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        "author": "Jo\u00e3o Victor Monte R Tavares",
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        "description": "# Ezdea promove facilidade ao usar DEA com Python\n\n## Como usar:\n\n```python\n# crie a situa\u00e7\u00e3o a ser analisada. No nosso caso ser\u00e1 a seguinte matriz:\nfrom ezdea import DEA\nimport pandas as pd\n\nmatriz = [[20, 151, 100, 90],\n          [19, 131, 150, 50],\n          [25, 160, 160, 55],\n          [27, 168, 180, 72],\n          [22, 158, 94, 66],\n          [55, 255, 230, 90],\n          [33, 235, 220, 88],\n          [31, 206, 152, 80],\n          [30, 244, 290, 100],\n          [50, 268, 250, 100],\n          [53, 306, 262, 147],\n          [38, 284, 250, 120]]\n          \n# declare as entradas e sa\u00eddas do problema\nnumero_inputs = 2\nnumero_outputs = 2\n\n# instancie o cen\u00e1rio\ncenario = DEA(matriz, numero_inputs, numero_outputs)\nresultado_bcc = cenario.bcc_dual_input(matrix=True) # matrix = True for\u00e7a que o retorno seja matriz e n\u00e3o json\ndf_bcc_dual = pd.DataFrame(resultado_bcc)\nprint(df_bcc_dual)\nmetas = cenario.calcular_metas(resultado_bcc)\ndf_metas = pd.DataFrame(metas)\nprint(df_metas)\n\n```\n\n## Lista de m\u00e9todos:\n\n* DEA.ccr_primal_input(matrix=False)\n\n* DEA.ccr_primal_output(matrix=False)\n\n* DEA.ccr_dual_input(matrix=False)\n\n* DEA.ccr_dual_output(matrix=False)\n\n* DEA.bcc_primal_input(matrix=False)\n\n* DEA.bcc_primal_output(matrix=False)\n\n* DEA.bcc_dual_input(matrix=False)\n\n* DEA.bcc_dual_output(matrix=False)\n\n* DEA.calcular_metas(matriz_dual)\n\n# pr\u00f3ximo passo: subir para o PyPi",
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