コード例 #1
0
def classifyZipcode(location_text):
    classifier = recognition_heuristic.naivebayes(recognition_heuristic.getwords)

    classifier.setdb('test_100.db')

    classified_zipcode = classifier.classify(location_text)

    zipcode = str(classified_zipcode[0])
    confidence = str(classfied_zipcode[1])

    #query MYSQL database and append to python list
    cur = db.cursor()
    cur.execute("SELECT latitude,longitude FROM sqlbook.zipcounty WHERE zipcode = %s", zipcode)
    rows = cur.fetchall()
    latitude = rows[0][0]
    longitude = rows[0][1]

    likely_zipcode = {"location_text": location_text, "zipcode": zipcode, "latitude": latitude, "longitude": longitude}
    weather = get__weather_data.getWeather(likely_zipcode['zipcode'])

    likely_zipcode.update(weather)
    return likely_zipcode
コード例 #2
0
location_text = 'Beverly Hills'
classified_zipcode = classifier.classify('Beverly Hills')

zipcode = str(classified_zipcode[0])
confidence = str(classfied_zipcode[1])

#query MYSQL database and append to python list
db = MySQLdb('')
cur = db.cursor()
cur.execute("SELECT latitude,longitude FROM sqlbook.zipcounty WHERE zipcode = %s", zipcode)
rows = cur.fetchall()
latitude = rows[0][0]
longitude = rows[0][1]

likely_zipcode = {"location_text": location_text, "zipcode": zipcode, "latitude": latitude, "longitude": longitude}
weather = get__weather_data.getWeather(likely_zipcode['zipcode'])

likely_zipcode.update(weather)
print likely_zipcode

#if not close enough
#select 10 close locations to the one classified.

cur.execute("""SELECT z.zipcode, z.state, zco.poname, distcirc, population, hh,
       hhmedincome, z.latitude, z.longitude
FROM (SELECT zips.*,
             ACOS(COS(comp.latrad)*COS(zips.latrad)*
                                   COS(comp.longrad - zips.longrad) +
                  SIN(comp.latrad)*SIN(zips.latrad))*radius as distcirc
      FROM (SELECT zc.*, latitude*PI()/180 as latrad,
                   longitude*PI()/180 as longrad, 3949.9 as radius