Update week37.ipynb

doc/pub/week37/ipynb/week37.ipynb

@@ -3518,7 +3518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "2ed0cafc",
    "metadata": {
     "collapsed": false,
@@ -3627,7 +3627,7 @@
     "y_offset = np.average(y, axis=0)\n",
     "X_offset = np.average(X, axis=0)\n",
     "\n",
-    "theta = fit_theta(X - X_offset, y)# - y_offset)\n",
+    "theta = fit_theta(X - X_offset, y)#-y_offset)\n",
     "intercept = np.mean(y_offset - X_offset @ theta)\n",
     "\n",
     "print(f\"Manual intercept: {intercept}\")\n",
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "id": "4f9b1fa0",
    "metadata": {
     "collapsed": false,
@@ -3943,7 +3943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "a731e32c",
    "metadata": {
     "collapsed": false,
@@ -4109,6 +4109,7 @@
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
     "#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable\n",
+    "\n",
     "X_train_mean = np.mean(X_train,axis=0)\n",
     "#Center by removing mean from each feature\n",
     "X_train_scaled = X_train - X_train_mean \n",