Apara Venkat AparaV

## sieve-eratosthenes-runtime-comparison.cpp
#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;

## regression_housing_1.py
import csv
import random
import numpy as np
import pandas as pd


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

## regression_housing_3.py
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]

## nn_housing.py
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]

## regression_housing_2.py
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)

## number-guessing-game.py
'''
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

## SIRSimulationsTestSuite.ipynb

      
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                AparaV
                / SIRSimulationsTestSuite.ipynb
            
            
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              November 27, 2019 23:51
            
          
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## streamer.py
import os
import time
import tweepy

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


def authenticate():

## zoe_fergusion_stat_consult_oct_25_2022.R
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.
	#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;
	import csv
	import random
	import numpy as np
	import pandas as pd


	def cleanup(df):
	'''
	Cleans data
	1. Creates new features:
	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]
	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]
	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)
	'''
	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
	import os
	import time
	import tweepy

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


	def authenticate():
	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.