tutorials/polynomial_regression.ipynb

@@ -258,19 +258,21 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
+     "name": "stdout",
-      "text/plain": [
+     "output_type": "stream",
-       "Matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])"
+     "text": [
+      "Matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n",
+      "Matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])\n",
+      "Matrix([[0.0, 0.0], [0.0, 0.0], [0.0, 1.0]])\n"
      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "A = Matrix.zeros(2, 3) # rows - columns\n",
-    "A # each row is a vector of length 3"
+    "print(A) # each row is a vector of length 3\n",
+    "A[1, 2] = 1.0\n",
+    "print(A)\n",
+    "print(A.transpose())"
    ]
   },
   {
@@ -311,7 +313,7 @@
     "    X = Matrix.zeros(n_samples, n_features) # `Matrix.ones` is needed\n",
     "    for row in range(n_samples):\n",
     "        for col in range(n_features):\n",
-    "            X.data[row][col] = x_data[row] ** col\n",
+    "            X[row, col] = x_data[row] ** col\n",
     "\n",
     "    return X"
    ]
@@ -326,7 +328,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix([[1, 0, 0, 0], [1, 1, 1, 1], [1, 2, 4, 8], [1, 3, 9, 27], [1, 4, 16, 64]])\n"
+      "Matrix([[1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 4.0, 8.0], [1.0, 3.0, 9.0, 27.0], [1.0, 4.0, 16.0, 64.0]])\n"
      ]
     }
    ],
@@ -378,7 +380,7 @@
    "outputs": [],
    "source": [
     "degree = 3\n",
-    "X_design = design_matrix_for_poly(xx.data, degree)"
+    "X_design = design_matrix_for_poly(xx, degree)"
    ]
   },
   {