remove blank code cells

Opsman · May 21, 2019 · f4a0a7e · f4a0a7e
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commit f4a0a7e
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diff --git a/jupyter_chinese/topic01-使用-Pandas-进行数据探索.ipynb b/jupyter_chinese/topic01-使用-Pandas-进行数据探索.ipynb
diff --git a/jupyter_chinese/topic02-Python-数据可视化分析.ipynb b/jupyter_chinese/topic02-Python-数据可视化分析.ipynb
diff --git a/jupyter_chinese/topic03-决策树和-K-近邻分类.ipynb b/jupyter_chinese/topic03-决策树和-K-近邻分类.ipynb
diff --git a/jupyter_chinese/topic04-线性回归和线性分类器.ipynb b/jupyter_chinese/topic04-线性回归和线性分类器.ipynb
diff --git a/jupyter_chinese/topic05-集成学习和随机森林方法.ipynb b/jupyter_chinese/topic05-集成学习和随机森林方法.ipynb
diff --git a/jupyter_chinese/topic06-特征工程和特征选择.ipynb b/jupyter_chinese/topic06-特征工程和特征选择.ipynb
diff --git a/jupyter_chinese/topic07-主成分分析和聚类.ipynb b/jupyter_chinese/topic07-主成分分析和聚类.ipynb
@@ -221,15 +221,6 @@
     "%config InlineBackend.figure_format = 'retina'"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -250,15 +241,6 @@
     "y = iris.target"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -296,15 +278,6 @@
     "ax.w_zaxis.set_ticklabels([])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -325,15 +298,6 @@
     "from sklearn.metrics import accuracy_score, roc_auc_score  # 识别准确率计算函数"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
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-   },
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -362,15 +326,6 @@
     "                                               preds.argmax(axis=1))))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -406,15 +361,6 @@
     "plt.legend(loc=0)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
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-   },
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -442,15 +388,6 @@
     "                                               preds.argmax(axis=1))))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
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-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -481,15 +418,6 @@
     "                                            iris.feature_names)))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -524,15 +452,6 @@
     "y = digits.target"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -554,15 +473,6 @@
     "    plt.imshow(X[i, :].reshape([8, 8]), cmap='gray')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -591,15 +501,6 @@
     "plt.title('MNIST. PCA projection')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -635,15 +536,6 @@
     "plt.title('MNIST. t-SNE projection')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -679,15 +571,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -780,15 +663,6 @@
     "plt.plot(X[:, 0], X[:, 1], 'bo')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -831,15 +705,6 @@
     "    cent_history.append(centroids)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -870,15 +735,6 @@
     "    plt.title('Step {:}'.format(i + 1))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -953,15 +809,6 @@
     "from sklearn.cluster import KMeans  # 导入 K-均值聚类模型"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -986,15 +833,6 @@
     "plt.ylabel('$J(C_k)$')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1213,15 +1051,6 @@
     "dn = hierarchy.dendrogram(Z, color_threshold=0.5)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1426,15 +1255,6 @@
     "from sklearn.cluster import KMeans, AgglomerativeClustering, AffinityPropagation, SpectralClustering"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1461,15 +1281,6 @@
     "algorithms.append(AgglomerativeClustering(n_clusters=10))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1503,15 +1314,6 @@
     "results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
   {
    "cell_type": "markdown",
    "metadata": {},