larger sample than population when replace is False
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149
model.py
149
model.py
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@ -19,7 +19,7 @@ class Model(ap.Model):
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self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod
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self.int_n_max_trial = int(self.p.n_max_trial)
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self.int_netw_sply_prf_size = int(self.p.netw_sply_prf_n)
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self.int_netw_sply_prf_n = int(self.p.netw_sply_prf_n)
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self.flt_netw_sply_prf_size = float(self.p.netw_sply_prf_size)
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self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
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self.flt_cap_limit_level = float(self.p.cap_limit_level)
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@ -65,58 +65,111 @@ class Model(ap.Model):
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firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict()
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nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict)
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# add edge to G_firm according to G_bom
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for node in nx.nodes(G_Firm):
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lst_pred_product_code = []
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for product_code in G_Firm.nodes[node]['Product_Code']:
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lst_pred_product_code += list(G_bom.predecessors(product_code))
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lst_pred_product_code = list(set(lst_pred_product_code))
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# to generate consistant graph
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lst_pred_product_code = list(sorted(lst_pred_product_code))
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for pred_product_code in lst_pred_product_code:
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# for each product predecessor (component) the firm need
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# get a list of firm producing this component
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lst_pred_firm = \
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Firm['Code'][Firm[pred_product_code] == 1].to_list()
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lst_pred_firm_size_damp = \
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[G_Firm.nodes[pred_firm]['Revenue_Log'] **
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self.flt_netw_sply_prf_size
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for pred_firm in lst_pred_firm]
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lst_prob = \
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[size_damp / sum(lst_pred_firm_size_damp)
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for size_damp in lst_pred_firm_size_damp]
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# select multiple supplier (multi-sourcing)
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n_pred_firm = self.int_netw_sply_prf_size
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lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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n_pred_firm,
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replace=False,
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p=lst_prob)
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lst_add_edge = [(pred_firm, node,
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{'Product': pred_product_code})
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for pred_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# # add edge to G_firm according to G_bom
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# for node in nx.nodes(G_Firm):
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# lst_pred_product_code = []
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# for product_code in G_Firm.nodes[node]['Product_Code']:
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# lst_pred_product_code += list(G_bom.predecessors(product_code))
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# lst_pred_product_code = list(set(lst_pred_product_code))
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# # to generate consistant graph
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# lst_pred_product_code = list(sorted(lst_pred_product_code))
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# for pred_product_code in lst_pred_product_code:
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# # for each product predecessor (component) the firm need
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# # get a list of firm producing this component
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# lst_pred_firm = \
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# Firm['Code'][Firm[pred_product_code] == 1].to_list()
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# lst_pred_firm_size_damp = \
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# [G_Firm.nodes[pred_firm]['Revenue_Log'] **
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# self.flt_netw_sply_prf_size
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# for pred_firm in lst_pred_firm]
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# lst_prob = \
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# [size_damp / sum(lst_pred_firm_size_damp)
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# for size_damp in lst_pred_firm_size_damp]
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# # select multiple supplier (multi-sourcing)
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# n_pred_firm = self.int_netw_sply_prf_n
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# if n_pred_firm > len(lst_pred_firm):
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# n_pred_firm = len(lst_pred_firm)
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# lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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# n_pred_firm,
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# replace=False,
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# p=lst_prob)
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# lst_add_edge = [(pred_firm, node,
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# {'Product': pred_product_code})
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# for pred_firm in lst_choose_firm]
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# G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(G_bom.successors(pred_product_code))
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lst_use_pred_prod_code = list(
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set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == pred_firm and
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v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == use_pred_prod_code][0]
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G_FirmProd.add_edge(pred_node, current_node)
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# # graph firm prod
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# set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
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# set_pred_succ_code = set(G_bom.successors(pred_product_code))
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# lst_use_pred_prod_code = list(
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# set_node_prod_code & set_pred_succ_code)
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# for pred_firm in lst_choose_firm:
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# pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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# if v['Firm_Code'] == pred_firm and
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# v['Product_Code'] == pred_product_code][0]
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# for use_pred_prod_code in lst_use_pred_prod_code:
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# current_node = \
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# [n for n, v in G_FirmProd.nodes(data=True)
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# if v['Firm_Code'] == node and
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# v['Product_Code'] == use_pred_prod_code][0]
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# G_FirmProd.add_edge(pred_node, current_node)
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# nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
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# nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
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# unconnected node
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# for node in nx.nodes(G_Firm):
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# if node.
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for node in nx.nodes(G_Firm):
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if G_Firm.degree(node) == 0:
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for product_code in G_Firm.nodes[node]['Product_Code']:
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# unconnect node does not have possible suppliers
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lst_succ_product_code = list(
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G_bom.successors(product_code))
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# different from for different types of product,
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# finding a common supplier (the logic above),
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# for different types of product,
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# finding a custormer for each product
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for succ_product_code in lst_succ_product_code:
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# for each product successor (finished product)
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# the firm sells to,
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# get a list of firm producing this finished product
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lst_succ_firm = \
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Firm['Code'][Firm[succ_product_code] == 1].to_list()
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lst_succ_firm_size_damp = \
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[G_Firm.nodes[succ_firm]['Revenue_Log'] **
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self.flt_netw_cust_prf_size
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for succ_firm in lst_succ_firm]
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lst_prob = \
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[size_damp / sum(lst_succ_firm_size_damp)
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for size_damp in lst_succ_firm_size_damp]
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# select multiple customer (multi-selling)
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n_succ_firm = self.int_netw_cust_prf_n
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if n_succ_firm > len(lst_succ_firm):
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n_succ_firm = len(lst_succ_firm)
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lst_choose_firm = self.nprandom.choice(lst_succ_firm,
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n_succ_firm,
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replace=False,
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p=lst_prob)
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lst_add_edge = [(node, succ_firm,
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{'Product': pred_product_code})
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for succ_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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set_node_prod_code = set(
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G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(
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G_bom.successors(pred_product_code))
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lst_use_pred_prod_code = list(
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set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == pred_firm and
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v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == use_pred_prod_code][0]
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G_FirmProd.add_edge(pred_node, current_node)
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self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm))
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self.firm_network = ap.Network(self, G_Firm)
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27
test.ipynb
27
test.ipynb
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@ -239,6 +239,33 @@
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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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