update the training number in 01_Simple_GP_Regression
" \\epsilon &\\sim \\mathcal{N}(0, 0.2)\n",
"\\end{align*}\n",
"\n",
- "with 11 training examples, and testing on 51 test examples.\n",
+ "with 100 training examples, and testing on 51 test examples.\n",
"\n",
"**Note:** this notebook is not necessarily intended to teach the mathematical background of Gaussian processes, but rather how to train a simple one and make predictions in GPyTorch. For a mathematical treatment, Chapter 2 of Gaussian Processes for Machine Learning provides a very thorough introduction to GP regression (this entire text is highly recommended): http://www.gaussianprocess.org/gpml/chapters/RW2.pdf"
]
"source": [
"### Set up training data\n",
"\n",
- "In the next cell, we set up the training data for this example. We'll be using 11 regularly spaced points on [0,1] which we evaluate the function on and add Gaussian noise to get the training labels."
+ "In the next cell, we set up the training data for this example. We'll be using 100 regularly spaced points on [0,1] which we evaluate the function on and add Gaussian noise to get the training labels."
]
},
{
"metadata": {},
"outputs": [],
"source": [
- "# Training data is 11 points in [0,1] inclusive regularly spaced\n",
+ "# Training data is 100 points in [0,1] inclusive regularly spaced\n",
"train_x = torch.linspace(0, 1, 100)\n",
"# True function is sin(2*pi*x) with Gaussian noise\n",
"train_y = torch.sin(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2"
