diff --git a/.idea/Multiview-Machine-Learning-OMIS.iml b/.idea/Multiview-Machine-Learning-OMIS.iml
index 6711606311e2664bd835f92b5c114681d2e284f5..327f097a397cdcb86929a6e87e2f03c96edf3852 100644
--- a/.idea/Multiview-Machine-Learning-OMIS.iml
+++ b/.idea/Multiview-Machine-Learning-OMIS.iml
@@ -1,8 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
- <component name="NewModuleRootManager">
+ <component name="NewModuleRootManager" inherit-compiler-output="false">
<content url="file://$MODULE_DIR$" />
- <orderEntry type="inheritedJdk" />
+ <orderEntry type="jdk" jdkName="Python 2.7.11 (~/anaconda2/bin/python)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="TestRunnerService">
diff --git a/.idea/compiler.xml b/.idea/compiler.xml
new file mode 100644
index 0000000000000000000000000000000000000000..96cc43efa6a0885098044e976cd780bb42c68a70
--- /dev/null
+++ b/.idea/compiler.xml
@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="CompilerConfiguration">
+ <resourceExtensions />
+ <wildcardResourcePatterns>
+ <entry name="!?*.java" />
+ <entry name="!?*.form" />
+ <entry name="!?*.class" />
+ <entry name="!?*.groovy" />
+ <entry name="!?*.scala" />
+ <entry name="!?*.flex" />
+ <entry name="!?*.kt" />
+ <entry name="!?*.clj" />
+ <entry name="!?*.aj" />
+ </wildcardResourcePatterns>
+ <annotationProcessing>
+ <profile default="true" name="Default" enabled="false">
+ <processorPath useClasspath="true" />
+ </profile>
+ </annotationProcessing>
+ </component>
+</project>
\ No newline at end of file
diff --git a/.idea/copyright/profiles_settings.xml b/.idea/copyright/profiles_settings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e7bedf3377d40335424fd605124d4761390218bb
--- /dev/null
+++ b/.idea/copyright/profiles_settings.xml
@@ -0,0 +1,3 @@
+<component name="CopyrightManager">
+ <settings default="" />
+</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9a70f565dd4fc3b16ac2318de4fadecb4a87dd87
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,17 @@
+<component name="InspectionProjectProfileManager">
+ <profile version="1.0">
+ <option name="myName" value="Project Default" />
+ <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+ <option name="ignoredIdentifiers">
+ <list>
+ <option value="Code.MultiView.Mumbo.Mumbo.len" />
+ <option value="Code.MultiView.Mumbo.Mumbo.range" />
+ <option value="Code.MultiView.Mumbo.Mumbo.zip" />
+ <option value="Code.MultiView.Mumbo.Mumbo.int" />
+ <option value="Code.MultiView.Mumbo.Mumbo.attr" />
+ <option value="Code.MultiView.Mumbo.Mumbo.getattr" />
+ </list>
+ </option>
+ </inspection_tool>
+ </profile>
+</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..3b312839bf2e939fea3ebdef15630a4b33e57caf
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,7 @@
+<component name="InspectionProjectProfileManager">
+ <settings>
+ <option name="PROJECT_PROFILE" value="Project Default" />
+ <option name="USE_PROJECT_PROFILE" value="true" />
+ <version value="1.0" />
+ </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 5f7ced0530cb25681f34d3f9d0808968d276fb72..31eea6ceaa9cd0369a7b9ba66caef8f489e3c061 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,4 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
- <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.4.3 (/usr/bin/python3.4)" project-jdk-type="Python SDK" />
+ <component name="ProjectRootManager" version="2" languageLevel="JDK_1_3" assert-keyword="false" jdk-15="false" project-jdk-name="Python 2.7.11 (~/anaconda2/bin/python)" project-jdk-type="Python SDK" />
</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 4996be3b86c569a0dcd209f3c8ec4ecbc92cba85..075917529dc5b2827566cef4f81011c92ef3028c 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,13 +1,24 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
+ <component name="AnalysisUIOptions">
+ <option name="SCOPE_TYPE" value="3" />
+ </component>
<component name="ChangeListManager">
<list default="true" id="b2cddca2-4818-4b60-b3ce-fb207e328b36" name="Default" comment="">
- <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py" />
+ <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/Code/MultiView/ExecMultiview.py" />
+ <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py" />
+ <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/__init__.py" />
+ <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/__init__.py" />
+ <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/Multiview-Machine-Learning-OMIS.iml" afterPath="$PROJECT_DIR$/.idea/Multiview-Machine-Learning-OMIS.iml" />
+ <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/misc.xml" afterPath="$PROJECT_DIR$/.idea/misc.xml" />
<change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/workspace.xml" afterPath="$PROJECT_DIR$/.idea/workspace.xml" />
+ <change type="MODIFICATION" beforePath="$PROJECT_DIR$/Code/MultiView/GetMutliviewDb.py" afterPath="$PROJECT_DIR$/Code/MultiView/GetMutliviewDb.py" />
+ <change type="MODIFICATION" beforePath="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py" />
<change type="MODIFICATION" beforePath="$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py" afterPath="$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py" />
</list>
<ignored path="Multiview-Machine-Learning-OMIS.iws" />
<ignored path=".idea/workspace.xml" />
+ <ignored path=".idea/dataSources.local.xml" />
<option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
<option name="TRACKING_ENABLED" value="true" />
<option name="SHOW_DIALOG" value="false" />
@@ -16,6 +27,9 @@
<option name="LAST_RESOLUTION" value="IGNORE" />
</component>
<component name="ChangesViewManager" flattened_view="true" show_ignored="false" />
+ <component name="CoverageDataManager">
+ <SUITE FILE_PATH="coverage/multiview-machine-learning-omis$Mumbo.coverage" NAME="Mumbo Coverage Results" MODIFIED="1459845546865" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/Code/MultiView/Mumbo" />
+ </component>
<component name="CreatePatchCommitExecutor">
<option name="PATCH_PATH" value="" />
</component>
@@ -25,11 +39,11 @@
</component>
<component name="FileEditorManager">
<leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
- <file leaf-file-name="Mumbo.py" pinned="false" current-in-tab="false">
+ <file leaf-file-name="Mumbo.py" pinned="false" current-in-tab="true">
<entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py">
<provider selected="true" editor-type-id="text-editor">
- <state vertical-scroll-proportion="0.0">
- <caret line="117" column="10" selection-start-line="117" selection-start-column="10" selection-end-line="117" selection-end-column="10" />
+ <state vertical-scroll-proportion="0.60912055">
+ <caret line="272" column="0" selection-start-line="272" selection-start-column="0" selection-end-line="272" selection-end-column="0" />
<folding>
<element signature="e#0#18#0" expanded="true" />
</folding>
@@ -37,13 +51,96 @@
</provider>
</entry>
</file>
- <file leaf-file-name="DecisionTree.py" pinned="false" current-in-tab="true">
+ <file leaf-file-name="__init__.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/__init__.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="0" column="0" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="__init__.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/__init__.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="11" column="10" selection-start-line="11" selection-start-column="10" selection-end-line="11" selection-end-column="10" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="DBCrawl.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/FeatExtraction/DBCrawl.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="24" column="49" selection-start-line="24" selection-start-column="8" selection-end-line="24" selection-end-column="49" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="GetMutliviewDb.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/GetMutliviewDb.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="29" column="58" selection-start-line="29" selection-start-column="58" selection-end-line="29" selection-end-column="58" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="ExecMultiview.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/ExecMultiview.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="15" column="0" selection-start-line="15" selection-start-column="0" selection-end-line="15" selection-end-column="0" />
+ <folding>
+ <element signature="e#0#27#0" expanded="true" />
+ </folding>
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="__init__.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/__init__.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="0" column="34" selection-start-line="0" selection-start-column="34" selection-end-line="0" selection-end-column="34" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="DecisionTree.py" pinned="false" current-in-tab="false">
<entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py">
<provider selected="true" editor-type-id="text-editor">
- <state vertical-scroll-proportion="0.16488549">
- <caret line="6" column="47" selection-start-line="6" selection-start-column="47" selection-end-line="6" selection-end-column="47" />
+ <state vertical-scroll-proportion="0.0">
+ <caret line="2" column="0" selection-start-line="2" selection-start-column="0" selection-end-line="2" selection-end-column="0" />
+ <folding />
+ </state>
+ </provider>
+ </entry>
+ </file>
+ <file leaf-file-name="ModifiedMulticlass.py" pinned="false" current-in-tab="false">
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="70" column="0" selection-start-line="70" selection-start-column="0" selection-end-line="70" selection-end-column="0" />
<folding>
- <element signature="e#0#24#0" expanded="true" />
+ <element signature="e#1464#1476#0" expanded="true" />
+ <element signature="e#2847#3198#0" expanded="false" />
+ <element signature="e#3238#3543#0" expanded="false" />
+ <element signature="e#3707#4412#0" expanded="false" />
+ <element signature="e#4589#5739#0" expanded="false" />
+ <element signature="e#5926#6407#0" expanded="false" />
+ <element signature="e#6456#6923#0" expanded="false" />
+ <element signature="e#7009#8968#1" expanded="false" />
+ <element signature="e#9023#9078#0" expanded="false" />
+ <element signature="e#16803#20674#0" expanded="false" />
+ <element signature="e#20746#22439#0" expanded="false" />
+ <element signature="e#22640#23628#0" expanded="false" />
</folding>
</state>
</provider>
@@ -61,18 +158,70 @@
<component name="Git.Settings">
<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
</component>
+ <component name="GradleLocalSettings">
+ <option name="externalProjectsViewState">
+ <projects_view />
+ </option>
+ </component>
<component name="IdeDocumentHistory">
<option name="CHANGED_PATHS">
<list>
- <option value="$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/ExecMultiview" />
+ <option value="$PROJECT_DIR$/Code/MultiView/Mumbo/__init__.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/__init__.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/GetMutliviewDb.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/ModifiedMulticlass.py" />
<option value="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/ExecMultiview.py" />
+ <option value="$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py" />
</list>
</option>
</component>
+ <component name="JsBuildToolGruntFileManager" detection-done="true" />
+ <component name="JsBuildToolPackageJson" detection-done="true" />
+ <component name="JsGulpfileManager">
+ <detection-done>true</detection-done>
+ </component>
<component name="ProjectFrameBounds">
- <option name="y" value="25" />
- <option name="width" value="1280" />
- <option name="height" value="775" />
+ <option name="x" value="1680" />
+ <option name="y" value="24" />
+ <option name="width" value="1920" />
+ <option name="height" value="1056" />
+ </component>
+ <component name="ProjectInspectionProfilesVisibleTreeState">
+ <entry key="Project Default">
+ <profile-state>
+ <expanded-state>
+ <State>
+ <id />
+ </State>
+ <State>
+ <id>Android Lint</id>
+ </State>
+ <State>
+ <id>Java</id>
+ </State>
+ <State>
+ <id>Numeric issuesJava</id>
+ </State>
+ <State>
+ <id>Probable bugsJava</id>
+ </State>
+ <State>
+ <id>Python</id>
+ </State>
+ <State>
+ <id>WebSocket issues</id>
+ </State>
+ </expanded-state>
+ <selected-state>
+ <State>
+ <id>Android</id>
+ </State>
+ </selected-state>
+ </profile-state>
+ </entry>
</component>
<component name="ProjectLevelVcsManager" settingsEditedManually="false">
<OptionsSetting value="true" id="Add" />
@@ -99,33 +248,31 @@
<foldersAlwaysOnTop value="true" />
</navigator>
<panes>
- <pane id="Scope" />
- <pane id="Scratches" />
<pane id="ProjectPane">
<subPane>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
</PATH>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
</PATH_ELEMENT>
</PATH>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
@@ -135,11 +282,11 @@
</PATH>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
@@ -153,11 +300,11 @@
</PATH>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
@@ -175,11 +322,11 @@
</PATH>
<PATH>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
- <option name="myItemId" value="Multiview-Machine-Learning-OMIS" />
+ <option name="myItemId" value="multiview-machine-learning-omis" />
<option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
</PATH_ELEMENT>
<PATH_ELEMENT>
@@ -201,12 +348,419 @@
</PATH>
</subPane>
</pane>
+ <pane id="PackagesPane" />
+ <pane id="Scratches" />
+ <pane id="Scope" />
</panes>
</component>
<component name="PropertiesComponent">
<property name="last_opened_file_path" value="$PROJECT_DIR$" />
+ <property name="aspect.path.notification.shown" value="true" />
+ <property name="WebServerToolWindowFactoryState" value="false" />
+ <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
+ <property name="settings.editor.splitter.proportion" value="0.2" />
+ </component>
+ <component name="PyConsoleOptionsProvider">
+ <option name="myPythonConsoleState">
+ <console-settings module-name="Multiview-Machine-Learning-OMIS" is-module-sdk="true">
+ <option name="myUseModuleSdk" value="true" />
+ <option name="myModuleName" value="Multiview-Machine-Learning-OMIS" />
+ </console-settings>
+ </option>
+ </component>
+ <component name="RecentsManager">
+ <key name="MoveFile.RECENT_KEYS">
+ <recent name="$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers" />
+ </key>
</component>
- <component name="RunManager">
+ <component name="RunManager" selected="Python.Mumbo">
+ <configuration default="false" name="Mumbo" type="PythonConfigurationType" factoryName="Python" temporary="true">
+ <option name="INTERPRETER_OPTIONS" value="" />
+ <option name="PARENT_ENVS" value="true" />
+ <envs>
+ <env name="PYTHONUNBUFFERED" value="1" />
+ </envs>
+ <option name="SDK_HOME" value="$USER_HOME$/anaconda2/bin/python2.7" />
+ <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Code/MultiView/Mumbo" />
+ <option name="IS_MODULE_SDK" value="false" />
+ <option name="ADD_CONTENT_ROOTS" value="true" />
+ <option name="ADD_SOURCE_ROOTS" value="true" />
+ <module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
+ <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py" />
+ <option name="PARAMETERS" value="" />
+ <option name="SHOW_COMMAND_LINE" value="false" />
+ <method />
+ </configuration>
+ <configuration default="true" type="AndroidRunConfigurationType" factoryName="Android Application">
+ <module name="" />
+ <option name="ACTIVITY_CLASS" value="" />
+ <option name="MODE" value="default_activity" />
+ <option name="DEPLOY" value="true" />
+ <option name="ARTIFACT_NAME" value="" />
+ <option name="TARGET_SELECTION_MODE" value="EMULATOR" />
+ <option name="USE_LAST_SELECTED_DEVICE" value="false" />
+ <option name="PREFERRED_AVD" value="" />
+ <option name="USE_COMMAND_LINE" value="true" />
+ <option name="COMMAND_LINE" value="" />
+ <option name="WIPE_USER_DATA" value="false" />
+ <option name="DISABLE_BOOT_ANIMATION" value="false" />
+ <option name="NETWORK_SPEED" value="full" />
+ <option name="NETWORK_LATENCY" value="none" />
+ <option name="CLEAR_LOGCAT" value="false" />
+ <option name="SHOW_LOGCAT_AUTOMATICALLY" value="true" />
+ <option name="FILTER_LOGCAT_AUTOMATICALLY" value="true" />
+ <option name="SELECTED_CLOUD_MATRIX_CONFIGURATION_ID" value="0" />
+ <option name="SELECTED_CLOUD_MATRIX_PROJECT_ID" value="" />
+ <option name="SELECTED_CLOUD_DEVICE_CONFIGURATION_ID" value="0" />
+ <option name="SELECTED_CLOUD_DEVICE_PROJECT_ID" value="" />
+ <option name="IS_VALID_CLOUD_MATRIX_SELECTION" value="false" />
+ <option name="INVALID_CLOUD_MATRIX_SELECTION_ERROR" value="" />
+ <option name="IS_VALID_CLOUD_DEVICE_SELECTION" value="false" />
+ <option name="INVALID_CLOUD_DEVICE_SELECTION_ERROR" value="" />
+ <option name="CLOUD_DEVICE_SERIAL_NUMBER" value="" />
+ <method />
+ </configuration>
+ <configuration default="true" type="AndroidTestRunConfigurationType" factoryName="Android Tests">
+ <module name="" />
+ <option name="TESTING_TYPE" value="0" />
+ <option name="INSTRUMENTATION_RUNNER_CLASS" value="" />
+ <option name="METHOD_NAME" value="" />
+ <option name="CLASS_NAME" value="" />
+ <option name="PACKAGE_NAME" value="" />
+ <option name="TARGET_SELECTION_MODE" value="EMULATOR" />
+ <option name="USE_LAST_SELECTED_DEVICE" value="false" />
+ <option name="PREFERRED_AVD" value="" />
+ <option name="USE_COMMAND_LINE" value="true" />
+ <option name="COMMAND_LINE" value="" />
+ <option name="WIPE_USER_DATA" value="false" />
+ <option name="DISABLE_BOOT_ANIMATION" value="false" />
+ <option name="NETWORK_SPEED" value="full" />
+ <option name="NETWORK_LATENCY" value="none" />
+ <option name="CLEAR_LOGCAT" value="false" />
+ <option name="SHOW_LOGCAT_AUTOMATICALLY" value="true" />
+ <option name="FILTER_LOGCAT_AUTOMATICALLY" value="true" />
+ <option name="SELECTED_CLOUD_MATRIX_CONFIGURATION_ID" value="0" />
+ <option name="SELECTED_CLOUD_MATRIX_PROJECT_ID" value="" />
+ <option name="SELECTED_CLOUD_DEVICE_CONFIGURATION_ID" value="0" />
+ <option name="SELECTED_CLOUD_DEVICE_PROJECT_ID" value="" />
+ <option name="IS_VALID_CLOUD_MATRIX_SELECTION" value="false" />
+ <option name="INVALID_CLOUD_MATRIX_SELECTION_ERROR" value="" />
+ <option name="IS_VALID_CLOUD_DEVICE_SELECTION" value="false" />
+ <option name="INVALID_CLOUD_DEVICE_SELECTION_ERROR" value="" />
+ <option name="CLOUD_DEVICE_SERIAL_NUMBER" value="" />
+ <method />
+ </configuration>
+ <configuration default="true" type="Application" factoryName="Application">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="VM_PARAMETERS" />
+ <option name="PROGRAM_PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="ENABLE_SWING_INSPECTOR" value="false" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <module name="" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="ArquillianJUnit" factoryName="">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="arquillianRunConfiguration">
+ <value>
+ <option name="containerStateName" value="" />
+ </value>
+ </option>
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="TEST_OBJECT" value="class" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <envs />
+ <patterns />
+ <method />
+ </configuration>
+ <configuration default="true" type="ArquillianTestNG" factoryName="">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="arquillianRunConfiguration">
+ <value>
+ <option name="containerStateName" value="" />
+ </value>
+ </option>
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="SUITE_NAME" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="GROUP_NAME" />
+ <option name="TEST_OBJECT" value="CLASS" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="OUTPUT_DIRECTORY" />
+ <option name="ANNOTATION_TYPE" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <option name="USE_DEFAULT_REPORTERS" value="false" />
+ <option name="PROPERTIES_FILE" />
+ <envs />
+ <properties />
+ <listeners />
+ <method />
+ </configuration>
+ <configuration default="true" type="CucumberJavaRunConfigurationType" factoryName="Cucumber java">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <option name="myFilePath" />
+ <option name="GLUE" />
+ <option name="myNameFilter" />
+ <option name="myGeneratedName" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="VM_PARAMETERS" />
+ <option name="PROGRAM_PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="ENABLE_SWING_INSPECTOR" value="false" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <module name="" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="DjangoTestsConfigurationType" factoryName="Django tests">
+ <option name="INTERPRETER_OPTIONS" value="" />
+ <option name="PARENT_ENVS" value="true" />
+ <envs>
+ <env name="PYTHONUNBUFFERED" value="1" />
+ </envs>
+ <option name="SDK_HOME" value="" />
+ <option name="WORKING_DIRECTORY" value="" />
+ <option name="IS_MODULE_SDK" value="false" />
+ <option name="ADD_CONTENT_ROOTS" value="true" />
+ <option name="ADD_SOURCE_ROOTS" value="true" />
+ <module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
+ <option name="TARGET" value="" />
+ <option name="SETTINGS_FILE" value="" />
+ <option name="CUSTOM_SETTINGS" value="false" />
+ <option name="USE_OPTIONS" value="false" />
+ <option name="OPTIONS" value="" />
+ <method />
+ </configuration>
+ <configuration default="true" type="FlashRunConfigurationType" factoryName="Flash App">
+ <option name="BCName" value="" />
+ <option name="IOSSimulatorSdkPath" value="" />
+ <option name="adlOptions" value="" />
+ <option name="airProgramParameters" value="" />
+ <option name="appDescriptorForEmulator" value="Android" />
+ <option name="debugTransport" value="USB" />
+ <option name="debuggerSdkRaw" value="BC SDK" />
+ <option name="emulator" value="NexusOne" />
+ <option name="emulatorAdlOptions" value="" />
+ <option name="fastPackaging" value="true" />
+ <option name="fullScreenHeight" value="0" />
+ <option name="fullScreenWidth" value="0" />
+ <option name="launchUrl" value="false" />
+ <option name="launcherParameters">
+ <LauncherParameters>
+ <option name="browser" value="a7bb68e0-33c0-4d6f-a81a-aac1fdb870c8" />
+ <option name="launcherType" value="OSDefault" />
+ <option name="newPlayerInstance" value="false" />
+ <option name="playerPath" value="/usr/bin/flashplayerdebugger" />
+ </LauncherParameters>
+ </option>
+ <option name="mobileRunTarget" value="Emulator" />
+ <option name="moduleName" value="" />
+ <option name="overriddenMainClass" value="" />
+ <option name="overriddenOutputFileName" value="" />
+ <option name="overrideMainClass" value="false" />
+ <option name="runTrusted" value="true" />
+ <option name="screenDpi" value="0" />
+ <option name="screenHeight" value="0" />
+ <option name="screenWidth" value="0" />
+ <option name="url" value="http://" />
+ <option name="usbDebugPort" value="7936" />
+ <method />
+ </configuration>
+ <configuration default="true" type="FlexUnitRunConfigurationType" factoryName="FlexUnit" appDescriptorForEmulator="Android" class_name="" emulatorAdlOptions="" method_name="" package_name="" scope="Class">
+ <option name="BCName" value="" />
+ <option name="launcherParameters">
+ <LauncherParameters>
+ <option name="browser" value="a7bb68e0-33c0-4d6f-a81a-aac1fdb870c8" />
+ <option name="launcherType" value="OSDefault" />
+ <option name="newPlayerInstance" value="false" />
+ <option name="playerPath" value="/usr/bin/flashplayerdebugger" />
+ </LauncherParameters>
+ </option>
+ <option name="moduleName" value="" />
+ <option name="trusted" value="true" />
+ <method />
+ </configuration>
+ <configuration default="true" type="GradleRunConfiguration" factoryName="Gradle">
+ <ExternalSystemSettings>
+ <option name="executionName" />
+ <option name="externalProjectPath" />
+ <option name="externalSystemIdString" value="GRADLE" />
+ <option name="scriptParameters" />
+ <option name="taskDescriptions">
+ <list />
+ </option>
+ <option name="taskNames">
+ <list />
+ </option>
+ <option name="vmOptions" />
+ </ExternalSystemSettings>
+ <method />
+ </configuration>
+ <configuration default="true" type="GrailsRunConfigurationType" factoryName="Grails">
+ <module name="" />
+ <setting name="vmparams" value="" />
+ <setting name="cmdLine" value="run-app" />
+ <setting name="depsClasspath" value="false" />
+ <setting name="passParentEnv" value="true" />
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <setting name="launchBrowser" value="false" />
+ <method />
+ </configuration>
+ <configuration default="true" type="JUnit" factoryName="JUnit">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="TEST_OBJECT" value="class" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <envs />
+ <patterns />
+ <method />
+ </configuration>
+ <configuration default="true" type="JUnitTestDiscovery" factoryName="JUnit Test Discovery" changeList="All">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="TEST_OBJECT" value="class" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <envs />
+ <patterns />
+ <method />
+ </configuration>
+ <configuration default="true" type="JarApplication" factoryName="JAR Application">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="Java Scratch" factoryName="Java Scratch">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <option name="SCRATCH_FILE_ID" value="0" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="VM_PARAMETERS" />
+ <option name="PROGRAM_PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="ENABLE_SWING_INSPECTOR" value="false" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <module name="" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="JavascriptDebugType" factoryName="JavaScript Debug">
+ <method />
+ </configuration>
+ <configuration default="true" type="JetRunConfigurationType" factoryName="Kotlin">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="VM_PARAMETERS" />
+ <option name="PROGRAM_PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <module name="Multiview-Machine-Learning-OMIS" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="KotlinStandaloneScriptRunConfigurationType" factoryName="Kotlin script">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <option name="filePath" />
+ <option name="vmParameters" />
+ <option name="alternativeJrePath" />
+ <option name="programParameters" />
+ <option name="passParentEnvs" value="true" />
+ <option name="workingDirectory" />
+ <option name="isAlternativeJrePathEnabled" value="false" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="NodeJSConfigurationType" factoryName="Node.js" working-dir="">
+ <method />
+ </configuration>
+ <configuration default="true" type="PyBehaveRunConfigurationType" factoryName="Behave">
+ <option name="INTERPRETER_OPTIONS" value="" />
+ <option name="PARENT_ENVS" value="true" />
+ <envs />
+ <option name="SDK_HOME" value="" />
+ <option name="WORKING_DIRECTORY" value="" />
+ <option name="IS_MODULE_SDK" value="false" />
+ <option name="ADD_CONTENT_ROOTS" value="true" />
+ <option name="ADD_SOURCE_ROOTS" value="true" />
+ <module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
+ <option name="ADDITIONAL_ARGS" value="" />
+ <method />
+ </configuration>
+ <configuration default="true" type="PyLettuceRunConfigurationType" factoryName="Lettuce">
+ <option name="INTERPRETER_OPTIONS" value="" />
+ <option name="PARENT_ENVS" value="true" />
+ <envs />
+ <option name="SDK_HOME" value="" />
+ <option name="WORKING_DIRECTORY" value="" />
+ <option name="IS_MODULE_SDK" value="false" />
+ <option name="ADD_CONTENT_ROOTS" value="true" />
+ <option name="ADD_SOURCE_ROOTS" value="true" />
+ <module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
+ <option name="ADDITIONAL_ARGS" value="" />
+ <method />
+ </configuration>
<configuration default="true" type="PythonConfigurationType" factoryName="Python">
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
@@ -219,11 +773,136 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="PARAMETERS" value="" />
<option name="SHOW_COMMAND_LINE" value="false" />
<method />
</configuration>
+ <configuration default="true" type="ScalaTestRunConfiguration" factoryName="ScalaTest">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <setting name="path" value="" />
+ <setting name="package" value="" />
+ <setting name="vmparams" value="" />
+ <setting name="params" value="" />
+ <setting name="workingDirectory" value="" />
+ <setting name="searchForTest" value="Across module dependencies" />
+ <setting name="testName" value="" />
+ <setting name="testKind" value="Class" />
+ <setting name="showProgressMessages" value="true" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="Specs2RunConfiguration" factoryName="Specs2">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <setting name="path" value="" />
+ <setting name="package" value="" />
+ <setting name="vmparams" value="" />
+ <setting name="params" value="" />
+ <setting name="workingDirectory" value="" />
+ <setting name="searchForTest" value="Across module dependencies" />
+ <setting name="testName" value="" />
+ <setting name="testKind" value="Class" />
+ <setting name="showProgressMessages" value="true" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="SpringBootApplicationConfigurationType" factoryName="Spring Boot">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="TestNG" factoryName="TestNG">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="SUITE_NAME" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="GROUP_NAME" />
+ <option name="TEST_OBJECT" value="CLASS" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="OUTPUT_DIRECTORY" />
+ <option name="ANNOTATION_TYPE" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <option name="USE_DEFAULT_REPORTERS" value="false" />
+ <option name="PROPERTIES_FILE" />
+ <envs />
+ <properties />
+ <listeners />
+ <method />
+ </configuration>
+ <configuration default="true" type="TestNGTestDiscovery" factoryName="TestNG Test Discovery" changeList="All">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <option name="ALTERNATIVE_JRE_PATH_ENABLED" value="false" />
+ <option name="ALTERNATIVE_JRE_PATH" />
+ <option name="SUITE_NAME" />
+ <option name="PACKAGE_NAME" />
+ <option name="MAIN_CLASS_NAME" />
+ <option name="METHOD_NAME" />
+ <option name="GROUP_NAME" />
+ <option name="TEST_OBJECT" value="CLASS" />
+ <option name="VM_PARAMETERS" />
+ <option name="PARAMETERS" />
+ <option name="WORKING_DIRECTORY" />
+ <option name="OUTPUT_DIRECTORY" />
+ <option name="ANNOTATION_TYPE" />
+ <option name="ENV_VARIABLES" />
+ <option name="PASS_PARENT_ENVS" value="true" />
+ <option name="TEST_SEARCH_SCOPE">
+ <value defaultName="singleModule" />
+ </option>
+ <option name="USE_DEFAULT_REPORTERS" value="false" />
+ <option name="PROPERTIES_FILE" />
+ <envs />
+ <properties />
+ <listeners />
+ <method />
+ </configuration>
+ <configuration default="true" type="js.build_tools.gulp" factoryName="Gulp.js">
+ <node-options />
+ <gulpfile />
+ <tasks />
+ <arguments />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="js.build_tools.npm" factoryName="npm">
+ <command value="run-script" />
+ <scripts />
+ <envs />
+ <method />
+ </configuration>
+ <configuration default="true" type="mocha-javascript-test-runner" factoryName="Mocha">
+ <node-options />
+ <working-directory>$PROJECT_DIR$</working-directory>
+ <pass-parent-env>true</pass-parent-env>
+ <envs />
+ <ui>bdd</ui>
+ <extra-mocha-options />
+ <test-kind>DIRECTORY</test-kind>
+ <test-directory />
+ <recursive>false</recursive>
+ <method />
+ </configuration>
+ <configuration default="true" type="osgi.bnd.run" factoryName="Run Launcher">
+ <method />
+ </configuration>
+ <configuration default="true" type="osgi.bnd.run" factoryName="Test Launcher (JUnit)">
+ <method />
+ </configuration>
<configuration default="true" type="tests" factoryName="Attests">
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
@@ -234,6 +913,7 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="CLASS_NAME" value="" />
<option name="METHOD_NAME" value="" />
@@ -253,6 +933,7 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="CLASS_NAME" value="" />
<option name="METHOD_NAME" value="" />
@@ -272,6 +953,7 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="CLASS_NAME" value="" />
<option name="METHOD_NAME" value="" />
@@ -293,6 +975,7 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="CLASS_NAME" value="" />
<option name="METHOD_NAME" value="" />
@@ -315,6 +998,7 @@
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<module name="Multiview-Machine-Learning-OMIS" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
<option name="SCRIPT_NAME" value="" />
<option name="CLASS_NAME" value="" />
<option name="METHOD_NAME" value="" />
@@ -329,6 +1013,34 @@
<option name="USE_KEYWORD" value="false" />
<method />
</configuration>
+ <configuration default="true" type="uTestRunConfiguration" factoryName="utest">
+ <extension name="coverage" enabled="false" merge="false" sample_coverage="true" runner="idea" />
+ <module name="" />
+ <setting name="path" value="" />
+ <setting name="package" value="" />
+ <setting name="vmparams" value="" />
+ <setting name="params" value="" />
+ <setting name="workingDirectory" value="" />
+ <setting name="searchForTest" value="Across module dependencies" />
+ <setting name="testName" value="" />
+ <setting name="testKind" value="Class" />
+ <setting name="showProgressMessages" value="true" />
+ <envs />
+ <method />
+ </configuration>
+ <list size="1">
+ <item index="0" class="java.lang.String" itemvalue="Python.Mumbo" />
+ </list>
+ <recent_temporary>
+ <list size="1">
+ <item index="0" class="java.lang.String" itemvalue="Python.Mumbo" />
+ </list>
+ </recent_temporary>
+ </component>
+ <component name="SbtLocalSettings">
+ <option name="externalProjectsViewState">
+ <projects_view />
+ </option>
</component>
<component name="ShelveChangesManager" show_recycled="false" />
<component name="SvnConfiguration">
@@ -340,31 +1052,68 @@
<created>1459504457506</created>
<option name="number" value="Default" />
<updated>1459504457506</updated>
+ <workItem from="1459841651297" duration="4166000" />
+ <workItem from="1459846109835" duration="713000" />
+ <workItem from="1459846850476" duration="8855000" />
</task>
<servers />
</component>
+ <component name="TimeTrackingManager">
+ <option name="totallyTimeSpent" value="13734000" />
+ </component>
<component name="ToolWindowManager">
- <frame x="0" y="25" width="1280" height="775" extended-state="6" />
+ <frame x="1680" y="24" width="1920" height="1056" extended-state="6" />
<editor active="true" />
<layout>
+ <window_info id="Palette	" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
+ <window_info id="UI Designer" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+ <window_info id="Designer" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
+ <window_info id="Palette" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
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+ <window_info id="Database" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+ <window_info id="Ant Build" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+ <window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
+ <window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="10" side_tool="true" content_ui="tabs" />
+ <window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="3" side_tool="true" content_ui="tabs" />
+ <window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="8" side_tool="false" content_ui="tabs" />
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- <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
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+ <window_info id="Maven Projects" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
+ <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.18125" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
+ <window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.15478615" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
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+ <window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="9" side_tool="false" content_ui="tabs" />
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<window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
</layout>
+ <layout-to-restore>
+ <window_info id="Cvs" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+ <window_info id="Palette	" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
+ <window_info id="UI Designer" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+ <window_info id="Designer" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
+ <window_info id="Message" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
+ <window_info id="Palette" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
+ <window_info id="Terminal" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="7" side_tool="false" content_ui="tabs" />
+ <window_info id="Find" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+ <window_info id="Database" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+ <window_info id="Ant Build" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+ <window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
+ <window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="10" side_tool="true" content_ui="tabs" />
+ <window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="3" side_tool="true" content_ui="tabs" />
+ <window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="8" side_tool="false" content_ui="tabs" />
+ <window_info id="TODO" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
+ <window_info id="Structure" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+ <window_info id="Commander" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
+ <window_info id="Maven Projects" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
+ <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.18125" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
+ <window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="9" side_tool="false" content_ui="tabs" />
+ <window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
+ <window_info id="Inspection" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
+ <window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.15478615" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
+ </layout-to-restore>
</component>
<component name="Vcs.Log.UiProperties">
<option name="RECENTLY_FILTERED_USER_GROUPS">
@@ -381,36 +1130,310 @@
<breakpoint-manager />
<watches-manager />
</component>
+ <component name="antWorkspaceConfiguration">
+ <option name="IS_AUTOSCROLL_TO_SOURCE" value="false" />
+ <option name="FILTER_TARGETS" value="false" />
+ </component>
<component name="editorHistoryManager">
<entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Mumbo.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="264" column="41" selection-start-line="264" selection-start-column="11" selection-end-line="264" selection-end-column="41" />
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+ </entry>
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/__init__.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
+ <caret line="0" column="0" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
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+ <state vertical-scroll-proportion="0.0">
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+ </provider>
+ </entry>
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/GetMutliviewDb.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
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+ </entry>
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/ExecMultiview.py">
+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
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+ <provider selected="true" editor-type-id="text-editor">
+ <state vertical-scroll-proportion="0.0">
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</entry>
+ <entry file="file://$PROJECT_DIR$/Code/MultiView/Mumbo/Classifiers/DecisionTree.py">
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diff --git a/Code/MultiView/ExecMultiview.py b/Code/MultiView/ExecMultiview.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e997a544c0a22dc72ee9581952e0ae320ecf2be
--- /dev/null
+++ b/Code/MultiView/ExecMultiview.py
@@ -0,0 +1,15 @@
+import Mumbo.Mumbo as Mumbo
+import GetMutliviewDb as DB
+
+NB_VIEW = 4
+DATASET_LENGTH = 300
+NB_CLASS = 4
+NB_ITER = 50
+classifierName="DecisionTree"
+NB_CORES = 4
+classifierConfig = 3
+
+DATASET, VIEW_DIMENSIONS, CLASS_LABELS = DB.createFakeData(NB_VIEW, DATASET_LENGTH, NB_CLASS)
+bestClassifiers, generalAlphas, bestViews = Mumbo.trainMumbo(DATASET, CLASS_LABELS, NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, classifierName, NB_CORES, classifierConfig)
+
+predictedLabels = Mumbo.classifyMumbo(DATASET, bestClassifiers, generalAlphas, bestViews, NB_CLASS)
diff --git a/Code/MultiView/GetMutliviewDb.py b/Code/MultiView/GetMutliviewDb.py
index f2aa9cb0adbec25b4103bb095e95fdc9032ef405..a3cb40eadfc0937d4df3433f74bd71840d7affa2 100644
--- a/Code/MultiView/GetMutliviewDb.py
+++ b/Code/MultiView/GetMutliviewDb.py
@@ -1,15 +1,31 @@
import numpy as np
-def getOneViewFromDB(viewName, pathToDB):
- view = np.genfromtxt(pathToDB + viewName, delimiter=';')
- return view
-
-def getClassLabels(pathToDB):
- labels = np.genfromtxt(pathToDB + "ClassLabels.csv", delimiter=';')
- return labels
-
-def getDataset(pathToDB, viewNames):
- dataset = []
- for viewName in viewNames:
- dataset.append(getOneViewFromDB(viewName, pathtoDB))
- return np.array(dataset)
+
+def getOneViewFromDB(viewName, pathToDB, DBName):
+ view = np.genfromtxt(pathToDB + DBName +"-" + viewName, delimiter=';')
+ return view
+
+
+def getClassLabels(pathToDB, DBName):
+ labels = np.genfromtxt(pathToDB + DBName + "-" + "ClassLabels.csv", delimiter=';')
+ return labels
+
+
+def getDataset(pathToDB, viewNames, DBName):
+ dataset = []
+ for viewName in viewNames:
+ dataset.append(getOneViewFromDB(viewName, pathToDB, DBName))
+ return np.array(dataset)
+
+
+def createFakeData(NB_VIEW, DATASET_LENGTH, NB_CLASS):
+ VIEW_DIMENSIONS = np.random.random_integers(5, 20, NB_VIEW)
+
+ DATA = [
+ np.array([
+ np.random.normal(0.0, 2, viewDimension)
+ for i in np.arange(DATASET_LENGTH)])
+ for viewDimension in VIEW_DIMENSIONS]
+
+ CLASS_LABELS = np.random.random_integers(0, NB_CLASS-1, DATASET_LENGTH)
+ return DATA, VIEW_DIMENSIONS, CLASS_LABELS
\ No newline at end of file
diff --git a/Code/MultiView/Mumbo/Classifiers/DecisionTree.py b/Code/MultiView/Mumbo/Classifiers/DecisionTree.py
index 64d28b6f6a23de2d53d4af1e44b986841b5ca7b6..b0dea346f15f67a9891dc1a7be6836b4d32b62d8 100644
--- a/Code/MultiView/Mumbo/Classifiers/DecisionTree.py
+++ b/Code/MultiView/Mumbo/Classifiers/DecisionTree.py
@@ -1,10 +1,11 @@
from sklearn import tree
-from sklearn.multiclass import OneVsRestClassifier
-
+# from sklearn.multiclass import OneVsRestClassifier
+from ModifiedMulticlass import OneVsRestClassifier
# Add weights
-def decisionTree(data, labels, arg):
+def DecisionTree(data, labels, arg, weights):
classifier = OneVsRestClassifier(tree.DecisionTreeClassifier(max_depth=arg))
- classifier.fit(data, labels)
- return classifier, classifier.predict(data)
\ No newline at end of file
+ classifier.fit(data, labels, sample_weight=weights)
+ return classifier, classifier.predict(data)
+
diff --git a/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py b/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py
new file mode 100644
index 0000000000000000000000000000000000000000..040342fd9fd542f6087566746e5d10f27e33e895
--- /dev/null
+++ b/Code/MultiView/Mumbo/Classifiers/ModifiedMulticlass.py
@@ -0,0 +1,718 @@
+"""
+Multiclass and multilabel classification strategies
+===================================================
+This module implements multiclass learning algorithms:
+ - one-vs-the-rest / one-vs-all
+ - one-vs-one
+ - error correcting output codes
+The estimators provided in this module are meta-estimators: they require a base
+estimator to be provided in their constructor. For example, it is possible to
+use these estimators to turn a binary classifier or a regressor into a
+multiclass classifier. It is also possible to use these estimators with
+multiclass estimators in the hope that their accuracy or runtime performance
+improves.
+All classifiers in scikit-learn implement multiclass classification; you
+only need to use this module if you want to experiment with custom multiclass
+strategies.
+The one-vs-the-rest meta-classifier also implements a `predict_proba` method,
+so long as such a method is implemented by the base classifier. This method
+returns probabilities of class membership in both the single label and
+multilabel case. Note that in the multilabel case, probabilities are the
+marginal probability that a given sample falls in the given class. As such, in
+the multilabel case the sum of these probabilities over all possible labels
+for a given sample *will not* sum to unity, as they do in the single label
+case.
+"""
+
+# Author: Mathieu Blondel <mathieu@mblondel.org>
+# Author: Hamzeh Alsalhi <93hamsal@gmail.com>
+#
+# License: BSD 3 clause
+
+import array
+import numpy as np
+import warnings
+import scipy.sparse as sp
+
+from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_classifier
+from sklearn.base import MetaEstimatorMixin, is_regressor
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.metrics.pairwise import euclidean_distances
+from sklearn.utils import check_random_state
+from sklearn.utils.validation import _num_samples
+from sklearn.utils.validation import check_consistent_length
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils import deprecated
+from sklearn.externals.joblib import Parallel
+from sklearn.externals.joblib import delayed
+
+__all__ = [
+ "OneVsRestClassifier",
+ "OneVsOneClassifier",
+ "OutputCodeClassifier",
+]
+
+
+def _fit_binary(estimator, X, y, classes=None, sample_weight=None):
+ """Fit a single binary estimator."""
+ unique_y = np.unique(y)
+ if len(unique_y) == 1:
+ if classes is not None:
+ if y[0] == -1:
+ c = 0
+ else:
+ c = y[0]
+ warnings.warn("Label %s is present in all training examples." %
+ str(classes[c]))
+ estimator = _ConstantPredictor().fit(X, unique_y)
+ else:
+ estimator = clone(estimator)
+ estimator.fit(X, y, sample_weight=sample_weight)
+ return estimator
+
+
+def _predict_binary(estimator, X):
+ """Make predictions using a single binary estimator."""
+ if is_regressor(estimator):
+ return estimator.predict(X)
+ try:
+ score = np.ravel(estimator.decision_function(X))
+ except (AttributeError, NotImplementedError):
+ # probabilities of the positive class
+ score = estimator.predict_proba(X)[:, 1]
+ return score
+
+
+def _check_estimator(estimator):
+ """Make sure that an estimator implements the necessary methods."""
+ if (not hasattr(estimator, "decision_function") and
+ not hasattr(estimator, "predict_proba")):
+ raise ValueError("The base estimator should implement "
+ "decision_function or predict_proba!")
+
+
+@deprecated("fit_ovr is deprecated and will be removed in 0.18."
+ "Use the OneVsRestClassifier instead.")
+def fit_ovr(estimator, X, y, n_jobs=1):
+ """Fit a one-vs-the-rest strategy.
+ Parameters
+ ----------
+ estimator : estimator object
+ An estimator object implementing `fit` and one of `decision_function`
+ or `predict_proba`.
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ y : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes]
+ Multi-class targets. An indicator matrix turns on multilabel
+ classification.
+ Returns
+ -------
+ estimators : list of estimators object
+ The list of fitted estimator.
+ lb : fitted LabelBinarizer
+ """
+ ovr = OneVsRestClassifier(estimator, n_jobs=n_jobs).fit(X, y)
+ return ovr.estimators_, ovr.label_binarizer_
+
+
+@deprecated("predict_ovr is deprecated and will be removed in 0.18."
+ "Use the OneVsRestClassifier instead.")
+def predict_ovr(estimators, label_binarizer, X):
+ """Predict multi-class targets using the one vs rest strategy.
+ Parameters
+ ----------
+ estimators : list of `n_classes` estimators, Estimators used for
+ predictions. The list must be homogeneous with respect to the type of
+ estimators. fit_ovr supplies this list as part of its output.
+ label_binarizer : LabelBinarizer object, Object used to transform
+ multiclass labels to binary labels and vice-versa. fit_ovr supplies
+ this object as part of its output.
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ Returns
+ -------
+ y : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes].
+ Predicted multi-class targets.
+ """
+ e_types = set([type(e) for e in estimators if not
+ isinstance(e, _ConstantPredictor)])
+ if len(e_types) > 1:
+ raise ValueError("List of estimators must contain estimators of the"
+ " same type but contains types {0}".format(e_types))
+
+ ovr = OneVsRestClassifier(clone(estimators[0]))
+ ovr.estimators_ = estimators
+ ovr.label_binarizer_ = label_binarizer
+
+ return ovr.predict(X)
+
+
+@deprecated("predict_proba_ovr is deprecated and will be removed in 0.18."
+ "Use the OneVsRestClassifier instead.")
+def predict_proba_ovr(estimators, X, is_multilabel):
+ e_types = set([type(e) for e in estimators if not
+ isinstance(e, _ConstantPredictor)])
+ if len(e_types) > 1:
+ raise ValueError("List of estimators must contain estimators of the"
+ " same type but contains types {0}".format(e_types))
+
+ Y = np.array([e.predict_proba(X)[:, 1] for e in estimators]).T
+
+ if not is_multilabel:
+ # Then, probabilities should be normalized to 1.
+ Y /= np.sum(Y, axis=1)[:, np.newaxis]
+
+ return Y
+
+
+class _ConstantPredictor(BaseEstimator):
+
+ def fit(self, X, y):
+ self.y_ = y
+ return self
+
+ def predict(self, X):
+ check_is_fitted(self, 'y_')
+
+ return np.repeat(self.y_, X.shape[0])
+
+ def decision_function(self, X):
+ check_is_fitted(self, 'y_')
+
+ return np.repeat(self.y_, X.shape[0])
+
+ def predict_proba(self, X):
+ check_is_fitted(self, 'y_')
+
+ return np.repeat([np.hstack([1 - self.y_, self.y_])],
+ X.shape[0], axis=0)
+
+
+class OneVsRestClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ """One-vs-the-rest (OvR) multiclass/multilabel strategy
+ Also known as one-vs-all, this strategy consists in fitting one classifier
+ per class. For each classifier, the class is fitted against all the other
+ classes. In addition to its computational efficiency (only `n_classes`
+ classifiers are needed), one advantage of this approach is its
+ interpretability. Since each class is represented by one and one classifier
+ only, it is possible to gain knowledge about the class by inspecting its
+ corresponding classifier. This is the most commonly used strategy for
+ multiclass classification and is a fair default choice.
+ This strategy can also be used for multilabel learning, where a classifier
+ is used to predict multiple labels for instance, by fitting on a 2-d matrix
+ in which cell [i, j] is 1 if sample i has label j and 0 otherwise.
+ In the multilabel learning literature, OvR is also known as the binary
+ relevance method.
+ Read more in the :ref:`User Guide <ovr_classification>`.
+ Parameters
+ ----------
+ estimator : estimator object
+ An estimator object implementing `fit` and one of `decision_function`
+ or `predict_proba`.
+ n_jobs : int, optional, default: 1
+ The number of jobs to use for the computation. If -1 all CPUs are used.
+ If 1 is given, no parallel computing code is used at all, which is
+ useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
+ used. Thus for n_jobs = -2, all CPUs but one are used.
+ Attributes
+ ----------
+ estimators_ : list of `n_classes` estimators
+ Estimators used for predictions.
+ classes_ : array, shape = [`n_classes`]
+ Class labels.
+ label_binarizer_ : LabelBinarizer object
+ Object used to transform multiclass labels to binary labels and
+ vice-versa.
+ multilabel_ : boolean
+ Whether a OneVsRestClassifier is a multilabel classifier.
+ """
+
+ def __init__(self, estimator, n_jobs=1):
+ self.estimator = estimator
+ self.n_jobs = n_jobs
+
+ def fit(self, X, y, sample_weight=None):
+ """Fit underlying estimators.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ y : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes]
+ Multi-class targets. An indicator matrix turns on multilabel
+ classification.
+ Returns
+ -------
+ self
+ """
+ # A sparse LabelBinarizer, with sparse_output=True, has been shown to
+ # outpreform or match a dense label binarizer in all cases and has also
+ # resulted in less or equal memory consumption in the fit_ovr function
+ # overall.
+ self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+ Y = self.label_binarizer_.fit_transform(y)
+ Y = Y.tocsc()
+ columns = (col.toarray().ravel() for col in Y.T)
+ # In cases where individual estimators are very fast to train setting
+ # n_jobs > 1 in can results in slower performance due to the overhead
+ # of spawning threads. See joblib issue #112.
+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(delayed(_fit_binary)(
+ self.estimator, X, column, classes=[
+ "not %s" % self.label_binarizer_.classes_[i],
+ self.label_binarizer_.classes_[i]], sample_weight=sample_weight)
+ for i, column in enumerate(columns))
+
+ return self
+
+ def predict(self, X):
+ """Predict multi-class targets using underlying estimators.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ Returns
+ -------
+ y : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes].
+ Predicted multi-class targets.
+ """
+ check_is_fitted(self, 'estimators_')
+ if (hasattr(self.estimators_[0], "decision_function") and
+ is_classifier(self.estimators_[0])):
+ thresh = 0
+ else:
+ thresh = .5
+
+ n_samples = _num_samples(X)
+ if self.label_binarizer_.y_type_ == "multiclass":
+ maxima = np.empty(n_samples, dtype=float)
+ maxima.fill(-np.inf)
+ argmaxima = np.zeros(n_samples, dtype=int)
+ for i, e in enumerate(self.estimators_):
+ pred = _predict_binary(e, X)
+ np.maximum(maxima, pred, out=maxima)
+ argmaxima[maxima == pred] = i
+ return self.label_binarizer_.classes_[np.array(argmaxima.T)]
+ else:
+ indices = array.array('i')
+ indptr = array.array('i', [0])
+ for e in self.estimators_:
+ indices.extend(np.where(_predict_binary(e, X) > thresh)[0])
+ indptr.append(len(indices))
+ data = np.ones(len(indices), dtype=int)
+ indicator = sp.csc_matrix((data, indices, indptr),
+ shape=(n_samples, len(self.estimators_)))
+ return self.label_binarizer_.inverse_transform(indicator)
+
+ def predict_proba(self, X):
+ """Probability estimates.
+ The returned estimates for all classes are ordered by label of classes.
+ Note that in the multilabel case, each sample can have any number of
+ labels. This returns the marginal probability that the given sample has
+ the label in question. For example, it is entirely consistent that two
+ labels both have a 90% probability of applying to a given sample.
+ In the single label multiclass case, the rows of the returned matrix
+ sum to 1.
+ Parameters
+ ----------
+ X : array-like, shape = [n_samples, n_features]
+ Returns
+ -------
+ T : (sparse) array-like, shape = [n_samples, n_classes]
+ Returns the probability of the sample for each class in the model,
+ where classes are ordered as they are in `self.classes_`.
+ """
+ check_is_fitted(self, 'estimators_')
+ # Y[i,j] gives the probability that sample i has the label j.
+ # In the multi-label case, these are not disjoint.
+ Y = np.array([e.predict_proba(X)[:, 1] for e in self.estimators_]).T
+
+ if len(self.estimators_) == 1:
+ # Only one estimator, but we still want to return probabilities
+ # for two classes.
+ Y = np.concatenate(((1 - Y), Y), axis=1)
+
+ if not self.multilabel_:
+ # Then, probabilities should be normalized to 1.
+ Y /= np.sum(Y, axis=1)[:, np.newaxis]
+ return Y
+
+ def decision_function(self, X):
+ """Returns the distance of each sample from the decision boundary for
+ each class. This can only be used with estimators which implement the
+ decision_function method.
+ Parameters
+ ----------
+ X : array-like, shape = [n_samples, n_features]
+ Returns
+ -------
+ T : array-like, shape = [n_samples, n_classes]
+ """
+ check_is_fitted(self, 'estimators_')
+ if not hasattr(self.estimators_[0], "decision_function"):
+ raise AttributeError(
+ "Base estimator doesn't have a decision_function attribute.")
+ return np.array([est.decision_function(X).ravel()
+ for est in self.estimators_]).T
+
+ @property
+ def multilabel_(self):
+ """Whether this is a multilabel classifier"""
+ return self.label_binarizer_.y_type_.startswith('multilabel')
+
+ @property
+ def classes_(self):
+ return self.label_binarizer_.classes_
+
+ @property
+ def coef_(self):
+ check_is_fitted(self, 'estimators_')
+ if not hasattr(self.estimators_[0], "coef_"):
+ raise AttributeError(
+ "Base estimator doesn't have a coef_ attribute.")
+ coefs = [e.coef_ for e in self.estimators_]
+ if sp.issparse(coefs[0]):
+ return sp.vstack(coefs)
+ return np.vstack(coefs)
+
+ @property
+ def intercept_(self):
+ check_is_fitted(self, 'estimators_')
+ if not hasattr(self.estimators_[0], "intercept_"):
+ raise AttributeError(
+ "Base estimator doesn't have an intercept_ attribute.")
+ return np.array([e.intercept_.ravel() for e in self.estimators_])
+
+
+def _fit_ovo_binary(estimator, X, y, i, j):
+ """Fit a single binary estimator (one-vs-one)."""
+ cond = np.logical_or(y == i, y == j)
+ y = y[cond]
+ y_binary = np.empty(y.shape, np.int)
+ y_binary[y == i] = 0
+ y_binary[y == j] = 1
+ ind = np.arange(X.shape[0])
+ return _fit_binary(estimator, X[ind[cond]], y_binary, classes=[i, j])
+
+
+@deprecated("fit_ovo is deprecated and will be removed in 0.18."
+ "Use the OneVsOneClassifier instead.")
+def fit_ovo(estimator, X, y, n_jobs=1):
+ ovo = OneVsOneClassifier(estimator, n_jobs=n_jobs).fit(X, y)
+ return ovo.estimators_, ovo.classes_
+
+
+@deprecated("predict_ovo is deprecated and will be removed in 0.18."
+ "Use the OneVsOneClassifier instead.")
+def predict_ovo(estimators, classes, X):
+ """Make predictions using the one-vs-one strategy."""
+
+ e_types = set([type(e) for e in estimators if not
+ isinstance(e, _ConstantPredictor)])
+ if len(e_types) > 1:
+ raise ValueError("List of estimators must contain estimators of the"
+ " same type but contains types {0}".format(e_types))
+
+ ovo = OneVsOneClassifier(clone(estimators[0]))
+ ovo.estimators_ = estimators
+ ovo.classes_ = classes
+ return ovo.predict(X)
+
+
+class OneVsOneClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ """One-vs-one multiclass strategy
+ This strategy consists in fitting one classifier per class pair.
+ At prediction time, the class which received the most votes is selected.
+ Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
+ this method is usually slower than one-vs-the-rest, due to its
+ O(n_classes^2) complexity. However, this method may be advantageous for
+ algorithms such as kernel algorithms which don't scale well with
+ `n_samples`. This is because each individual learning problem only involves
+ a small subset of the data whereas, with one-vs-the-rest, the complete
+ dataset is used `n_classes` times.
+ Read more in the :ref:`User Guide <ovo_classification>`.
+ Parameters
+ ----------
+ estimator : estimator object
+ An estimator object implementing `fit` and one of `decision_function`
+ or `predict_proba`.
+ n_jobs : int, optional, default: 1
+ The number of jobs to use for the computation. If -1 all CPUs are used.
+ If 1 is given, no parallel computing code is used at all, which is
+ useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
+ used. Thus for n_jobs = -2, all CPUs but one are used.
+ Attributes
+ ----------
+ estimators_ : list of `n_classes * (n_classes - 1) / 2` estimators
+ Estimators used for predictions.
+ classes_ : numpy array of shape [n_classes]
+ Array containing labels.
+ """
+
+ def __init__(self, estimator, n_jobs=1):
+ self.estimator = estimator
+ self.n_jobs = n_jobs
+
+ def fit(self, X, y):
+ """Fit underlying estimators.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ y : array-like, shape = [n_samples]
+ Multi-class targets.
+ Returns
+ -------
+ self
+ """
+ y = np.asarray(y)
+ check_consistent_length(X, y)
+
+ self.classes_ = np.unique(y)
+ n_classes = self.classes_.shape[0]
+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+ delayed(_fit_ovo_binary)(
+ self.estimator, X, y, self.classes_[i], self.classes_[j])
+ for i in range(n_classes) for j in range(i + 1, n_classes))
+
+ return self
+
+ def predict(self, X):
+ """Estimate the best class label for each sample in X.
+ This is implemented as ``argmax(decision_function(X), axis=1)`` which
+ will return the label of the class with most votes by estimators
+ predicting the outcome of a decision for each possible class pair.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ Returns
+ -------
+ y : numpy array of shape [n_samples]
+ Predicted multi-class targets.
+ """
+ Y = self.decision_function(X)
+ return self.classes_[Y.argmax(axis=1)]
+
+ def decision_function(self, X):
+ """Decision function for the OneVsOneClassifier.
+ The decision values for the samples are computed by adding the
+ normalized sum of pair-wise classification confidence levels to the
+ votes in order to disambiguate between the decision values when the
+ votes for all the classes are equal leading to a tie.
+ Parameters
+ ----------
+ X : array-like, shape = [n_samples, n_features]
+ Returns
+ -------
+ Y : array-like, shape = [n_samples, n_classes]
+ """
+ check_is_fitted(self, 'estimators_')
+
+ predictions = np.vstack([est.predict(X) for est in self.estimators_]).T
+ confidences = np.vstack([_predict_binary(est, X) for est in self.estimators_]).T
+ return _ovr_decision_function(predictions, confidences,
+ len(self.classes_))
+
+
+def _ovr_decision_function(predictions, confidences, n_classes):
+ """Compute a continuous, tie-breaking ovr decision function.
+ It is important to include a continuous value, not only votes,
+ to make computing AUC or calibration meaningful.
+ Parameters
+ ----------
+ predictions : array-like, shape (n_samples, n_classifiers)
+ Predicted classes for each binary classifier.
+ confidences : array-like, shape (n_samples, n_classifiers)
+ Decision functions or predicted probabilities for positive class
+ for each binary classifier.
+ n_classes : int
+ Number of classes. n_classifiers must be
+ ``n_classes * (n_classes - 1 ) / 2``
+ """
+ n_samples = predictions.shape[0]
+ votes = np.zeros((n_samples, n_classes))
+ sum_of_confidences = np.zeros((n_samples, n_classes))
+
+ k = 0
+ for i in range(n_classes):
+ for j in range(i + 1, n_classes):
+ sum_of_confidences[:, i] -= confidences[:, k]
+ sum_of_confidences[:, j] += confidences[:, k]
+ votes[predictions[:, k] == 0, i] += 1
+ votes[predictions[:, k] == 1, j] += 1
+ k += 1
+
+ max_confidences = sum_of_confidences.max()
+ min_confidences = sum_of_confidences.min()
+
+ if max_confidences == min_confidences:
+ return votes
+
+ # Scale the sum_of_confidences to (-0.5, 0.5) and add it with votes.
+ # The motivation is to use confidence levels as a way to break ties in
+ # the votes without switching any decision made based on a difference
+ # of 1 vote.
+ eps = np.finfo(sum_of_confidences.dtype).eps
+ max_abs_confidence = max(abs(max_confidences), abs(min_confidences))
+ scale = (0.5 - eps) / max_abs_confidence
+ return votes + sum_of_confidences * scale
+
+
+@deprecated("fit_ecoc is deprecated and will be removed in 0.18."
+ "Use the OutputCodeClassifier instead.")
+def fit_ecoc(estimator, X, y, code_size=1.5, random_state=None, n_jobs=1):
+ """Fit an error-correcting output-code strategy.
+ Parameters
+ ----------
+ estimator : estimator object
+ An estimator object implementing `fit` and one of `decision_function`
+ or `predict_proba`.
+ code_size : float, optional
+ Percentage of the number of classes to be used to create the code book.
+ random_state : numpy.RandomState, optional
+ The generator used to initialize the codebook. Defaults to
+ numpy.random.
+ Returns
+ --------
+ estimators : list of `int(n_classes * code_size)` estimators
+ Estimators used for predictions.
+ classes : numpy array of shape [n_classes]
+ Array containing labels.
+ code_book_ : numpy array of shape [n_classes, code_size]
+ Binary array containing the code of each class.
+ """
+ ecoc = OutputCodeClassifier(estimator, random_state=random_state,
+ n_jobs=n_jobs).fit(X, y)
+ return ecoc.estimators_, ecoc.classes_, ecoc.code_book_
+
+
+@deprecated("predict_ecoc is deprecated and will be removed in 0.18."
+ "Use the OutputCodeClassifier instead.")
+def predict_ecoc(estimators, classes, code_book, X):
+ """Make predictions using the error-correcting output-code strategy."""
+ ecoc = OutputCodeClassifier(clone(estimators[0]))
+ ecoc.classes_ = classes
+ ecoc.estimators_ = estimators
+ ecoc.code_book_ = code_book
+
+ return ecoc.predict(X)
+
+
+class OutputCodeClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ """(Error-Correcting) Output-Code multiclass strategy
+ Output-code based strategies consist in representing each class with a
+ binary code (an array of 0s and 1s). At fitting time, one binary
+ classifier per bit in the code book is fitted. At prediction time, the
+ classifiers are used to project new points in the class space and the class
+ closest to the points is chosen. The main advantage of these strategies is
+ that the number of classifiers used can be controlled by the user, either
+ for compressing the model (0 < code_size < 1) or for making the model more
+ robust to errors (code_size > 1). See the documentation for more details.
+ Read more in the :ref:`User Guide <ecoc>`.
+ Parameters
+ ----------
+ estimator : estimator object
+ An estimator object implementing `fit` and one of `decision_function`
+ or `predict_proba`.
+ code_size : float
+ Percentage of the number of classes to be used to create the code book.
+ A number between 0 and 1 will require fewer classifiers than
+ one-vs-the-rest. A number greater than 1 will require more classifiers
+ than one-vs-the-rest.
+ random_state : numpy.RandomState, optional
+ The generator used to initialize the codebook. Defaults to
+ numpy.random.
+ n_jobs : int, optional, default: 1
+ The number of jobs to use for the computation. If -1 all CPUs are used.
+ If 1 is given, no parallel computing code is used at all, which is
+ useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
+ used. Thus for n_jobs = -2, all CPUs but one are used.
+ Attributes
+ ----------
+ estimators_ : list of `int(n_classes * code_size)` estimators
+ Estimators used for predictions.
+ classes_ : numpy array of shape [n_classes]
+ Array containing labels.
+ code_book_ : numpy array of shape [n_classes, code_size]
+ Binary array containing the code of each class.
+ References
+ ----------
+ .. [1] "Solving multiclass learning problems via error-correcting output
+ codes",
+ Dietterich T., Bakiri G.,
+ Journal of Artificial Intelligence Research 2,
+ 1995.
+ .. [2] "The error coding method and PICTs",
+ James G., Hastie T.,
+ Journal of Computational and Graphical statistics 7,
+ 1998.
+ .. [3] "The Elements of Statistical Learning",
+ Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
+ 2008.
+ """
+
+ def __init__(self, estimator, code_size=1.5, random_state=None, n_jobs=1):
+ self.estimator = estimator
+ self.code_size = code_size
+ self.random_state = random_state
+ self.n_jobs = n_jobs
+
+ def fit(self, X, y):
+ """Fit underlying estimators.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ y : numpy array of shape [n_samples]
+ Multi-class targets.
+ Returns
+ -------
+ self
+ """
+ if self.code_size <= 0:
+ raise ValueError("code_size should be greater than 0, got {1}"
+ "".format(self.code_size))
+
+ _check_estimator(self.estimator)
+ random_state = check_random_state(self.random_state)
+
+ self.classes_ = np.unique(y)
+ n_classes = self.classes_.shape[0]
+ code_size_ = int(n_classes * self.code_size)
+
+ # FIXME: there are more elaborate methods than generating the codebook
+ # randomly.
+ self.code_book_ = random_state.random_sample((n_classes, code_size_))
+ self.code_book_[self.code_book_ > 0.5] = 1
+
+ if hasattr(self.estimator, "decision_function"):
+ self.code_book_[self.code_book_ != 1] = -1
+ else:
+ self.code_book_[self.code_book_ != 1] = 0
+
+ classes_index = dict((c, i) for i, c in enumerate(self.classes_))
+
+ Y = np.array([self.code_book_[classes_index[y[i]]]
+ for i in range(X.shape[0])], dtype=np.int)
+
+ self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+ delayed(_fit_binary)(self.estimator, X, Y[:, i])
+ for i in range(Y.shape[1]))
+
+ return self
+
+ def predict(self, X):
+ """Predict multi-class targets using underlying estimators.
+ Parameters
+ ----------
+ X : (sparse) array-like, shape = [n_samples, n_features]
+ Data.
+ Returns
+ -------
+ y : numpy array of shape [n_samples]
+ Predicted multi-class targets.
+ """
+ check_is_fitted(self, 'estimators_')
+ Y = np.array([_predict_binary(e, X) for e in self.estimators_]).T
+ pred = euclidean_distances(Y, self.code_book_).argmin(axis=1)
+ return self.classes_[pred]
+
diff --git a/Code/MultiView/Mumbo/Classifiers/__init__.py b/Code/MultiView/Mumbo/Classifiers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..67fe9de36bef451e807afd5bebf524100535612a
--- /dev/null
+++ b/Code/MultiView/Mumbo/Classifiers/__init__.py
@@ -0,0 +1,13 @@
+# from os import listdir
+# from os.path import isfile, join
+# mypath="."
+# modules = [f[:-3] for f in listdir(mypath) if isfile(join(mypath, f)) and f[-3:] == ".py" and f!="__init__.py" ]
+# __all__ = modules
+
+import os
+for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
+ if module == '__init__.py' or module[-3:] != '.py':
+ continue
+ __import__(module[:-3], locals(), globals())
+del module
+del os
diff --git a/Code/MultiView/Mumbo/Mumbo.py b/Code/MultiView/Mumbo/Mumbo.py
index db1e02134c750c7a4b4f76e1f8dbf6df4fdeb945..8fd0cb7d890eff42c0e3fd0ca95cce5027a9934e 100644
--- a/Code/MultiView/Mumbo/Mumbo.py
+++ b/Code/MultiView/Mumbo/Mumbo.py
@@ -1,7 +1,7 @@
import numpy as np
import math
from joblib import Parallel, delayed
-import Classifers
+from Classifiers import *
# Data shape : ((Views, Examples, Corrdinates))
@@ -29,7 +29,7 @@ def initialize(NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, CLASS_LABELS):
])
fs = np.zeros((NB_ITER, NB_VIEW, DATASET_LENGTH, NB_CLASS))
ds = np.zeros((NB_ITER, NB_VIEW, DATASET_LENGTH))
- edges = np.zeros((NB_ITER, NB_VIEW, DATASET_LENGTH))
+ edges = np.zeros((NB_ITER, NB_VIEW))
alphas = np.zeros((NB_ITER, NB_VIEW))
predictions = np.zeros((NB_ITER, NB_VIEW, DATASET_LENGTH))
generalAlphas = np.zeros(NB_ITER)
@@ -41,7 +41,7 @@ def initialize(NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, CLASS_LABELS):
def computeWeights(costMatrices, NB_CLASS, DATASET_LENGTH, iterIndice,
viewIndice, CLASS_LABELS):
dist = np.sum(costMatrices[iterIndice, viewIndice]) # ATTENTION
- weights = np.array([costMatrices[iterIndice, viewIndice,
+ weights = np.array([costMatrices[iterIndice, viewIndice,
exampleIndice, CLASS_LABELS[exampleIndice]]/dist\
for exampleIndice in range(DATASET_LENGTH)])
return weights
@@ -51,30 +51,38 @@ def trainWeakClassifier(classifierName, monoviewDataset, CLASS_LABELS, costMatri
NB_CLASS, DATASET_LENGTH, iterIndice, viewIndice,
classifier_config):
weights = computeWeights(costMatrices, NB_CLASS, DATASET_LENGTH,
- iterIndice, viewIndice)
- #Train classifier(classifierName, weights, DATASET, CLASS_LABEL, classifier_config)
+ iterIndice, viewIndice, CLASS_LABELS)
+ classifierMethod=globals()["DecisionTree"].DecisionTree #Permet d'appeler une fonction avec une string
+ classifier, classes = classifierMethod(monoviewDataset, CLASS_LABELS, classifier_config, weights)
return classifier, classes
-def trainWeakClassifers(classifierName, DATASET, CLASS_LABELS, costMatrices,
+def trainWeakClassifers(classifierName, DATASET, CLASS_LABELS, costMatrices,
NB_CLASS, DATASET_LENGTH, iterIndice, classifier_config,
- NB_CORES):
+ NB_CORES, NB_VIEW):
+ trainedClassifiers = []
+ labelsMatrix = []
if NB_CORES > NB_VIEW:
NB_JOBS = NB_VIEW
else:
NB_JOBS = NB_CORES
- trainedClassifiers, classesMatrix = Parallel(n_jobs=NB_JOBS)(
+ trainedClassifiersAndLabels = Parallel(n_jobs=NB_JOBS)(
delayed(trainWeakClassifier)(classifierName, DATASET[viewIndice], CLASS_LABELS,
costMatrices, NB_CLASS, DATASET_LENGTH,
iterIndice, viewIndice, classifier_config)
for viewIndice in range(NB_VIEW))
- return trainedClassifiers, classesMatrix
+ for (classifier, labelsArray) in trainedClassifiersAndLabels:
+ trainedClassifiers.append(classifier)
+ labelsMatrix.append(labelsArray)
-def computeEdge (predictionMatrix, costMatrix):
- return np.sum(predictionMatrix*costMatrix)
+ return np.array(trainedClassifiers), np.array(labelsMatrix)
+
+
+def computeEdge(predictionMatrix, costMatrix, NB_CLASS):
+ return np.sum(np.array([np.sum(predictionMatrix*costMatrix[:,classIndice]) for classIndice in range(NB_CLASS)]))
def computeAlpha(edge):
@@ -131,7 +139,7 @@ def updateCostmatrices(costMatrices, fs, iterIndice, NB_VIEW, DATASET_LENGTH,
for exampleIndice in range(DATASET_LENGTH):
for classe in range(NB_CLASS):
if classe != CLASS_LABELS[exampleIndice]:
- costMatrices[iterIndice, viewIndice, exampleIndice, classe] \
+ costMatrices[iterIndice+1, viewIndice, exampleIndice, classe] \
= math.exp(fs[iterIndice, viewIndice, exampleIndice, classe] - \
fs[iterIndice, viewIndice,
exampleIndice, CLASS_LABELS[exampleIndice]])
@@ -142,9 +150,9 @@ def updateCostmatrices(costMatrices, fs, iterIndice, NB_VIEW, DATASET_LENGTH,
return costMatrices
-def chooseView(predictions, generalCostMatrix, iterIndice, NB_VIEW):
+def chooseView(predictions, generalCostMatrix, iterIndice, NB_VIEW, NB_CLASS):
edges = np.array([computeEdge(predictions[iterIndice, viewIndice],
- generalCostMatrix) \
+ generalCostMatrix[iterIndice], NB_CLASS) \
for viewIndice in range(NB_VIEW)])
bestView = np.argmax(edges)
return bestView, edges[bestView]
@@ -204,7 +212,7 @@ def computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions,
def trainMumbo(DATASET, CLASS_LABELS, NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH,
- classifierName, NB_CORES):
+ classifierName, NB_CORES, classifierConfig):
# Initialization
costMatrices, \
@@ -218,57 +226,62 @@ def trainMumbo(DATASET, CLASS_LABELS, NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH
# Learning
for iterIndice in range(NB_ITER):
- classifiers, predictedLabels = trainWeakClassifers(classifierName,
- DATASET,
- CLASS_LABELS,
- costMatrices,
- NB_CLASS,
- DATASET_LENGTH,
- iterIndice,
- classifier_config,
- NB_CORES)
+
+ classifiers, predictedLabels = trainWeakClassifers(classifierName,
+ DATASET,
+ CLASS_LABELS,
+ costMatrices,
+ NB_CLASS,
+ DATASET_LENGTH,
+ iterIndice,
+ classifierConfig,
+ NB_CORES, NB_VIEW)
+
predictions[iterIndice] = predictedLabels
-
+
for viewIndice in range(NB_VIEW):
-
+
edges[iterIndice, viewIndice] = computeEdge(predictions[iterIndice,
- viewIndice],
- costMatrices[iterIndice+1,
- viewIndice])
- alphas[iterIndice, viewIndice] = computeAlpha(edges[iterIndice,
+ viewIndice],
+ costMatrices[iterIndice+1,
+ viewIndice], NB_CLASS)
+
+ alphas[iterIndice, viewIndice] = computeAlpha(edges[iterIndice,
viewIndice])
-
- ds = updateDs(ds, predictions, CLASS_LABELS, NB_VIEW, DATASET_LENGTH,
- NB_CLASS, iterIndice)
- fs = updateFs(predictions, ds, alphas, fs, iterIndice, NB_VIEW,
- DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
- costMatrices = updateCostmatrices(costMatrices, fs, iterIndice,
- NB_VIEW, DATASET_LENGTH,
- NB_CLASS, CLASS_LABELS)
-
- bestView, edge = chooseView(predictions, generalCostMatrix,
- iterIndice, NB_VIEW)
-
+
+ ds = updateDs(ds, predictions, CLASS_LABELS, NB_VIEW, DATASET_LENGTH,
+ NB_CLASS, iterIndice)
+ fs = updateFs(predictions, ds, alphas, fs, iterIndice, NB_VIEW,
+ DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
+ costMatrices = updateCostmatrices(costMatrices, fs, iterIndice,
+ NB_VIEW, DATASET_LENGTH,
+ NB_CLASS, CLASS_LABELS)
+
+ bestView, edge = chooseView(predictions, generalCostMatrix,
+ iterIndice, NB_VIEW, NB_CLASS)
+
bestViews[iterIndice] = bestView
generalAlphas[iterIndice] = computeAlpha(edge)
bestClassifiers.append(classifiers[bestView])
generalFs = updateGeneralFs(generalFs, iterIndice, predictions, alphas,
- DATASET_LENGTH, NB_CLASS, bestView,
- generalAlphas, CLASS_LABELS)
- generalCostMatrix = updateGeneralCostMatrix(generalCostMatrix,
- generalFs, iterIndice,
- DATASET_LENGTH, NB_CLASS,
+ DATASET_LENGTH, NB_CLASS, bestView,
+ generalAlphas, CLASS_LABELS)
+ generalCostMatrix = updateGeneralCostMatrix(generalCostMatrix,
+ generalFs, iterIndice,
+ DATASET_LENGTH, NB_CLASS,
CLASS_LABELS)
-
+
# finalFs = computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions, bestViews, CLASS_LABELS, NB_ITER)
- return bestClassifiers, generalAlphas
+ return bestClassifiers, generalAlphas, bestViews
-def classifyMumbo(DATASET, classifiers, alphas, NB_CLASS):
- DATASET_LENGTH = len(DATASET)
+def classifyMumbo(DATASET, classifiers, alphas, views, NB_CLASS):
+ DATASET_LENGTH = len(DATASET[0])
predictedLabels = np.zeros(DATASET_LENGTH)
+
for exampleIndice in range(DATASET_LENGTH):
votes = np.zeros(NB_CLASS)
- for classifier, alpha in zip(classifiers, alphas):
- votes[int(classifier.predict(DATASET[exampleIndice]))]+=alpha
+ for classifier, alpha, view in zip(classifiers, alphas, views):
+ data = np.array([np.array(DATASET[int(view)][exampleIndice])])
+ votes[int(classifier.predict(data))]+=alpha
predictedLabels[exampleIndice] = np.argmax(votes)
return predictedLabels
\ No newline at end of file
diff --git a/Code/MultiView/Mumbo/__init__.py b/Code/MultiView/Mumbo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b937cb86efd2e73baabbcfcfe235faf8154faa04
--- /dev/null
+++ b/Code/MultiView/Mumbo/__init__.py
@@ -0,0 +1 @@
+__all__ = ["Mumbo", "Classifiers"]
\ No newline at end of file
diff --git a/multiview-machine-learning-omis.iml b/multiview-machine-learning-omis.iml
new file mode 100644
index 0000000000000000000000000000000000000000..ad3c0a365c8cd79b6f3291a01ea24ccdc75c0de0
--- /dev/null
+++ b/multiview-machine-learning-omis.iml
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+ <component name="NewModuleRootManager" inherit-compiler-output="true">
+ <exclude-output />
+ <content url="file://$MODULE_DIR$" />
+ <orderEntry type="inheritedJdk" />
+ <orderEntry type="sourceFolder" forTests="false" />
+ </component>
+</module>
\ No newline at end of file