From fc6fd7e610cef41686cad7f1ec331f5b3f657308 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Fri, 17 Feb 2017 14:47:18 +0100
Subject: [PATCH] Versicolor instead of Versicolour

---
 04_training_linear_models.ipynb  |  2 +-
 05_support_vector_machines.ipynb | 10 +++++-----
 06_decision_trees.ipynb          | 10 +++++-----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/04_training_linear_models.ipynb b/04_training_linear_models.ipynb
index 777c8f4..b9bc7b2 100644
--- a/04_training_linear_models.ipynb
+++ b/04_training_linear_models.ipynb
@@ -1245,7 +1245,7 @@
     "\n",
     "plt.figure(figsize=(10, 4))\n",
     "plt.plot(X[y==2, 0], X[y==2, 1], \"g^\", label=\"Iris-Virginica\")\n",
-    "plt.plot(X[y==1, 0], X[y==1, 1], \"bs\", label=\"Iris-Versicolour\")\n",
+    "plt.plot(X[y==1, 0], X[y==1, 1], \"bs\", label=\"Iris-Versicolor\")\n",
     "plt.plot(X[y==0, 0], X[y==0, 1], \"yo\", label=\"Iris-Setosa\")\n",
     "\n",
     "from matplotlib.colors import ListedColormap\n",
diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index e76258b..ef8ae75 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -100,9 +100,9 @@
     "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
     "y = iris[\"target\"]\n",
     "\n",
-    "setosa_or_versicolour = (y == 0) | (y == 1)\n",
-    "X = X[setosa_or_versicolour]\n",
-    "y = y[setosa_or_versicolour]\n",
+    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
+    "X = X[setosa_or_versicolor]\n",
+    "y = y[setosa_or_versicolor]\n",
     "\n",
     "# SVM Classifier model\n",
     "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
@@ -150,7 +150,7 @@
     "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
     "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
     "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
-    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolour\")\n",
+    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
     "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
     "plt.xlabel(\"Petal length\", fontsize=14)\n",
     "plt.ylabel(\"Petal width\", fontsize=14)\n",
@@ -373,7 +373,7 @@
     "plt.figure(figsize=(12,3.2))\n",
     "plt.subplot(121)\n",
     "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
-    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolour\")\n",
+    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
     "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
     "plt.xlabel(\"Petal length\", fontsize=14)\n",
     "plt.ylabel(\"Petal width\", fontsize=14)\n",
diff --git a/06_decision_trees.ipynb b/06_decision_trees.ipynb
index a152c93..2178678 100644
--- a/06_decision_trees.ipynb
+++ b/06_decision_trees.ipynb
@@ -159,7 +159,7 @@
     "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
     "    if plot_training:\n",
     "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
-    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolour\")\n",
+    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
     "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
     "        plt.axis(axes)\n",
     "    if iris:\n",
@@ -241,7 +241,7 @@
    },
    "outputs": [],
    "source": [
-    "X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # widest Iris-Versicolour flower"
+    "X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # widest Iris-Versicolor flower"
    ]
   },
   {
@@ -254,9 +254,9 @@
    },
    "outputs": [],
    "source": [
-    "not_widest_versicolour = (X[:, 1]!=1.8) | (y==2)\n",
-    "X_tweaked = X[not_widest_versicolour]\n",
-    "y_tweaked = y[not_widest_versicolour]\n",
+    "not_widest_versicolor = (X[:, 1]!=1.8) | (y==2)\n",
+    "X_tweaked = X[not_widest_versicolor]\n",
+    "y_tweaked = y[not_widest_versicolor]\n",
     "\n",
     "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
     "tree_clf_tweaked.fit(X_tweaked, y_tweaked)"