{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from lets_plot import *\n", "\n", "LetsPlot.setup_html()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import make_moons\n", "from sklearn.preprocessing import scale\n", "from sklearn.metrics.pairwise import check_pairwise_arrays\n", "from sklearn.metrics import euclidean_distances\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X, y = make_moons(noise = 0.1, n_samples = 1000)\n", "X = scale(X)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data = {'x1':X.T[0], 'x2':X.T[1], 'target':y}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ], "text/plain": [ "