{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D Gaussians\n",
    "\n",
    "## Two gaussians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from fastabx import Dataset, Score, Task\n",
    "\n",
    "n = 100\n",
    "diagonal_shift = 4\n",
    "mean = np.zeros(2)\n",
    "cov = np.array([[4, -2], [-2, 3]])\n",
    "\n",
    "rng = np.random.default_rng(seed=0)\n",
    "first = rng.multivariate_normal(mean, cov, n)\n",
    "second = rng.multivariate_normal(mean + np.ones(2) * diagonal_shift, cov, n)\n",
    "\n",
    "dataset = Dataset.from_numpy(np.vstack([first, second]), {\"label\": [0] * n + [1] * n})\n",
    "task = Task(dataset, on=\"label\")\n",
    "score = Score(task, \"euclidean\")\n",
    "\n",
    "plt.scatter(*first.T, alpha=0.5)\n",
    "plt.scatter(*second.T, alpha=0.5)\n",
    "plt.axis(\"equal\")\n",
    "plt.grid()\n",
    "plt.title(f\"ABX: {score.collapse():.3%}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two gaussians with increasing shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from fastabx import Dataset, Score, Task\n",
    "\n",
    "n = 100\n",
    "shift = np.ones(1)\n",
    "mean = np.zeros(2)\n",
    "cov = np.array([[4, -2], [-2, 3]])\n",
    "\n",
    "rng = np.random.default_rng(seed=0)\n",
    "first = rng.multivariate_normal(mean, cov, n)\n",
    "second = rng.multivariate_normal(mean, cov, n)\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(10, 8), nrows=3, ncols=3, sharex=True, sharey=True)\n",
    "for ax in axes.flatten():\n",
    "    dataset = Dataset.from_numpy(np.vstack([first, second]), {\"label\": [0] * n + [1] * n})\n",
    "    task = Task(dataset, on=\"label\")\n",
    "    score = Score(task, \"euclidean\")\n",
    "\n",
    "    ax.scatter(*first.T, s=10, alpha=0.5)\n",
    "    ax.scatter(*second.T, s=10, alpha=0.5)\n",
    "    ax.grid()\n",
    "    ax.set_title(f\"ABX: {score.collapse():.3%}\")\n",
    "    second += shift\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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