299 lines
8.8 KiB
Plaintext
299 lines
8.8 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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"cell_type": "code",
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"execution_count": 4,
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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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"[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]\n",
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"[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]\n",
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"[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]\n",
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"[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]\n"
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]
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}
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],
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"source": [
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"import math\n",
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"size = [18,20,21,22,23]\n",
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"p = [s / sum(size) for s in size]\n",
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"print(p)\n",
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"for beta in [0.1, 0.2, 0.3]:\n",
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" damp_size = [math.exp(beta*s) for s in size]\n",
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" print([s / sum(damp_size) for s in damp_size])\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": 10,
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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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"[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]\n",
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"[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]\n",
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"[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]\n",
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"[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]\n"
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]
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}
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],
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"source": [
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"import math\n",
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"size = [18,20,21,22,23]\n",
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"p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]\n",
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"print(p)\n",
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"for beta in [0.1, 0.5, 0.8]:\n",
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" p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]\n",
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" print(p)"
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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": 1,
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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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"32\n"
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]
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}
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],
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"source": [
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"import multiprocess as mp\n",
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"\n",
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"print(mp.cpu_count())"
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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": 18,
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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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"71\n"
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]
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}
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],
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"source": [
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"from orm import engine\n",
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"import pandas as pd\n",
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"import pickle\n",
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"str_sql = \"select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a \" \\\n",
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"\"inner join \" \\\n",
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"\"(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a \" \\\n",
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"\"inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b \" \\\n",
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"\"on a.id = b.s_id \" \\\n",
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"\"group by e_id) as b \" \\\n",
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"\"on a.id = b.e_id \" \\\n",
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"\"order by count desc;\"\n",
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"result = pd.read_sql(sql=str_sql, con=engine)\n",
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"result['dct_lst_init_remove_firm_prod'] = result['dct_lst_init_remove_firm_prod'].apply(lambda x: pickle.loads(x))\n",
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"# print(result)\n",
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"list_dct = result.loc[result['count']>=9, 'dct_lst_init_remove_firm_prod'].to_list()\n",
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"print(len(list_dct))"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "probabilities do not sum to 1",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m np\u001b[39m.\u001b[39;49mrandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m], p\u001b[39m=\u001b[39;49m[\u001b[39m0.9\u001b[39;49m])\n",
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"File \u001b[1;32mmtrand.pyx:933\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: probabilities do not sum to 1"
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]
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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.choice([1], p=[0.9])"
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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": 46,
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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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"0.004495606232695251"
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]
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},
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"execution_count": 46,
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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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"prob_remove = 0\n",
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"prob_remove = np.random.uniform(\n",
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" prob_remove - 0.1, prob_remove + 0.1)\n",
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"prob_remove = 1 if prob_remove > 1 else prob_remove\n",
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"prob_remove = 0 if prob_remove < 0 else prob_remove\n",
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"prob_remove"
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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": 66,
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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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"[8]\n"
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]
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}
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],
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"source": [
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"nprandom = np.random.default_rng(0)\n",
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"lst_choose_firm = nprandom.choice(range(10),\n",
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" 1,\n",
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" replace=False\n",
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" )\n",
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"print(lst_choose_firm)"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Cannot take a larger sample than population when replace is False",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m nprandom \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(\u001b[39m0\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m lst_choose_firm \u001b[39m=\u001b[39m nprandom\u001b[39m.\u001b[39;49mchoice([\u001b[39m1\u001b[39;49m,\u001b[39m2\u001b[39;49m],\n\u001b[0;32m 3\u001b[0m \u001b[39m3\u001b[39;49m,\n\u001b[0;32m 4\u001b[0m replace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[0;32m 5\u001b[0m )\n\u001b[0;32m 6\u001b[0m lst_choose_firm\n",
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"File \u001b[1;32m_generator.pyx:753\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.choice\u001b[1;34m()\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when replace is False"
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]
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}
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],
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"source": [
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"nprandom = np.random.default_rng(0)\n",
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"lst_choose_firm = nprandom.choice([1,2],\n",
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" 3,\n",
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" replace=False\n",
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" )\n",
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"lst_choose_firm"
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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": "3.8.8"
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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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