Deep GP notebook now downloads elevators
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "from deep_gp import AbstractDeepGPHiddenLayer, AbstractDeepGP, DeepGaussianLikelihood"
+ "from gpytorch.models.deep_gps import AbstractDeepGPHiddenLayer, AbstractDeepGP, DeepGaussianLikelihood"
]
},
{
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading 'elevators' UCI dataset...\n"
+ ]
+ }
+ ],
"source": [
"import urllib.request\n",
"import os.path\n",
"from math import floor\n",
"import numpy as np\n",
"\n",
- "data = torch.Tensor(loadmat('/home/jake.gardner/data/elevators.mat')['data'])\n",
+ "if not os.path.isfile('elevators.mat'):\n",
+ " print('Downloading \\'elevators\\' UCI dataset...')\n",
+ " urllib.request.urlretrieve('https://drive.google.com/uc?export=download&id=1jhWL3YUHvXIaftia4qeAyDwVxo6j1alk', 'elevators.mat')\n",
+ " \n",
+ "data = torch.Tensor(loadmat('elevators.mat')['data'])\n",
"X = data[:, :-1]\n",
"y = data[:, -1]\n",
"\n",
