'].attrs['concomitant'],
-        'n' : len(problem['data/X']),
-        'd' : len(problem['data/X'][0]),
-        'k' : len(problem['data/C'])
-    }
-    context['dico']=dico
-
-    dico_ms = problem['model_selection'].attrs.asdict()
-
-    if dico_ms['PATH']:
-        context['path'] = True
-        context['tabs'].append({'title': 'Lambda-path','url': 'path.html'})
-        dico_path = { **problem['model_selection/PATHparameters'].attrs.asdict(), **problem['solution/PATH'].attrs.asdict() }
-        dico_path['lambdas']= problem['solution/PATH/LAMBDAS']
-        dico_path['lamin']= min(dico_path['lambdas'])
-        dico_path['Nlam'] = len(dico_path['lambdas'])
-        data = pd.DataFrame(np.array(problem['solution/PATH/BETAS']),index=dico_path['lambdas'], columns=labels)
-        data.to_csv(os.path.join(output_dir, 'path.csv'),header=True, index=True)
-        SIGMAS = None
-        if dico['concomitant']: 
-            SIGMAS = problem['solution/PATH/SIGMAS']
-
-        context['dicopath']= dico_path
-
-        plot_path(np.array(problem['solution/PATH/BETAS']),SIGMAS,problem['solution/PATH/LAMBDAS'],output_dir,labels)
-
-    if dico_ms['CV']:
-        context['cv'] = True
-        xGraph, yGraph, standard_error = problem['solution/CV/xGraph'], problem['solution/CV/yGraph'], problem['solution/CV/standard_error']
-        context['tabs'].append({'title': 'Cross-Validation','url': 'cv.html'})
-        dico_cv = { **problem['model_selection/CVparameters'].attrs.asdict(), **problem['solution/CV'].attrs.asdict() }
-        dico_cv['lamin']= min(xGraph)
-        dico_cv['Nlam']= len(xGraph)
-        beta = pd.DataFrame(data={'label':problem['label'],'beta':problem['solution/CV/refit']})
-        beta.to_csv(os.path.join(output_dir,'CV-beta.csv'),header=True, index=False)
-        selected_param = np.array(problem['solution/CV/selected_param'])
-        beta_support = beta[selected_param]
-        dico_cv['htmlbeta']=q2templates.df_to_html(beta_support, index=False)
-
-        context['dicocv']= dico_cv
-
-        if (dico_cv['oneSE']): lam = dico_cv['lambda_1SE']
-        else : lam = dico_cv['lambda_min']
-
-        plot_beta(np.array(problem['solution/CV/refit']),selected_param,output_dir,labels,'cv-refit.png',r"Refitted coefficients of $\beta$ after CV model selection finds $\lambda$ = "+str(lam))
-        plot_cv(xGraph, yGraph,dico_cv['index_1SE'],dico_cv['index_min'],standard_error, output_dir, 'cv-graph.png')
-
-    if dico_ms['StabSel']:
-        context['stabsel'] = True
-        context['tabs'].append({'title': 'Stability Selection','url': 'stabsel.html'})
-        dico_stabsel = { **problem['model_selection/StabSelparameters'].attrs.asdict(), **problem['solution/StabSel'].attrs.asdict() }
-        
-        stability = pd.DataFrame(data={'label':problem['label'],'stability-probability':problem['solution/StabSel/distribution']})
-        stability.to_csv(os.path.join(output_dir,'StabSel-prob.csv'),header=True, index=False)
-        selected_param = np.array(problem['solution/StabSel/selected_param'])
-        stability_support = stability[selected_param]
-
-        dico_stabsel['nsel']=len(stability_support)
-        dico_stabsel['htmlstab']=q2templates.df_to_html(stability_support, index=False)
-
-        context['dicostabsel']= dico_stabsel
-
-        plot_beta(np.array(problem['solution/StabSel/refit']),selected_param,output_dir,labels,'stabsel-refit.png',r"Refitted coefficients of $\beta$ after stability selection")
-        plot_stability(problem['solution/StabSel/distribution'], selected_param, dico_stabsel['threshold'],dico_stabsel['method'], labels, output_dir,'stabsel-graph.png')
-
-    if dico_ms['LAMfixed']:
-        context['lam'] = True
-        context['tabs'].append({'title': 'LAM fixed','url': 'lam-fixed.html'})
-        dico_lam = { **problem['model_selection/LAMfixedparameters'].attrs.asdict(), **problem['solution/LAMfixed'].attrs.asdict() }
-        dico_lam['lamtype']= problem['model_selection/LAMfixedparameters'].attrs['lam']
-
-        beta = pd.DataFrame(data={'label':problem['label'],'beta':problem['solution/LAMfixed/refit']})
-        beta.to_csv(os.path.join(output_dir,'LAM-beta.csv'),header=True, index=False)
-        selected_param = np.array(problem['solution/LAMfixed/selected_param'])
-        beta_support = beta[selected_param]
-        dico_lam['htmlbeta']=q2templates.df_to_html(beta_support, index=False)
-
-        context['dicolam']= dico_lam
-
-        plot_beta(np.array(problem['solution/LAMfixed/beta']),None,output_dir,labels,'lam-beta.png',r"Coefficients of $\beta$ at $\lambda$ = "+str(dico_lam['lam']))
-        plot_beta(np.array(problem['solution/LAMfixed/refit']),selected_param,output_dir,labels,'lam-refit.png',r"Reffited coefficients of $\beta$ at $\lambda$ = "+str(dico_lam['lam']))
-
-    return context
-
-
-
-
-
-
-
-
-
-
-
-def name_formulation(dictio,output_dir):
-    if dictio['concomitant']:
-        if dictio['huber']:
-            shutil.copy(os.path.join(dir_form, 'R4.png'),os.path.join(output_dir, 'formula.png'))
-            return 'R4 (concomitant and huber with e = '+ str(dictio['e']) +' and rho = '+str(dictio['rho']) +' )'
-        else :
-            shutil.copy(os.path.join(dir_form, 'R3.png'),os.path.join(output_dir, 'formula.png'))
-            return 'R3 (concomitant with e = ' + str(dictio['e']) + ' )'
-    else : 
-        if dictio['huber']:
-            shutil.copy(os.path.join(dir_form, 'R2.png'),os.path.join(output_dir, 'formula.png'))
-            return 'R2 (huber with rho = '+str(dictio['rho']) +' )'
-        else :
-            shutil.copy(os.path.join(dir_form, 'R1.png'),os.path.join(output_dir, 'formula.png'))
-            return 'R1 (classic lasso formulation)'
-
-def plot_path(BETAS, SIGMAS, LAMBDAS,output_dir, labels):
-    fig = plt.figure()
-    ax = fig.subplots()
-    l_index = influence(BETAS, 10)
-    j=0
-    for i in range(len(BETAS[0])):
-        if j<len(l_index) and i==l_index[j]: 
-            label = str(labels[i])
-            j+=1
-        else : 
-            label=None
-        ax.plot(LAMBDAS,BETAS[:,i],label=label,color=colo[i])
-    ax.set_title(r"Coefficients across $\lambda$-path")
-    ax.legend(loc=4, borderaxespad=0.)
-    ax.set_xlabel(r"$\lambda$"), ax.set_ylabel(r"Coefficients $\beta_i$ ")
-    
-    fig.savefig(os.path.join(output_dir, 'beta-path.png'))
-    if not SIGMAS is None:
-        fig2 = plt.figure()
-        ax2 = fig2.subplots()
-        ax2.plot(LAMBDAS, SIGMAS, color='blue')
-        ax2.set_title(r"Scale estimate across $\lambda$-path"),ax2.set_xlabel(r"$\lambda$"), ax2.set_ylabel(r"Scale $\sigma$ ")
-        fig2.savefig(os.path.join(output_dir, 'sigma-path.png'))
-
-
-def plot_beta(beta,selected_param,output_dir,labels,name,title):
-    fig = plt.figure()
-    ax = fig.subplots()
-    ax.bar(range(len(beta)), beta)
-    ax.set_title(title)
-    ax.set_xlabel(r"Feature of index $i$" )
-    ax.set_ylabel(r"Coefficients $\beta_i$ ")
-    ax.axhline(xmax=len(beta),color='k')
-    if not selected_param is None : 
-        ax.set_xticks(np.where(selected_param)[0])
-        ax.set_xticklabels(np.array(labels)[selected_param], rotation=30)
-    fig.savefig(os.path.join(output_dir, name))
-
-
-def plot_cv(xGraph, yGraph,index_1SE, index_min,SE, output_dir, name):
-    fig = plt.figure()
-    ax = fig.subplots()
-
-    mse_max = 1.
-    for j in range(len(xGraph)):
-        if (yGraph[j] < mse_max): break
-
-    ax.errorbar(xGraph[j:], yGraph[j:], SE[j:], label='mean over the k groups of data', errorevery = 10 )
-    ax.axvline(x=xGraph[index_min], color='k', label=r'$\lambda$ (min MSE)')
-    ax.axvline(x=xGraph[index_1SE],color='r',label=r'$\lambda$ (1SE) ')
-    ax.set_title(r" " )
-    ax.set_xlabel(r"$\lambda / \lambda_{max}$")
-    ax.set_ylabel(r"Mean-Squared Error (MSE) ")
-    ax.legend()
-
-    fig.savefig(os.path.join(output_dir, name))
-
-
-def plot_stability(distribution, selected_param, threshold, method, labels, output_dir,name):
-    fig = plt.figure()
-    ax = fig.subplots()
-
-    D, selected = np.array(distribution), selected_param
-    unselected = [not i for i in selected]
-    Dselected, Dunselected  = np.zeros(len(D)), np.zeros(len(D))
-    Dselected[selected], Dunselected[unselected] = D[selected], D[unselected]
-
-    ax.bar(range(len(Dselected)), Dselected, color='r', label='selected coefficients')
-    ax.bar(range(len(Dunselected)), Dunselected, color='b', label='unselected coefficients')
-    ax.axhline(y=threshold, color='g',label='Threshold : thresh = '+ str(threshold))
-
-    ax.set_xticks(np.where(selected_param)[0])
-    ax.set_xticklabels(np.array(labels)[selected_param], rotation=30)
-    
-    ax.set_xlabel(r"Coefficient index $i$")
-    ax.set_ylabel(r"Selection probability ")
-    ax.set_title(r"Stability selection profile with method "+ method)
-    ax.legend()
-
-
-    fig.savefig(os.path.join(output_dir, name))
-
diff --git a/build/lib/q2_classo/_summarize/assets/cv.html b/build/lib/q2_classo/_summarize/assets/cv.html
deleted file mode 100644
index 0750c1a..0000000
--- a/build/lib/q2_classo/_summarize/assets/cv.html
+++ /dev/null
@@ -1,42 +0,0 @@
-{% extends "tab-child.html" %}
-
-{% block content %}
-{% if cv %}
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Parameters</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>numerical_method :</td><td>{{ dicocv.numerical_method }}</td></tr>
-            <tr><td>Number of subsets :</td><td>{{ dicocv.Nsubset }}</td></tr>
-            <tr><td>Using 1-standard-deviation method :</td><td>{{ dicocv.oneSE }}</td></tr>
-            <tr><td>Lambda min :</td><td>{{ dicocv.lamin }}</td></tr>
-            <tr><td>number of lambdas :</td><td>{{ dicocv.Nlam}}</td></tr>
-            <tr><td>Seed for randomization :</td><td>{{ dicocv.seed }}</td></tr>
-        </table>
-        
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Solution</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Running time :</td><td>{{ dicocv.time }}</td></tr>
-            <tr><td> lambda / lambda_max for 1-standard-deviation method :</td><td>{{ dicocv.lambda_1SE }}</td></tr>
-            <tr><td> lambda / lambda_max for min method :</td><td>{{ dicocv.lambda_min }}</td></tr>
-        </table>
-        <h3>Beta for selected parameters</h3>
-            {{dicocv.htmlbeta}}
-            <h4><a href="CV-beta.csv">Download beta solution after refit as CSV</a></h4>   
-            <img src="cv-refit.png">
-
-        <h3>Graphic of cross validation</h3>
-            <img src="cv-graph.png">
-        
-    </div>
-</div>
-
-
-{% endif %}
-
-{% endblock %}
\ No newline at end of file
diff --git a/build/lib/q2_classo/_summarize/assets/index.html b/build/lib/q2_classo/_summarize/assets/index.html
deleted file mode 100644
index 6186a45..0000000
--- a/build/lib/q2_classo/_summarize/assets/index.html
+++ /dev/null
@@ -1 +0,0 @@
-{% extends "tab-parent.html" %}
diff --git a/build/lib/q2_classo/_summarize/assets/lam-fixed.html b/build/lib/q2_classo/_summarize/assets/lam-fixed.html
deleted file mode 100644
index 7ad2527..0000000
--- a/build/lib/q2_classo/_summarize/assets/lam-fixed.html
+++ /dev/null
@@ -1,41 +0,0 @@
-{% extends "tab-child.html" %}
-
-{% block content %}
-{% if stabsel %}
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Parameters</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Numerical method :</td><td>{{ dicolam.numerical_method }}</td></tr>
-            <tr><td> Lambda given :</td><td>{{ dicolam.lamtype }}</td></tr>
-            <tr><td> Real given lambda (on contrast to giving lambda/lambdamax) :</td><td>{{ dicolam.true_lam }}</td></tr>
-            <tr><td> Lambdamax :</td><td>{{ dicolam.lambdamax }}</td></tr>
-            <tr><td> Theoretical lambda :</td><td>{{ dicolam.theoretical_lam }}</td></tr>
-        </table>
-        
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Solution</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Running time :</td><td>{{ dicolam.time }}</td></tr>
-            <tr><td>Lambda used : </td><td>{{ dicolam.lam }}</td></tr>
-            <tr><td>Sigma : </td><td>{{ dicolam.sigma }}</td></tr>
-            <h4>Beta for selected parameters</h4>
-            {{dicolam.htmlbeta}}
-            <h4><a href="LAM-beta.csv">Download beta solution after refit as CSV</a></h4>  
-        </table>
-        <img src="lam-refit.png">
-        <h3>Beta-solution before refit </h3>
-            <img src="lam-beta.png">
-
-    </div>
-</div>
-
-
-
-{% endif %}
-
-{% endblock %}
\ No newline at end of file
diff --git a/build/lib/q2_classo/_summarize/assets/overview.html b/build/lib/q2_classo/_summarize/assets/overview.html
deleted file mode 100644
index 6ebb355..0000000
--- a/build/lib/q2_classo/_summarize/assets/overview.html
+++ /dev/null
@@ -1,28 +0,0 @@
-{% extends "tab-child.html" %}
-
-{% block content %}
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Dimension of the problem</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Number of samples :</td><td>{{ dico.n }}</td></tr>
-            <tr><td>Number of features :</td><td>{{ dico.d }}</td></tr>
-            <tr><td>Number of constraints :</td><td>{{ dico.k }}</td></tr>
-        </table>
-
-        <h4><a href="features.csv">Download features matrix as CSV</a></h4>   
-        <h4><a href="samples.csv">Download samples column as CSV</a></h4>   
-        <h4><a href="constraints.csv">Download constraints matrix as CSV</a></h4>   
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Formulation : {{ dico.formulation }}</h1>
-        <img src="formula.png">
-    </div>
-</div>
-
-
-{% endblock %}
\ No newline at end of file
diff --git a/build/lib/q2_classo/_summarize/assets/path.html b/build/lib/q2_classo/_summarize/assets/path.html
deleted file mode 100644
index 17cb70e..0000000
--- a/build/lib/q2_classo/_summarize/assets/path.html
+++ /dev/null
@@ -1,45 +0,0 @@
-{% extends "tab-child.html" %}
-
-{% block content %}
-{% if path %}
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Parameters</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>numerical_method :</td><td>{{ dicopath.numerical_method }}</td></tr>
-            <tr><td>Lambda min :</td><td>{{ dicopath.lamin }}</td></tr>
-            <tr><td>number of lambdas :</td><td>{{ dicopath.Nlam}}</td></tr>
-        </table>
-        
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Solution</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Running time :</td><td>{{ dicopath.time }}</td></tr>
-        </table>
-        <h3>Beta-path</h3>
-        <h4><a href="path.csv">Download beta/lambda path as csv</a></h4>  
-            <img src="beta-path.png"> 
-        {% if dico.concomitant %}
-            <h3>Sigma-path</h3>
-                <img src="sigma-path.png">
-
-        {% endif %}
-        
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12 text-center">
-        
-    </div>
-</div>
-
-
-
-{% endif %}
-
-{% endblock %}
\ No newline at end of file
diff --git a/build/lib/q2_classo/_summarize/assets/stabsel.html b/build/lib/q2_classo/_summarize/assets/stabsel.html
deleted file mode 100644
index 812101c..0000000
--- a/build/lib/q2_classo/_summarize/assets/stabsel.html
+++ /dev/null
@@ -1,45 +0,0 @@
-{% extends "tab-child.html" %}
-
-{% block content %}
-{% if stabsel %}
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Parameters</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>numerical_method :</td><td>{{ dicostabsel.numerical_method }}</td></tr>
-            <tr><td> Method of stability selection used :</td><td>{{ dicostabsel.method }}</td></tr>
-            <tr><td> Real given lambda (on contrast to giving lambda/lambdamax) :</td><td>{{ dicostabsel.true_lam }}</td></tr>
-            <tr><td> Theoretical lambda :</td><td>{{ dicostabsel.theoretical_lam }}</td></tr>
-            <tr><td> Lambda used if method is lam :</td><td>{{ dicostabsel.lam }}</td></tr>
-            <tr><td> Lambda min used if method is max:</td><td>{{ dicostabsel.lamin }}</td></tr>
-            <tr><td> B (number of split on the dataset ) :</td><td>{{ dicostabsel.B }}</td></tr>
-            <tr><td> q (used for the q-first method or the q-max method) :</td><td>{{ dicostabsel.q }}</td></tr>
-            <tr><td> Percent of the dataset for each split :</td><td>{{ dicostabsel.percent_nS }}</td></tr>
-            <tr><td> Threshold :</td><td>{{ dicostabsel.threshold }}</td></tr>
-            <tr><td>Seed for randomization :</td><td>{{ dicostabsel.seed }}</td></tr>
-        </table>
-        
-    </div>
-</div>
-
-<div class="row">
-    <div class="col-lg-12">
-        <h1>Solution</h1>
-        <table class="table table-striped table-hover">
-            <tr><td>Running time :</td><td>{{ dicostabsel.time }}</td></tr>
-            <tr><td>Number of selected parameters :</td><td>{{ dicostabsel.nsel }}</td></tr>
-
-        </table>
-        <h2>Stability Selection profile</h2>
-            {{dicostabsel.htmlstab}}
-            <h4><a href="StabSel-prob.csv">Download stability selection profile as CSV</a></h4>
-            <img src="stabsel-graph.png">   
-            <h3>Beta-solution after refit </h3>
-            <img src="stabsel-refit.png">
-    </div>
-</div>
-
-
-{% endif %}
-
-{% endblock %}
\ No newline at end of file
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diff --git a/build/lib/q2_classo/_transformer.py b/build/lib/q2_classo/_transformer.py
deleted file mode 100644
index 45d5264..0000000
--- a/build/lib/q2_classo/_transformer.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import numpy as np 
-import zarr
-
-def classo_to_dir(problem):
-    result = CLASSOProblemDirectoryFormat()
-    
-    # only do it for PATH for now
-    solPATH_classo, solPATH_file = problem.solution.PATH , result.PATH
-    if type(solPATH_classo) != str : 
-        if type(solPATH_classo.SIGMAS)==str : 
-            data = zarr.array(np.array([solPATH_classo.LAMBDAS,solPATH_classo.BETAS]) )
-        else :                                
-            data = zarr.array(np.array([solPATH_classo.LAMBDAS,solPATH_classo.BETAS,solPATH_classo.SIGMAS]) ) 
-        
-        with solPATH_file.open() as fh : 
-            data.write(fh,format=SolutionFormat) # is it how it is supposed to be done ??
-  
-    return result
diff --git a/build/lib/q2_classo/_types.py b/build/lib/q2_classo/_types.py
deleted file mode 100644
index 4616794..0000000
--- a/build/lib/q2_classo/_types.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from qiime2.plugin import SemanticType
-from CLasso import *
-from q2_classo.plugin_setup import plugin
-from ._formats import CLASSOProblemDirectoryFormat
-
-CLASSOProblem = SemanticType("CLASSOProblem")
-
-ConstraintMatrix = SemanticType("ConstraintMatrix")
-
-plugin.register_semantic_types(CLASSOProblem)
-
-plugin.register_semantic_type_to_format(CLASSOProblem, 
-                                        artifact_format=CLASSOProblemDirectoryFormat)
-
-
-
-
-
-'''
-Beta_type         = SemanticType('Beta_type') # array of size Npath x d with the solution beta for each lambda on each row
-Sigma_type        = SemanticType('Sigma_type') # array of size Npath with the solution sigma for each lambda when the formulation of the problem is R2 or R4
-Lambda_type       = SemanticType('Lambda_type') # array of size Npath with the lambdas (real lambdas, not divided by lambda_max) for which the solution is computed
-Method_type       = SemanticType('Method_type') # name of the numerical method that has been used. It can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-Formulation_type  = SemanticType('Formulation_type') # can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-Time_type         = SemanticType('Time_type') # running time of this action
-
-Path_type = SemanticType('Path_type', field_names=['betas','sigmas','lambdas', 'method', 'formulation', 'time'],
-                      field_members={
-                          'betas' : Beta_type,
-                          'sigmas': Sigma_type,
-                          'lambdas': Lambda_type,
-                          'method' : Method_type,
-                          'formulation' : Formulation_type,
-                          'time'   : Time_type
-                       })
-
-Path = Path_type[Beta_type,Sigma_type,Lambda_type, Method_type, Formulation_type, Time_type]
-'''
\ No newline at end of file
diff --git a/build/lib/q2_classo/_visualizer.py b/build/lib/q2_classo/_visualizer.py
deleted file mode 100644
index 6a13ee4..0000000
--- a/build/lib/q2_classo/_visualizer.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import os
-import json
-import zarr
-import pandas as pd
-import numpy as np
-import q2templates
-import pkg_resources
-
-TEMPLATES = pkg_resources.resource_filename('q2_classo','q2_classo')
-
-
-
-def summarize(output_dir: str, problem : zarr.hierarchy.Group):
-    print(TEMPLATES)
-
-    beta = pd.DataFrame(data={'label':problem['label'],'beta':problem['solution/LAMfixed/refit']})
-    beta.to_csv(os.path.join(output_dir,'beta.csv'),header=True, index=False)
-    show_plot = False
-    if show_plot : 
-        x = np.linspace(0,1)
-        y = x**2
-        fig = plt.figure()
-        ax = fig.add_subplot(1, 1, 1)
-        ax.plot(x, y, color='blue')
-        fig.savefig(os.path.join(output_dir, 'test-plot.png'))
-    
-    
-
-    html = q2templates.df_to_html(beta, index=False)
-    context = {
-        'dico': {
-            'un': 1, 'deux':2
-        },
-        'result': html,
-        'n_features':len(beta),
-        'beta' : beta,
-        'show_plot': show_plot,
-        'tabs': [{'title': 'Overview',
-                  'url': 'overview.html'},
-                 {'title': 'LAM fixed',
-                  'url': 'lam-fixed.html'}],
-        'dangers': [],
-        'warnings': [],
-    }
-    
-    
-    index = os.path.join(TEMPLATES, 'assets', 'index.html')
-    overview_template = os.path.join(TEMPLATES, 'assets', 'overview.html')
-    quality_template = os.path.join(TEMPLATES, 'assets', 'quality-plot.html')
-    templates = [index, overview_template, quality_template]
-    q2templates.render(templates, output_dir, context=context)
-
-    shutil.copytree(os.path.join(TEMPLATES, 'assets', 'dist'),
-                    os.path.join(output_dir, 'dist'))
-
-    
-
-
-    with open(os.path.join(output_dir, 'data.jsonp'), 'w') as fh:
-        fh.write("app.init(")
-        json.dump({'selected param' : 10}, fh)
-        fh.write(',')
-        beta.to_json(fh)
-        fh.write(');')
-
-
-'''
-Example from : 
-https://github.com/qiime2/q2-demux/blob/master/q2_demux/_summarize/_visualizer.py
-
-
-
-import collections
-import os
-import pkg_resources
-import shutil
-import random
-import json
-
-import pandas as pd
-import seaborn as sns
-import numpy as np
-
-from q2_demux._demux import _read_fastq_seqs
-import q2templates
-
-TEMPLATES = pkg_resources.resource_filename('q2_demux', '_summarize')
-
-
-def _decode_qual_to_phred33(qual_str):
-    # this function is adapted from scikit-bio
-    qual = np.frombuffer(qual_str.encode('ascii'), dtype=np.uint8) - 33
-    return qual
-
-
-# TODO: Remove _PlotQualView once QIIME 2 #220 completed
-class _PlotQualView:
-    """
-    A very simple pass-through view which is made up of a single-end or
-    paired-end directory format with a bool indicating if single or paired.
-    """
-    def __init__(self, directory_format, paired):
-        self.directory_format = directory_format
-        self.paired = paired
-
-
-def _link_sample_n_to_file(file_records, counts, subsample_ns):
-    results = collections.defaultdict(list)
-    for num in subsample_ns:
-        total = 0
-        for file, sample_id in file_records:
-            total += counts[sample_id]
-            if num < total:
-                idx = counts[sample_id] - (total - num)
-                results[file].append(idx)
-                break
-    return results
-
-
-def _subsample_paired(fastq_map):
-    qual_sample = collections.defaultdict(list)
-    min_seq_len = {'forward': float('inf'), 'reverse': float('inf')}
-    for fwd, rev, index in fastq_map:
-        file_pair = zip(_read_fastq_seqs(fwd), _read_fastq_seqs(rev))
-        for i, (fseq, rseq) in enumerate(file_pair):
-            if i == index[0]:
-                min_seq_len['forward'] = min(min_seq_len['forward'],
-                                             len(fseq[1]))
-                min_seq_len['reverse'] = min(min_seq_len['reverse'],
-                                             len(rseq[1]))
-                qual_sample['forward'].append(_decode_qual_to_phred33(fseq[3]))
-                qual_sample['reverse'].append(_decode_qual_to_phred33(rseq[3]))
-                index.pop(0)
-                if len(index) == 0:
-                    break
-    return qual_sample, min_seq_len
-
-
-def _subsample_single(fastq_map):
-    qual_sample = collections.defaultdict(list)
-    min_seq_len = {'forward': float('inf'), 'reverse': None}
-    for file, index in fastq_map:
-        for i, seq in enumerate(_read_fastq_seqs(file)):
-            if i == index[0]:
-                min_seq_len['forward'] = min(min_seq_len['forward'],
-                                             len(seq[1]))
-                qual_sample['forward'].append(_decode_qual_to_phred33(seq[3]))
-                index.pop(0)
-                if len(index) == 0:
-                    break
-    return qual_sample, min_seq_len
-
-
-def _compute_stats_of_df(df):
-    df_stats = df.describe(
-        percentiles=[0.02, 0.09, 0.25, 0.5, 0.75, 0.91, 0.98])
-    drop_cols = df_stats.index.isin(['std', 'mean', 'min', 'max'])
-    df_stats = df_stats[~drop_cols]
-    return df_stats
-
-
-def _build_seq_len_table(qscores: pd.DataFrame) -> str:
-    sequence_lengths = qscores.notnull().sum(axis=1).copy()
-    stats = _compute_stats_of_df(sequence_lengths)
-
-    stats[stats.index != 'count'] = \
-        stats[stats.index != 'count'].astype(int).apply('{} nts'.format)
-
-    stats.rename(index={'50%': '50% (Median)',
-                        'count': 'Total Sequences Sampled'},
-                 inplace=True)
-    frame = stats.to_frame(name="")
-    return q2templates.df_to_html(frame)
-
-
-def summarize(output_dir: str, data: _PlotQualView, n: int = 10000) -> None:
-    paired = data.paired
-    data = data.directory_format
-    dangers = []
-    warnings = []
-
-    manifest = pd.read_csv(os.path.join(str(data), data.manifest.pathspec),
-                           header=0, comment='#')
-    manifest.filename = manifest.filename.apply(
-        lambda x: os.path.join(str(data), x))
-
-    fwd = manifest[manifest.direction == 'forward'].filename.tolist()
-    rev = manifest[manifest.direction == 'reverse'].filename.tolist()
-
-    per_sample_fastq_counts = {}
-    reads = rev if not fwd and rev else fwd
-    file_records = []
-    for file in reads:
-        count = 0
-        for seq in _read_fastq_seqs(file):
-            count += 1
-        sample_id = manifest.loc[manifest.filename == file,
-                                 'sample-id'].iloc[0]
-        per_sample_fastq_counts[sample_id] = count
-        file_records.append((file, sample_id))
-
-    result = pd.Series(per_sample_fastq_counts)
-    result.name = 'Sequence count'
-    result.index.name = 'Sample name'
-    result.sort_values(inplace=True, ascending=False)
-    result.to_csv(os.path.join(output_dir, 'per-sample-fastq-counts.csv'),
-                  header=True, index=True)
-    sequence_count = result.sum()
-
-    if n > sequence_count:
-        n = sequence_count
-        warnings.append('A subsample value was provided that is greater than '
-                        'the amount of sequences across all samples. The plot '
-                        'was generated using all available sequences.')
-
-    subsample_ns = sorted(random.sample(range(sequence_count), n))
-    link = _link_sample_n_to_file(file_records,
-                                  per_sample_fastq_counts,
-                                  subsample_ns)
-    if paired:
-        sample_map = [(file, rev[fwd.index(file)], link[file])
-                      for file in link]
-        quality_scores, min_seq_len = _subsample_paired(sample_map)
-    else:
-        sample_map = [(file, link[file]) for file in link]
-        quality_scores, min_seq_len = _subsample_single(sample_map)
-
-    forward_scores = pd.DataFrame(quality_scores['forward'])
-    forward_stats = _compute_stats_of_df(forward_scores)
-    forward_stats.to_csv(os.path.join(output_dir,
-                         'forward-seven-number-summaries.csv'),
-                         header=True, index=True)
-    forward_length_table = _build_seq_len_table(forward_scores)
-
-    if (forward_stats.loc['50%'] > 45).any():
-        dangers.append('Some of the PHRED quality values are out of range. '
-                       'This is likely because an incorrect PHRED offset '
-                       'was chosen on import of your raw data. You can learn '
-                       'how to choose your PHRED offset during import in the '
-                       'importing tutorial.')
-
-    # Required initilization for conditional display of the table
-    reverse_length_table = None
-    if paired:
-        reverse_scores = pd.DataFrame(quality_scores['reverse'])
-        reverse_stats = _compute_stats_of_df(reverse_scores)
-        reverse_stats.to_csv(os.path.join(output_dir,
-                             'reverse-seven-number-summaries.csv'),
-                             header=True, index=True)
-        reverse_length_table = _build_seq_len_table(reverse_scores)
-
-    show_plot = len(fwd) > 1
-    if show_plot:
-        ax = sns.distplot(result, kde=False)
-        ax.set_xlabel('Number of sequences')
-        ax.set_ylabel('Frequency')
-        fig = ax.get_figure()
-        fig.savefig(os.path.join(output_dir, 'demultiplex-summary.png'))
-        fig.savefig(os.path.join(output_dir, 'demultiplex-summary.pdf'))
-
-    html_df = result.to_frame().reset_index(drop=False)
-    html = q2templates.df_to_html(html_df, index=False)
-    index = os.path.join(TEMPLATES, 'assets', 'index.html')
-    overview_template = os.path.join(TEMPLATES, 'assets', 'overview.html')
-    quality_template = os.path.join(TEMPLATES, 'assets', 'quality-plot.html')
-    context = {
-        'result_data': {
-            'min': result.min(),
-            'median': result.median(),
-            'mean': result.mean(),
-            'max': result.max(),
-            'sum': sequence_count
-        },
-        'forward_length_table': forward_length_table,
-        'reverse_length_table': reverse_length_table,
-        'result': html,
-        'n_samples': result.count(),
-        'show_plot': show_plot,
-        'paired': paired,
-        'tabs': [{'title': 'Overview',
-                  'url': 'overview.html'},
-                 {'title': 'Interactive Quality Plot',
-                  'url': 'quality-plot.html'}],
-        'dangers': dangers,
-        'warnings': warnings,
-    }
-    templates = [index, overview_template, quality_template]
-    q2templates.render(templates, output_dir, context=context)
-
-    shutil.copytree(os.path.join(TEMPLATES, 'assets', 'dist'),
-                    os.path.join(output_dir, 'dist'))
-
-    with open(os.path.join(output_dir, 'data.jsonp'), 'w') as fh:
-        fh.write("app.init(")
-        json.dump({'n': int(n), 'totalSeqCount': int(sequence_count),
-                   'minSeqLen': min_seq_len}, fh)
-        fh.write(',')
-        forward_stats.to_json(fh)
-        if paired:
-            fh.write(',')
-            reverse_stats.to_json(fh)
-        fh.write(');')
-'''
\ No newline at end of file
diff --git a/build/lib/q2_classo/citations.bib b/build/lib/q2_classo/citations.bib
deleted file mode 100644
index e69de29..0000000
diff --git a/build/lib/q2_classo/dictionaries.py b/build/lib/q2_classo/dictionaries.py
deleted file mode 100644
index 15a0dd4..0000000
--- a/build/lib/q2_classo/dictionaries.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from qiime2.plugin import (SemanticType,Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric)
-from q2_types.feature_table import FeatureTable, Composition
-from q2_types.feature_data import FeatureData
-
-
-regress_parameters={
-     'y': MetadataColumn[Numeric],
-    #Formulation parameters
-    'concomitant': Bool,
-    'huber'      : Bool,
-    'rho'        : Float,
-    'rescale'    : Bool,
-
-
-    #PATH parameters :
-    'PATH' : Bool,
-    'PATH_numerical_method' : Str,
-    'PATH_n_active'         : Int, # do something here ! for now it can be a bool !
-    'PATH_lambdas'          : List[Float],
-    'PATH_nlam_log'         : Int,
-    'PATH_lamin_log'        : Float,
-
-
-    #CV parameters :
-    'CV' : Bool,
-    'CV_numerical_method' : Str,
-    'CV_seed'             : Int, # do something here ! for now it can be a bool !
-    'CV_lambdas'          : List[Float],
-    'CV_oneSE'            : Bool,
-    'CV_subsets'          : Int,
-
-    #StabSel parameters :
-    'StabSel' : Bool,
-    'StabSel_numerical_method' : Str,
-    'StabSel_seed'             : Int, # do something here ! for now it can be a bool !
-    'StabSel_lam'              : Float, # can be str as well for now !
-    'StabSel_true_lam'         : Bool,
-    'StabSel_method'           : Str,
-    'StabSel_B'                : Int,
-    'StabSel_q'                : Int,
-    'StabSel_percent_nS'       : Float,
-    'StabSel_lamin'            : Float,
-    'StabSel_threshold'        : Float,
-    'StabSel_threshold_label'  : Float, # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'LAMfixed' : Bool,
-    'LAMfixed_numerical_method' : Str,
-    'LAMfixed_lam'              : Float, # can be str as well for now !
-    'LAMfixed_true_lam'         : Bool
-}
-regress_parameter_descriptions={
-    'y': 'Vector representing the output of the problem',
-    #Formulation parameters
-    'concomitant': 'True if the formulation of the problem should be with an M_estimation of sigma. Default value = True',
-    'huber'      : 'True if the formulation of the problem should be robust Default value = False',
-    'rho'        : 'Value of rho for robust problem. Default value = 1.345',
-    'rescale'    : 'if True, then the function rescale() will be applied to data when solving the problem',
-
-
-    #PATH parameters :
-    'PATH' : 'True if path should be computed. Default Value = False',
-    'PATH_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'PATH_n_active'         : 'if it is an integer, then the algo stop computing the path when n_active variables are actives. then the solution does not change from this point. Dafault value : False',
-    'PATH_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.array([10**(-delta * float(i) / nlam) for i in range(0,nlam) ] ) with delta=2. and nlam = 40',
-    'PATH_nlam_log'         : ' number of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-    'PATH_lamin_log'        : 'minimum of lambdas required, if the list of lambdas is not specified, in order to use a log-ratio list of lambdas',
-
-    #CV parameters :
-    'CV' : 'True if Cross Validation should be computed. Default Value = False',
-    'CV_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'CV_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'CV_lambdas'          : 'list of lambdas for computinf lasso-path for cross validation on lambda. Default value : np.linspace(1., 1e-3, 500)',
-    'CV_oneSE'            : 'if set to True, the selected lambda if computed with method ‘one-standard-error’ Default value : True',
-    'CV_subsets'          : 'number of subset in the cross validation method Dafault value : 5',
-    #StabSel parameters :
-    'StabSel' : 'True if Stability Selection should be computed. Default Value = True',
-    'StabSel_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'StabSel_seed'             : 'Seed for random values, for an equal seed, the result will be the same. If set to False/None: pseudo-random vectors Default value : None',
-    'StabSel_lam'              : '(only used if method = ‘lam’) lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed',
-    'StabSel_true_lam'         : '(only used if method = ‘lam’) True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True',
-    'StabSel_method'           : '‘first’, ‘lam’ or ‘max’ depending on the type of stability selection we do. Default value : ‘first’',
-    'StabSel_B'                : 'number of subsample considered. Default value : 50',
-    'StabSel_q'                : 'number of selected variable per subsample. Default value : 10',
-    'StabSel_percent_nS'       : 'size of subsample relatively to the total amount of sample Default value : 0.5',
-    'StabSel_lamin'            : 'lamin when computing the lasso-path for method ‘max’ Default value : 1e-2',
-    'StabSel_threshold'        : 'threhold for stability selection Default value : 0.7',
-    'StabSel_threshold_label'  : 'threshold to know when the label should be plot on the graph. Default value : 0.4', # might unneeded here, but needed for visualisation
-
-    #LAMfixed parameters :
-    'LAMfixed' : 'True if solution for a fixed lambda should be computed. Default Value = False',
-    'LAMfixed_numerical_method' : 'name of the numerical method that is used, it can be : ‘Path-Alg’ (path algorithm) , ‘P-PDS’ (Projected primal-dual splitting method) , ‘PF-PDS’ (Projection-free primal-dual splitting method) or ‘DR’ (Douglas-Rachford-type splitting method) Default value : ‘choose’, which means that the function choose_numerical_method() will choose it accordingly to the formulation',
-    'LAMfixed_lam'              : 'lam for which the lasso should be computed. Default value : ‘theoretical’ which mean it will be equal to theoretical_lam once it is computed', # can be str as well for now !
-    'LAMfixed_true_lam'         : 'True if the lambda given is the real lambda, False if it lambda/lambdamax which is between 0 and 1. If True and lam = ‘theoretical’ , then it will takes the value n*theoretical_lam. Default value : True'
-}
-
-
-
diff --git a/build/lib/q2_classo/functions.py b/build/lib/q2_classo/functions.py
deleted file mode 100644
index 8105af0..0000000
--- a/build/lib/q2_classo/functions.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-from q2_classo.CLasso import *
-from qiime2.plugin import (SemanticType,Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric)
-from q2_types.feature_table import FeatureTable, Composition
-from q2_types.feature_data import FeatureData
-
-
-
-Beta_type         = SemanticType('Beta_type') # array of size Npath x d with the solution beta for each lambda on each row
-Sigma_type        = SemanticType('Sigma_type') # array of size Npath with the solution sigma for each lambda when the formulation of the problem is R2 or R4
-Lambda_type       = SemanticType('Lambda_type') # array of size Npath with the lambdas (real lambdas, not divided by lambda_max) for which the solution is computed
-Method_type       = SemanticType('Method_type') # name of the numerical method that has been used. It can be 'Path-Alg', 'P-PDS' , 'PF-PDS' or 'DR'
-Formulation_type  = SemanticType('Formulation_type') # can be 'R1' ; 'R2' ; 'R3' ; 'R4' ; 'C1' ; 'C2'
-Time_type         = SemanticType('Time_type') # running time of this action
-
-Path_type = SemanticType('Path_type', field_names=['betas','sigmas','lambdas', 'method', 'formulation', 'time'],
-                      field_members={
-                          'betas' : Beta_type,
-                          'sigmas': Sigma_type,
-                          'lambdas': Lambda_type,
-                          'method' : Method_type,
-                          'formulation' : Formulation_type,
-                          'time'   : Time_type
-                       })
-
-Test_type = SemanticType('Test_type', variant_of=FeatureData.field['type'])
-
-# plugin.register_semantic_types(Test_type)
-# plugin.register_semantic_type_to_format(FeatureData[Test_type],artifact_format=BIOMV210DirFmt)
-
-
-def regress(X : FeatureTable[Composition],
-            y : MetadataColumn[Numeric],
-            #PATH parameters :
-            PATH : Bool                         = True,
-            PATH_numerical_method : Str         = 'not specified',
-            PATH_n_active         : Int         = 0,
-            PATH_lambdas          : List[Float] = None,
-            PATH_nlam_log         : Int         = 40,
-            PATH_lamin_log        : Float       = 1e-2,
-
-            #CV parameters :
-            CV : Bool                         = False,
-            CV_numerical_method : Str         = 'not specified',
-            CV_seed             : Int         = None, # do something here ! for now it can be a bool !
-            CV_lambdas          : List[Float] = None, # to do 
-            CV_oneSE            : Bool        = True,
-            CV_subsets          : Int         = 5,
-
-            #StabSel parameters :
-            StabSel : Bool = False,
-            StabSel_numerical_method : Str    = 'not specified',
-            StabSel_seed             : Int    = None, # do something here ! for now it can be a bool !
-            StabSel_lam              : Float  = -1.0, # if negative, then it means 'theoretical'
-            StabSel_true_lam         : Bool   = True,
-            StabSel_method           : Str    = 'first',
-            StabSel_B                : Int    = 50, 
-            StabSel_q                : Int    = 10,
-            StabSel_percent_nS       : Float  = 0.5,
-            StabSel_lamin            : Float  = 1e-2,
-            StabSel_threshold        : Float  = 0.7,
-            StabSel_threshold_label  : Float  = 0.4, # might unneeded here, but needed for visualisation
-
-            #LAMfixed parameters :
-            LAMfixed : Bool = False,
-            LAMfixed_numerical_method : Str  = 'not specified',
-            LAMfixed_lam              : Float = -1.0, # if negative, then it means 'theoretical'
-            LAMfixed_true_lam         : Bool  = True,
-            
-            #Formulation parameters
-            concomitant: Bool      = True,
-            huber      : Bool      = False,
-            rho        : Float     = 1.345 ,
-            rescale    : Bool      = False) -> Path_type[Beta_type,Sigma_type,Lambda_type, Method_type, Formulation_type, Time_type]:
-    
-    
-    X_matrix = np.array(X)
-    y_matrix = np.array(y)
-
-    problem = classo_problem(X_matrix, y_matrix, rescale=rescale)
-    problem.formulation.huber       = huber
-    problem.formulation.concomitant = concomitant
-    problem.formulation.rho         = rho
-
-    #PATH
-    if PATH:
-        problem.model_selection.PATH                            = PATH
-        problem.model_selection.PATHparameters.numerical_method = PATH_numerical_method
-        problem.model_selection.PATHparameters.n_active         = PATH_n_active
-        if PATH_lambdas is None: 
-            nlam, lamin = PATH_nlam_log, PATH_lamin_log
-            log_lambdas =  np.array([10**(np.log10(lamin) * float(i) / nlam) for i in range(0,nlam) ] )
-            problem.model_selection.PATHparameters.lambdas = log_lambdas
-        else : problem.model_selection.PATHparameters.lambdas          =  PATH_lambdas
-
-    if CV :
-        problem.moodel.selection.LAMfixed = LAMfixed
-        if CV_lambdas is None: problem.moodel.selection.CVparameters.lambdas   =  np.linspace(1., 1e-3, 500)
-        else                 :  problem.model_selection.CVparameters.lambdas   =  CV_lambdas
-
-    if StabSel : 
-        problem.moodel.selection.StabSel = StabSel
-        if (StabSel_lam>0.): problem.moodel.selection.StabSelparameters.lam = StabSel_lam
-        else               : problem.moodel.selection.StabSelparameters.lam = 'theoretical'
-
-    if LAMfixed: 
-        problem.moodel.selection.LAMfixed = LAMfixed
-        if (LAMfixed_lam>0.): problem.moodel.selection.LAMfixedparameters.lam = LAMfixed_lam
-        else                : problem.moodel.selection.LAMfixedparameters.lam = 'theoretical'
-
-
-    problem.solve()
-    solution_PATH, solution_CV, solution_StabSel, solution_LAM = problem.solution.PATH, problem.solution.CV, problem.solution.StabSel, problem.solution.LAMfixed
-
-    return( solution_PATH.BETAS, solution_PATH.SIGMAS, solution_PATH.LAMBDAS, solution_PATH.method, solution_PATH.formulation, solution_PATH.time  )
-
-
-
-
-
-            
diff --git a/build/lib/q2_classo/plugin_setup.py b/build/lib/q2_classo/plugin_setup.py
deleted file mode 100644
index f1df040..0000000
--- a/build/lib/q2_classo/plugin_setup.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from qiime2.plugin import (Plugin, Int, Float, Range, Metadata, Str, Bool,
-     Choices, MetadataColumn, Categorical, List,
-     Citations, TypeMatch, Numeric, SemanticType)
-from q2_types.feature_table import FeatureTable, Composition, BIOMV210Format
-import qiime2
-from . import  *
-import numpy as np
-import biom
-import zarr
-import pandas as pd
-version=qiime2.__version__
-#citations = Citations.load('citations.bib', package='q2_classo') 
-plugin = Plugin(
-name='classo',
-                version='0.0.0.dev0',
-website='https://github.com/Leo-Simpson/q2-classo',
-package='q2-classo',
-short_description=('Package for constrained sparse regression and classification'),
-description=('This is QIIME 2 plugin that enables sparse and robust linear regression and classification with linear equality constraints on the model parameters.')
-)
-
-
-CLASSOProblem    = SemanticType("CLASSOProblem")
-ConstraintMatrix = SemanticType("ConstraintMatrix")
-
-
-plugin.register_formats(CLASSOProblemDirectoryFormat,ConstraintDirectoryFormat)
-plugin.register_semantic_type_to_format(ConstraintMatrix,
-                                        artifact_format=ConstraintDirectoryFormat)
-plugin.register_semantic_type_to_format(CLASSOProblem, 
-                                        artifact_format=CLASSOProblemDirectoryFormat)
-
-
-
-
-plugin.methods.register_function(
-           function=regress,
-           inputs={'features': FeatureTable[Composition], 'c':ConstraintMatrix},
-           parameters=regress_parameters,
-           outputs= [('result',CLASSOProblem)],
-           input_descriptions={'features': 'Matrix representing the data of the problem','c': 'Constraint matrix, default is the zero-sum',},
-           parameter_descriptions=regress_parameter_descriptions,
-           output_descriptions= {
-               'result':"Directory format that will contain all information about the problem solved"
-               },
-           name='regress',
-           description=("The function computes the constrainted_sparse_regression vector with respect to the formulation of regression that is asked and with respect to the model selection parameters given")
-           #citations=[citations['Weiss2017']]
-           )
-
-
-
-plugin.methods.register_function(
-           function=generate_data,
-           inputs={},
-           parameters={'n':Int, 'd':Int, 'd_nonzero':Int},
-           outputs= [('x',FeatureTable[Composition]),('c',ConstraintMatrix)],
-           input_descriptions={},
-           parameter_descriptions={'n': 'number of sample', 'd': 'number of features','d_nonzero': 'number of non nul componants in beta' },
-           output_descriptions= {'x': 'Matrix representing the data of the problem','c':'Matrix representing the constraint of the problem'},
-           name='generate_data',
-           description=("Function that build random data")
-           )
-
-
-plugin.visualizers.register_function(
-    function=summarize,
-    inputs={'problem':CLASSOProblem},
-    parameters={},
-    input_descriptions={'problem': 'classo problem object containing the solution of the regression'},
-    parameter_descriptions={},
-    name='Summarize regression solution',
-    description=('Summarize the object created by the regression with its characteristics')
-)
-
-
-
-
-
-'''
-Transformers
-'''
-
-@plugin.register_transformer
-def _0(obj: classo_problem) -> CLASSOProblemDirectoryFormat :
-    # for output of regress
-    ff = CLASSOProblemDirectoryFormat()
-    zipfile = str(ff.path/'problem.zip')
-    store = zarr.ZipStore(zipfile,mode='w')
-    root = zarr.open(store=store)
-    to_zarr(obj,'problem',root)
-    store.close()
-    return ff 
-
-@plugin.register_transformer
-def _1(ff : ZarrProblemFormat) -> zarr.hierarchy.Group : 
-    # for visualizers
-    store = zarr.ZipStore(str(ff),mode='r')
-    root = zarr.open(store=store)
-    return root
-
-
-
-@plugin.register_transformer
-def _2(obj : np.ndarray) -> BIOMV210Format :
-    # for X in generate data, or generate constraint
-    ff = BIOMV210Format()
-    l1, l2 = [str(i) for i in range(len(obj[0]))],[str(i) for i in range(len(obj))]
-    data = biom.Table(obj.T,observation_ids=l1,sample_ids=l2)
-    with ff.open() as fh:
-        data.to_hdf5(fh, generated_by='qiime2 %s' % version)
-    return ff
-
-@plugin.register_transformer
-def _3(ff: BIOMV210Format) -> np.ndarray:
-    # for X in regress
-    with ff.open() as fh:
-        table = biom.Table.from_hdf5(fh)
-    array = table.matrix_data.toarray().T # numpy array
-    return array
-
-
-
-
-
-
-@plugin.register_transformer
-def _4(obj : np.ndarray) -> ConstraintDirectoryFormat :
-    # for C in generate data, or generate constraint
-    ff = ConstraintDirectoryFormat()
-    filename = str(ff.path/'cmatrix.zip')
-    zarr.save(filename, obj)
-    return ff
-
-
-@plugin.register_transformer
-def _5(obj : ConstraintFormat) -> np.ndarray : 
-    # for C in regress
-    return zarr.load(str(obj))
-
diff --git a/dist/q2_classo-0.0.0.dev0-py3.6.egg b/dist/q2_classo-0.0.0.dev0-py3.6.egg
deleted file mode 100644
index 96d2aa35f38c2d9259cf29cfc67abd15ac9ee5ec..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/q2_classo.egg-info/PKG-INFO b/q2_classo.egg-info/PKG-INFO
deleted file mode 100644
index 8ef0b06..0000000
--- a/q2_classo.egg-info/PKG-INFO
+++ /dev/null
@@ -1,10 +0,0 @@
-Metadata-Version: 1.0
-Name: q2-classo
-Version: 0.0.0.dev0
-Summary: Package for constrained sparse regression and classification
-Home-page: https://qiime2.org
-Author: UNKNOWN
-Author-email: UNKNOWN
-License: BSD-3-Clause
-Description: UNKNOWN
-Platform: UNKNOWN
diff --git a/q2_classo.egg-info/SOURCES.txt b/q2_classo.egg-info/SOURCES.txt
deleted file mode 100644
index 6b6ccc6..0000000
--- a/q2_classo.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-README.md
-setup.py
-q2_classo/__init__.py
-q2_classo/_dict.py
-q2_classo/_formats.py
-q2_classo/_func.py
-q2_classo/citations.bib
-q2_classo/plugin_setup.py
-q2_classo.egg-info/PKG-INFO
-q2_classo.egg-info/SOURCES.txt
-q2_classo.egg-info/dependency_links.txt
-q2_classo.egg-info/entry_points.txt
-q2_classo.egg-info/not-zip-safe
-q2_classo.egg-info/top_level.txt
-q2_classo/CLasso/__init__.py
-q2_classo/CLasso/compact_func.py
-q2_classo/CLasso/cross_validation.py
-q2_classo/CLasso/misc_functions.py
-q2_classo/CLasso/path_alg.py
-q2_classo/CLasso/solve_R1.py
-q2_classo/CLasso/solve_R2.py
-q2_classo/CLasso/solve_R3.py
-q2_classo/CLasso/solve_R4.py
-q2_classo/CLasso/solver 2.py
-q2_classo/CLasso/solver.py
-q2_classo/CLasso/stability_selection 2.py
-q2_classo/CLasso/stability_selection.py
-q2_classo/_summarize/__init__.py
-q2_classo/_summarize/_visualizer.py
-q2_classo/_summarize/assets/cv.html
-q2_classo/_summarize/assets/index.html
-q2_classo/_summarize/assets/lam-fixed.html
-q2_classo/_summarize/assets/overview.html
-q2_classo/_summarize/assets/path.html
-q2_classo/_summarize/assets/stabsel.html
-q2_classo/_summarize/form/R1.png
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