#processed by genderzyer 3.0 -- genderzye -at- jofish dot com
#short version:
#output format for each line is: rating name
#if rating is >1, then it's a man's name
#if rating is <1, it's a woman's name
#if rating is 1, it's ambiguous
#if rating is 0, it doesn't know the name.
#
#longer version:
#so for example, jordan is both a boys' and a girls' name.
#but according to the 2005 US census (ymmv for other demographics, of course)
#it is the 46th most popular boys name and the 86th most popular girls name.
#
#this is encoded internally as boyscore girlscore name, like this:
#1.954 0.914 jordan 
#
#the algorithm in the code does this:
#if abs(boyscore-1) > abs(girlscore-1): return boyscore
#   else: return girlscore
#
#so for jordan, 0.954 > 0.086 so most likely male and it would return 1.954
#
#0 means we have no data either way
#
#1 means ambiguous: we know it can be both male and female.
#this indicates that these names were found in a baby name book/site/list, but we have no
#concept of relative popularity of the two terms. 
#
0 First Name
1.996 Matthew
1.5 King
0.924 Sara
0.5 Kelli
0.5 Asha
1.054 Yusuf
1.839 Peter
0 Wainwright
0.977 Anna
1.5 Maxime
1.886 Mark
0 Mailyn
0 Swathi
0 MEGHBARTMA
1.1111 Ching Man
0.924 Sara
0.563 Anne
0.924 Sara
0 Riddhima
0 Bowman
1.5 Ayman
0 Sarun
1.5 Ulrich
1.874 Josiah
0.5 Vidya
0.977 Anna
0.62 Annie
1.985 Nicholas
0 Yula
0 Aneeqa
1 Ange
1.998 Michael
0.5 Nisha
0.761 Monica
0.5 Diane
0.985 Sarah
0.999 Emily 
1.944 Charles
0 Adeel
0.8888 Zhihui
0.5 Chiara
1.993 Daniel
0 Anay
0.979 Lauren
0 Krithika
0.5 Joanne
0.086 Mira 
1.892 Patrick
0.955 Allison
0 Sajid
0 Anant
0.958 Jennifer
0.5 Simona
1.651 Jay
0.958 Jennifer
1.935 Adrian
1 Ollie
1.62 Chad
0.5 Silke Noa
0 Sakshi
0.5 Sylvie
1 Robin
1.982 John