jakevdp · teoguso · Dec 17, 2016 · Dec 19, 2016
diff --git a/notebooks/01-Preliminaries.ipynb b/notebooks/01-Preliminaries.ipynb
@@ -115,11 +115,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython: 5.1.0\n",
+      "numpy: 1.11.2\n",
+      "scipy: 0.18.1\n",
+      "matplotlib: 1.5.3\n",
+      "scikit-learn: 0.18\n",
+      "seaborn 0.7.1\n"
+     ]
+    }
+   ],
    "source": [
     "from __future__ import print_function\n",
     "\n",
@@ -162,7 +175,7 @@
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
-   "language": "",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -175,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/02.1-Machine-Learning-Intro.ipynb b/notebooks/02.1-Machine-Learning-Intro.ipynb
diff --git a/notebooks/02.2-Basic-Principles.ipynb b/notebooks/02.2-Basic-Principles.ipynb
diff --git a/notebooks/03.1-Classification-SVMs.ipynb b/notebooks/03.1-Classification-SVMs.ipynb
diff --git a/notebooks/03.2-Regression-Forests.ipynb b/notebooks/03.2-Regression-Forests.ipynb
diff --git a/notebooks/04.1-Dimensionality-PCA.ipynb b/notebooks/04.1-Dimensionality-PCA.ipynb
diff --git a/notebooks/04.2-Clustering-KMeans.ipynb b/notebooks/04.2-Clustering-KMeans.ipynb
diff --git a/notebooks/04.3-Density-GMM.ipynb b/notebooks/04.3-Density-GMM.ipynb
diff --git a/notebooks/05-Validation.ipynb b/notebooks/05-Validation.ipynb
diff --git a/notebooks/Index.ipynb b/notebooks/Index.ipynb
@@ -1,62 +1,72 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:60efb1a2f4e1815d3e1a54160adf3184ab956d9a4487c226bde9b22aa2728f94"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scikit-Learn Tutorial\n",
+    "\n",
+    "*Jake VanderPlas*\n",
+    "\n",
+    "This is the main index for the materials of my Scikit-Learn tutorial.\n",
+    "Please refer to the [github repository](http://github.com/jakevdp/sklearn_tutorial) for this tutorial for any updates."
+   ]
+  },
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Scikit-Learn Tutorial\n",
-      "\n",
-      "*Jake VanderPlas*\n",
-      "\n",
-      "This is the main index for the materials of my Scikit-Learn tutorial.\n",
-      "Please refer to the [github repository](http://github.com/jakevdp/sklearn_tutorial) for this tutorial for any updates."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Tutorial Notebooks\n",
-      "\n",
-      "The following links are to notebooks containing the tutorial materials.\n",
-      "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_tutorial) in which they are contained.\n",
-      "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n",
-      "\n",
-      "### 1. Preliminaries\n",
-      "\n",
-      "  + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n",
-      "  \n",
-      "### 2. Introduction to Machine Learning with Scikit-Learn\n",
-      "\n",
-      "  + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n",
-      "  + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n",
-      "  \n",
-      "### 3. Supervised Learning In-Depth\n",
-      "\n",
-      "  + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n",
-      "  + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n",
-      "\n",
-      "### 4. Unsupervised Learning In-Depth\n",
-      "\n",
-      "  + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n",
-      "  + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n",
-      "  + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n",
-      "  \n",
-      "### 5. Model Validation In-Depth\n",
-      "\n",
-      "  + [05-Validation.ipynb](05-Validation.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorial Notebooks\n",
+    "\n",
+    "The following links are to notebooks containing the tutorial materials.\n",
+    "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_tutorial) in which they are contained.\n",
+    "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n",
+    "\n",
+    "### 1. Preliminaries\n",
+    "\n",
+    "  + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n",
+    "  \n",
+    "### 2. Introduction to Machine Learning with Scikit-Learn\n",
+    "\n",
+    "  + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n",
+    "  + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n",
+    "  \n",
+    "### 3. Supervised Learning In-Depth\n",
+    "\n",
+    "  + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n",
+    "  + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n",
+    "\n",
+    "### 4. Unsupervised Learning In-Depth\n",
+    "\n",
+    "  + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n",
+    "  + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n",
+    "  + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n",
+    "  \n",
+    "### 5. Model Validation In-Depth\n",
+    "\n",
+    "  + [05-Validation.ipynb](05-Validation.ipynb)"
+   ]
   }
- ]
-}
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/URL.ipynb b/notebooks/URL.ipynb
@@ -1,4 +1,24 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scikit-Learn Tutorial\n",
+    "\n",
+    "---\n",
+    "\n",
+    "<br><br><br>\n",
+    "\n",
+    "# Download all materials here:\n",
+    "# http://github.com/jakevdp/sklearn_tutorial\n",
+    "\n",
+    "<br><br><br><br><br><br>\n",
+    "\n",
+    "---"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -15,36 +35,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.3.5"
-  },
-  "name": "",
-  "signature": "sha256:5941485b19c03919025faae3887fd7ef842661eae6780930de41597909742c69"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Scikit-Learn Tutorial\n",
-      "\n",
-      "---\n",
-      "\n",
-      "<br><br><br>\n",
-      "\n",
-      "# Download all materials here:\n",
-      "# http://github.com/jakevdp/sklearn_tutorial\n",
-      "\n",
-      "<br><br><br><br><br><br>\n",
-      "\n",
-      "---"
-     ]
-    }
-   ],
-   "metadata": {}
+   "version": "3.5.2"
   }
- ]
-}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/fig_code/sgd_separator.py b/notebooks/fig_code/sgd_separator.py
@@ -22,7 +22,7 @@ def plot_sgd_separator():
     for (i, j), val in np.ndenumerate(X1):
         x1 = val
         x2 = X2[i, j]
-        p = clf.decision_function([x1, x2])
+        p = clf.decision_function([[x1, x2]])
         Z[i, j] = p[0]
     levels = [-1.0, 0.0, 1.0]
     linestyles = ['dashed', 'solid', 'dashed']