Removed 3rd year lab answers

AIC.ipynb

@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "98abd4a7",
+   "metadata": {},
+   "source": [
+    "AIC to be added to doenut.oy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c2e0b5a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of parameters: 3\n",
+      "MSE: 0.011\n",
+      "AIC: -442.128\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# calculate akaike information criterion for a linear regression model\n",
+    "from math import log\n",
+    "from sklearn.datasets import make_regression\n",
+    "from sklearn.line`ar_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    " \n",
+    "# calculate aic for regression\n",
+    "def calculate_aic(n, mse, num_params):\n",
+    "\taic = n * log(mse) + 2 * num_params\n",
+    "\treturn aic\n",
+    "\n",
+    "# calculate bic for regression\n",
+    "def calculate_bic(n, mse, num_params):\n",
+    "\tbic = n * log(mse) + num_params * log(n)\n",
+    "\treturn bic\n",
+    " \n",
+    "# generate dataset\n",
+    "X, y = make_regression(n_samples=100, n_features=2, noise=0.1)\n",
+    "# define and fit the model on all data\n",
+    "model = LinearRegression()\n",
+    "model.fit(X, y)\n",
+    "# number of parameters\n",
+    "num_params = len(model.coef_) + 1\n",
+    "print('Number of parameters: %d' % (num_params))\n",
+    "# predict the training set\n",
+    "yhat = model.predict(X)\n",
+    "# calculate the error\n",
+    "mse = mean_squared_error(y, yhat)\n",
+    "print('MSE: %.3f' % mse)\n",
+    "# calculate the aic\n",
+    "aic = calculate_aic(len(y), mse, num_params)\n",
+    "print('AIC: %.3f' % aic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0426c7b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "67.09209781943903"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "N=22\n",
+    "rmse=6.1168\n",
+    "num_params=N-16\n",
+    "calculate_aic(n=N, mse=rmse*2, num_params=num_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "b8977c33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "75.33333333333333\n",
+      "0.23570226039551584\n",
+      "0.4714045207910317\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x=[76,75,75]\n",
+    "print(np.mean(x))\n",
+    "print(np.std(x)/np.sqrt(4))\n",
+    "print(np.std(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d0b546d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84-75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "26b39103",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "82-61"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "c135332b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean([32,42])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5bffb591",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "82-50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "900fc756",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "82-40"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "27d7a7cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "88-60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4ae6376",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}