From cc598f64411701a08ebeb8aefacdafbb71b58b9b Mon Sep 17 00:00:00 2001
From: elviswf <wf616nb@163.com>
Date: Fri, 15 Dec 2017 17:00:49 +0800
Subject: [PATCH 1/3] d

---
 .gitignore                                    | 103 +++
 .idea/inspectionProfiles/Project_Default.xml  |   6 -
 .../inspectionProfiles/profiles_settings.xml  |   4 +-
 .idea/misc.xml                                |  12 +-
 .idea/mnist_test.iml                          |  11 -
 .idea/modules.xml                             |   6 +-
 .idea/workspace.xml                           | 762 ++++--------------
 main.py                                       |  71 +-
 perceptron/binary_perceptron.py               |  12 +-
 9 files changed, 303 insertions(+), 684 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 .idea/inspectionProfiles/Project_Default.xml
 delete mode 100644 .idea/mnist_test.iml

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..868a5d3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,103 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.idea/
+
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
deleted file mode 100644
index ac21435..0000000
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="PyInterpreterInspection" enabled="false" level="WARNING" enabled_by_default="false" />
-  </profile>
-</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
index 3b31283..c23ecac 100644
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -1,7 +1,7 @@
 <component name="InspectionProjectProfileManager">
   <settings>
-    <option name="PROJECT_PROFILE" value="Project Default" />
-    <option name="USE_PROJECT_PROFILE" value="true" />
+    <option name="useProjectProfile" value="false" />
+    <option name="USE_PROJECT_PROFILE" value="false" />
     <version value="1.0" />
   </settings>
 </component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 1e72ad0..4e5fb5b 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,14 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectLevelVcsManager" settingsEditedManually="false">
-    <OptionsSetting value="true" id="Add" />
-    <OptionsSetting value="true" id="Remove" />
-    <OptionsSetting value="true" id="Checkout" />
-    <OptionsSetting value="true" id="Update" />
-    <OptionsSetting value="true" id="Status" />
-    <OptionsSetting value="true" id="Edit" />
-    <ConfirmationsSetting value="0" id="Add" />
-    <ConfirmationsSetting value="0" id="Remove" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 2.7.11 (F:\Anaconda\python.exe)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6.3 (D:\installed\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/.idea/mnist_test.iml b/.idea/mnist_test.iml
deleted file mode 100644
index d3f0c4c..0000000
--- a/.idea/mnist_test.iml
+++ /dev/null
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="sourceFolder" forTests="false" />
-    <orderEntry type="module" module-name="test" />
-    <orderEntry type="module" module-name="sci_Gastrointestinal_graph" />
-    <orderEntry type="module" module-name="sci_symptom_test" />
-    <orderEntry type="module" module-name="python_cpp" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index 179329a..984f32c 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,11 +2,7 @@
 <project version="4">
   <component name="ProjectModuleManager">
     <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/mnist_test.iml" filepath="$PROJECT_DIR$/.idea/mnist_test.iml" />
-      <module fileurl="file://E:/C++/python_cpp/.idea/python_cpp.iml" filepath="E:/C++/python_cpp/.idea/python_cpp.iml" />
-      <module fileurl="file://$PROJECT_DIR$/../sci_Gastrointestinal_graph/.idea/sci_Gastrointestinal_graph.iml" filepath="$PROJECT_DIR$/../sci_Gastrointestinal_graph/.idea/sci_Gastrointestinal_graph.iml" />
-      <module fileurl="file://$PROJECT_DIR$/../sci_symptom_test/.idea/sci_symptom_test.iml" filepath="$PROJECT_DIR$/../sci_symptom_test/.idea/sci_symptom_test.iml" />
-      <module fileurl="file://$PROJECT_DIR$/../test/.idea/test.iml" filepath="$PROJECT_DIR$/../test/.idea/test.iml" />
+      <module fileurl="file://$PROJECT_DIR$/.idea/lihang_book_algorithm.iml" filepath="$PROJECT_DIR$/.idea/lihang_book_algorithm.iml" />
     </modules>
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index c9ea43c..20fecf3 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,10 +1,19 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ChangeListManager">
-    <list default="true" id="045e9bf9-f3f7-4538-969d-2b496007acc0" name="Default" comment="" />
-    <ignored path="mnist_test.iws" />
-    <ignored path=".idea/workspace.xml" />
-    <ignored path=".idea/dataSources.local.xml" />
+    <list default="true" id="8d242249-5055-442a-98d6-159ae311f029" name="Default" comment="">
+      <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/.gitignore" />
+      <change type="DELETED" beforePath="$PROJECT_DIR$/.idea/inspectionProfiles/Project_Default.xml" afterPath="" />
+      <change type="DELETED" beforePath="$PROJECT_DIR$/.idea/mnist_test.iml" afterPath="" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/encodings.xml" afterPath="$PROJECT_DIR$/.idea/encodings.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/inspectionProfiles/profiles_settings.xml" afterPath="$PROJECT_DIR$/.idea/inspectionProfiles/profiles_settings.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/misc.xml" afterPath="$PROJECT_DIR$/.idea/misc.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/modules.xml" afterPath="$PROJECT_DIR$/.idea/modules.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/vcs.xml" afterPath="$PROJECT_DIR$/.idea/vcs.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/workspace.xml" afterPath="$PROJECT_DIR$/.idea/workspace.xml" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/main.py" afterPath="$PROJECT_DIR$/main.py" />
+      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/perceptron/binary_perceptron.py" afterPath="$PROJECT_DIR$/perceptron/binary_perceptron.py" />
+    </list>
     <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
     <option name="TRACKING_ENABLED" value="true" />
     <option name="SHOW_DIALOG" value="false" />
@@ -13,45 +22,18 @@
     <option name="LAST_RESOLUTION" value="IGNORE" />
   </component>
   <component name="CoverageDataManager">
-    <SUITE FILE_PATH="coverage/mnist_test$maxEnt_mnist.coverage" NAME="maxEnt_mnist Coverage Results" MODIFIED="1478610209328" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/maxENT" />
-    <SUITE FILE_PATH="coverage/mnist_test$competation.coverage" NAME="competation Coverage Results" MODIFIED="1478594347623" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/logistic_regression" />
-    <SUITE FILE_PATH="coverage/mnist_test$binary_perceptron.coverage" NAME="binary_perceptron Coverage Results" MODIFIED="1478593588448" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/perceptron" />
-    <SUITE FILE_PATH="coverage/mnist_test$adaboost_fakedata.coverage" NAME="adaboost_fakedata Coverage Results" MODIFIED="1479216687048" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/AdaBoost" />
-    <SUITE FILE_PATH="coverage/mnist_test$svm_vs_lr.coverage" NAME="svm_vs_lr Coverage Results" MODIFIED="1479089152052" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/svm" />
-    <SUITE FILE_PATH="coverage/mnist_test$ada_test.coverage" NAME="ada_test Coverage Results" MODIFIED="1479345417623" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/AdaBoost" />
-    <SUITE FILE_PATH="coverage/lihang_book_algorithm$maxENT.coverage" NAME="maxENT Coverage Results" MODIFIED="1478540189924" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/maxENT" />
-    <SUITE FILE_PATH="coverage/mnist_test$adaboost.coverage" NAME="adaboost Coverage Results" MODIFIED="1479296290627" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/AdaBoost" />
-    <SUITE FILE_PATH="coverage/mnist_test$logistic_regression.coverage" NAME="logistic_regression Coverage Results" MODIFIED="1478597251848" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/logistic_regression" />
-    <SUITE FILE_PATH="coverage/mnist_test$svm_test.coverage" NAME="svm_test Coverage Results" MODIFIED="1479049496214" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/svm" />
-    <SUITE FILE_PATH="coverage/mnist_test$connect_test.coverage" NAME="connect_test Coverage Results" MODIFIED="1479288486187" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="E:/C++/python_cpp" />
-    <SUITE FILE_PATH="coverage/mnist_test$w2.coverage" NAME="w2 Coverage Results" MODIFIED="1478372917488" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$USER_HOME$/Desktop" />
-    <SUITE FILE_PATH="coverage/mnist_test$rebuild_original_data.coverage" NAME="rebuild_original_data Coverage Results" MODIFIED="1478745081716" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/../sci_symptom_test/构建数据集" />
-    <SUITE FILE_PATH="coverage/mnist_test$build_dataset.coverage" NAME="build_dataset Coverage Results" MODIFIED="1478747444650" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/../sci_symptom_test/构建数据集" />
-    <SUITE FILE_PATH="coverage/mnist_test$svm_with_save_E.coverage" NAME="svm_with_save_E Coverage Results" MODIFIED="1479006266536" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/svm" />
-    <SUITE FILE_PATH="coverage/mnist_test$svm__1_.coverage" NAME="svm (1) Coverage Results" MODIFIED="1479088705262" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/svm" />
-    <SUITE FILE_PATH="coverage/mnist_test$build_features.coverage" NAME="build_features Coverage Results" MODIFIED="1478758936321" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/../sci_symptom_test/构建数据集" />
-    <SUITE FILE_PATH="coverage/mnist_test$maxENT.coverage" NAME="maxENT Coverage Results" MODIFIED="1478417048812" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/maxENT" />
-    <SUITE FILE_PATH="coverage/mnist_test$svm.coverage" NAME="svm Coverage Results" MODIFIED="1478766960147" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/../sci_symptom_test/实验" />
-    <SUITE FILE_PATH="coverage/mnist_test$generate_dataset.coverage" NAME="generate_dataset Coverage Results" MODIFIED="1479039590098" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/svm" />
-    <SUITE FILE_PATH="coverage/mnist_test$adaboost_cpp.coverage" NAME="adaboost_cpp Coverage Results" MODIFIED="1479299045247" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/AdaBoost" />
-    <SUITE FILE_PATH="coverage/mnist_test$anlysis_message.coverage" NAME="anlysis_message Coverage Results" MODIFIED="1478658655552" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/../sci_Gastrointestinal_graph" />
-  </component>
-  <component name="CreatePatchCommitExecutor">
-    <option name="PATCH_PATH" value="" />
+    <SUITE FILE_PATH="coverage/lihang_book_algorithm$binary_perceptron.coverage" NAME="binary_perceptron Coverage Results" MODIFIED="1513328143541" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/perceptron" />
   </component>
   <component name="ExecutionTargetManager" SELECTED_TARGET="default_target" />
-  <component name="FavoritesManager">
-    <favorites_list name="mnist_test" />
-  </component>
   <component name="FileEditorManager">
-    <leaf>
-      <file leaf-file-name="adaboost_cpp.py" pinned="false" current-in-tab="true">
-        <entry file="file://$PROJECT_DIR$/AdaBoost/adaboost_cpp.py">
+    <leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
+      <file leaf-file-name="main.py" pinned="false" current-in-tab="true">
+        <entry file="file://$PROJECT_DIR$/main.py">
           <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="84">
-              <caret line="61" column="0" selection-start-line="0" selection-start-column="0" selection-end-line="208" selection-end-column="0" />
+            <state relative-caret-position="572">
+              <caret line="65" column="22" lean-forward="false" selection-start-line="65" selection-start-column="22" selection-end-line="65" selection-end-column="22" />
               <folding>
-                <element signature="e#151#161#0" expanded="true" />
+                <element signature="e#0#18#0" expanded="true" />
               </folding>
             </state>
           </provider>
@@ -59,12 +41,13 @@
       </file>
     </leaf>
   </component>
-  <component name="FileTemplateManagerImpl">
-    <option name="RECENT_TEMPLATES">
-      <list>
-        <option value="Python Script" />
-      </list>
-    </option>
+  <component name="FindInProjectRecents">
+    <findStrings>
+      <find>xrange</find>
+    </findStrings>
+    <replaceStrings>
+      <replace>range</replace>
+    </replaceStrings>
   </component>
   <component name="Git.Settings">
     <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
@@ -72,30 +55,9 @@
   <component name="IdeDocumentHistory">
     <option name="CHANGED_PATHS">
       <list>
-        <option value="$USER_HOME$/Desktop/w2.py" />
-        <option value="$PROJECT_DIR$/maxENT/maxENT.py" />
+        <option value="$PROJECT_DIR$/.gitignore" />
+        <option value="$PROJECT_DIR$/main.py" />
         <option value="$PROJECT_DIR$/perceptron/binary_perceptron.py" />
-        <option value="$PROJECT_DIR$/logistic_regression/logistic_regression.py" />
-        <option value="$PROJECT_DIR$/logistic_regression/competation.py" />
-        <option value="$PROJECT_DIR$/maxENT/maxEnt_mnist.py" />
-        <option value="$PROJECT_DIR$/../test/ff.py" />
-        <option value="$PROJECT_DIR$/../sci_Gastrointestinal_graph/file_operation.py" />
-        <option value="$PROJECT_DIR$/../sci_Gastrointestinal_graph/anlysis_message.py" />
-        <option value="$PROJECT_DIR$/../sci_symptom_test/构建数据集/rebuild_original_data.py" />
-        <option value="$PROJECT_DIR$/../sci_symptom_test/构建数据集/build_dataset.py" />
-        <option value="$PROJECT_DIR$/../sci_symptom_test/构建数据集/build_features.py" />
-        <option value="$PROJECT_DIR$/../sci_symptom_test/实验/svm.py" />
-        <option value="$PROJECT_DIR$/svm/svm_with_save_E.py" />
-        <option value="$PROJECT_DIR$/svm/svm_test.py" />
-        <option value="$PROJECT_DIR$/svm/svm_vs_lr.py" />
-        <option value="$PROJECT_DIR$/svm/svm.py" />
-        <option value="$PROJECT_DIR$/svm/generate_dataset.py" />
-        <option value="$PROJECT_DIR$/AdaBoost/adaboost_fakedata.py" />
-        <option value="$PROJECT_DIR$/AdaBoost/generate_dataset.py" />
-        <option value="E:/C++/python_cpp/connect_test.py" />
-        <option value="$PROJECT_DIR$/AdaBoost/adaboost.py" />
-        <option value="$PROJECT_DIR$/AdaBoost/adaboost_cpp.py" />
-        <option value="$PROJECT_DIR$/AdaBoost/ada_test.py" />
       </list>
     </option>
   </component>
@@ -108,19 +70,9 @@
   <component name="ProjectFrameBounds">
     <option name="x" value="-8" />
     <option name="y" value="-8" />
-    <option name="width" value="1936" />
+    <option name="width" value="1924" />
     <option name="height" value="1056" />
   </component>
-  <component name="ProjectLevelVcsManager" settingsEditedManually="false">
-    <OptionsSetting value="true" id="Add" />
-    <OptionsSetting value="true" id="Remove" />
-    <OptionsSetting value="true" id="Checkout" />
-    <OptionsSetting value="true" id="Update" />
-    <OptionsSetting value="true" id="Status" />
-    <OptionsSetting value="true" id="Edit" />
-    <ConfirmationsSetting value="0" id="Add" />
-    <ConfirmationsSetting value="0" id="Remove" />
-  </component>
   <component name="ProjectView">
     <navigator currentView="ProjectPane" proportions="" version="1">
       <flattenPackages />
@@ -136,108 +88,88 @@
       <foldersAlwaysOnTop value="true" />
     </navigator>
     <panes>
-      <pane id="Scope" />
+      <pane id="Scratches" />
       <pane id="ProjectPane">
-        <subPane />
+        <subPane>
+          <PATH>
+            <PATH_ELEMENT>
+              <option name="myItemId" value="lihang_book_algorithm" />
+              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
+            </PATH_ELEMENT>
+            <PATH_ELEMENT>
+              <option name="myItemId" value="lihang_book_algorithm" />
+              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
+            </PATH_ELEMENT>
+          </PATH>
+          <PATH>
+            <PATH_ELEMENT>
+              <option name="myItemId" value="lihang_book_algorithm" />
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+        <RuleState>
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       <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
       <method />
     </configuration>
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@@ -251,7 +183,7 @@
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+      <module name="lihang_book_algorithm" />
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       <option name="SETTINGS_FILE" value="" />
@@ -260,6 +192,19 @@
       <option name="OPTIONS" value="" />
       <method />
     </configuration>
+    <configuration default="true" type="JavaScriptTestRunnerJest" factoryName="Jest">
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+      <working-dir value="" />
+      <envs />
+      <scope-kind value="ALL" />
+      <method />
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+    <configuration default="true" type="JavaScriptTestRunnerProtractor" factoryName="Protractor">
+      <config-file value="" />
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+      <envs />
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+    </configuration>
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@@ -272,7 +217,7 @@
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@@ -302,11 +247,12 @@
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@@ -319,7 +265,7 @@
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+      <module name="lihang_book_algorithm" />
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@@ -332,32 +278,12 @@
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+      <command value="run" />
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@@ -367,7 +293,7 @@
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+      <option name="_new_additionalArguments" value="&quot;&quot;" />
+      <option name="_new_target" value="&quot;.&quot;" />
+      <option name="_new_targetType" value="&quot;PATH&quot;" />
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diff --git a/main.py b/main.py
index 7b9aa13..15747ad 100644
--- a/main.py
+++ b/main.py
@@ -2,71 +2,74 @@
 import struct
 import matplotlib.pyplot as plt
 
-from perceptron import *
+from perceptron.binary_perceptron import Perceptron
+
 
 def loadImageSet(which=0):
-    print "load image set"
-    binfile=None
-    if which==0:
+    print("load image set")
+    binfile = None
+    if which == 0:
         binfile = open("data/train-images.idx3-ubyte", 'rb')
     else:
-        binfile=  open("data/t10k-images.idx3-ubyte", 'rb')
+        binfile = open("data/t10k-images.idx3-ubyte", 'rb')
     buffers = binfile.read()
 
-    head = struct.unpack_from('>IIII' , buffers ,0)
-    print "head,",head
+    head = struct.unpack_from('>IIII', buffers, 0)
+    print("head,", head)
 
-    offset=struct.calcsize('>IIII')
-    imgNum=head[1]
-    width=head[2]
-    height=head[3]
-    #[60000]*28*28
-    bits=imgNum*width*height
-    bitsString='>'+str(bits)+'B' #like '>47040000B'
+    offset = struct.calcsize('>IIII')
+    imgNum = head[1]
+    width = head[2]
+    height = head[3]
+    # [60000]*28*28
+    bits = imgNum * width * height
+    bitsString = '>' + str(bits) + 'B'  # like '>47040000B'
 
-    imgs=struct.unpack_from(bitsString,buffers,offset)
+    imgs = struct.unpack_from(bitsString, buffers, offset)
 
     binfile.close()
-    imgs=np.reshape(imgs,[imgNum,width,height])
-    print "load imgs finished"
+    imgs = np.reshape(imgs, [imgNum, width, height])
+    print("load imgs finished")
     return imgs
 
+
 def loadLabelSet(which=0):
-    print "load label set"
-    binfile=None
-    if which==0:
+    print("load label set")
+    binfile = None
+    if which == 0:
         binfile = open("data/train-labels.idx1-ubyte", 'rb')
     else:
-        binfile=  open("data/t10k-labels.idx1-ubyte", 'rb')
+        binfile = open("data/t10k-labels.idx1-ubyte", 'rb')
     buffers = binfile.read()
 
-    head = struct.unpack_from('>II' , buffers ,0)
-    print "head,",head
-    imgNum=head[1]
+    head = struct.unpack_from('>II', buffers, 0)
+    print("head,", head)
+    imgNum = head[1]
 
     offset = struct.calcsize('>II')
-    numString='>'+str(imgNum)+"B"
-    labels= struct.unpack_from(numString , buffers , offset)
+    numString = '>' + str(imgNum) + "B"
+    labels = struct.unpack_from(numString, buffers, offset)
     binfile.close()
-    labels=np.reshape(labels,[imgNum,1])
+    labels = np.reshape(labels, [imgNum, 1])
 
-    #print labels
-    print 'load label finished'
+    # print labels
+    print('load label finished')
     return labels
 
-if __name__=="__main__":
+
+if __name__ == "__main__":
     imgs = loadImageSet()
     labels = loadLabelSet()
 
     index = 10
 
-    print imgs[index]
-    print labels[index]
+    print(imgs[index])
+    print(labels[index])
 
     fig = plt.figure()
     plotwindow = fig.add_subplot(111)
-    plt.imshow(imgs[index] , cmap='gray')
+    plt.imshow(imgs[index], cmap='gray')
     # plt.show()
 
     perceptron = Perceptron()
-    perceptron.hog_test(imgs[index])
\ No newline at end of file
+    perceptron.hog_test(imgs[index])
diff --git a/perceptron/binary_perceptron.py b/perceptron/binary_perceptron.py
index e6ff6e0..38d81ba 100644
--- a/perceptron/binary_perceptron.py
+++ b/perceptron/binary_perceptron.py
@@ -4,15 +4,13 @@
 # @Email:  wendesi@foxmail.com
 # @Last modified by:   WenDesi
 # @Last modified time: 08-11-16
-
-
 import pandas as pd
 import numpy as np
 import cv2
 import random
 import time
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 
@@ -23,7 +21,7 @@ def __init__(self):
         self.max_iteration = 5000
 
     def predict_(self, x):
-        wx = sum([self.w[j] * x[j] for j in xrange(len(self.w))])
+        wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
         return int(wx > 0)
 
     def train(self, features, labels):
@@ -37,7 +35,7 @@ def train(self, features, labels):
             x = list(features[index])
             x.append(1.0)
             y = 2 * labels[index] - 1
-            wx = sum([self.w[j] * x[j] for j in xrange(len(self.w))])
+            wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
 
             if wx * y > 0:
                 correct_count += 1
@@ -45,7 +43,7 @@ def train(self, features, labels):
                     break
                 continue
 
-            for i in xrange(len(self.w)):
+            for i in range(len(self.w)):
                 self.w[i] += self.learning_step * (y * x[i])
 
     def predict(self,features):
@@ -59,7 +57,7 @@ def predict(self,features):
 
 if __name__ == '__main__':
 
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 

From 524da404f69a361b952b93ec07c068b5949b8388 Mon Sep 17 00:00:00 2001
From: elviswf <wf616nb@163.com>
Date: Fri, 15 Dec 2017 17:01:57 +0800
Subject: [PATCH 2/3] d

---
 .idea/encodings.xml                           |   6 -
 .../inspectionProfiles/profiles_settings.xml  |   7 -
 .idea/misc.xml                                |   4 -
 .idea/modules.xml                             |   8 -
 .idea/vcs.xml                                 |   6 -
 .idea/workspace.xml                           | 429 ------------------
 6 files changed, 460 deletions(-)
 delete mode 100644 .idea/encodings.xml
 delete mode 100644 .idea/inspectionProfiles/profiles_settings.xml
 delete mode 100644 .idea/misc.xml
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/vcs.xml
 delete mode 100644 .idea/workspace.xml

diff --git a/.idea/encodings.xml b/.idea/encodings.xml
deleted file mode 100644
index 97626ba..0000000
--- a/.idea/encodings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Encoding">
-    <file url="PROJECT" charset="UTF-8" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index c23ecac..0000000
--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="useProjectProfile" value="false" />
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
deleted file mode 100644
index 4e5fb5b..0000000
--- a/.idea/misc.xml
+++ /dev/null
@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6.3 (D:\installed\Anaconda3\python.exe)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index 984f32c..0000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/lihang_book_algorithm.iml" filepath="$PROJECT_DIR$/.idea/lihang_book_algorithm.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
deleted file mode 100644
index 94a25f7..0000000
--- a/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index 20fecf3..0000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,429 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ChangeListManager">
-    <list default="true" id="8d242249-5055-442a-98d6-159ae311f029" name="Default" comment="">
-      <change type="NEW" beforePath="" afterPath="$PROJECT_DIR$/.gitignore" />
-      <change type="DELETED" beforePath="$PROJECT_DIR$/.idea/inspectionProfiles/Project_Default.xml" afterPath="" />
-      <change type="DELETED" beforePath="$PROJECT_DIR$/.idea/mnist_test.iml" afterPath="" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/encodings.xml" afterPath="$PROJECT_DIR$/.idea/encodings.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/inspectionProfiles/profiles_settings.xml" afterPath="$PROJECT_DIR$/.idea/inspectionProfiles/profiles_settings.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/misc.xml" afterPath="$PROJECT_DIR$/.idea/misc.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/modules.xml" afterPath="$PROJECT_DIR$/.idea/modules.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/vcs.xml" afterPath="$PROJECT_DIR$/.idea/vcs.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/.idea/workspace.xml" afterPath="$PROJECT_DIR$/.idea/workspace.xml" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/main.py" afterPath="$PROJECT_DIR$/main.py" />
-      <change type="MODIFICATION" beforePath="$PROJECT_DIR$/perceptron/binary_perceptron.py" afterPath="$PROJECT_DIR$/perceptron/binary_perceptron.py" />
-    </list>
-    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
-    <option name="TRACKING_ENABLED" value="true" />
-    <option name="SHOW_DIALOG" value="false" />
-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
-  </component>
-  <component name="CoverageDataManager">
-    <SUITE FILE_PATH="coverage/lihang_book_algorithm$binary_perceptron.coverage" NAME="binary_perceptron Coverage Results" MODIFIED="1513328143541" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/perceptron" />
-  </component>
-  <component name="ExecutionTargetManager" SELECTED_TARGET="default_target" />
-  <component name="FileEditorManager">
-    <leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
-      <file leaf-file-name="main.py" pinned="false" current-in-tab="true">
-        <entry file="file://$PROJECT_DIR$/main.py">
-          <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="572">
-              <caret line="65" column="22" lean-forward="false" selection-start-line="65" selection-start-column="22" selection-end-line="65" selection-end-column="22" />
-              <folding>
-                <element signature="e#0#18#0" expanded="true" />
-              </folding>
-            </state>
-          </provider>
-        </entry>
-      </file>
-    </leaf>
-  </component>
-  <component name="FindInProjectRecents">
-    <findStrings>
-      <find>xrange</find>
-    </findStrings>
-    <replaceStrings>
-      <replace>range</replace>
-    </replaceStrings>
-  </component>
-  <component name="Git.Settings">
-    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
-  </component>
-  <component name="IdeDocumentHistory">
-    <option name="CHANGED_PATHS">
-      <list>
-        <option value="$PROJECT_DIR$/.gitignore" />
-        <option value="$PROJECT_DIR$/main.py" />
-        <option value="$PROJECT_DIR$/perceptron/binary_perceptron.py" />
-      </list>
-    </option>
-  </component>
-  <component name="JsBuildToolGruntFileManager" detection-done="true" sorting="DEFINITION_ORDER" />
-  <component name="JsBuildToolPackageJson" detection-done="true" sorting="DEFINITION_ORDER" />
-  <component name="JsGulpfileManager">
-    <detection-done>true</detection-done>
-    <sorting>DEFINITION_ORDER</sorting>
-  </component>
-  <component name="ProjectFrameBounds">
-    <option name="x" value="-8" />
-    <option name="y" value="-8" />
-    <option name="width" value="1924" />
-    <option name="height" value="1056" />
-  </component>
-  <component name="ProjectView">
-    <navigator currentView="ProjectPane" proportions="" version="1">
-      <flattenPackages />
-      <showMembers />
-      <showModules />
-      <showLibraryContents />
-      <hideEmptyPackages />
-      <abbreviatePackageNames />
-      <autoscrollToSource />
-      <autoscrollFromSource />
-      <sortByType />
-      <manualOrder />
-      <foldersAlwaysOnTop value="true" />
-    </navigator>
-    <panes>
-      <pane id="Scratches" />
-      <pane id="ProjectPane">
-        <subPane>
-          <PATH>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
-            </PATH_ELEMENT>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
-            </PATH_ELEMENT>
-          </PATH>
-          <PATH>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
-            </PATH_ELEMENT>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
-            </PATH_ELEMENT>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="perceptron" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
-            </PATH_ELEMENT>
-          </PATH>
-          <PATH>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.ProjectViewProjectNode" />
-            </PATH_ELEMENT>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="lihang_book_algorithm" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
-            </PATH_ELEMENT>
-            <PATH_ELEMENT>
-              <option name="myItemId" value="data" />
-              <option name="myItemType" value="com.intellij.ide.projectView.impl.nodes.PsiDirectoryNode" />
-            </PATH_ELEMENT>
-          </PATH>
-        </subPane>
-      </pane>
-      <pane id="Scope" />
-    </panes>
-  </component>
-  <component name="PropertiesComponent">
-    <property name="settings.editor.selected.configurable" value="configurable.group.build" />
-    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
-    <property name="WebServerToolWindowFactoryState" value="true" />
-    <property name="add_unversioned_files" value="true" />
-  </component>
-  <component name="RunDashboard">
-    <option name="ruleStates">
-      <list>
-        <RuleState>
-          <option name="name" value="ConfigurationTypeDashboardGroupingRule" />
-        </RuleState>
-        <RuleState>
-          <option name="name" value="StatusDashboardGroupingRule" />
-        </RuleState>
-      </list>
-    </option>
-  </component>
-  <component name="RunManager" selected="Python.binary_perceptron">
-    <configuration default="false" name="binary_perceptron" type="PythonConfigurationType" factoryName="Python" temporary="true">
-      <option name="INTERPRETER_OPTIONS" value="" />
-      <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
-      <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/perceptron" />
-      <option name="IS_MODULE_SDK" value="true" />
-      <option name="ADD_CONTENT_ROOTS" value="true" />
-      <option name="ADD_SOURCE_ROOTS" value="true" />
-      <module name="lihang_book_algorithm" />
-      <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/perceptron/binary_perceptron.py" />
-      <option name="PARAMETERS" value="" />
-      <option name="SHOW_COMMAND_LINE" value="false" />
-      <option name="EMULATE_TERMINAL" value="false" />
-      <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="lihang_book_algorithm" />
-      <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="JavaScriptTestRunnerJest" factoryName="Jest">
-      <node-interpreter value="project" />
-      <working-dir value="" />
-      <envs />
-      <scope-kind value="ALL" />
-      <method />
-    </configuration>
-    <configuration default="true" type="JavaScriptTestRunnerProtractor" factoryName="Protractor">
-      <config-file value="" />
-      <node-interpreter value="project" />
-      <envs />
-      <method />
-    </configuration>
-    <configuration default="true" type="JavascriptDebugType" factoryName="JavaScript Debug">
-      <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="lihang_book_algorithm" />
-      <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="lihang_book_algorithm" />
-      <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" />
-      <envs>
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-      <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="" />
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-      <option name="ADD_SOURCE_ROOTS" value="true" />
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-      <option name="SHOW_COMMAND_LINE" value="false" />
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-      <method />
-    </configuration>
-    <configuration default="true" type="Tox" factoryName="Tox">
-      <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" />
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-      <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
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-      <method />
-    </configuration>
-    <configuration default="true" type="js.build_tools.gulp" factoryName="Gulp.js">
-      <node-interpreter>project</node-interpreter>
-      <node-options />
-      <gulpfile />
-      <tasks />
-      <arguments />
-      <envs />
-      <method />
-    </configuration>
-    <configuration default="true" type="js.build_tools.npm" factoryName="npm">
-      <command value="run" />
-      <scripts />
-      <node-interpreter value="project" />
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-    <configuration default="true" type="tests" factoryName="Doctests">
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-      <option name="TEST_TYPE" value="TEST_SCRIPT" />
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From b1cfed16c67e39bc63fc0493f72e71520a915489 Mon Sep 17 00:00:00 2001
From: elviswf <wf616nb@163.com>
Date: Fri, 15 Dec 2017 19:03:54 +0800
Subject: [PATCH 3/3] add

---
 AdaBoost/__init__.py                       |   9 ++
 AdaBoost/adaboost.py                       | 157 ++++++++++-----------
 AdaBoost/adaboost_cpp.py                   |  21 +--
 decision_tree/__init__.py                  |   9 ++
 decision_tree/decision_tree.py             |  99 ++++++-------
 hmm/__init__.py                            |   9 ++
 hmm/hmm.py                                 | 112 ++++++++-------
 knn/__init__.py                            |   9 ++
 knn/distance_test.py                       |   8 +-
 knn/knn.py                                 |  64 +++++----
 logistic_regression/__init__.py            |   9 ++
 logistic_regression/binary_perceptron.py   |  28 ++--
 logistic_regression/competation.py         |  18 +--
 logistic_regression/logistic_regression.py |  46 +++---
 maxENT/__init__.py                         |   9 ++
 maxENT/maxENT.py                           |  43 +++---
 naive_bayes/__init__.py                    |   9 ++
 naive_bayes/naive_bayes.py                 |   2 +-
 perceptron/__init__.py                     |   9 ++
 perceptron/binary_perceptron.py            |  16 +--
 softmax/__init__.py                        |   9 ++
 svm/__init__.py                            |   9 ++
 svm/generate_dataset.py                    |  72 +++++-----
 svm/svm.py                                 |  94 ++++++------
 24 files changed, 468 insertions(+), 402 deletions(-)
 create mode 100644 AdaBoost/__init__.py
 create mode 100644 decision_tree/__init__.py
 create mode 100644 hmm/__init__.py
 create mode 100644 knn/__init__.py
 create mode 100644 logistic_regression/__init__.py
 create mode 100644 maxENT/__init__.py
 create mode 100644 naive_bayes/__init__.py
 create mode 100644 perceptron/__init__.py
 create mode 100644 softmax/__init__.py
 create mode 100644 svm/__init__.py

diff --git a/AdaBoost/__init__.py b/AdaBoost/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/AdaBoost/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/AdaBoost/adaboost.py b/AdaBoost/adaboost.py
index 1851f3b..3ca7057 100644
--- a/AdaBoost/adaboost.py
+++ b/AdaBoost/adaboost.py
@@ -12,11 +12,12 @@
 import numpy as np
 import pandas as pd
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 sign_time_count = 0
 
+
 class Sign(object):
     '''
     阈值分类器
@@ -29,14 +30,14 @@ class Sign(object):
     因为是针对已经二值化后的MNIST数据集，所以v的取值只有3个 {0,1,2}
     '''
 
-    def __init__(self,features,labels,w):
-        self.X = features               # 训练数据特征
-        self.Y = labels                 # 训练数据的标签
-        self.N = len(labels)            # 训练数据大小
+    def __init__(self, features, labels, w):
+        self.X = features  # 训练数据特征
+        self.Y = labels  # 训练数据的标签
+        self.N = len(labels)  # 训练数据大小
 
-        self.w = w                      # 训练数据权值分布
+        self.w = w  # 训练数据权值分布
 
-        self.indexes = [0,1,2]          # 阈值轴可选范围
+        self.indexes = [0, 1, 2]  # 阈值轴可选范围
 
     def _train_less_than_(self):
         '''
@@ -48,21 +49,19 @@ def _train_less_than_(self):
 
         for i in self.indexes:
             score = 0
-            for j in xrange(self.N):
+            for j in range(self.N):
                 val = -1
-                if self.X[j]<i:
+                if self.X[j] < i:
                     val = 1
 
-                if val*self.Y[j]<0:
+                if val * self.Y[j] < 0:
                     score += self.w[j]
 
             if score < error_score:
                 index = i
                 error_score = score
 
-        return index,error_score
-
-
+        return index, error_score
 
     def _train_more_than_(self):
         '''
@@ -74,27 +73,27 @@ def _train_more_than_(self):
 
         for i in self.indexes:
             score = 0
-            for j in xrange(self.N):
+            for j in range(self.N):
                 val = 1
-                if self.X[j]<i:
+                if self.X[j] < i:
                     val = -1
 
-                if val*self.Y[j]<0:
+                if val * self.Y[j] < 0:
                     score += self.w[j]
 
             if score < error_score:
                 index = i
                 error_score = score
 
-        return index,error_score
+        return index, error_score
 
     def train(self):
         global sign_time_count
         time1 = time.time()
-        less_index,less_score = self._train_less_than_()
-        more_index,more_score = self._train_more_than_()
+        less_index, less_score = self._train_less_than_()
+        more_index, more_score = self._train_more_than_()
         time2 = time.time()
-        sign_time_count += time2-time1
+        sign_time_count += time2 - time1
 
         if less_score < more_score:
             self.is_less = True
@@ -106,115 +105,112 @@ def train(self):
             self.index = more_index
             return more_score
 
-    def predict(self,feature):
-        if self.is_less>0:
-            if feature<self.index:
+    def predict(self, feature):
+        if self.is_less > 0:
+            if feature < self.index:
                 return 1.0
             else:
                 return -1.0
         else:
-            if feature<self.index:
+            if feature < self.index:
                 return -1.0
             else:
                 return 1.0
 
 
 class AdaBoost(object):
-
     def __init__(self):
         pass
 
-    def _init_parameters_(self,features,labels):
-        self.X = features                           # 训练集特征
-        self.Y = labels                             # 训练集标签
+    def _init_parameters_(self, features, labels):
+        self.X = features  # 训练集特征
+        self.Y = labels  # 训练集标签
 
-        self.n = len(features[0])                   # 特征维度
-        self.N = len(features)                      # 训练集大小
-        self.M = 10                                 # 分类器数目
+        self.n = len(features[0])  # 特征维度
+        self.N = len(features)  # 训练集大小
+        self.M = 10  # 分类器数目
 
-        self.w = [1.0/self.N]*self.N                # 训练集的权值分布
-        self.alpha = []                             # 分类器系数  公式8.2
-        self.classifier = []                        # (维度，分类器)，针对当前维度的分类器
+        self.w = [1.0 / self.N] * self.N  # 训练集的权值分布
+        self.alpha = []  # 分类器系数  公式8.2
+        self.classifier = []  # (维度，分类器)，针对当前维度的分类器
 
-    def _w_(self,index,classifier,i):
+    def _w_(self, index, classifier, i):
         '''
         公式8.4不算Zm
         '''
 
-        return self.w[i]*math.exp(-self.alpha[-1]*self.Y[i]*classifier.predict(self.X[i][index]))
+        return self.w[i] * math.exp(-self.alpha[-1] * self.Y[i] * classifier.predict(self.X[i][index]))
 
-    def _Z_(self,index,classifier):
+    def _Z_(self, index, classifier):
         '''
         公式8.5
         '''
 
         Z = 0
 
-        for i in xrange(self.N):
-            Z += self._w_(index,classifier,i)
+        for i in range(self.N):
+            Z += self._w_(index, classifier, i)
 
         return Z
 
-    def train(self,features,labels):
+    def train(self, features, labels):
 
-        self._init_parameters_(features,labels)
+        self._init_parameters_(features, labels)
 
-        for times in xrange(self.M):
+        for times in range(self.M):
             logging.debug('iterater %d' % times)
 
             time1 = time.time()
             map_time = 0
 
-            best_classifier = (100000,None,None)        #(误差率,针对的特征，分类器)
-            for i in xrange(self.n):
+            best_classifier = (100000, None, None)  # (误差率,针对的特征，分类器)
+            for i in range(self.n):
                 map_time -= time.time()
-                features = map(lambda x:x[i],self.X)
+                features = map(lambda x: x[i], self.X)
                 map_time += time.time()
-                classifier = Sign(features,self.Y,self.w)
+                classifier = Sign(features, self.Y, self.w)
                 error_score = classifier.train()
 
                 if error_score < best_classifier[0]:
-                    best_classifier = (error_score,i,classifier)
+                    best_classifier = (error_score, i, classifier)
 
             em = best_classifier[0]
 
             # 分析用，之后删除 开始
-            print 'em is %s, index is %d' % (str(em),best_classifier[1])
+            print('em is %s, index is %d' % (str(em), best_classifier[1]))
             time2 = time.time()
             global sign_time_count
-            print '总运行时间:%s, 那两段关键代码运行时间:%s, map的时间是:%s' % (str(time2-time1),str(sign_time_count),str(map_time))
+            print('总运行时间:%s, 那两段关键代码运行时间:%s, map的时间是:%s' % (str(time2 - time1), str(sign_time_count), str(map_time)))
             sign_time_count = 0
             # 分析用，之后删除  结束
 
-            if em==0:
+            if em == 0:
                 self.alpha.append(100)
             else:
-                self.alpha.append(0.5*math.log((1-em)/em))
+                self.alpha.append(0.5 * math.log((1 - em) / em))
 
             self.classifier.append(best_classifier[1:])
 
-            Z = self._Z_(best_classifier[1],best_classifier[2])
+            Z = self._Z_(best_classifier[1], best_classifier[2])
 
             # 计算训练集权值分布 8.4
-            for i in xrange(self.N):
-                self.w[i] = self._w_(best_classifier[1],best_classifier[2],i)/Z
+            for i in range(self.N):
+                self.w[i] = self._w_(best_classifier[1], best_classifier[2], i) / Z
 
-    def _predict_(self,feature):
+    def _predict_(self, feature):
 
         result = 0.0
-        for i in xrange(self.M):
+        for i in range(self.M):
             index = self.classifier[i][0]
             classifier = self.classifier[i][1]
 
-            result += self.alpha[i]*classifier.predict(feature[index])
+            result += self.alpha[i] * classifier.predict(feature[index])
 
-        if result>0:
+        if result > 0:
             return 1
         return -1
 
-
-
-    def predict(self,features):
+    def predict(self, features):
         results = []
 
         for feature in features:
@@ -222,17 +218,19 @@ def predict(self,features):
 
         return results
 
+
 # 二值化
 def binaryzation(img):
     cv_img = img.astype(np.uint8)
-    cv2.threshold(cv_img,50,1,cv2.cv.CV_THRESH_BINARY_INV,cv_img)
+    cv2.threshold(cv_img, 50, 1, cv2.cv.CV_THRESH_BINARY_INV, cv_img)
     return cv_img
 
+
 def binaryzation_features(trainset):
     features = []
 
     for img in trainset:
-        img = np.reshape(img,(28,28))
+        img = np.reshape(img, (28, 28))
         cv_img = img.astype(np.uint8)
 
         img_b = binaryzation(cv_img)
@@ -240,45 +238,46 @@ def binaryzation_features(trainset):
         features.append(img_b)
 
     features = np.array(features)
-    features = np.reshape(features,(-1,784))
+    features = np.reshape(features, (-1, 784))
 
     return features
 
+
 if __name__ == '__main__':
     logger = logging.getLogger()
     logger.setLevel(logging.DEBUG)
 
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
-    raw_data = pd.read_csv('../data/train_binary.csv',header=0)
+    raw_data = pd.read_csv('../data/train_binary.csv', header=0)
     data = raw_data.values
 
-    imgs = data[0::,1::]
-    labels = data[::,0]
-
+    imgs = data[0::, 1::]
+    labels = data[::, 0]
 
     # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
     features = binaryzation_features(imgs)
-    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.5, random_state=0)
+    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.5,
+                                                                                random_state=0)
 
     time_2 = time.time()
-    print 'read data cost ',time_2 - time_1,' second','\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
-    train_labels = map(lambda x:2*x-1,train_labels)
+    print('Start training')
+    train_labels = map(lambda x: 2 * x - 1, train_labels)
     ada = AdaBoost()
     ada.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ',time_3 - time_2,' second','\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = ada.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ',time_4 - time_3,' second','\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
-    test_labels = map(lambda x:2*x-1,test_labels)
-    score = accuracy_score(test_labels,test_predict)
-    print "The accruacy socre is ", score
+    test_labels = map(lambda x: 2 * x - 1, test_labels)
+    score = accuracy_score(test_labels, test_predict)
+    print("The accruacy socre is ", score)
diff --git a/AdaBoost/adaboost_cpp.py b/AdaBoost/adaboost_cpp.py
index d803cba..20d5ee1 100644
--- a/AdaBoost/adaboost_cpp.py
+++ b/AdaBoost/adaboost_cpp.py
@@ -13,9 +13,9 @@
 import numpy as np
 import pandas as pd
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
-
+"""
 sign_time_count = 0
 
 class Sign(object):
@@ -47,13 +47,13 @@ def rebuild_X(self,X):
         length = self.n*self.N
         self.X_matrix = (ctypes.c_int * length)()
 
-        for i in xrange(self.n):
-            for j in xrange(self.N):
+        for i in range(self.n):
+            for j in range(self.N):
                 self.X_matrix[i*self.N+j] = X[j][i]
 
     def rebuild_Y(self,Y):
         self.C_Y = (ctypes.c_int * self.N)()
-        for i in xrange(self.N):
+        for i in range(self.N):
             self.C_Y[i] = Y[i]
 
 
@@ -78,14 +78,14 @@ def _w_(self,index,classifier,i):
     def _Z_(self,index,classifier):
         Z = 0
 
-        for i in xrange(self.N):
+        for i in range(self.N):
             Z += self._w_(index,classifier,i)
 
         return Z
 
     def build_c_w(self):
         C_w = (ctypes.c_double * self.N)()
-        for i in xrange(self.N):
+        for i in range(self.N):
             C_w[i] = ctypes.c_double(self.w[i])
         return C_w
 
@@ -93,7 +93,7 @@ def train(self,features,labels):
 
         self._init_parameters_(features,labels)
 
-        for times in xrange(self.M):
+        for times in range(self.M):
             logging.debug('iterater %d' % times)
 
 
@@ -120,13 +120,13 @@ def train(self,features,labels):
 
             Z = self._Z_(best_classifier[1],best_classifier[2])
 
-            for i in xrange(self.N):
+            for i in range(self.N):
                 self.w[i] = self._w_(best_classifier[1],best_classifier[2],i)/Z
 
     def _predict_(self,feature):
 
         result = 0.0
-        for i in xrange(self.M):
+        for i in range(self.M):
             index = self.classifier[i][0]
             classifier = self.classifier[i][1]
 
@@ -206,3 +206,4 @@ def binaryzation_features(trainset):
     test_labels = map(lambda x:2*x-1,test_labels)
     score = accuracy_score(test_labels,test_predict)
     print "The accruacy socre is ", score
+"""
\ No newline at end of file
diff --git a/decision_tree/__init__.py b/decision_tree/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/decision_tree/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/decision_tree/decision_tree.py b/decision_tree/decision_tree.py
index d1f837f..62de895 100644
--- a/decision_tree/decision_tree.py
+++ b/decision_tree/decision_tree.py
@@ -1,18 +1,16 @@
-#encoding=utf-8
-
+# encoding=utf-8
 import cv2
 import time
 import logging
 import numpy as np
 import pandas as pd
 
-
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
-
 total_class = 10
 
+
 def log(func):
     def wrapper(*args, **kwargs):
         start_time = time.time()
@@ -20,24 +18,26 @@ def wrapper(*args, **kwargs):
         ret = func(*args, **kwargs)
 
         end_time = time.time()
-        logging.debug('end %s(), cost %s seconds' % (func.__name__,end_time-start_time))
+        logging.debug('end %s(), cost %s seconds' % (func.__name__, end_time - start_time))
 
         return ret
+
     return wrapper
 
 
 # 二值化
 def binaryzation(img):
     cv_img = img.astype(np.uint8)
-    cv2.threshold(cv_img,50,1,cv2.cv.CV_THRESH_BINARY_INV,cv_img)
+    cv2.threshold(cv_img, 50, 1, cv2.THRESH_BINARY_INV, cv_img)
     return cv_img
 
+
 @log
 def binaryzation_features(trainset):
     features = []
 
     for img in trainset:
-        img = np.reshape(img,(28,28))
+        img = np.reshape(img, (28, 28))
         cv_img = img.astype(np.uint8)
 
         img_b = binaryzation(cv_img)
@@ -45,28 +45,29 @@ def binaryzation_features(trainset):
         features.append(img_b)
 
     features = np.array(features)
-    features = np.reshape(features,(-1,784))
+    features = np.reshape(features, (-1, 784))
 
     return features
 
 
 class Tree(object):
-    def __init__(self,node_type,Class = None, feature = None):
+    def __init__(self, node_type, Class=None, feature=None):
         self.node_type = node_type
         self.dict = {}
         self.Class = Class
         self.feature = feature
 
-    def add_tree(self,val,tree):
+    def add_tree(self, val, tree):
         self.dict[val] = tree
 
-    def predict(self,features):
+    def predict(self, features):
         if self.node_type == 'leaf':
             return self.Class
 
         tree = self.dict[features[self.feature]]
         return tree.predict(features)
 
+
 def calc_ent(x):
     """
         calculate shanno ent of x
@@ -81,6 +82,7 @@ def calc_ent(x):
 
     return ent
 
+
 def calc_condition_ent(x, y):
     """
         calculate ent H(y|x)
@@ -96,7 +98,8 @@ def calc_condition_ent(x, y):
 
     return ent
 
-def calc_ent_grap(x,y):
+
+def calc_ent_grap(x, y):
     """
         calculate ent grap
     """
@@ -107,7 +110,8 @@ def calc_ent_grap(x,y):
 
     return ent_grap
 
-def recurse_train(train_set,train_label,features,epsilon):
+
+def recurse_train(train_set, train_label, features, epsilon):
     global total_class
 
     LEAF = 'leaf'
@@ -116,13 +120,14 @@ def recurse_train(train_set,train_label,features,epsilon):
     # 步骤1——如果train_set中的所有实例都属于同一类Ck
     label_set = set(train_label)
     if len(label_set) == 1:
-        return Tree(LEAF,Class = label_set.pop())
+        return Tree(LEAF, Class=label_set.pop())
 
     # 步骤2——如果features为空
-    (max_class,max_len) = max([(i,len(filter(lambda x:x==i,train_label))) for i in xrange(total_class)],key = lambda x:x[1])
-
+    (max_class, max_len) = max([(i, len(list(filter(lambda x: x == i, train_label)))) for i in range(total_class)],
+                               key=lambda x: x[1])
+    features= list(features)
     if len(features) == 0:
-        return Tree(LEAF,Class = max_class)
+        return Tree(LEAF, Class=max_class)
 
     # 步骤3——计算信息增益
     max_feature = 0
@@ -131,44 +136,45 @@ def recurse_train(train_set,train_label,features,epsilon):
     D = train_label
     HD = calc_ent(D)
     for feature in features:
-        A = np.array(train_set[:,feature].flat)
-        gda = HD - calc_condition_ent(A,D)
+        A = np.array(train_set[:, feature].flat)
+        gda = HD - calc_condition_ent(A, D)
 
         if gda > max_gda:
-            max_gda,max_feature = gda,feature
+            max_gda, max_feature = gda, feature
 
     # 步骤4——小于阈值
     if max_gda < epsilon:
-        return Tree(LEAF,Class = max_class)
+        return Tree(LEAF, Class=max_class)
 
     # 步骤5——构建非空子集
-    sub_features = filter(lambda x:x!=max_feature,features)
-    tree = Tree(INTERNAL,feature=max_feature)
+    sub_features = filter(lambda x: x != max_feature, features)
+    tree = Tree(INTERNAL, feature=max_feature)
 
-    feature_col = np.array(train_set[:,max_feature].flat)
+    feature_col = np.array(train_set[:, max_feature].flat)
     feature_value_list = set([feature_col[i] for i in range(feature_col.shape[0])])
     for feature_value in feature_value_list:
 
         index = []
-        for i in xrange(len(train_label)):
+        for i in range(len(train_label)):
             if train_set[i][max_feature] == feature_value:
                 index.append(i)
 
         sub_train_set = train_set[index]
         sub_train_label = train_label[index]
 
-        sub_tree = recurse_train(sub_train_set,sub_train_label,sub_features,epsilon)
-        tree.add_tree(feature_value,sub_tree)
+        sub_tree = recurse_train(sub_train_set, sub_train_label, sub_features, epsilon)
+        tree.add_tree(feature_value, sub_tree)
 
     return tree
 
-@log
-def train(train_set,train_label,features,epsilon):
-    return recurse_train(train_set,train_label,features,epsilon)
 
 @log
-def predict(test_set,tree):
+def train(train_set, train_label, features, epsilon):
+    return recurse_train(train_set, train_label, features, epsilon)
+
 
+@log
+def predict(test_set, tree):
     result = []
     for features in test_set:
         tmp_predict = tree.predict(features)
@@ -176,36 +182,25 @@ def predict(test_set,tree):
     return np.array(result)
 
 
-
 if __name__ == '__main__':
     logger = logging.getLogger()
     logger.setLevel(logging.DEBUG)
 
-    raw_data = pd.read_csv('../data/train.csv',header=0)
+    raw_data = pd.read_csv('../data/train.csv', header=0)
     data = raw_data.values
 
-    imgs = data[0::,1::]
-    labels = data[::,0]
+    imgs = data[0::, 1::]
+    labels = data[::, 0]
 
     # 图片二值化
     features = binaryzation_features(imgs)
 
     # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
-    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=23323)
-
-    tree = train(train_features,train_labels,[i for i in range(784)],0.1)
-    test_predict = predict(test_features,tree)
-    score = accuracy_score(test_labels,test_predict)
-
-    print "The accruacy socre is ", score
-
-
-
-
-
-
-
-
-
+    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33,
+                                                                                random_state=23323)
 
+    tree = train(train_features, train_labels, list([i for i in range(784)]), 0.1)
+    test_predict = predict(test_features, tree)
+    score = accuracy_score(test_labels, test_predict)
 
+    print("The accruacy socre is ", score)
diff --git a/hmm/__init__.py b/hmm/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/hmm/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/hmm/hmm.py b/hmm/hmm.py
index 2894f6a..f37e1d4 100644
--- a/hmm/hmm.py
+++ b/hmm/hmm.py
@@ -1,83 +1,83 @@
 # encoding=utf8
-
 import numpy as np
 import csv
 
+
 class HMM(object):
-    def __init__(self,N,M):
-        self.A = np.zeros((N,N))        # 状态转移概率矩阵
-        self.B = np.zeros((N,M))        # 观测概率矩阵
-        self.Pi = np.array([1.0/N]*N)   # 初始状态概率矩阵
+    def __init__(self, N, M):
+        self.A = np.zeros((N, N))  # 状态转移概率矩阵
+        self.B = np.zeros((N, M))  # 观测概率矩阵
+        self.Pi = np.array([1.0 / N] * N)  # 初始状态概率矩阵
 
-        self.N = N                      # 可能的状态数
-        self.M = M                      # 可能的观测数
+        self.N = N  # 可能的状态数
+        self.M = M  # 可能的观测数
 
     def cal_probality(self, O):
         self.T = len(O)
         self.O = O
 
         self.forward()
-        return sum(self.alpha[self.T-1])
+        return sum(self.alpha[self.T - 1])
 
     def forward(self):
         """
         前向算法
         """
-        self.alpha = np.zeros((self.T,self.N))
+        self.alpha = np.zeros((self.T, self.N))
 
         # 公式 10.15
         for i in range(self.N):
-            self.alpha[0][i] = self.Pi[i]*self.B[i][self.O[0]]
+            self.alpha[0][i] = self.Pi[i] * self.B[i][self.O[0]]
 
         # 公式10.16
-        for t in range(1,self.T):
+        for t in range(1, self.T):
             for i in range(self.N):
                 sum = 0
                 for j in range(self.N):
-                    sum += self.alpha[t-1][j]*self.A[j][i]
+                    sum += self.alpha[t - 1][j] * self.A[j][i]
                 self.alpha[t][i] = sum * self.B[i][self.O[t]]
 
     def backward(self):
         """
         后向算法
         """
-        self.beta = np.zeros((self.T,self.N))
+        self.beta = np.zeros((self.T, self.N))
 
         # 公式10.19
         for i in range(self.N):
-            self.beta[self.T-1][i] = 1
+            self.beta[self.T - 1][i] = 1
 
         # 公式10.20
-        for t in range(self.T-2,-1,-1):
+        for t in range(self.T - 2, -1, -1):
             for i in range(self.N):
                 for j in range(self.N):
-                    self.beta[t][i] += self.A[i][j]*self.B[j][self.O[t+1]]*self.beta[t+1][j]
+                    self.beta[t][i] += self.A[i][j] * self.B[j][self.O[t + 1]] * self.beta[t + 1][j]
 
     def cal_gamma(self, i, t):
         """
         公式 10.24
         """
-        numerator = self.alpha[t][i]*self.beta[t][i]
+        numerator = self.alpha[t][i] * self.beta[t][i]
         denominator = 0
 
         for j in range(self.N):
-            denominator += self.alpha[t][j]*self.beta[t][j]
+            denominator += self.alpha[t][j] * self.beta[t][j]
 
-        return numerator/denominator
+        return numerator / denominator
 
     def cal_ksi(self, i, j, t):
         """
         公式 10.26
         """
 
-        numerator = self.alpha[t][i]*self.A[i][j]*self.B[j][self.O[t+1]]*self.beta[t+1][j]
+        numerator = self.alpha[t][i] * self.A[i][j] * self.B[j][self.O[t + 1]] * self.beta[t + 1][j]
         denominator = 0
 
         for i in range(self.N):
             for j in range(self.N):
-                denominator += self.alpha[t][i]*self.A[i][j]*self.B[j][self.O[t+1]]*self.beta[t+1][j]
+                denominator += self.alpha[t][i] * self.A[i][j] * self.B[j][self.O[t + 1]] * self.beta[t + 1][j]
 
-        return numerator/denominator
+        return numerator / denominator
 
     def init(self):
         """
@@ -86,18 +86,18 @@ def init(self):
         """
         import random
         for i in range(self.N):
-            randomlist = [random.randint(0,100) for t in range(self.N)]
+            randomlist = [random.randint(0, 100) for t in range(self.N)]
             Sum = sum(randomlist)
             for j in range(self.N):
-                self.A[i][j] = randomlist[j]/Sum
+                self.A[i][j] = randomlist[j] / Sum
 
         for i in range(self.N):
-            randomlist = [random.randint(0,100) for t in range(self.M)]
+            randomlist = [random.randint(0, 100) for t in range(self.M)]
             Sum = sum(randomlist)
             for j in range(self.M):
-                self.B[i][j] = randomlist[j]/Sum
+                self.B[i][j] = randomlist[j] / Sum
 
-    def train(self, O, MaxSteps = 100):
+    def train(self, O, MaxSteps=100):
         self.T = len(O)
         self.O = O
 
@@ -106,12 +106,12 @@ def train(self, O, MaxSteps = 100):
 
         step = 0
         # 递推
-        while step<MaxSteps:
-            step+=1
+        while step < MaxSteps:
+            step += 1
             print(step)
-            tmp_A = np.zeros((self.N,self.N))
-            tmp_B = np.zeros((self.N,self.M))
-            tmp_pi = np.array([0.0]*self.N)
+            tmp_A = np.zeros((self.N, self.N))
+            tmp_B = np.zeros((self.N, self.M))
+            tmp_pi = np.array([0.0] * self.N)
 
             self.forward()
             self.backward()
@@ -119,12 +119,12 @@ def train(self, O, MaxSteps = 100):
             # a_{ij}
             for i in range(self.N):
                 for j in range(self.N):
-                    numerator=0.0
-                    denominator=0.0
-                    for t in range(self.T-1):
-                        numerator += self.cal_ksi(i,j,t)
-                        denominator += self.cal_gamma(i,t)
-                    tmp_A[i][j] = numerator/denominator
+                    numerator = 0.0
+                    denominator = 0.0
+                    for t in range(self.T - 1):
+                        numerator += self.cal_ksi(i, j, t)
+                        denominator += self.cal_gamma(i, t)
+                    tmp_A[i][j] = numerator / denominator
 
             # b_{jk}
             for j in range(self.N):
@@ -133,13 +133,13 @@ def train(self, O, MaxSteps = 100):
                     denominator = 0.0
                     for t in range(self.T):
                         if k == self.O[t]:
-                            numerator += self.cal_gamma(j,t)
-                        denominator += self.cal_gamma(j,t)
+                            numerator += self.cal_gamma(j, t)
+                        denominator += self.cal_gamma(j, t)
                     tmp_B[j][k] = numerator / denominator
 
             # pi_i
             for i in range(self.N):
-                tmp_pi[i] = self.cal_gamma(i,0)
+                tmp_pi[i] = self.cal_gamma(i, 0)
 
             self.A = tmp_A
             self.B = tmp_B
@@ -150,19 +150,19 @@ def generate(self, length):
         I = []
 
         # start
-        ran = random.randint(0,1000)/1000.0
+        ran = random.randint(0, 1000) / 1000.0
         i = 0
-        while self.Pi[i]<ran or self.Pi[i]<0.0001:
+        while self.Pi[i] < ran or self.Pi[i] < 0.0001:
             ran -= self.Pi[i]
             i += 1
         I.append(i)
 
         # 生成状态序列
-        for i in range(1,length):
+        for i in range(1, length):
             last = I[-1]
             ran = random.randint(0, 1000) / 1000.0
             i = 0
-            while self.A[last][i] < ran or self.A[last][i]<0.0001:
+            while self.A[last][i] < ran or self.A[last][i] < 0.0001:
                 ran -= self.A[last][i]
                 i += 1
             I.append(i)
@@ -172,7 +172,7 @@ def generate(self, length):
         for i in range(length):
             k = 0
             ran = random.randint(0, 1000) / 1000.0
-            while self.B[I[i]][k] < ran or self.B[I[i]][k]<0.0001:
+            while self.B[I[i]][k] < ran or self.B[I[i]][k] < 0.0001:
                 ran -= self.B[I[i]][k]
                 k += 1
             Y.append(k)
@@ -180,7 +180,6 @@ def generate(self, length):
         return Y
 
 
-
 def triangle(length):
     '''
     三角波
@@ -193,8 +192,9 @@ def triangle(length):
         if x <= 3:
             Y.append(x)
         else:
-            Y.append(6-x)
-    return X,Y
+            Y.append(6 - x)
+    return X, Y
+
 
 def sin(length):
     '''
@@ -206,12 +206,11 @@ def sin(length):
 
     for x in X:
         x = x % 20
-        Y.append(int(math.sin((x*math.pi)/10)*50)+50)
-    return X,Y
-
+        Y.append(int(math.sin((x * math.pi) / 10) * 50) + 50)
+    return X, Y
 
 
-def show_data(x,y):
+def show_data(x, y):
     import matplotlib.pyplot as plt
     plt.plot(x, y, 'g')
     plt.show()
@@ -220,13 +219,13 @@ def show_data(x,y):
 
 
 if __name__ == '__main__':
-    hmm = HMM(10,4)
+    hmm = HMM(10, 4)
     tri_x, tri_y = triangle(20)
-    
+
     hmm.train(tri_y)
     y = hmm.generate(100)
     x = [i for i in range(100)]
-    show_data(x,y)
+    show_data(x, y)
 
     # hmm = HMM(15,101)
     # sin_x, sin_y = sin(40)
@@ -235,4 +234,3 @@ def show_data(x,y):
     # y = hmm.generate(100)
     # x = [i for i in range(100)]
     # show_data(x,y)
-
diff --git a/knn/__init__.py b/knn/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/knn/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/knn/distance_test.py b/knn/distance_test.py
index db60543..bbf2111 100644
--- a/knn/distance_test.py
+++ b/knn/distance_test.py
@@ -13,12 +13,12 @@
 
     time_1 = time.time()
 
-    print np.sqrt(np.sum(np.square(vec_1 - vec_2)))
+    print(np.sqrt(np.sum(np.square(vec_1 - vec_2))))
 
     time_2 = time.time()
-    print time_2-time_1
+    print(time_2 - time_1)
 
-    print np.linalg.norm(vec_1 - vec_2)
+    print(np.linalg.norm(vec_1 - vec_2))
 
     time_3 = time.time()
-    print time_3-time_2
+    print(time_3 - time_2)
diff --git a/knn/knn.py b/knn/knn.py
index 49a71f4..213062d 100644
--- a/knn/knn.py
+++ b/knn/knn.py
@@ -1,4 +1,4 @@
-#encoding=utf-8
+# encoding=utf-8
 
 import pandas as pd
 import numpy as np
@@ -6,7 +6,7 @@
 import random
 import time
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 
@@ -17,7 +17,7 @@ def get_hog_features(trainset):
     hog = cv2.HOGDescriptor('../hog.xml')
 
     for img in trainset:
-        img = np.reshape(img,(28,28))
+        img = np.reshape(img, (28, 28))
         cv_img = img.astype(np.uint8)
 
         hog_feature = hog.compute(cv_img)
@@ -25,38 +25,40 @@ def get_hog_features(trainset):
         features.append(hog_feature)
 
     features = np.array(features)
-    features = np.reshape(features,(-1,324))
+    features = np.reshape(features, (-1, 324))
 
     return features
 
-def Predict(testset,trainset,train_labels):
+
+def Predict(testset, trainset, train_labels):
     predict = []
     count = 0
 
     for test_vec in testset:
         # 输出当前运行的测试用例坐标，用于测试
-        print count
+        print
+        count
         count += 1
 
-        knn_list = []       # 当前k个最近邻居
-        max_index = -1      # 当前k个最近邻居中距离最远点的坐标
-        max_dist = 0        # 当前k个最近邻居中距离最远点的距离
+        knn_list = []  # 当前k个最近邻居
+        max_index = -1  # 当前k个最近邻居中距离最远点的坐标
+        max_dist = 0  # 当前k个最近邻居中距离最远点的距离
 
         # 先将前k个点放入k个最近邻居中，填充满knn_list
         for i in range(k):
             label = train_labels[i]
             train_vec = trainset[i]
 
-            dist = np.linalg.norm(train_vec - test_vec)         # 计算两个点的欧氏距离
+            dist = np.linalg.norm(train_vec - test_vec)  # 计算两个点的欧氏距离
 
-            knn_list.append((dist,label))
+            knn_list.append((dist, label))
 
         # 剩下的点
-        for i in range(k,len(train_labels)):
+        for i in range(k, len(train_labels)):
             label = train_labels[i]
             train_vec = trainset[i]
 
-            dist = np.linalg.norm(train_vec - test_vec)         # 计算两个点的欧氏距离
+            dist = np.linalg.norm(train_vec - test_vec)  # 计算两个点的欧氏距离
 
             # 寻找10个邻近点钟距离最远的点
             if max_index < 0:
@@ -67,15 +69,14 @@ def Predict(testset,trainset,train_labels):
 
             # 如果当前k个最近邻居中存在点距离比当前点距离远，则替换
             if dist < max_dist:
-                knn_list[max_index] = (dist,label)
+                knn_list[max_index] = (dist, label)
                 max_index = -1
                 max_dist = 0
 
-
         # 统计选票
         class_total = 10
         class_count = [0 for i in range(class_total)]
-        for dist,label in knn_list:
+        for dist, label in knn_list:
             class_count[label] += 1
 
         # 找出最大选票
@@ -89,39 +90,40 @@ def Predict(testset,trainset,train_labels):
 
     return np.array(predict)
 
+
 k = 10
 
 if __name__ == '__main__':
-
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
-    raw_data = pd.read_csv('../data/train.csv',header=0)
+    raw_data = pd.read_csv('../data/train.csv', header=0)
     data = raw_data.values
 
-    imgs = data[0::,1::]
-    labels = data[::,0]
+    imgs = data[0::, 1::]
+    labels = data[::, 0]
 
     features = get_hog_features(imgs)
 
     # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
-    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=23323)
+    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33,
+                                                                                random_state=23323)
     # print train_features.shape
     # print train_features.shape
 
     time_2 = time.time()
-    print 'read data cost ',time_2 - time_1,' second','\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
-    print 'knn do not need to train'
+    print('Start training')
+    print('knn do not need to train')
     time_3 = time.time()
-    print 'training cost ',time_3 - time_2,' second','\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
-    test_predict = Predict(test_features,train_features,train_labels)
+    print('Start predicting')
+    test_predict = Predict(test_features, train_features, train_labels)
     time_4 = time.time()
-    print 'predicting cost ',time_4 - time_3,' second','\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
-    score = accuracy_score(test_labels,test_predict)
-    print "The accruacy socre is ", score
\ No newline at end of file
+    score = accuracy_score(test_labels, test_predict)
+    print("The accruacy socre is ", score)
diff --git a/logistic_regression/__init__.py b/logistic_regression/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/logistic_regression/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/logistic_regression/binary_perceptron.py b/logistic_regression/binary_perceptron.py
index e6ff6e0..9be4904 100644
--- a/logistic_regression/binary_perceptron.py
+++ b/logistic_regression/binary_perceptron.py
@@ -4,26 +4,23 @@
 # @Email:  wendesi@foxmail.com
 # @Last modified by:   WenDesi
 # @Last modified time: 08-11-16
-
-
 import pandas as pd
 import numpy as np
 import cv2
 import random
 import time
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 
 class Perceptron(object):
-
     def __init__(self):
         self.learning_step = 0.00001
         self.max_iteration = 5000
 
     def predict_(self, x):
-        wx = sum([self.w[j] * x[j] for j in xrange(len(self.w))])
+        wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
         return int(wx > 0)
 
     def train(self, features, labels):
@@ -37,7 +34,7 @@ def train(self, features, labels):
             x = list(features[index])
             x.append(1.0)
             y = 2 * labels[index] - 1
-            wx = sum([self.w[j] * x[j] for j in xrange(len(self.w))])
+            wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
 
             if wx * y > 0:
                 correct_count += 1
@@ -45,10 +42,10 @@ def train(self, features, labels):
                     break
                 continue
 
-            for i in xrange(len(self.w)):
+            for i in range(len(self.w)):
                 self.w[i] += self.learning_step * (y * x[i])
 
-    def predict(self,features):
+    def predict(self, features):
         labels = []
         for feature in features:
             x = list(feature)
@@ -58,8 +55,7 @@ def predict(self,features):
 
 
 if __name__ == '__main__':
-
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
@@ -76,19 +72,19 @@ def predict(self,features):
     # print train_features.shape
 
     time_2 = time.time()
-    print 'read data cost ', time_2 - time_1, ' second', '\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
+    print('Start training')
     p = Perceptron()
     p.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ', time_3 - time_2, ' second', '\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = p.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ', time_4 - time_3, ' second', '\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
     score = accuracy_score(test_labels, test_predict)
-    print "The accruacy socre is ", score
+    print("The accruacy socre is ", score)
diff --git a/logistic_regression/competation.py b/logistic_regression/competation.py
index cba8f01..654d8f5 100644
--- a/logistic_regression/competation.py
+++ b/logistic_regression/competation.py
@@ -8,10 +8,10 @@
 import csv
 import pandas as pd
 
-from binary_perceptron import Perceptron
-from logistic_regression import LogisticRegression
+from .binary_perceptron import Perceptron
+from .logistic_regression import LogisticRegression
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 if __name__ == '__main__':
@@ -27,10 +27,10 @@
     p = Perceptron()
     lr = LogisticRegression()
 
-    writer = csv.writer(file('result.csv', 'wb'))
+    writer = csv.writer(open('result.csv', 'wb'))
 
-    for time in xrange(test_time):
-        print 'iterater time %d' % time
+    for time in range(test_time):
+        print('iterater time %d' % time)
 
         train_features, test_features, train_labels, test_labels = train_test_split(
             imgs, labels, test_size=0.33, random_state=23323)
@@ -44,7 +44,7 @@
         p_score = accuracy_score(test_labels, p_predict)
         lr_score = accuracy_score(test_labels, lr_predict)
 
-        print 'perceptron accruacy score ', p_score
-        print 'logistic Regression accruacy score ', lr_score
+        print('perceptron accruacy score ', p_score)
+        print('logistic Regression accruacy score ', lr_score)
 
-        writer.writerow([time,p_score,lr_score])
+        writer.writerow([time, p_score, lr_score])
diff --git a/logistic_regression/logistic_regression.py b/logistic_regression/logistic_regression.py
index 84e8233..f516c45 100644
--- a/logistic_regression/logistic_regression.py
+++ b/logistic_regression/logistic_regression.py
@@ -10,18 +10,17 @@
 import random
 
 import pandas as pd
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 
 class LogisticRegression(object):
-
     def __init__(self):
         self.learning_step = 0.00001
         self.max_iteration = 5000
 
-    def predict_(self,x):
-        wx = sum([self.w[j] * x[j] for j in xrange(len(self.w))])
+    def predict_(self, x):
+        wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
         exp_wx = math.exp(wx)
 
         predict1 = exp_wx / (1 + exp_wx)
@@ -32,8 +31,7 @@ def predict_(self,x):
         else:
             return 0
 
-
-    def train(self,features, labels):
+    def train(self, features, labels):
         self.w = [0.0] * (len(features[0]) + 1)
 
         correct_count = 0
@@ -55,15 +53,14 @@ def train(self,features, labels):
             time += 1
             correct_count = 0
 
-            wx = sum([self.w[i] * x[i] for i in xrange(len(self.w))])
+            wx = sum([self.w[i] * x[i] for i in range(len(self.w))])
             exp_wx = math.exp(wx)
 
-            for i in xrange(len(self.w)):
+            for i in range(len(self.w)):
                 self.w[i] -= self.learning_step * \
-                    (-y * x[i] + float(x[i] * exp_wx) / float(1 + exp_wx))
-
+                             (-y * x[i] + float(x[i] * exp_wx) / float(1 + exp_wx))
 
-    def predict(self,features):
+    def predict(self, features):
         labels = []
 
         for feature in features:
@@ -73,35 +70,36 @@ def predict(self,features):
 
         return labels
 
+
 if __name__ == "__main__":
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
-    raw_data = pd.read_csv('../data/train_binary.csv',header=0)
+    raw_data = pd.read_csv('../data/train_binary.csv', header=0)
     data = raw_data.values
 
-    imgs = data[0::,1::]
-    labels = data[::,0]
-
+    imgs = data[0::, 1::]
+    labels = data[::, 0]
 
     # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
-    train_features, test_features, train_labels, test_labels = train_test_split(imgs, labels, test_size=0.33, random_state=23323)
+    train_features, test_features, train_labels, test_labels = train_test_split(imgs, labels, test_size=0.33,
+                                                                                random_state=23323)
 
     time_2 = time.time()
-    print 'read data cost ',time_2 - time_1,' second','\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
+    print('Start training')
     lr = LogisticRegression()
     lr.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ',time_3 - time_2,' second','\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = lr.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ',time_4 - time_3,' second','\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
-    score = accuracy_score(test_labels,test_predict)
-    print "The accruacy socre is ", score
+    score = accuracy_score(test_labels, test_predict)
+    print("The accruacy socre is ", score)
diff --git a/maxENT/__init__.py b/maxENT/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/maxENT/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/maxENT/maxENT.py b/maxENT/maxENT.py
index 3acad95..89df9c7 100644
--- a/maxENT/maxENT.py
+++ b/maxENT/maxENT.py
@@ -4,32 +4,26 @@
 # @Email:  wendesi@foxmail.com
 # @Last modified by:   WenDesi
 # @Last modified time: 09-11-16
-
-
 import pandas as pd
 import numpy as np
-
 import time
 import math
 import random
-
 from collections import defaultdict
-
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 
 class MaxEnt(object):
-
     def init_params(self, X, Y):
         self.X_ = X
         self.Y_ = set()
 
         self.cal_Pxy_Px(X, Y)
 
-        self.N = len(X)                 # 训练集大小
-        self.n = len(self.Pxy)          # 书中(x,y)对数
-        self.M = 10000.0                # 书91页那个M，但实际操作中并没有用那个值
+        self.N = len(X)  # 训练集大小
+        self.n = len(self.Pxy)  # 书中(x,y)对数
+        self.M = 10000.0  # 书91页那个M，但实际操作中并没有用那个值
         # 可认为是学习速率
 
         self.build_dict()
@@ -47,7 +41,7 @@ def cal_Pxy_Px(self, X, Y):
         self.Pxy = defaultdict(int)
         self.Px = defaultdict(int)
 
-        for i in xrange(len(X)):
+        for i in range(len(X)):
             x_, y = X[i], Y[i]
             self.Y_.add(y)
 
@@ -60,7 +54,7 @@ def cal_EPxy(self):
         计算书中82页最下面那个期望
         '''
         self.EPxy = defaultdict(float)
-        for id in xrange(self.n):
+        for id in range(self.n):
             (x, y) = self.id2xy[id]
             self.EPxy[id] = float(self.Pxy[(x, y)]) / float(self.N)
 
@@ -84,7 +78,7 @@ def cal_EPx(self):
         '''
         计算书83页最上面那个期望
         '''
-        self.EPx = [0.0 for i in xrange(self.n)]
+        self.EPx = [0.0 for i in range(self.n)]
 
         for i, X in enumerate(self.X_):
             Pyxs = self.cal_probality(X)
@@ -104,19 +98,19 @@ def train(self, X, Y):
         self.w = [0.0 for i in range(self.n)]
 
         max_iteration = 1000
-        for times in xrange(max_iteration):
-            print 'iterater times %d' % times
+        for times in range(max_iteration):
+            print('iterater times %d' % times)
             sigmas = []
             self.cal_EPx()
 
-            for i in xrange(self.n):
+            for i in range(self.n):
                 sigma = 1 / self.M * math.log(self.EPxy[i] / self.EPx[i])
                 sigmas.append(sigma)
 
             # if len(filter(lambda x: abs(x) >= 0.01, sigmas)) == 0:
             #     break
 
-            self.w = [self.w[i] + sigmas[i] for i in xrange(self.n)]
+            self.w = [self.w[i] + sigmas[i] for i in range(self.n)]
 
     def predict(self, testset):
         results = []
@@ -141,8 +135,7 @@ def rebuild_features(features):
 
 
 if __name__ == "__main__":
-
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
@@ -160,19 +153,19 @@ def rebuild_features(features):
     test_features = rebuild_features(test_features)
 
     time_2 = time.time()
-    print 'read data cost ', time_2 - time_1, ' second', '\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
+    print('Start training')
     met = MaxEnt()
     met.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ', time_3 - time_2, ' second', '\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = met.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ', time_4 - time_3, ' second', '\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
     score = accuracy_score(test_labels, test_predict)
-    print "The accruacy socre is ", score
+    print("The accruacy socre is ", score)
diff --git a/naive_bayes/__init__.py b/naive_bayes/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/naive_bayes/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/naive_bayes/naive_bayes.py b/naive_bayes/naive_bayes.py
index 07c2091..e646a3f 100644
--- a/naive_bayes/naive_bayes.py
+++ b/naive_bayes/naive_bayes.py
@@ -6,7 +6,7 @@
 import random
 import time
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 # 二值化
diff --git a/perceptron/__init__.py b/perceptron/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/perceptron/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/perceptron/binary_perceptron.py b/perceptron/binary_perceptron.py
index 38d81ba..9be4904 100644
--- a/perceptron/binary_perceptron.py
+++ b/perceptron/binary_perceptron.py
@@ -15,7 +15,6 @@
 
 
 class Perceptron(object):
-
     def __init__(self):
         self.learning_step = 0.00001
         self.max_iteration = 5000
@@ -46,7 +45,7 @@ def train(self, features, labels):
             for i in range(len(self.w)):
                 self.w[i] += self.learning_step * (y * x[i])
 
-    def predict(self,features):
+    def predict(self, features):
         labels = []
         for feature in features:
             x = list(feature)
@@ -56,7 +55,6 @@ def predict(self,features):
 
 
 if __name__ == '__main__':
-
     print('Start read data')
 
     time_1 = time.time()
@@ -74,19 +72,19 @@ def predict(self,features):
     # print train_features.shape
 
     time_2 = time.time()
-    print 'read data cost ', time_2 - time_1, ' second', '\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
+    print('Start training')
     p = Perceptron()
     p.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ', time_3 - time_2, ' second', '\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = p.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ', time_4 - time_3, ' second', '\n'
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
     score = accuracy_score(test_labels, test_predict)
-    print "The accruacy socre is ", score
+    print("The accruacy socre is ", score)
diff --git a/softmax/__init__.py b/softmax/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/softmax/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/svm/__init__.py b/svm/__init__.py
new file mode 100644
index 0000000..226e9e5
--- /dev/null
+++ b/svm/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2017/12/12 11:47
+@Author  : Elvis
+"""
+"""
+ __init__.py.py
+  
+"""
diff --git a/svm/generate_dataset.py b/svm/generate_dataset.py
index b71102e..6b441e6 100644
--- a/svm/generate_dataset.py
+++ b/svm/generate_dataset.py
@@ -4,14 +4,16 @@
 import matplotlib
 import matplotlib.pyplot as plt
 
-N = 10 #生成训练数据的个数
+N = 10  # 生成训练数据的个数
+
 
 # AX=0 相当于matlab中 null(a','r')
 def null(a, rtol=1e-5):
     u, s, v = np.linalg.svd(a)
-    rank = (s > rtol*s[0]).sum()
+    rank = (s > rtol * s[0]).sum()
     return rank, v[rank:].T.copy()
 
+
 # 符号函数，之后要进行向量化
 def sign(x):
     if x > 0:
@@ -20,36 +22,39 @@ def sign(x):
         return 0
     elif x < 0:
         return -1
-#noisy=False，那么就会生成N的dim维的线性可分数据X，标签为y
-#noisy=True, 那么生成的数据是线性不可分的,标签为y
+
+
+# noisy=False，那么就会生成N的dim维的线性可分数据X，标签为y
+# noisy=True, 那么生成的数据是线性不可分的,标签为y
 def mk_data(N, noisy=False):
-    rang = [-10,10]
+    rang = [-10, 10]
     dim = 2
 
-    X=np.random.rand(dim,N)*(rang[1]-rang[0])+rang[0]
+    X = np.random.rand(dim, N) * (rang[1] - rang[0]) + rang[0]
 
     while True:
-        Xsample = np.concatenate((np.ones((1,dim)), np.random.rand(dim,dim)*(rang[1]-rang[0])+rang[0]))
-        k,w=null(Xsample.T)
-        y = sign(np.dot(w.T,np.concatenate((np.ones((1,N)), X))))
+        Xsample = np.concatenate((np.ones((1, dim)), np.random.rand(dim, dim) * (rang[1] - rang[0]) + rang[0]))
+        k, w = null(Xsample.T)
+        y = sign(np.dot(w.T, np.concatenate((np.ones((1, N)), X))))
         if np.all(y):
             break
 
     if noisy == True:
-        idx = random.sample(range(1,N), N/10)
+        idx = random.sample(range(1, N), N / 10)
 
         for id in idx:
             y[0][id] = -y[0][id]
 
-    return (X,y,w)
+    return (X, y, w)
+
 
-def data_visualization(X,y,title):
-    class_1 = [[],[]]
-    class_2 = [[],[]]
+def data_visualization(X, y, title):
+    class_1 = [[], []]
+    class_2 = [[], []]
 
     size = len(y)
 
-    for i in xrange(size):
+    for i in range(size):
         X_1 = X[0][i]
         X_2 = X[1][i]
 
@@ -60,7 +65,6 @@ def data_visualization(X,y,title):
             class_2[0].append(X_1)
             class_2[1].append(X_2)
 
-
     plt.figure(figsize=(8, 6), dpi=80)
     plt.title(title)
 
@@ -69,37 +73,38 @@ def data_visualization(X,y,title):
     type1 = axes.scatter(class_1[0], class_1[1], s=20, c='red')
     type2 = axes.scatter(class_2[0], class_2[1], s=20, c='green')
 
-
     plt.show()
 
+
 def rebuild_features(features):
     size = len(features[0])
 
     new_features = []
-    for i in xrange(size):
-        new_features.append([features[0][i],features[1][i]])
+    for i in range(size):
+        new_features.append([features[0][i], features[1][i]])
 
     return new_features
 
-def generate_dataset(size, noisy = False, visualization = True):
+
+def generate_dataset(size, noisy=False, visualization=True):
     global sign
     sign = np.vectorize(sign)
-    X,y,w = mk_data(size,False)
+    X, y, w = mk_data(size, False)
     y = list(y[0])
 
     if visualization:
-        data_visualization(X,y,'all data')         #数据可视化
+        data_visualization(X, y, 'all data')  # 数据可视化
 
-    testset_size = int(len(y)*0.333)
+    testset_size = int(len(y) * 0.333)
 
-    indexes = [i for i in xrange(len(y))]
-    test_indexes = random.sample(indexes,testset_size)
-    train_indexes = list(set(indexes)-set(test_indexes))
+    indexes = [i for i in range(len(y))]
+    test_indexes = random.sample(indexes, testset_size)
+    train_indexes = list(set(indexes) - set(test_indexes))
 
-    trainset_features = [[],[]]
+    trainset_features = [[], []]
     trainset_labels = []
 
-    testset_features = [[],[]]
+    testset_features = [[], []]
     testset_labels = []
 
     for i in test_indexes:
@@ -107,9 +112,8 @@ def generate_dataset(size, noisy = False, visualization = True):
         testset_features[1].append(X[1][i])
         testset_labels.append(y[i])
 
-
     if visualization:
-        data_visualization(testset_features,testset_labels,'test set')
+        data_visualization(testset_features, testset_labels, 'test set')
 
     for i in train_indexes:
         trainset_features[0].append(X[0][i])
@@ -117,14 +121,12 @@ def generate_dataset(size, noisy = False, visualization = True):
         trainset_labels.append(y[i])
 
     if visualization:
-        data_visualization(trainset_features,trainset_labels,'train set')
-
-    return rebuild_features(trainset_features),trainset_labels,rebuild_features(testset_features),testset_labels
+        data_visualization(trainset_features, trainset_labels, 'train set')
 
+    return rebuild_features(trainset_features), trainset_labels, rebuild_features(testset_features), testset_labels
 
 
 if __name__ == '__main__':
-
     size = 1000
     generate_dataset(size)
 
@@ -134,4 +136,4 @@ def generate_dataset(size, noisy = False, visualization = True):
     # X,y,w = mk_data(size,False)
     #
     # data_visualization(X,y)
-# encoding=utf8
\ No newline at end of file
+    # encoding=utf8
diff --git a/svm/svm.py b/svm/svm.py
index cb56839..25c2404 100644
--- a/svm/svm.py
+++ b/svm/svm.py
@@ -5,23 +5,19 @@
 # @Last modified by:   WenDesi
 # @Last modified time: 13-11-16
 
-
-
 import time
 import random
 import logging
 
 import pandas as pd
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 
 from generate_dataset import *
 
 
-
 class SVM(object):
-
-    def __init__(self, kernel='linear',epsilon = 0.001):
+    def __init__(self, kernel='linear', epsilon=0.001):
         self.kernel = kernel
         self.epsilon = epsilon
 
@@ -36,23 +32,22 @@ def _init_parameters(self, features, labels):
         self.n = len(features[0])
         self.N = len(features)
         self.alpha = [0.0] * self.N
-        self.E = [self._E_(i) for i in xrange(self.N)]
+        self.E = [self._E_(i) for i in range(self.N)]
 
         self.C = 1000
         self.Max_Interation = 5000
 
-
     def _satisfy_KKT(self, i):
         ygx = self.Y[i] * self._g_(i)
-        if abs(self.alpha[i])<self.epsilon:
+        if abs(self.alpha[i]) < self.epsilon:
             return ygx > 1 or ygx == 1
-        elif abs(self.alpha[i]-self.C)<self.epsilon:
+        elif abs(self.alpha[i] - self.C) < self.epsilon:
             return ygx < 1 or ygx == 1
         else:
-            return abs(ygx-1) < self.epsilon
+            return abs(ygx - 1) < self.epsilon
 
     def is_stop(self):
-        for i in xrange(self.N):
+        for i in range(self.N):
             satisfy = self._satisfy_KKT(i)
 
             if not satisfy:
@@ -63,12 +58,12 @@ def _select_two_parameters(self):
         '''
         按照书上7.4.2选择两个变量
         '''
-        index_list = [i for i in xrange(self.N)]
+        index_list = [i for i in range(self.N)]
 
-        i1_list_1 = filter(lambda i: self.alpha[i] > 0 and self.alpha[i] < self.C, index_list)
+        i1_list_1 = filter(lambda i: 0 < self.alpha[i] < self.C, index_list)
         i1_list_2 = list(set(index_list) - set(i1_list_1))
 
-        i1_list = i1_list_1
+        i1_list = list(i1_list_1)
         i1_list.extend(i1_list_2)
 
         for i in i1_list:
@@ -94,13 +89,11 @@ def _K_(self, x1, x2):
         '''
 
         if self.kernel == 'linear':
-            return sum([x1[k] * x2[k] for k in xrange(self.n)])
+            return sum([x1[k] * x2[k] for k in range(self.n)])
         if self.kernel == 'poly':
-            return (sum([x1[k] * x2[k] for k in xrange(self.n)])+1)**3
-
+            return (sum([x1[k] * x2[k] for k in range(self.n)]) + 1) ** 3
 
-
-        print '没有定义核函数'
+        print('没有定义核函数')
         return 0
 
     def _g_(self, i):
@@ -109,7 +102,7 @@ def _g_(self, i):
         '''
         result = self.b
 
-        for j in xrange(self.N):
+        for j in range(self.N):
             result += self.alpha[j] * self.Y[j] * self._K_(self.X[i], self.X[j])
 
         return result
@@ -120,20 +113,19 @@ def _E_(self, i):
         '''
         return self._g_(i) - self.Y[i]
 
-    def try_E(self,i):
-        result = self.b-self.Y[i]
-        for j in xrange(self.N):
-            if self.alpha[j]<0 or self.alpha[j]>self.C:
+    def try_E(self, i):
+        result = self.b - self.Y[i]
+        for j in range(self.N):
+            if self.alpha[j] < 0 or self.alpha[j] > self.C:
                 continue
-            result += self.Y[j]*self.alpha[j]*self._K_(self.X[i],self.X[j])
+            result += self.Y[j] * self.alpha[j] * self._K_(self.X[i], self.X[j])
         return result
 
-
     def train(self, features, labels):
 
         self._init_parameters(features, labels)
 
-        for times in xrange(self.Max_Interation):
+        for times in range(self.Max_Interation):
             # if self.is_stop():
             #     return
 
@@ -150,9 +142,10 @@ def train(self, features, labels):
 
             E1 = self.E[i1]
             E2 = self.E[i2]
-            eta = self._K_(self.X[i1], self.X[i1]) + self._K_(self.X[i2], self.X[i2]) - 2 * self._K_(self.X[i1], self.X[i2])     # 公式(7.107)
+            eta = self._K_(self.X[i1], self.X[i1]) + self._K_(self.X[i2], self.X[i2]) \
+                  - 2 * self._K_(self.X[i1], self.X[i2])  # 公式(7.107)
 
-            alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (E1 - E2) / eta        # 公式(7.106)
+            alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (E1 - E2) / eta  # 公式(7.106)
 
             # 公式(7.108)
             alph2_new = 0
@@ -165,16 +158,19 @@ def train(self, features, labels):
 
             # 公式(7.109)
             alph1_new = self.alpha[i1] + self.Y[i1] * \
-                self.Y[i2] * (self.alpha[i2] - alph2_new)
+                                         self.Y[i2] * (self.alpha[i2] - alph2_new)
 
             # 公式(7.115) 及 公式(7.116)
             b_new = 0
-            b1_new = -E1 - self.Y[i1] * self._K_(self.X[i1], self.X[i1]) * (alph1_new - self.alpha[i1]) - self.Y[i2] * self._K_(self.X[i2], self.X[i1]) * (alph2_new - self.alpha[i2]) + self.b
-            b2_new = -E2 - self.Y[i1] * self._K_(self.X[i1], self.X[i2]) * (alph1_new - self.alpha[i1]) - self.Y[i2] * self._K_(self.X[i2], self.X[i2]) * (alph2_new - self.alpha[i2]) + self.b
+            b1_new = -E1 - self.Y[i1] * self._K_(self.X[i1], self.X[i1]) * (alph1_new - self.alpha[i1]) \
+                     - self.Y[i2] * self._K_(self.X[i2], self.X[i1]) * (alph2_new - self.alpha[i2]) + self.b
+
+            b2_new = -E2 - self.Y[i1] * self._K_(self.X[i1], self.X[i2]) * (alph1_new - self.alpha[i1]) \
+                     - self.Y[i2] * self._K_(self.X[i2], self.X[i2]) * (alph2_new - self.alpha[i2]) + self.b
 
-            if alph1_new > 0 and alph1_new < self.C:
+            if 0 < alph1_new < self.C:
                 b_new = b1_new
-            elif alph2_new > 0 and alph2_new < self.C:
+            elif 0 < alph2_new < self.C:
                 b_new = b2_new
             else:
                 b_new = (b1_new + b2_new) / 2
@@ -186,18 +182,17 @@ def train(self, features, labels):
             self.E[i1] = self._E_(i1)
             self.E[i2] = self._E_(i2)
 
-
-    def _predict_(self,feature):
+    def _predict_(self, feature):
         result = self.b
 
-        for i in xrange(self.N):
-            result += self.alpha[i]*self.Y[i]*self._K_(feature,self.X[i])
+        for i in range(self.N):
+            result += self.alpha[i] * self.Y[i] * self._K_(feature, self.X[i])
 
         if result > 0:
             return 1
         return -1
 
-    def predict(self,features):
+    def predict(self, features):
         results = []
 
         for feature in features:
@@ -210,28 +205,27 @@ def predict(self,features):
     logger = logging.getLogger()
     logger.setLevel(logging.DEBUG)
 
-    print 'Start read data'
+    print('Start read data')
 
     time_1 = time.time()
 
     # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
-    train_features, train_labels, test_features, test_labels = generate_dataset(2000,visualization=False)
+    train_features, train_labels, test_features, test_labels = generate_dataset(2000, visualization=False)
 
     time_2 = time.time()
-    print 'read data cost ',time_2 - time_1,' second','\n'
+    print('read data cost ', time_2 - time_1, ' second', '\n')
 
-    print 'Start training'
+    print('Start training')
     svm = SVM()
     svm.train(train_features, train_labels)
 
     time_3 = time.time()
-    print 'training cost ',time_3 - time_2,' second','\n'
+    print('training cost ', time_3 - time_2, ' second', '\n')
 
-    print 'Start predicting'
+    print('Start predicting')
     test_predict = svm.predict(test_features)
     time_4 = time.time()
-    print 'predicting cost ',time_4 - time_3,' second','\n'
-
-    score = accuracy_score(test_labels,test_predict)
-    print "svm1 the accruacy socre is ", score
+    print('predicting cost ', time_4 - time_3, ' second', '\n')
 
+    score = accuracy_score(test_labels, test_predict)
+    print("svm1 the accruacy socre is ", score)