增加部分实验结果,中期材料

1 files changed, 20 insertions, 19 deletions

diff --git a/Experiment/statFeature/StatFeature.ipynb b/Experiment/statFeature/StatFeature.ipynb
index 5da283f..b0573cd 100644
--- a/Experiment/statFeature/StatFeature.ipynb
+++ b/Experiment/statFeature/StatFeature.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 31,
    "metadata": {
     "collapsed": true
    },
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 32,
    "metadata": {
     "collapsed": true
    },
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 33,
    "metadata": {
     "collapsed": true
    },
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 36,
    "metadata": {
     "collapsed": true
    },
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 37,
    "metadata": {
     "collapsed": true
    },
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -407,7 +407,7 @@
        "Name: label, dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -415,7 +415,7 @@
      "data": {
       "image/png": 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0NknSlKWqpvdmyRnAiVX1G23+JcAxVfXysT5rgDVt9nHAV6dW4K49CvjO0EUsEn4WO/hZ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\nAEnq1P8HV9/axntrFV8AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ac1f320>"
+       "<matplotlib.figure.Figure at 0x113f03390>"
       ]
      },
      "metadata": {},
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -440,18 +440,18 @@
      "text": [
       "183\n",
       "                    precision    recall        f1\n",
-      "LogisticRegression   0.773019  0.773019  0.773019\n",
-      "SVM                  0.841328  0.841328  0.841328\n",
-      "GaussianNB           0.730193  0.730193  0.730193\n",
-      "tree                 0.984154  0.984154  0.984154\n",
-      "RandomForest         0.988651  0.988651  0.988651\n"
+      "LogisticRegression   0.775161  0.775161  0.775161\n",
+      "SVM                  0.831906  0.831906  0.831906\n",
+      "GaussianNB           0.729122  0.729122  0.729122\n",
+      "tree                 0.984582  0.984582  0.984582\n",
+      "RandomForest         0.989507  0.989507  0.989507\n"
      ]
     },
     {
      "data": {
-      "image/png": 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65vtYkiP2saw9T6IVz/Ukwz6vBL5IMu3gzZlWZNVi+5L722VWRTMV9YTqYknf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\n",
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HrUzPfB9DcsQ+lrXnSbTiuY5k2OeVwBdJph28KdOKrFpsV3J/28yqaKainlBd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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x112e0e550>"
+       "<matplotlib.figure.Figure at 0x10e68fa58>"
       ]
      },
      "metadata": {},
@@ -475,6 +475,7 @@
     "                        columns = ['precision', 'recall', 'f1'])\n",
     "#def a():\n",
     "\n",
+    "\n",
     "f1_score_list = list()\n",
     "recall_score_list = list()\n",
     "precision_score_list = list()\n",
@@ -499,7 +500,7 @@
     "recall_score_list = list()\n",
     "precision_score_list = list()\n",
     "for i in range(1):\n",
-    "    #np.random.shuffle(examples)\n",
+    "    np.random.shuffle(examples)\n",
     "    examples_train = examples[:int(len(examples)*0.75)]\n",
     "    examples_test = examples[int(len(examples)*0.75):]\n",
     "    x_train = examples_train[:,0:-1]\n",
@@ -559,7 +560,7 @@
     "recall_score_list = list()\n",
     "precision_score_list = list()\n",
     "for i in range(1):\n",
-    "    #np.random.shuffle(examples)\n",
+    "    np.random.shuffle(examples)\n",
     "    examples_train = examples[:int(len(examples)*0.75)]\n",
     "    examples_test = examples[int(len(examples)*0.75):]\n",
     "    x_train = examples_train[:,0:-1]\n",