98 lines
1.8 KiB
Plaintext
98 lines
1.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"85\n",
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"63\n",
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"51\n",
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"26\n",
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"30\n",
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"4\n",
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"7\n",
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"1\n",
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"17\n",
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"81\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([2, 2])"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"np.random.randint(0.5, 3.5)\n",
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"p_remove = 0.9\n",
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"np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
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"rng = np.random.default_rng(0)\n",
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"for _ in range(10):\n",
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" print(rng.integers(0,100))\n",
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"np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"share = 0.8\n",
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"list_succ_firms = [1, 1]\n",
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"round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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