Created
October 20, 2022 01:56
-
-
Save minghao912/8829c5eee279cb2e0414affc32732f20 to your computer and use it in GitHub Desktop.
MATH 170E HW 4 Q3
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import random\n", | |
"\n", | |
"def roll():\n", | |
" # Roll 3 dice\n", | |
" dice = [random.randint(1,6) for i in range(3)]\n", | |
" \n", | |
" # Calculate X\n", | |
" maxRoll = max(dice)\n", | |
" minRoll = min(dice)\n", | |
" return maxRoll - minRoll" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"results = [roll() for i in range(1000000)]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import matplotlib.pyplot as plt" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"outcomes = [0, 0, 0, 0, 0, 0]\n", | |
"\n", | |
"for result in results:\n", | |
" outcomes[result] += 1" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"[27688, 139601, 222186, 250116, 221788, 138621]\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x1b6c0d1e410>]" | |
] | |
}, | |
"execution_count": 19, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAmO0lEQVR4nO3deXhV5bn+8e9DQgKEKcyQEQRkUoFsIVqHqhVwBD3OTD1Hpa12sLVXawelVfuz7Wlr6zlFi+hPJqXWEUdEKrVWAoR5CEMYMjEkECAJkJDhPX9kYVNKIECStYf7c137ys6711772a3sO2utd7+POecQERE5mRZ+FyAiIsFLISEiIvVSSIiISL0UEiIiUi+FhIiI1Cva7wIaW5cuXVxqaqrfZYiIhJQVK1bsc851PXE87EIiNTWVzMxMv8sQEQkpZpZzsnGdbhIRkXopJEREpF4KCRERqZdCQkRE6qWQEBGRep02JMwsycw+MbONZrbBzL7jjf/MzArMbLV3u77Oc35kZtlmttnMRtcZH+ONZZvZI3XGe5vZUm/8z2YW443Her9ne4+nNuq7FxGRU2rIkUQV8LBzbhCQDjxoZoO8x552zg31bu8DeI/dBQwGxgDTzCzKzKKAPwLXAYOAu+vs51fevvoCB4B7vfF7gQPe+NPediIi0kxOGxLOud3OuZXe/VIgC0g4xVPGAvOccxXOuR1ANjDCu2U757Y7544B84CxZmbA1cBr3vNnAuPq7Gumd/814BpvexFpgAOHj/Hy0lw+3VJETY3aAsiZO6Mv03mne4YBS4EvAd80s0lAJrVHGweoDZCMOk/L55+hknfC+EigM3DQOVd1ku0Tjj/HOVdlZoe87fedUNcUYApAcnLymbwlkbDjnGNl7gHmZOTy3rrdHKuqASClcxsmjEzh9kAiHdvE+FylhIoGh4SZtQVeBx5yzpWY2bPAE4Dzfv4W+K8mqfI0nHPTgekAgUBAfy5JRCotr+St1buYm5HDpj2ltI2N5q6Lk7gjkMS2ojLmZOTwi/ez+M1Hm7npol5MTE/hoqSOfpctQa5BIWFmLakNiLnOuTcAnHN76zz+PPCu92sBkFTn6YneGPWM7wc6mlm0dzRRd/vj+8o3s2igg7e9iHg27DrEnIxc3l5dwJFj1QxJaM8vb72Amy7qRVxs7T/xIQkdGDs0gazdJczJyOHNVQW8tiKfCxM7MCE9hZsu7EXrmCif34kEIztd+1LvGsBMoNg591Cd8Z7Oud3e/e8CI51zd5nZYOBlaq9B9AIWAf0AA7YA11D74b8cuMc5t8HM/gK87pybZ2bPAWudc9PM7EHgAufc183sLuBW59wdp6o3EAg4rd0k4a68spp31uxi7tJcVucdpFXLFtx8US/Gj0zhwsQOnO7SXWl5JW+uKmD2khy2FpbRoXVLbk9LZHx6Cr27xDXTu5BgYmYrnHOBfxtvQEhcBvwdWAfUeMM/Bu4GhlJ7umkn8LU6ofETak89VVF7euoDb/x64PdAFPCic+4X3ngfai9kdwJWAROccxVm1gqYTe11kGLgLufc9lPVq5CQcJZdWMbLS3N5bUUeJeVV9O3WlvEjk7l1WCId2rQ84/0551i2o5jZGTl8uH4PVTWOy/t1YUJ6CtcM6EZ0lL5KFSnOOiRCjUJCws2xqho+2riHuRm5LNm+n5ZRxujBPZiQnsLI3p1Oe9TQUIWl5fx5WR4vL8tl96FyenZoxT0jkrlzRBLd2rVqlNeQ4KWQEAkxecVHmLc8lz8vz2dfWQWJ8a25Z2Qyt6cl0bVdbJO9blV1DYs2FTInI4e/b91HdAtjzJAeTExPYUQjhpIEl/pCIuz6SYiEsuoax+LNhcxdmssnmwsx4OoB3RmfnswV/boS1aLpP6Cjo1owenAPRg/uwY59h5mbkcOrmXm8u3Y3/bu3ZWJ6CuOGJdCu1Zmf3pLQoyMJkSBQWFrOq8vzeGVZHgUHj9KtXSx3XZzEnSOSSejY2u/yOHqsmnfW7mL2khzWFRwiLiaKccMSmHhJCgN6tPe7PGkEOt0kEmSccyzZtp+5S3NZsKH2ovFlfbswfmQyXxnUnZZBetF4Td5BZmfk8M6aXVRU1XBxajwT0lMYM6QHsdGaRhuqFBIiQeLgkWO8tiKfl5fmsn3fYTq2qZ1+eveIZPp0bet3eQ124HDt+5i7NIed+4/QpW0Md16cxN0jkkmMb+N3eXKGFBIiPnLOsSrvIHMzcnl3be1f4Gkp8Ywfmcz1F/SkVcvQ/Qu8psbxWfY+ZmfksCir9ju2Vw/oxoT0FK7o15UWzXAdRc6dLlyL+KCsooq3VxcwJyOXrN0lxMVEcXsgkfEjUxjYMzzO5bdoYVzRvytX9O9KwcGjvLI0l3nLc/k4q5CUzm0Y783Iio/TelGhSEcSIk3g+PIXb60q4PCxagb2bM+E9GTGDk2gbWz4/212rKqGDzfsYc6SHJbtLCYmugU3XdiLiZekcFEDvhEuzU+nm0SaWHllNe+t3c3cpTmszD1IbHQLbrywF+PTkxmW1DFiPxg37yllTkYOb6zM5/Cxai5I6MCE9GRuvihB60UFEYWESBPZXuQtlbEyn4NHKunTJY57RiZzW5qW5K6rrKKKN1cVMGdJDpv3ltK+VTS3pSUxIT20LtiHK4WESCOqrK5h4ca9zF2awz+y9xPdonapjPHpyVzSp3PEHjU0hHOO5TsPeOtF7aayunbq74T0FL4yUOtF+UUhIdIICg4eZd6yXOYtz6OotIKEjt5SGYFErW90FopKK3g1M4+5GTnsOlROj/atuGdkMnddnES39vrfszkpJETOUnWN49MtRcxdmsNfNxXigKvP78b49GSu7N+tWZbKCHdV1TV8srmI2Rk5fLql6IsjswnpKaT30XpRzUEhIXKGjv+V+8qyXPIPHKVL29qlMu4akaQvizWhnfsOM3dpDq9m5nPoaCV9u9WuF3XL8ATaa72oJqOQEGkA5xwZ24uZuzSHBRv2UFntuKRPZyakp3DtoO7EROt8eXM53lhpztJc1uQdpI23XtSEkSkM6hUe3zEJJgoJkVM4dKSS11fWLjGxregwHVq35DZvqYy+3TTzxm9r8w8yJyOHt1f/89vqE9NTuO4CrRfVWBQSIidwzrEm/xBzM3J4Z+0uyitrGJbckfEjU7jxwtBeKiNcHV/3au7SXHbsO0znuBjuuDiJe0Ykk9RJpwDPhUJCxHO4oor5a3YxJyOHDbtKvjiNcc+IZIYkdPC7PGmAmhrHP7btY/aSHD7O2vvFZIIJl6RwpdaLOisKCYl4zjn+56/ZTP90O2UVVQzo0Y7x6SmMG9pLDXRC2C5vWvLLy/LYV1ZBUqfWjB+ZwuRLUvWN7jOgkJCI99I/dvCzdzYyalB3vnZlH4Ynx2tqZRg53gt89pIclu4o5isDuzN9YpqOKhqovpDQVA2JCJ9uKeLxdzdy7aDuPDchjbQUzb0PNzHeWll//tol/OymQXyctZfffLTZ77JCXvgvRykRb1tRGQ++vJL+3dvx9J1D9ZdlBJh8aSqb95YybfE2zu/RjrFDE/wuKWTpSELC2qEjldw/M5OYqBbMmByIiGW6BcyMn988hBG9O/GD19ayJu+g3yWFLIWEhK2q6hoefHkleQeO8NzENH1LOsLERLfg2fHD6doulimzM9lbUu53SSFJISFh68n3svgsex+/GHcBF6d28rsc8UHntrE8PylAaXkVU2avoLyy2u+SQo5CQsLS3KU5vPT5Tu67rDd3XJzkdznio4E92/O7O4ayJu8gj7y+lnCb0dnUFBISdj7fto+pb2/gy+d35UfXD/S7HAkCY4b04OFr+/PW6l386dPtfpcTUnQVT8JKzv7DPDB3Jald4njm7mFaxlu+8M2r+7J5bym/+nAT/bq15ZqB3f0uKSToSELCRkl5JffOrP0i5QuTA1pWWv6FmfHft13EkF4d+M681WzZW+p3SSFBISFhobrG8e1XVrFz32GmjR9OSuc4v0uSINQ6Jorpk9JoHRPFfTMzOXD4mN8lBT2FhISFX36QxeLNRfx87GAuPa+L3+VIEOvZoTV/mpjGnkPlPDB3JZXVNX6XFNQUEhLyXs3M4/m/72DyJSmMH5nidzkSAoYnx/PUrRewZPt+Hn9no9/lBDVduJaQtnxnMT95cx2X9e3CozcO8rscCSH/kZbI5r2lTP90O+f3aMeEdP2BcTI6kpCQlVd8hK/PXkFifBv+eM9woqP0n7OcmR+OGcBV53flZ/M3sGTbfr/LCUr6VyUhqayiivtnZVJZXcOMyQE6tNFMJjlzUS2MP9w9jNQucTwwdwW5+4/4XVLQUUhIyKmpcTw0bzVbC8v44/jhnNdVPajl7LVv1ZIZkwLUOLh/ViZlFVV+lxRUThsSZpZkZp+Y2UYz22Bm3/HGO5nZQjPb6v2M98bNzJ4xs2wzW2tmw+vsa7K3/VYzm1xnPM3M1nnPeca8hf7rew2JbL/5aDMfZ+3l0RsGcnm/rn6XI2EgtUsc08YPJ7uojIfmraamRkt3HNeQI4kq4GHn3CAgHXjQzAYBjwCLnHP9gEXe7wDXAf282xTgWaj9wAemAiOBEcDUOh/6zwL313neGG+8vteQCPXWqgKmLd7G3SOSmXxpqt/lSBj5Ut8uPHrDQDUrOsFpQ8I5t9s5t9K7XwpkAQnAWGCmt9lMYJx3fywwy9XKADqaWU9gNLDQOVfsnDsALATGeI+1d85luNqVt2adsK+TvYZEoFW5B/jB62tJ79OJx8cOVmc5aXSTL03l7hFJTFu8jbdXF/hdTlA4o2sSZpYKDAOWAt2dc7u9h/YAxxdCSQDy6jwt3xs71Xj+ScY5xWucWNcUM8s0s8yioqIzeUsSInYdPMr9s1bQo30rnh2fRkvNZJImoGZF/67B/9LMrC3wOvCQc66k7mPeEUCTnsQ71Ws456Y75wLOuUDXrjpHHW6OHKudyVReWc2MyQHi42L8LknCmJoV/asGhYSZtaQ2IOY6597whvd6p4rwfhZ64wVA3QX8E72xU40nnmT8VK8hEaKmxvH9v6xh4+4S/ufuYfTv3s7vkiQCqFnRPzVkdpMBLwBZzrnf1XloPnB8htJk4O0645O8WU7pwCHvlNECYJSZxXsXrEcBC7zHSsws3XutSSfs62SvIRHiD4u28v66Pfz4uoFcNaCb3+VIBBnYsz1P36lmRQ05kvgSMBG42sxWe7frgV8C15rZVuAr3u8A7wPbgWzgeeABAOdcMfAEsNy7Pe6N4W0zw3vONuADb7y+15AI8O7aXfxh0VZuT0vkvst7+12ORKDRg//ZrOi5v0VmsyILt3QMBAIuMzPT7zLkHK3LP8Ttf/qcIb06MPf+kcRGR/ldkkQo5xzfemUV763bzYxJgbBtVmRmK5xzgRPHNUVEgs7eknLum7WcznGxPDcxTQEhvor0ZkUKCQkq5ZXVTJmVSWl5FTMmB+jSNtbvkkQiulmRQkKChnOOH7y2lrUFh/j9nUMZ2LO93yWJfOGLZkUlkdWsSCEhQWPa4m3MX7OL7486n1GDe/hdjsi/GZ4cz1O3RFazIjUdkqDw4fo9/PeCzYwb2osHvnye3+WI1CvSmhXpSEJ8t3FXCd97dTUXJXXkl/9xodZkkqAXSc2KFBLiq6LSCu6buZz2rVry/MQ0WrXUTCYJfpHUrEghIb6pqKrm63NWUHzkGDMmB+jWvpXfJYk0WKQ0K1JIiC+cc/z4jfWsyDnAb28fypCEDn6XJHLGIqFZkUJCfPH837fz+sp8HvpKP264sKff5YictS/17cJjNw4K22ZFmt0kze6vm/by1AebuOGCnnz76n5+lyNyziZdksKmPSVMW7yN83u0Y+zQhNM/KUToSEKa1Za9pXz7ldUM7tWe39x+ES1aaCaThL5wblakkJBmU3z4GPfOXE7rmCienxSgdYxmMkn4CNdmRQoJaRbHqmr4xpwV7C2pYPrENHp2aO13SSKNLhybFSkkpMk555g6fz1LdxTz37ddyLDkeL9LEmky4dasSCEhTe6lz3fyyrI8HrzqvLC6oCdSn9GDe/D9UeHRrEghIU3q0y1FPPHuRkYN6s7D157vdzkizebBq/py44U9+fWCTSzK2ut3OWdNISFNZltRGQ++vJL+3dvx9J1DNZNJIkq4NCtSSEiTOHjkGPfNzCQmqgUzJgeIi9VXciTyhEOzIoWENLrK6hoefHklBQeO8qeJaSTGt/G7JBHfhHqzIoWENLon393IP7L384tbhhBI7eR3OSK+G54czy9vDc1mRToHII1qTkYOM5fkMOWKPtweSPK7HJGgcevwRDbvKeVPIdasSEcS0mg+z97H1PkbuHpAN344ZoDf5YgEnR+EYLMihYQ0ip37DvONuSvp0yWOP9w1lCjNZBL5N6HYrEghIeespLyS+2Zl0sLghckX065VS79LEglaodasSCEh56S6xvGtl1exc99hpo1PI7mzZjKJnE4oNStSSMg5eer9LP62pYgnxg3hkvM6+12OSMgIlWZFmt0kZ+3Py3OZ8dkOvnppKnePSPa7HJGQU9usqDSomxXpSELOyrIdxfz0rfVc3q8LP71hoN/liISk2mZFg4O6WZFCQs5YXvERvj5nBUnxbfjfe4YTHaX/jETOVrA3K9K/bjkjZRVV3Dczk6rqGmZMDtChtWYyiZyrYG5WpJCQBquucTw0bxXZRWVMG59Gn65t/S5JJGwEa7MihYQ02G8+2szHWYVMvWkQl/Xr4nc5ImEnGJsVKSSkQd5Ymc+zi7cxfmQyE0NkzRmRUFS3WdHHG/1vVqSQkNNamXuAR15fxyV9OvOzmwdjpiU3RJrKvzYrWuV7s6LThoSZvWhmhWa2vs7Yz8yswMxWe7fr6zz2IzPLNrPNZja6zvgYbyzbzB6pM97bzJZ64382sxhvPNb7Pdt7PLXR3rU02K6DR5kyawU9O7Zi2vjhtNRMJpEmd7xZUZvYaN+bFTXkX/xLwJiTjD/tnBvq3d4HMLNBwF3AYO8508wsysyigD8C1wGDgLu9bQF+5e2rL3AAuNcbvxc44I0/7W0nzejIsSrun5VJRWU1L0wOEB8X43dJIhEjWJoVnTYknHOfAsUN3N9YYJ5zrsI5twPIBkZ4t2zn3Hbn3DFgHjDWas9bXA285j1/JjCuzr5mevdfA64xnedoNjU1jodfXUPW7hKeuWcYfbu187skkYgTDM2KzuXcwTfNbK13OireG0sA8upsk++N1TfeGTjonKs6Yfxf9uU9fsjb/t+Y2RQzyzSzzKKionN4S3Lc7xdt5YP1e/jx9QO56vxufpcjErFuHZ7I167ow+yMHOZk5DT7659tSDwLnAcMBXYDv22sgs6Gc266cy7gnAt07drVz1LCwjtrdvHMoq3cEUjk3st6+12OSMTzs1nRWYWEc26vc67aOVcDPE/t6SSAAqBuz8pEb6y+8f1ARzOLPmH8X/blPd7B216a0Nr8g3z/L2u4ODWeJ8YN0UwmkSDgZ7OiswoJM+tZ59dbgOMzn+YDd3kzk3oD/YBlwHKgnzeTKYbai9vzXe1XCj8BbvOePxl4u86+Jnv3bwP+6oLlK4hham9JOffPyqRL21iem5BGbHSU3yWJiMevZkUNmQL7CrAEON/M8s3sXuDXZrbOzNYCVwHfBXDObQBeBTYCHwIPekccVcA3gQVAFvCqty3AD4HvmVk2tdccXvDGXwA6e+PfA76YNiuNr7yymimzMikrr+KFrwbo3DbW75JE5AR+NCuycPvjPBAIuMzMTL/LCCnOOb49bzXvrt3F9IkBrh3U3e+SROQUZn6+k6nzN/DAl8/jB2MGNMo+zWyFcy5w4riaDgl//CSbd9bs4odjBiggREJAczYr0tdnI9yH6/fwm4+2cMuwBL5+ZR+/yxGRBmjOZkUKiQi2Ydchvvvn1QxL7shTt16gmUwiIaS5mhUpJCJURVU133plFR3btORPE9No1VIzmURCTXM0K1JIRKgZf9/B9qLDPHXrBXRr18rvckTkLB1vVrQ2/yCLNxc2+v514ToC5RUf4ZlFW7n+gh58WUtuiIS80YN7sPC7V9K3W+N3i9SRRIRxzjF1/gaiWxiP3jjo9E8QkZDQFAEBComIs2DDXv66qZDvXtufnh1a+12OiAQ5hUQEOVxRxePvbGBAj3Z89dJUv8sRkRCgkIggzyzayq5D5Tw5bgjR6jAnIg2gT4oIsXlPKS98toM7A0kEUjv5XY6IhAiFRARwzvHTt9bRrlU0j1zXOOu8iEhkUEhEgNdW5LN85wEeuW6A+lSLyBlRSIS5A4eP8dQHm0hLief2tKTTP0FEpA6FRJj79YLNHDpayZPjhtCihdZmEpEzo5AIYytzD/DKslz+89JUBvZs73c5IhKCFBJhqqq6hp+8uZ4e7Vvx0LX9/S5HREKUQiJMzVqSQ9buEqbeNIi2sVqiS0TOjkIiDO0tKed3C7dwZf+ujBnSw+9yRCSEKSTC0OPvbqSyuobHxw5WIyEROScKiTDz6ZYi3lu7mwev6ktK5zi/yxGREKeQCCPlldU89vZ6eneJ42vqVy0ijUBXNMPIc3/bxs79R5hz70hio9WOVETOnY4kwsTOfYeZtngbN13Ui8v6dfG7HBEJEwqJMOCc47H5G4iJasGjNwz0uxwRCSMKiTDw/ro9fLqliIdH9adb+1Z+lyMiYUQhEeJKyyt5/N0NDO7VnonpKX6XIyJhRheuQ9zvP95KYWkFz01IU7c5EWl0+lQJYRt3lfDS5zu5e0Qyw5Lj/S5HRMKQQiJE1dTUdpvr2LolPxytbnMi0jQUEiHq1cw8VuYe5MfXD6RDm5Z+lyMiYUohEYKKDx/jlx9uYkTvTtw6PMHvckQkjCkkQtBT72dRVl7Fk+OGaAE/EWlSCokQs3xnMX9Zkc99l/ehf/d2fpcjImFOIRFCKqtr+Omb60no2JpvX9PX73JEJAKcNiTM7EUzKzSz9XXGOpnZQjPb6v2M98bNzJ4xs2wzW2tmw+s8Z7K3/VYzm1xnPM3M1nnPeca88yf1vUYk+///2MHmvaVMvWkQbWL0FRcRaXoNOZJ4CRhzwtgjwCLnXD9gkfc7wHVAP+82BXgWaj/wganASGAEMLXOh/6zwP11njfmNK8RkXYdPMrvP97KVwZ2Y9RgdZsTkeZx2pBwzn0KFJ8wPBaY6d2fCYyrMz7L1coAOppZT2A0sNA5V+ycOwAsBMZ4j7V3zmU45xww64R9new1ItLj72ykxjmm3jTY71JEJIKc7TWJ7s653d79PUB3734CkFdnu3xv7FTj+ScZP9VrRJxPNhXy4YY9fOvqfiR1auN3OSISQc75wrV3BOAaoZazfg0zm2JmmWaWWVRU1JSlNLujx6p5bP56zusax/2Xq9uciDSvsw2Jvd6pIryfhd54AZBUZ7tEb+xU44knGT/Va/wb59x051zAORfo2rXrWb6l4DRtcTZ5xUd5YtwQYqI1GU1EmtfZfurMB47PUJoMvF1nfJI3yykdOOSdMloAjDKzeO+C9ShggfdYiZmle7OaJp2wr5O9RsTYVlTGc3/bxi3DErj0PHWbE5Hmd9p5lGb2CvBloIuZ5VM7S+mXwKtmdi+QA9zhbf4+cD2QDRwB/hPAOVdsZk8Ay73tHnfOHb8Y/gC1M6haAx94N07xGhHBOcejb62nVcsofny9us2JiD9OGxLOubvreeiak2zrgAfr2c+LwIsnGc8EhpxkfP/JXiNSzF+zi8+37eeJsYPp2i7W73JEJELpJHcQKimv5Mn3srgwsQP3jFS3ORHxj762G4R+u2Az+8oqeHHyxUS10AJ+IuIfHUkEmXX5h5idkcPE9BQuSOzgdzkiEuEUEkGk2us21ykulodHne93OSIiColg8vKyXNbkH+LRGwfSobW6zYmI/xQSQaKotIJff7iJS8/rzM0X9fK7HBERQCERNJ56P4vyymoeH6tucyISPBQSQWDJtv28saqAr11xHn27tfW7HBGRLygkfHasqoZH315PYnxrHrxK3eZEJLjoexI+m/HZdrILy3jxqwFax0T5XY6IyL/QkYSP8oqP8MyirYwe3J2rB0RsuwwRCWIKCR/9/J0NGMZj6jYnIkFKIeGThRv38nFWIQ99pR8JHVv7XY6IyEkpJHxw5FgVP5u/gf7d2/Jfl/X2uxwRkXrpwrUPnlmUTcHBo7z6tUtoGaWcFpHgpU+oZrZ1bykz/r6d29ISGdG7k9/liIickkKiGTnn+Olb64mLjeZH1w3wuxwRkdNSSDSjN1YWsHRHMT8cM4DObdVtTkSCn0KimRw6Usn/ez+LYckdueviJL/LERFpEF24bia/XrCJA0eOMeveEbRQtzkRCRE6kmgGq/MO8vKyXCZfmsrgXuo2JyKhQyHRxKprHD95cx3d2sXyvWv7+12OiMgZUUg0sdlLdrJhVwmP3jiIdq3UbU5EQotCogkVlpTz24+2cHm/LtxwQU+/yxEROWMKiSb05HtZVFTXqNuciIQshUQT+WzrPuav2cU3rjyP3l3i/C5HROSsKCSaQEVVNY+9vZ6Uzm34xpfP87scEZGzpu9JNIHpf9vO9n2HmflfI2jVUt3mRCR06UiikeXuP8L/fpLNDRf05Mr+Xf0uR0TknCgkGpFzjsfmrye6hfHojYP8LkdE5JwpJBrRh+v3sHhzEd+9tj89OrTyuxwRkXOmkGgkZRVV/PydjQzs2Z6vXprqdzkiIo1CIdFI/vDxFvaUlPPkuCFEq9uciIQJfZo1gqzdJbz4j53cdXESaSnxfpcjItJoFBLnqKamtttch9Yt+eEYdZsTkfCikDhHr63IZ0XOAR65bgDxcTF+lyMi0qjOKSTMbKeZrTOz1WaW6Y11MrOFZrbV+xnvjZuZPWNm2Wa21syG19nPZG/7rWY2uc54mrf/bO+5QbUA0oHDx3jqgywCKfHcNjzR73JERBpdYxxJXOWcG+qcC3i/PwIscs71AxZ5vwNcB/TzblOAZ6E2VICpwEhgBDD1eLB429xf53ljGqHeRvOrDzdRUl7Fk7cMUbc5EQlLTXG6aSww07s/ExhXZ3yWq5UBdDSznsBoYKFzrtg5dwBYCIzxHmvvnMtwzjlgVp19+W5FTjHzludx72W9GdCjvd/liIg0iXMNCQd8ZGYrzGyKN9bdObfbu78H6O7dTwDy6jw33xs71Xj+Scb/jZlNMbNMM8ssKio6l/fTIFXVNfzkzfX07NCK71zTr8lfT0TEL+e6wN9lzrkCM+sGLDSzTXUfdM45M3Pn+Bqn5ZybDkwHCAQCTf56L32+k017SnluwnDiYrVGooiEr3M6knDOFXg/C4E3qb2msNc7VYT3s9DbvABIqvP0RG/sVOOJJxn31e5DR3l64RauOr8rowf38LscEZEmddYhYWZxZtbu+H1gFLAemA8cn6E0GXjbuz8fmOTNckoHDnmnpRYAo8ws3rtgPQpY4D1WYmbp3qymSXX25Zsn3t1IVY3j5zer25yIhL9zOVfSHXjT+6CMBl52zn1oZsuBV83sXiAHuMPb/n3geiAbOAL8J4BzrtjMngCWe9s97pwr9u4/ALwEtAY+8G6+Wby5kPfX7eHha/uT3LmNn6WIiDQLq504FD4CgYDLzMxs9P2WV1Yz+vefEmXGBw9dTmy0mgmJSPgwsxV1vsrwBV11baBpi7eRs/8Ic+8bqYAQkYihZTkaYHtRGc8t3sbNF/XiS327+F2OiEizUUichnOOx97eQGx0C356w0C/yxERaVYKidN4d+1uPsvex8Oj+tOtvbrNiUhkUUicQml5JU+8u5EhCe2ZeEmq3+WIiDQ7Xbg+hd8t3EJRWQXTJwWI0gJ+IhKBdCRRj/UFh5j5+U7uGZHM0KSOfpcjIuILhcRJHO821ykuhh+MVrc5EYlcComTmLc8j9V5B/nx9QPp0Kal3+WIiPhGIXGCfWUV/OrDTYzs3Ylbhp10ZXIRkYihkDjBU+9v4nBFFU+O0wJ+IiIKiTqWbt/P6yvzuf+KPvTr3s7vckREfKeQ8FRW1/Do2+tJ6Niab13d1+9yRESCgr4n4Xnhsx1s2VvG85MCtInR/ywiIqAjiS90axfLbWmJXDuo++k3FhGJEPqT2XPr8ERuHZ54+g1FRCKIjiRERKReCgkREamXQkJEROqlkBARkXopJEREpF4KCRERqZdCQkRE6qWQEBGReplzzu8aGpWZFQE5Z/n0LsC+RiwnFOg9Rwa958hwLu85xTnX9cTBsAuJc2Fmmc65gN91NCe958ig9xwZmuI963STiIjUSyEhIiL1Ukj8q+l+F+ADvefIoPccGRr9PeuahIiI1EtHEiIiUi+FhIiI1Esh4TGzMWa22cyyzewRv+tpamb2opkVmtl6v2tpDmaWZGafmNlGM9tgZt/xu6amZmatzGyZma3x3vPP/a6puZhZlJmtMrN3/a6lOZjZTjNbZ2arzSyzUfetaxK1/0EBW4BrgXxgOXC3c26jr4U1ITO7AigDZjnnhvhdT1Mzs55AT+fcSjNrB6wAxoX5/8cGxDnnysysJfAZ8B3nXIbPpTU5M/seEADaO+du9LuepmZmO4GAc67RvzyoI4laI4Bs59x259wxYB4w1ueampRz7lOg2O86motzbrdzbqV3vxTIAhL8rappuVpl3q8tvVvY/1VoZonADcAMv2sJBwqJWglAXp3f8wnzD5BIZmapwDBgqc+lNDnvtMtqoBBY6JwL+/cM/B74AVDjcx3NyQEfmdkKM5vSmDtWSEhEMbO2wOvAQ865Er/raWrOuWrn3FAgERhhZmF9atHMbgQKnXMr/K6lmV3mnBsOXAc86J1ObhQKiVoFQFKd3xO9MQkj3nn514G5zrk3/K6nOTnnDgKfAGN8LqWpfQm42TtHPw+42szm+FtS03POFXg/C4E3qT2F3igUErWWA/3MrLeZxQB3AfN9rkkakXcR9wUgyzn3O7/raQ5m1tXMOnr3W1M7MWOTr0U1Mefcj5xzic65VGr/Hf/VOTfB57KalJnFeZMxMLM4YBTQaLMWFRKAc64K+CawgNoLmq865zb4W1XTMrNXgCXA+WaWb2b3+l1TE/sSMJHavyxXe7fr/S6qifUEPjGztdT+IbTQORcRU0IjTHfgMzNbAywD3nPOfdhYO9cUWBERqZeOJEREpF4KCRERqZdCQkRE6qWQEBGReikkRESkXgoJERGpl0JCRETq9X+MZn2GdiDmQwAAAABJRU5ErkJggg==", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"print(outcomes)\n", | |
"\n", | |
"plt.plot([0,1,2,3,4,5], outcomes)" | |
] | |
} | |
], | |
"metadata": { | |
"interpreter": { | |
"hash": "b89b5cfaba6639976dc87ff2fec6d58faec662063367e2c229c520fe71072417" | |
}, | |
"kernelspec": { | |
"display_name": "Python 3.10.0 64-bit", | |
"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": "3.10.0" | |
}, | |
"orig_nbformat": 4 | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment