gp in GPy added
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ritchie46 committed
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commit
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1 file changed
+390
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {},
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"outputs": [],
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"source": [
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"import GPy\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"from scipy import stats\n",
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"from scipy.stats import multivariate_normal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f9ad4bde5f8>]"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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truncated
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"<Figure size 432x288 with 1 Axes>"
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]
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"np.random.seed(5)\n",
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"x = np.linspace(0, 10)[:, None]\n",
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"y = np.sin(x)\n",
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"\n",
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"X = np.random.choice(x.flatten(), 7, replace=False)[:, None]\n",
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"Y = np.sin(X)\n",
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"\n",
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"plt.plot(x, y)\n",
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"plt.plot(X, Y, 'o')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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".tg {font-family:\"Courier New\", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}\n",
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".tg td{font-family:\"Courier New\", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
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".tg th{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;color:#fff;background-color:#26ADE4;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
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".tg .tg-right{font-family:\"Courier New\", Courier, monospace !important;font-weight:normal;text-align:right;}\n",
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"</style>\n",
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"<table class=\"tg\"><tr><th><b> rbf. </b></th><th><b>value</b></th><th><b>constraints</b></th><th><b>priors</b></th></tr>\n",
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"<tr><td class=tg-left> variance </td><td class=tg-right> 1.0</td><td class=tg-center> +ve </td><td class=tg-center> </td></tr>\n",
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"<tr><td class=tg-left> lengthscale</td><td class=tg-right> 1.0</td><td class=tg-center> +ve </td><td class=tg-center> </td></tr>\n",
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"</table>"
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],
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"text/plain": [
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"<GPy.kern.src.rbf.RBF at 0x7f9ad4b41eb8>"
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]
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},
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"kernel = GPy.kern.RBF(input_dim=1)\n",
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"kernel"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
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".pd{\n",
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" font-family: \"Courier New\", Courier, monospace !important;\n",
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" width: 100%;\n",
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" padding: 3px;\n",
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"}\n",
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"</style>\n",
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"\n",
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"<p class=pd>\n",
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"<b>Model</b>: GP regression<br>\n",
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"<b>Objective</b>: 9.071735102889447<br>\n",
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"<b>Number of Parameters</b>: 3<br>\n",
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"<b>Number of Optimization Parameters</b>: 3<br>\n",
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"<b>Updates</b>: True<br>\n",
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"</p>\n",
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"<style type=\"text/css\">\n",
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".tg {font-family:\"Courier New\", Courier, monospace !important;padding:2px 3px;word-break:normal;border-collapse:collapse;border-spacing:0;border-color:#DCDCDC;margin:0px auto;width:100%;}\n",
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".tg td{font-family:\"Courier New\", Courier, monospace !important;font-weight:bold;color:#444;background-color:#F7FDFA;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:#DCDCDC;}\n",
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"</style>\n",
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"<table class=\"tg\"><tr><th><b> GP_regression. </b></th><th><b>value</b></th><th><b>constraints</b></th><th><b>priors</b></th></tr>\n",
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"<tr><td class=tg-left> rbf.variance </td><td class=tg-right> 1.0</td><td class=tg-center> +ve </td><td class=tg-center> </td></tr>\n",
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"<tr><td class=tg-left> rbf.lengthscale </td><td class=tg-right> 1.0</td><td class=tg-center> +ve </td><td class=tg-center> </td></tr>\n",
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"<tr><td class=tg-left> Gaussian_noise.variance</td><td class=tg-right> 1.0</td><td class=tg-center> +ve </td><td class=tg-center> </td></tr>\n",
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"</table>"
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],
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"<GPy.models.gp_regression.GPRegression at 0x7f9acf6c27b8>"
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},
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"execution_count": 129,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"m = GPy.models.GPRegression(X, Y, kernel)\n",
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"m"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 153,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'dataplot': [<matplotlib.collections.PathCollection at 0x7f9acd282278>],\n",
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" 'gpmean': [[<matplotlib.lines.Line2D at 0x7f9acd1d9470>]],\n",
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" 'gpconfidence': [<matplotlib.collections.PolyCollection at 0x7f9acd1d98d0>]}"
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]
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},
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"execution_count": 153,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"kernel = GPy.kern.RBF(input_dim=1)\n",
|
||||
"m = GPy.models.GPRegression(X, Y, kernel)\n",
|
||||
"m.plot(plot_density=False, figsize=(16, 4))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 172,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<paramz.optimization.optimization.opt_lbfgsb at 0x7f9ace7c5160>"
|
||||
]
|
||||
},
|
||||
"execution_count": 172,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"m.optimize()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 173,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'dataplot': [<matplotlib.collections.PathCollection at 0x7f9acd268978>],\n",
|
||||
" 'gpmean': [[<matplotlib.lines.Line2D at 0x7f9acd268940>]],\n",
|
||||
" 'gpconfidence': [<matplotlib.collections.PolyCollection at 0x7f9acd2689e8>]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 173,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" /opt/miniconda3/lib/python3.7/site-packages/matplotlib/figure.py:2369: UserWarning:This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"m.plot(plot_density=0, figsize=(16, 4))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 174,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# make prediction by parameterizing Gaussian\n",
|
||||
"mu, cov = m.predict(x, full_cov=True)\n",
|
||||
"a = multivariate_normal(mu.flatten(), cov).rvs(75)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(16, 4))\n",
|
||||
"for i in range(a.shape[0]):\n",
|
||||
" plt.plot(a[i, :], alpha=0.5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 176,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# or sample directly from posterior\n",
|
||||
"a = m.posterior_samples(x, size=75)\n",
|
||||
"a = np.squeeze(a)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(16, 4))\n",
|
||||
"for i in range(a.shape[0]):\n",
|
||||
" plt.plot(a[:, i], alpha=0.5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 269,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'gpmean': [[<matplotlib.lines.Line2D at 0x7f9ace055ef0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace31e0f0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace04b9b0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace04bb70>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace0564e0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace056828>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace056b70>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace056eb8>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace056f98>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accff1588>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accff18d0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9ace060160>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accff1b70>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfd3fd0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfd7e80>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfc1c18>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfd8d30>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfebb00>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfebbe0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accffdf98>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfe87b8>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accfe8898>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accedb400>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accedb5c0>],\n",
|
||||
" [<matplotlib.lines.Line2D at 0x7f9accedbd68>]]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 269,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" /opt/miniconda3/lib/python3.7/site-packages/matplotlib/figure.py:2369: UserWarning:This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1152x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# plot prior\n",
|
||||
"kernel = GPy.kern.RBF(input_dim=1, lengthscale=1, variance=0.001)\n",
|
||||
"m = GPy.models.GPRegression(X, Y, kernel)\n",
|
||||
"m.plot_samples(samples=25, figsize=(16, 4))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in new issue
Block a user