def __init__(self, train_mode=0):
self.train_mode = train_mode
agent = DQNAgent(mode=self.train_mode)
user = user_simulator()
self.manager = dialog_manager(agent,
user,
self.train_mode,
maximum_turn=20)
self.simulation_epoch_size = 800
# Python Lib Imports
import string,cgi,time
from os import curdir, sep, getenv
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
import urlparse
import dialog_manager as dm
# File Imports
import trainer as tr
import utility as ut
import trainer_reader as rdr
# Create Dialog Manager Object to perform
# classification
Dialog_Manager = dm.dialog_manager()
class MyHandler(BaseHTTPRequestHandler):
"""HTTP Server Handler Class; Responds to Questions Posed by Get Messages. """
def do_GET(self):
"""Use Get Method to perform Natural Language Processing.
Get Args:
anno: Annotation for the NLP.
message: The Message to be responded to.
Returns:
A response as obtained from the dialogue Manager
for the given Question
"""
This file is the main file of the project. Run this to see the results.
"""
import dialog_manager as DM
import q_classify as QC
from nltk.tag import pos_tag
import utility as ut
import nltk
import trysearch as ts
import AlchemyAPI as AP
Dialog_Manager = DM.dialog_manager()
q_class = QC.q_classification()
var = 1
while var:
print '\n'
string = raw_input(" Enter the question: ")
if string in ["end","End","exit","Exit"]:
var = 0
else:
temp = [ (a,b) for (a,b) in pos_tag(nltk.tokenize.word_tokenize(ut.clean(string)))]
temp1 = dict()
temp2 = ''
for (a,b) in temp:
if a == 'i':
a = 'i'
elif b == 'RB' or b == 'VB':
"""
This file is the main file of the project. Run this to see the results.
"""
import dialog_manager as DM
import q_classify as QC
from nltk.tag import pos_tag
import utility as ut
import nltk
import trysearch as ts
import AlchemyAPI as AP
Dialog_Manager = DM.dialog_manager()
q_class = QC.q_classification()
var = 1
while var:
print '\n'
string = raw_input(" Enter the question: ")
if string in ["end", "End", "exit", "Exit"]:
var = 0
else:
temp = [
(a, b)
for (a,
b) in pos_tag(nltk.tokenize.word_tokenize(ut.clean(string)))
]
temp1 = dict()
temp2 = ''