prakhar21

#!/usr/bin/env python

"""
@uthor: Prakhar Mishra
date: Dec, 12 2017
"""
# importing packages
from numpy import array
from keras.preprocessing.text import Tokenizer
from numpy import array

prakhar21

#!/urs/bin/env python

"""
@uthor: Prakhar Mishra
date: Dec, 13 2017
desc: Read URL for more information on the same.
"""

import os
import csv

prakhar21

#!/usr/bin/env python

from nltk.translate.bleu_score import sentence_bleu

def perfect():
    machine_translation = "this blog talks about scoring machine translated text" 
    machine_trans = machine_translation.split()
    reference_translation = "this blog talks about scoring machine translated text"
    reference_trans = [reference_translation.split()]
    score = sentence_bleu(reference_trans,machine_trans)

prakhar21

#!/usr/bin/env python


'''
@author: Prakhar
@date: 3rd, January 2018
'''

prakhar21

import os
import time
import random
import re
from slackclient import SlackClient
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions

prakhar21

'''
@uthor: Prakhar Mishra
'''

import gym
import numpy as np
env = gym.make('CartPole-v0')

class LinearCombinationPolicy():
    

prakhar21

'''
@author: Prakhar
Motivation: http://mnemstudio.org/path-finding-q-learning-tutorial.htm
'''

import random
from termcolor import colored

class QLearning:
     

prakhar21

#!/usr/bin/env python

import requests
import config

def run():
        city_id_mapping = {}

        for city in config.CITIES:
                parameters = {'q': city}

prakhar21

#!/usr/bin/env python


import requests
import config
import get_cityids

def run():
    collection_id = {}

prakhar21

{
	"Delhi": {
		"rating": "4.0",
		"votes": "553",
		"score": 2212.0,
		"name": "Jung Bahadur Kachori Wala",
		"cuisine": "Street Food"
	},
	"Bangalore": {
		"rating": "4.4",