Added problems 9.1

PlayX-2333 · Jul 10, 2020 · c97f35d · c97f35d
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diff --git a/Solutions to Chapter 6 Similarity-Based Methods.ipynb b/Solutions to Chapter 6 Similarity-Based Methods.ipynb
@@ -1377,31 +1377,6 @@
     "#### Problem 6.14 (a) Prepare Zip Code Data"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def split_zip_data(zip_data_path, splits = 1, train_size = 500):\n",
-    "    # Split the raw data into train and test\n",
-    "    # splits: specify the number of random splits for each train-test pair\n",
-    "    X_tr, y_tr, X_te, y_te = data.load_zip_data(zip_data_path)\n",
-    "    train_size = train_size\n",
-    "    splits = splits\n",
-    "    data_splits = data.sample_zip_data(X_tr, y_tr, train_size, splits)\n",
-    "    return data_splits\n",
-    "\n",
-    "def set_two_classes(y_train, y_test, digit):    \n",
-    "    # Classify digit '1' vs. not '1'\n",
-    "    y_train[y_train==digit] = 1\n",
-    "    y_test[y_test==digit] = 1\n",
-    "    \n",
-    "    y_train[y_train!=digit] = -1\n",
-    "    y_test[y_test!=digit] = -1\n",
-    "    return y_train, y_test"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -1419,7 +1394,7 @@
    ],
    "source": [
     "zip_data_path = './data/usps.h5'\n",
-    "data_splits = split_zip_data(zip_data_path, splits = 1)\n",
+    "data_splits = data.split_zip_data(zip_data_path, splits = 1)\n",
     "\n",
     "X_train, y_train, X_test, y_test = data_splits[0]\n",
     "\n",
@@ -1428,7 +1403,7 @@
     "freqs = counts/len(y_train)\n",
     "print('Frequencies of the digits: \\n', dict(zip(unique, freqs)))\n",
     "\n",
-    "y_train, y_test = set_two_classes(y_train, y_test, 1)"
+    "y_train, y_test = data.set_two_classes(y_train, y_test, 1)"
    ]
   },
   {
@@ -1692,15 +1667,15 @@
     "k=3\n",
     "tot_exps = 1000    \n",
     "zip_data_path = './data/usps.h5'\n",
-    "data_splits = split_zip_data(zip_data_path, splits = tot_exps)\n",
+    "data_splits = data.split_zip_data(zip_data_path, splits = tot_exps)\n",
     "digit = 1 #we classify digit '1' vs. non '1'\n",
     "nn_Eins, nn_Eouts = [], []\n",
     "cnn_Eins, cnn_Eouts = [], []\n",
     "for it in range(tot_exps):\n",
     "    if (it + 100) % 100 == 0:\n",
     "        print('---- Working on iteration: ', it)\n",
     "    X_train, y_train, X_test, y_test = data_splits[it]\n",
-    "    y_train, y_test = set_two_classes(y_train, y_test, digit)\n",
+    "    y_train, y_test = data.set_two_classes(y_train, y_test, digit)\n",
     "    X_tr, X_te = data.compute_features(X_train, X_test)\n",
     "    \n",
     "    nn_cls = nn.NearestNeighbors(X_tr, y_train, k)\n",