Clean up.

Line fits.ipynb

@@ -1,104 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1bba0128",
-   "metadata": {},
-   "source": [
-    "# Simple fits\n",
-    "\n",
-    "Here we are fitting a line from scratch.\n",
-    "In the next notebook, we will do fancier fits with neural networks, but let's start with a basic problem and complicate it as we go along.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "23feddde",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Tuple\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb1286f0",
-   "metadata": {},
-   "source": [
-    "We start by generating some fake dataset, which is simple enough that we can visualize the results easily. For this reason, the dataset will contain only two  variables.\n",
-    "\n",
-    "The simulated example data will be $f(x) = 3 x + \\epsilon$, where $\\epsilon \\sim \\mathcal{N}(\\mu=0, \\sigma=0.5)$.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "5d457cd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_data(N: int) -> np.ndarray:\n",
-    "    x = 2*np.random.randn(N, 1)\n",
-    "    epsilon = 0.5*np.random.randn(N, 1)\n",
-    "    z = 3*x + epsilon\n",
-    "    return np.concatenate((x, z), axis=1).astype(np.float32)\n",
-    "\n",
-    "data = generate_data(N=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48433f6f",
-   "metadata": {},
-   "source": [
-    "We can fit this line from scratch, assuming $y = f(x) = \\beta x + \\alpha + \\epsilon$, where $\\epsilon$ is a zero-mean Gaussian noise.\n",
-    "\n",
-    "How would you do it? Feel free to use standard Python modules. Look at the solution for a simple mathematical expression for this fit with a full derivation.\n",
-    "\n",
-    "Tip: Look for the documentation for `numpy.linalg.lstsq`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "85040eef-3558-4531-9bce-4f29d520f86b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e9170af-ca77-4526-b3d2-e19e77fef44e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}