done pw4 but not sure about ex7
This commit is contained in:
Joachim Bach committed
1 parent
d23d2a51c4
commit
14cf888160
1 file changed
+247
-35
@@ -18,7 +18,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 312,
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"execution_count": 429,
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"id": "d6eb2aab",
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"id": "d6eb2aab",
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"metadata": {},
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"outputs": [],
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@@ -48,7 +48,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 313,
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"execution_count": 430,
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"id": "5f3f924f",
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"metadata": {},
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"outputs": [],
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"outputs": [],
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@@ -66,7 +66,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 314,
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"execution_count": 431,
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"id": "5108e239",
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"id": "5108e239",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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@@ -84,7 +84,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 315,
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"execution_count": 432,
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"id": "34222aff",
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"id": "34222aff",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -158,7 +158,7 @@
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"4 33 1.5 830"
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"4 33 1.5 830"
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]
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]
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},
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},
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"execution_count": 315,
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"execution_count": 432,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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}
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@@ -185,7 +185,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 316,
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"execution_count": 433,
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"id": "ad07d4cd",
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"id": "ad07d4cd",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -219,7 +219,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 317,
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"execution_count": 434,
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"id": "ac0c9f07",
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"id": "ac0c9f07",
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"metadata": {},
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"outputs": [
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@@ -253,7 +253,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 318,
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"execution_count": 435,
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"id": "6d015af9",
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"id": "6d015af9",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -312,7 +312,7 @@
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 319,
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"execution_count": 436,
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"id": "19ffbbd9",
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"id": "19ffbbd9",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -375,7 +375,7 @@
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},
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 320,
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"execution_count": 437,
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"id": "db65a2fa",
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"id": "db65a2fa",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -442,7 +442,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 438,
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"id": "9ca79a3d",
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"id": "9ca79a3d",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -501,7 +501,7 @@
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 330,
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"execution_count": 439,
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"id": "5a34ad43",
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"id": "5a34ad43",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -557,13 +557,13 @@
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 323,
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"execution_count": 440,
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"id": "e80c04b3",
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"id": "e80c04b3",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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truncated
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"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -593,7 +593,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 324,
|
"execution_count": 441,
|
||||||
"id": "5e594f32",
|
"id": "5e594f32",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -645,7 +645,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 325,
|
"execution_count": 442,
|
||||||
"id": "3f693388",
|
"id": "3f693388",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -653,13 +653,13 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Theta = [ 0.32873692 29.18572953]\n",
|
"Theta = [ 0.338377 28.68612806]\n",
|
||||||
"Cost = 145335.46042386265\n"
|
"Cost = 144162.5602768206\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGzCAYAAAD9pBdvAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAASFdJREFUeJzt3Xl8E3X+P/DXJGmSpm3S+4JeHHJfgkABBbVSkXWpB4ssK4eCx4KCeIG7Kx7r1l3E1XWVY/0JqIsgKrCLKPTLuUiRs8ohRRBajqYt0CY90zb5/P5IGwg9aNo007Sv58N50Ew+k7zn09q8+pnPzEhCCAEiIiIimSjkLoCIiIjaN4YRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlkxjBAREZGsGEaIiIhIVgwjREREJCuGESJqdeLj4zF16lS5y3CJJEl45ZVX5C6DyCsxjBC5yenTp/H444+jU6dO0Gq10Ov1GD58ON59912UlZW5/f1KS0vxyiuvYMeOHW5/7dbm+PHjeOWVV3D27FlZ69i0aRMDB1ELUMldAFFb8PXXX2P8+PHQaDSYPHkyevfujYqKCuzevRvPP/88jh07hmXLlrn1PUtLS/Hqq68CAEaNGuXW15ZbZmYmFIqrfysdP34cr776KkaNGoX4+HjZ6tq0aRPef//9OgNJWVkZVCr+SiVqCv6fQ9RMZ86cwUMPPYS4uDhs27YNUVFRjudmzpyJU6dO4euvv5axwtanqqoKNpsNarW6zuc1Go1H6igpKYGfn59bXkur1brldYjaJUFEzfLEE08IAOK7775rVPvKykrx2muviU6dOgm1Wi3i4uLE/PnzRXl5uVO7/fv3i9GjR4uQkBCh1WpFfHy8mDZtmhBCiDNnzggAtZYFCxY0+N6nT58WDz74oAgKChK+vr5iyJAhYuPGjY7njUajUCqV4pVXXqm17YkTJwQA8d577znWFRQUiNmzZ4uOHTsKtVotOnfuLN58801htVodbWpqXbhwofj73/8uOnXqJBQKhTh8+HC9dcbFxYkpU6YIIYRYvnx5nfu6fft2R/tNmzaJESNGCJ1OJ/z9/cU999wjjh496vSaU6ZMEX5+fuLUqVNizJgxwt/fX4wbN04IIcSuXbvEgw8+KGJiYoRarRYdO3YUc+bMEaWlpU7b11VHjbr6/9ChQ+Luu+8WAQEBws/PT9xxxx0iPT3dqU3N/u3evVs888wzIjQ0VOh0OpGSkiLy8vKc2jb0M0HkzTgyQtRM//3vf9GpUycMGzasUe2nT5+OlStX4sEHH8Szzz6L77//Hqmpqfjpp5+wbt06AEBeXh5Gjx6NsLAwzJs3D4GBgTh79iy++uorAEBYWBgWL16MJ598Evfddx/uv/9+AEDfvn3rfd/c3FwMGzYMpaWlePrppxESEoKVK1fi17/+Nb744gvcd999iIiIwMiRI/H5559jwYIFTtuvWbMGSqUS48ePB2A/TDRy5EhcuHABjz/+OGJjY7Fnzx7Mnz8fOTk5eOedd5y2X758OcrLy/HYY49Bo9EgODi4Uf1122234emnn8Y//vEPvPTSS+jRowcAOP795JNPMGXKFCQnJ+Ovf/0rSktLsXjxYowYMQKHDx92OqxTVVWF5ORkjBgxAm+99RZ0Oh0AYO3atSgtLcWTTz6JkJAQ7Nu3D++99x7Onz+PtWvXAgAef/xxXLx4EWlpafjkk09uWPexY8dw6623Qq/X44UXXoCPjw+WLl2KUaNGYefOnRgyZIhT+6eeegpBQUFYsGABzp49i3feeQezZs3CmjVrANz4Z4LIq8mdhoi8mclkEgAcf2HfSEZGhgAgpk+f7rT+ueeeEwDEtm3bhBBCrFu3TgAQ+/fvr/e18vPzGzUaUmPOnDkCgPjf//7nWFdUVCQSEhJEfHy8YzRj6dKlAoA4cuSI0/Y9e/YUd9xxh+Px66+/Lvz8/MTJkyed2s2bN08olUqRnZ0thLg6MqLX62v9pV+fa0dGhBBi7dq1tUZDauoPDAwUM2bMcFpvNBqFwWBwWl8zsjFv3rxa73ftCEiN1NRUIUmSyMrKcqybOXOmqO/X5vXfi5SUFKFWq8Xp06cd6y5evCgCAgLEbbfd5lhXMzKSlJQkbDabY/0zzzwjlEqlKCwsFEI07meCyFvxbBqiZjCbzQCAgICARrXftGkTAGDu3LlO65999lkAcMwtCQwMBABs3LgRlZWV7igVmzZtwuDBgzFixAjHOn9/fzz22GM4e/Ysjh8/DgC4//77oVKpHH+RA8DRo0dx/PhxTJgwwbFu7dq1uPXWWxEUFIRLly45lqSkJFitVuzatcvp/R944AGEhYW5ZV9qpKWlobCwEBMnTnSqQalUYsiQIdi+fXutbZ588sla63x9fR1fl5SU4NKlSxg2bBiEEDh8+LDLdVmtVmzZsgUpKSno1KmTY31UVBR++9vfYvfu3Y6fnRqPPfYYJElyPL711lthtVqRlZUFoGV+JohaC68KI7t27cK9996L6OhoSJKE9evXu/waQgi89dZbuOmmm6DRaNChQwe88cYb7i+W2gW9Xg8AKCoqalT7rKwsKBQKdOnSxWl9ZGQkAgMDHR88I0eOxAMPPIBXX30VoaGhGDduHJYvXw6LxdLkWrOystCtW7da62sOd9S8d2hoKO688058/vnnjjZr1qyBSqVyHA4CgJ9//hnffvstwsLCnJakpCQA9sMK10pISGhy7fX5+eefAQB33HFHrTq2bNlSqwaVSoWOHTvWep3s7GxMnToVwcHB8Pf3R1hYGEaOHAkAMJlMLteVn5+P0tLSevvbZrPh3LlzTutjY2OdHgcFBQEACgoKALTMzwRRa+FVc0ZKSkrQr18/PPLII06/FF0xe/ZsbNmyBW+99Rb69OmDK1eu4MqVK26ulNoLvV6P6OhoHD161KXtrv0LuL7nv/jiC+zduxf//e9/sXnzZjzyyCNYtGgR9u7dC39//+aUfUMPPfQQpk2bhoyMDPTv3x+ff/457rzzToSGhjra2Gw23HXXXXjhhRfqfI2bbrrJ6fG1ow/uYrPZANjnjURGRtZ6/vpTbTUajdMpw4B9FOOuu+7ClStX8OKLL6J79+7w8/PDhQsXMHXqVMd7tDSlUlnneiEEAPl/JohakleFkTFjxmDMmDH1Pm+xWPCHP/wBn332GQoLC9G7d2/89a9/dVyD4aeffsLixYtx9OhRx18sLfHXGrUvv/rVr7Bs2TKkp6cjMTGxwbZxcXGw2Wz4+eefHSMSgH1yaWFhIeLi4pzaDx06FEOHDsUbb7yBVatWYdKkSVi9ejWmT59+w0BT13tnZmbWWn/ixAnH8zVSUlLw+OOPOw7VnDx5EvPnz3farnPnziguLnaMhLSk+va1c+fOAIDw8PAm13HkyBGcPHkSK1euxOTJkx3r09LSGl3H9cLCwqDT6ertb4VCgZiYmCbV29DPBJG38qrDNDcya9YspKenY/Xq1fjxxx8xfvx43H333Y6h3JqzHjZu3IiEhATEx8dj+vTpHBmhZnnhhRfg5+eH6dOnIzc3t9bzp0+fxrvvvgsAuOeeewCg1pkmb7/9NgBg7NixAOxD8zV/Edfo378/ADiG5WvOBCksLGxUnffccw/27duH9PR0x7qSkhIsW7YM8fHx6Nmzp2N9YGAgkpOT8fnnn2P16tVQq9VISUlxer3f/OY3SE9Px+bNm2u9V2FhIaqqqhpVV2PUXAvk+n1NTk6GXq/HX/7ylzrnUeTn59/wtWtGJK7tbyGE43vWmDrqes3Ro0djw4YNTleNzc3NxapVqzBixAjHIb7GaszPBJG38qqRkYZkZ2dj+fLlyM7ORnR0NADgueeew7fffovly5fjL3/5C3755RdkZWVh7dq1+Pjjj2G1WvHMM8/gwQcfxLZt22TeA/JWnTt3xqpVqzBhwgT06NHD6Qqse/bswdq1ax33WenXrx+mTJmCZcuWobCwECNHjsS+ffuwcuVKpKSk4PbbbwcArFy5Eh988AHuu+8+dO7cGUVFRfjXv/4FvV7vCDS+vr7o2bMn1qxZg5tuugnBwcHo3bs3evfuXWed8+bNw2effYYxY8bg6aefRnBwMFauXIkzZ87gyy+/rHX4YsKECfjd736HDz74AMnJyY4JlDWef/55/Oc//8GvfvUrTJ06FQMHDkRJSQmOHDmCL774AmfPnnU6rNMc/fv3h1KpxF//+leYTCZoNBrccccdCA8Px+LFi/Hwww/j5ptvxkMPPYSwsDBkZ2fj66+/xvDhw/HPf/6zwdfu3r07OnfujOeeew4XLlyAXq/Hl19+6Zirca2BAwcCAJ5++mkkJydDqVTioYceqvN1//znPyMtLQ0jRozA73//e6hUKixduhQWiwV/+9vfXO6DxvxMEHktGc/kaRYAYt26dY7HGzduFACEn5+f06JSqcRvfvMbIYQQM2bMEABEZmamY7uDBw8KAOLEiROe3gVqY06ePClmzJgh4uPjhVqtFgEBAWL48OHivffec7qgWWVlpXj11VdFQkKC8PHxETExMbUuenbo0CExceJEERsbKzQajQgPDxe/+tWvxIEDB5zec8+ePWLgwIFCrVa7dNGzwMBAodVqxeDBg50uenYts9ksfH19BQDx6aef1tmmqKhIzJ8/X3Tp0kWo1WoRGhoqhg0bJt566y1RUVEhhHC+6FljXX9qrxBC/Otf/xKdOnUSSqWy1mm+27dvF8nJycJgMAitVis6d+4spk6d6tRfNRc9q8vx48dFUlKS8Pf3F6GhoWLGjBnihx9+EADE8uXLHe2qqqrEU089JcLCwoQkSY266FlycrLw9/cXOp1O3H777WLPnj1ObWpO7b3+lN3t27c77WdjfyaIvJEkxHXjfl5CkiSsW7fOMXS8Zs0aTJo0CceOHas1Eczf3x+RkZFYsGBBreHcsrIy6HQ6bNmyBXfddZcnd4GIiIjQhg7Line truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -695,8 +695,9 @@
|
|||||||
" plt.plot(range(0, len(costs)), costs)\n",
|
" plt.plot(range(0, len(costs)), costs)\n",
|
||||||
" plt.title(\"Cost over iterations\")\n",
|
" plt.title(\"Cost over iterations\")\n",
|
||||||
" plt.show()\n",
|
" plt.show()\n",
|
||||||
|
" return theta\n",
|
||||||
"\n",
|
"\n",
|
||||||
"stochastic_gradient_descent(X, y, max_iter = 1000, alpha = 0.000001)"
|
"theta = stochastic_gradient_descent(X, y, max_iter = 1000, alpha = 0.000001)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -709,13 +710,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 326,
|
"execution_count": 443,
|
||||||
"id": "f73c6af3",
|
"id": "f73c6af3",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -778,7 +779,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 327,
|
"execution_count": 444,
|
||||||
"id": "dbcb6f22",
|
"id": "dbcb6f22",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -786,8 +787,8 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Theta = [240.07710713 26.33242457]\n",
|
"Theta = [ 0.338377 28.68612806]\n",
|
||||||
"Cost = 138034.95779787414\n"
|
"Cost = 144162.5602768206\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -912,7 +913,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 445,
|
||||||
"id": "8604ed56",
|
"id": "8604ed56",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@@ -921,13 +922,13 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Stochastic : \n",
|
"Stochastic : \n",
|
||||||
"Theta = [ 0.31013607 29.29520581]\n",
|
"Theta = [ 0.32518013 29.02018324]\n",
|
||||||
"Cost = 145871.70198566347\n"
|
"Cost = 144715.36532107095\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -940,13 +941,13 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"batch of 1 : \n",
|
"batch of 1 : \n",
|
||||||
"Theta = [ 0.33356128 29.13574287]\n",
|
"Theta = [ 0.31051073 29.06744375]\n",
|
||||||
"Cost = 145123.94167962432\n"
|
"Cost = 144869.2393588724\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -958,19 +959,30 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Theta = [ 0.32898838 28.78840806]\n",
|
"batch of 10 : \n",
|
||||||
"Cost = 144232.79516214516\n"
|
"Theta = [ 0.32754329 28.6711317 ]\n",
|
||||||
|
"Cost = 144160.22579802573\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 640x480 with 1 Axes>"
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([ 0.32754329, 28.6711317 ])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 445,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
@@ -998,6 +1010,7 @@
|
|||||||
" plt.plot(range(0, len(costs)), costs)\n",
|
" plt.plot(range(0, len(costs)), costs)\n",
|
||||||
" plt.title(\"Cost over iterations\")\n",
|
" plt.title(\"Cost over iterations\")\n",
|
||||||
" plt.show()\n",
|
" plt.show()\n",
|
||||||
|
" return theta\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"print(\"Stochastic : \")\n",
|
"print(\"Stochastic : \")\n",
|
||||||
@@ -1007,6 +1020,205 @@
|
|||||||
"print(\"batch of 10 : \")\n",
|
"print(\"batch of 10 : \")\n",
|
||||||
"batch_gradient_descent(X, y, batch_size = 10, max_iter = 1000, alpha = 0.000001)"
|
"batch_gradient_descent(X, y, batch_size = 10, max_iter = 1000, alpha = 0.000001)"
|
||||||
]
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "35e0d314",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Exercice 7 Optional – multi-variable linear regression"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "de60cd7e",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## A)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 446,
|
||||||
|
"id": "928deeaa",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Theta = [2302.75644692 1057.56722311 65.54208244]\n",
|
||||||
|
"Cost = 133826.31478540495\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def multivariate_batch_gradient_descent(X, y, batch_size, max_iter, alpha):\n",
|
||||||
|
" theta = np.zeros((X.shape[1]))\n",
|
||||||
|
"\n",
|
||||||
|
" costs = []\n",
|
||||||
|
"\n",
|
||||||
|
" for i in range(0, max_iter): # we could also use a delta on the cost function with a while to stop when the cost is not improving but we wanted to see the curve after x iterations\n",
|
||||||
|
" batch_idx = np.random.choice(y.shape[0], batch_size)\n",
|
||||||
|
" y_stoch = y[batch_idx]\n",
|
||||||
|
" y_pred_stoch = X[batch_idx, :] @ theta\n",
|
||||||
|
"\n",
|
||||||
|
" for j in range(0, X.shape[1]):\n",
|
||||||
|
" theta[j] = theta[j] - alpha * (1/len(y_stoch)) * np.sum((y_pred_stoch - y_stoch) * X[batch_idx, j])\n",
|
||||||
|
"\n",
|
||||||
|
" y_pred = X @ theta\n",
|
||||||
|
" costs.append(cost_J(y_pred, y))\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
" y_pred = X @ theta\n",
|
||||||
|
" print(f\"Theta = {theta}\")\n",
|
||||||
|
" print(f\"Cost = {cost_J(y_pred, y)}\")\n",
|
||||||
|
"\n",
|
||||||
|
" plt.plot(range(0, len(costs)), costs)\n",
|
||||||
|
" plt.title(\"Cost over iterations\")\n",
|
||||||
|
" plt.show()\n",
|
||||||
|
"\n",
|
||||||
|
" return theta\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# need to normalize the data or else overflows will occur\n",
|
||||||
|
"living = dataset[\"living_area\"].to_frame()\n",
|
||||||
|
"mean_living = living.mean()\n",
|
||||||
|
"std_living = living.std()\n",
|
||||||
|
"living_normalized = (living - mean_living) / std_living\n",
|
||||||
|
"\n",
|
||||||
|
"X_norm = living_normalized\n",
|
||||||
|
"X_norm.insert(0, \"x0\", 1) # add the column full of ones at the start, this represents x0\n",
|
||||||
|
"X_norm.insert(2, \"x2\", X_norm.iloc[:,1]**2) # add the squared value of column 1 to column 2\n",
|
||||||
|
"X_norm = np.array(X_norm)\n",
|
||||||
|
"\n",
|
||||||
|
"y = np.array(dataset[\"rent_price\"])\n",
|
||||||
|
"\n",
|
||||||
|
"theta = multivariate_batch_gradient_descent(X_norm, y, batch_size=10, max_iter = 10000, alpha=0.001)\n",
|
||||||
|
"\n",
|
||||||
|
"plt.scatter(dataset[\"living_area\"], dataset[\"rent_price\"])\n",
|
||||||
|
"plt.xlabel(\"Living Area\")\n",
|
||||||
|
"plt.ylabel(\"Rent price\")\n",
|
||||||
|
"\n",
|
||||||
|
"y_regr = theta[0] + theta[1] * X_norm[:,1] + theta[2] * X_norm[:,2]\n",
|
||||||
|
"plt.plot(dataset[\"living_area\"], y_regr, color='red', label='Regression line')\n",
|
||||||
|
"\n",
|
||||||
|
"plt.show()\n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "64f2201b",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"The convergence is still happening. We observed that we could use a way bigger alpha to achieve the same results"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "a48af614",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## B)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "dcfa5986",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Theta = [ 1.05482247 0.33405956 28.44182263]\n",
|
||||||
|
"Cost = 144284.47491785986\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 640x480 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"X = dataset[\"living_area\"].to_frame()\n",
|
||||||
|
"X.insert(0, \"x0\", 1) # add the column full of ones at the start, this represents x0\n",
|
||||||
|
"X.insert(0, \"x2\", dataset[\"nb_rooms\"].to_frame()) # add the column full of ones at the start, this represents x0\n",
|
||||||
|
"X = np.array(X)\n",
|
||||||
|
"\n",
|
||||||
|
"y = np.array(dataset[\"rent_price\"])\n",
|
||||||
|
"\n",
|
||||||
|
"theta = multivariate_batch_gradient_descent(X, y, batch_size=10, max_iter = 1000, alpha=0.000001)\n",
|
||||||
|
"\n",
|
||||||
|
"plt.scatter(dataset[\"living_area\"], dataset[\"rent_price\"])\n",
|
||||||
|
"plt.xlabel(\"Living Area\")\n",
|
||||||
|
"plt.ylabel(\"Rent price\")\n",
|
||||||
|
"\n",
|
||||||
|
"y_regr = theta[0] + theta[1] * X[:,1] + theta[2] * X[:,2]\n",
|
||||||
|
"plt.plot(dataset[\"living_area\"], y_regr, color='red', label='Regression line')\n",
|
||||||
|
"\n",
|
||||||
|
"plt.show()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "ed1e2d4f",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"The algorithm still manages to converge, but no clear improvements is seen here"
|
||||||
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|||||||
Reference in new issue
Block a user