{
"cells": [
{
"cell_type": "markdown",
"id": "24039ebc-170b-41bb-a0aa-95d223486c29",
"metadata": {},
"source": [
"# Removed Data Points Messages by sampling\n",
"\n",
"Small examples for checking computation messages from sampling."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "07eaa2cd-b165-4af5-8aea-1f1284d627a3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from lets_plot import *\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d59908e1-fc2e-4fad-9713-9a54cb893f13",
"metadata": {},
"outputs": [],
"source": [
"def data1(n, seed=123):\n",
" np.random.seed(seed)\n",
" cov = [[1, -.8], \n",
" [-.8, 1]]\n",
" x, y = np.random.multivariate_normal(mean=[0, 0], cov=cov, size=n).T\n",
" return {\"x\": x, \"y\": y}"
]
},
{
"cell_type": "markdown",
"id": "f2287203-2ba1-40f7-b344-f10b8f455fc4",
"metadata": {},
"source": [
"## Message format"
]
},
{
"cell_type": "markdown",
"id": "aeb5d1ae-fb8b-4aac-b697-f8760daec7aa",
"metadata": {},
"source": [
"One sampling function"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b80cbd8b-e254-4d30-9f76-ca75dbaa3dc6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"