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My profile is forever under construction .... I`m a passionated for declarative languages during the past 30 years with experience in programming for AI. At the beginning, I falling in love with Prolog, as my first serious programming language that I learned by myself. During this period, I taught several CS courses in many universities in Brazil. Recently, I got involved with Constraint Programming (CP) developing experiments with declarative programming languages. Additionally, I experimented with many tools for CP, finding a perfect match with languages like PICAT, MINIZINC, HASKELL, etc. Others classical programming languages, such C, C++, Python, et all, I have some knowledge to become an expert. In November 2019, I retired from UDESC where I am so proud of my academic career during these 36 years, working for many industries and universities. By the way, I am still felling with good energy to work with certain activities. Although I am 60, my health is fine, since I`m used to swim in many open water competitions (from 3000 up to 8000 meters). This way I am ready for new challenges in programming and research: combinatorial problems, declarative programming, programming in logic, constraint programming, and so much more! Any proposal in Brazil or abroad are welcome, as well as a home office work is suitable for me in this moment. Finally, some jobs in these areas as freelancer are also wellcome. There is a bit timid blog in: to contact me, et all. ©

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Linux, security and infrastructure geek. Travelling in 2016, part of the 48% and looking for a new home. ©

import pandas as pd import numpy as np import matplotlib.pyplot as plt from fuzzywuzzy import fuzz pd.set_option(`max_columns`, 50) import pyodbc from pandas.io import sql cols=[`ISSUER_NAME`,`CPTY_NAME`,`SIMILARITY_1`,`SIMILARITY_2`,`FLAG`] data = [] sql_conn=pyodbc.connect(`DRIVER={ODBC Driver 11 for SQL Server}; SERVER=RADO-THINKPAD; DATABASE=IVAN; Trusted_Connection=yes`) query = SELECT * FROM dbo.issuer TABLE = pd.read_sql(query, sql_conn) final = fuzz.token_set_ratio(s1, s2) for s1, s2 in TABLE.itertuples(index=False): final = fuzz.token_set_ratio(s1, s2) final1 = fuzz.partial_ratio(s1, s2) if final>50 and final1>50: flag=`YES` else: flag=`NO` data.append((s1, s2, final, final1, flag)) result = pd.DataFrame(data, columns=cols) result.to_csv(file_name, sep=`,`) ©