{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4099c66d", "metadata": {}, "outputs": [], "source": [ "import GPy\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "e355bc94", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "id": "c70ef438", "metadata": {}, "outputs": [], "source": [ "true_kernel_lengthscale = 2.0\n", "true_kernel_variance = 1.0\n", "true_noise = 0.5" ] }, { "cell_type": "code", "execution_count": 4, "id": "6efdbc47", "metadata": {}, "outputs": [], "source": [ "kernel = GPy.kern.RBF(\n", " input_dim=1, variance=true_kernel_variance, lengthscale=true_kernel_lengthscale\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "44f9be85", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
rbf. | value | constraints | priors |
---|---|---|---|
variance | 1.0 | +ve | |
lengthscale | 2.0 | +ve |
\n",
"Model: GP regression
\n",
"Objective: 195.84754738371487
\n",
"Number of Parameters: 3
\n",
"Number of Optimization Parameters: 3
\n",
"Updates: True
\n",
"
GP_regression. | value | constraints | priors |
---|---|---|---|
rbf.variance | 3.173279047762375 | +ve | |
rbf.lengthscale | 2.3422120232188184 | +ve | |
Gaussian_noise.variance | 0.25032019995651095 | +ve |
\n",
"Model: GP regression
\n",
"Objective: 195.84754738371876
\n",
"Number of Parameters: 3
\n",
"Number of Optimization Parameters: 3
\n",
"Updates: True
\n",
"
GP_regression. | value | constraints | priors |
---|---|---|---|
rbf.variance | 3.173275150587815 | +ve | |
rbf.lengthscale | 2.342210766061685 | +ve | |
Gaussian_noise.variance | 0.2503201961365106 | +ve |