{
"cells": [
{
"attachments": {},
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"metadata": {},
"source": [
"# Cluster Resampler\n",
"\n",
"This notebook shows how `clusterResampler` methods are used to create synthetic samples. `clusterResampler` relies on a Python package [k-means-constrained](https://pypi.org/project/k-means-constrained/) to cluster the data. There are two methods demonstrated in this notebook. The first one draws synthetic values from a multivariate normal distribution. The second one draws synthetic values from a gaussian copula. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from synloc import sample_circulars_xy, clusterCov, clusterGaussCopula"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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1000 rows × 2 columns
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],
"source": [
"syn_cov.comparePlots(['x', 'y'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Gaussian Copula"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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1000 rows × 2 columns
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"metadata": {},
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],
"source": [
"syn_cop.comparePlots(['x', 'y'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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