change samples to examples

Author: rasbt

ch02/ch02.ipynb

@@ -287,10 +287,10 @@
     "\n",
     "        Parameters\n",
     "        ----------\n",
-    "        X : {array-like}, shape = [n_samples, n_features]\n",
-    "          Training vectors, where n_samples is the number of samples and\n",
+    "        X : {array-like}, shape = [n_examples, n_features]\n",
+    "          Training vectors, where n_examples is the number of examples and\n",
     "          n_features is the number of features.\n",
-    "        y : array-like, shape = [n_samples]\n",
+    "        y : array-like, shape = [n_examples]\n",
     "          Target values.\n",
     "\n",
     "        Returns\n",
@@ -744,7 +744,7 @@
     "    plt.xlim(xx1.min(), xx1.max())\n",
     "    plt.ylim(xx2.min(), xx2.max())\n",
     "\n",
-    "    # plot class samples\n",
+    "    # plot class examples\n",
     "    for idx, cl in enumerate(np.unique(y)):\n",
     "        plt.scatter(x=X[y == cl, 0], \n",
     "                    y=X[y == cl, 1],\n",
@@ -916,10 +916,10 @@
     "\n",
     "        Parameters\n",
     "        ----------\n",
-    "        X : {array-like}, shape = [n_samples, n_features]\n",
-    "          Training vectors, where n_samples is the number of samples and\n",
+    "        X : {array-like}, shape = [n_examples, n_features]\n",
+    "          Training vectors, where n_examples is the number of examples and\n",
     "          n_features is the number of features.\n",
-    "        y : array-like, shape = [n_samples]\n",
+    "        y : array-like, shape = [n_examples]\n",
     "          Target values.\n",
     "\n",
     "        Returns\n",
@@ -1178,7 +1178,7 @@
     "      Weights after fitting.\n",
     "    cost_ : list\n",
     "      Sum-of-squares cost function value averaged over all\n",
-    "      training samples in each epoch.\n",
+    "      training examples in each epoch.\n",
     "\n",
     "        \n",
     "    \"\"\"\n",
@@ -1194,10 +1194,10 @@
     "\n",
     "        Parameters\n",
     "        ----------\n",
-    "        X : {array-like}, shape = [n_samples, n_features]\n",
-    "          Training vectors, where n_samples is the number of samples and\n",
+    "        X : {array-like}, shape = [n_examples, n_features]\n",
+    "          Training vectors, where n_examples is the number of examples and\n",
     "          n_features is the number of features.\n",
-    "        y : array-like, shape = [n_samples]\n",
+    "        y : array-like, shape = [n_examples]\n",
     "          Target values.\n",
     "\n",
     "        Returns\n",
@@ -1401,7 +1401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.1"
   }
  },
  "nbformat": 4,