AparaV

library(geepack)    # Run install.packages("geepack") if necessary
library(tidyr)      # Run install.packages("tidyr") if necessary



### Generate synthetic data
### For demonstration purposes only.
### You do not need to understand how this section works.
### Please see line 43 for format of data frame and line 58 for fitting model.

AparaV

AparaV

'''
Author: Aparajithan Venkateswaran
Companion Article: https://aparav.github.io/2018/01/08/the-number-guessing-game/
'''

from scipy.special import comb

def conditional_prob(i, k, n, m):
	'''
	Calculates the probability of event C conditioned on Ri

AparaV

import os
import csv
import numpy as np
import pandas as pd
import tensorflow as tf

train_size = np.shape(x_train)[0]
valid_size = np.shape(x_valid)[0]
test_size = np.shape(x_test)[0]
num_features = np.shape(x_train)[1]

AparaV

import tensorflow as tf
import numpy as np

def accuracy(prediction, labels):
    return 0.5 * np.sqrt(((prediction - labels) ** 2).mean(axis=None))

train_size = np.shape(x_train)[0]
valid_size = np.shape(x_valid)[0]
test_size = np.shape(x_test)[0]
num_features = np.shape(x_train)[1]

AparaV

def split(train_dataset):
    '''
    Shuffle data and split into 3 datasets
        1. Training - 60%
        2. Validation - 20%
        3. Testing - 20%
    '''
    # Shuffle data
    train_dataset = train_dataset.sample(frac=1)

AparaV

import csv
import random
import numpy as np
import pandas as pd


def cleanup(df):
    '''
    Cleans data
        1. Creates new features:

AparaV

#include <iostream>
#include <vector>
#include <cstring>

using namespace std;

bool* boolPrimeSieveMemset(int64_t size) {
	bool* prime = new bool[size + 1];
	memset(prime, true, size + 1);				//faster than loops and vectors
	prime[0] = false;

AparaV

import os
import time
import tweepy

from tweepy import OAuthHandler
from tweepy import Stream
from tweepy import StreamListener


def authenticate():