Venkatramani Rajgopal Venkat-Rajgopal

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	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split

	# Load the raw training data and replace missing values with NA
	training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

	# Output the number of missing values for each column
	sapply(training.data.raw,function(x) sum(is.na(x)))

	# Quick check for how many different values for each feature
	sapply(training.data.raw, function(x) length(unique(x)))

	# A visual way to check for missing data

	#!/usr/bin/env python

	import networkx as nx
	import matplotlib.pyplot as plt
	import math
	from random import random
	from numpy import arange

	N = 1000
	K = 5