def main():
rec = recorder.Recorder()
processorThread = processor.run(rec)
rec.start()
rec.record(5)
rec.stop()
def main():
rec = recorder.Recorder()
processorThread = processor.run(rec)
rec.start()
rec.record(5)
rec.stop()
import manageFiles
import processor
import score
import recommender
processor.parameters['index'] = "codigo"
processor.parameters['properties'] = ["desc","titulo","area_atuacao"]
processor.parameters['new_path'] = reachrAPI.getJobToRecommend()
processor.parameters['current_path'] = reachrAPI.getAllJobs()
print(processor.parameters['new_path'])
print(processor.parameters['current_path'])
score.parameters['properties'] = ["desc","titulo","area_atuacao"]
score.parameters['weights'] = {"desc":0.3, "titulo":0.6, "area_atuacao":0.1}
score.parameters['input'] = processor.run()
recommender.parameters['threshold'] = 0.2
recommender.parameters['selection-step'] = 2
recommender.parameters['key'] = processor.parameters['index']
recommender.parameters['inner-list'] = 'processo_seletivo'
recommender.parameters['candidates-list'] = 'candidatos'
recommender.parameters['input-jobs'] = manageFiles.read(processor.parameters['current_path'])
recommender.parameters['input-similars'] = score.run()
ret = reachrAPI.postJobRecommendation(recommender.run())
print(ret)
args = parser.parse_args()
existed_video = []
# path_download = os.path.join(os.path.dirname(os.path.abspath(__file__)), args.download)
# path_output = os.path.join(os.path.dirname(os.path.abspath(__file__)), args.out_folder)
if not os.path.exists(args.download):
os.makedirs(args.download)
if not os.path.exists(args.out_folder):
os.makedirs(args.out_folder)
existed_video = os.listdir(args.out_folder)
video_ids = crawling(args.keyword, args.num_video)
video_ids = [x for x in video_ids if x not in existed_video]
for video in video_ids:
print(video)
try:
download(video, args.download) # 동영상 다운로드
except:
continue
run(args.download, args.accuracy, args.image_shape, args.out_folder,
video, args.class_name)
if os.path.exists(os.path.join(args.download, video + ".mp4")):
os.remove(os.path.join(args.download, video + ".mp4")) #동영상 삭제
def spark_setup():
""" Sets configuration and creates Spark session """
conf = SparkConf()
spark_context = SparkContext().getOrCreate(conf)
spark_session = SparkSession(spark_context)
spark_session.sparkContext.setLogLevel('WARN')
return spark_session
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Input and output file names')
parser.add_argument('student_file',
help='the path to the file of student information')
parser.add_argument('teacher_file',
help='the path to the file of teacher information')
parser.add_argument('--out',
dest='output_file',
help='the desired output path for the json report')
input_args = parser.parse_args()
print(input_args)
spark = spark_setup()
if input_args.output_file:
processor.run(spark, input_args.student_file, input_args.teacher_file,
input_args.output_file)
else:
processor.run(spark, input_args.student_file, input_args.teacher_file)
import processor
import os
import sys
import traceback
try:
datadir = os.environ.get('KBC_DATADIR') or '/data/'
processor.run(datadir)
except ValueError as err:
print(err, file=sys.stderr)
sys.exit(1)
except Exception as err:
print(err, file=sys.stderr)
traceback.print_exc(file=sys.stderr)
sys.exit(2)
import sys
import time
import cProfile
import memory
import stack
import streams
import processor
memory = memory.MemoryV1(sys.argv[1])
stack = stack.Stack()
input = streams.KeyboardInputStreamV1()
screen = streams.ScreenOutputStreamV1()
transcript = streams.FileOutputStreamV1(time.strftime('%Y%m%d%H%M%S') + '.log')
output = streams.OutputDemuxV1(screen, transcript, memory.get_header())
processor = processor.ProcessorV1(memory, stack, input, output)
#profile = ''
#cProfile.run(processor.run(), profile)
processor.run()
print 'Goodbye'
def test_run():
run(data_dir)
assert out_file_exists("kml/kml-with-extended-data.csv")
assert out_file_exists("shapefile/stations.csv")
assert out_file_exists("geojson/stations.csv")
import setup
setup.load()
import manageFiles
import processor
import similarity
root = '/home/mnf/reachr/projects/RecommenderSystem/test'
processor.parameters['index'] = "codigo"
processor.parameters['properties'] = ["desc", "titulo", "area_atuacao"]
processor.parameters['current_path'] = root + "/support/vagas.json"
processor.parameters['new_path'] = root + "/support/new_vaga.json"
computed_similarity = processor.run()
manageFiles.parameters['path'] = root + '/support/similiars.json'
manageFiles.write(computed_similarity)
#test jaccard
import pandas as pd
similarity.parameters['matrix'] = pd.DataFrame(
[[1, 0, 1, 0, 1], [0, 0, 1, 1, 1]], columns=list('ABCDE')).transpose()
print(similarity.run_jaccard())
def run(self):
import processor # Imported here to avoid circular imports
processor.run()
def listings():
loans = processor.run()
return render_template("listings.html", loans=loans)
