diff --git a/4candpartialorder.txt b/4candpartialorder.txt
new file mode 100644
index 0000000..326b234
--- /dev/null
+++ b/4candpartialorder.txt
@@ -0,0 +1,8 @@
+4
+1,a
+2,b
+3,c
+4,d
+2,2,2
+1,1,2
+1,2,3
\ No newline at end of file
diff --git a/input b/input
new file mode 100644
index 0000000..4bebc23
--- /dev/null
+++ b/input
@@ -0,0 +1,7 @@
+3
+1,a
+2,b
+3,c
+4,4,2
+1,1,2,3
+3,3,1,2
diff --git a/input0 b/input0
new file mode 100644
index 0000000..4aa9fbe
--- /dev/null
+++ b/input0
@@ -0,0 +1,11 @@
+3
+1,a
+2,b
+3,c
+6,6,6
+1,1,2,3
+1,1,3,2
+1,2,3,1
+1,2,1,3
+1,3,1,2
+1,3,2,1
\ No newline at end of file
diff --git a/input1.txt b/input1.txt
new file mode 100644
index 0000000..0057edf
--- /dev/null
+++ b/input1.txt
@@ -0,0 +1,13 @@
+6
+1,a
+2,b
+3,c
+4,d
+5,e
+6,f
+5,5,5
+1,4,2
+1,4,1
+1,3,4
+1,6,4
+1,1,6
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..b2cbad1
--- /dev/null
+++ b/main.py
@@ -0,0 +1,21 @@
+import prefpy
+from prefpy import preference
+from prefpy import profile
+
+from prefpy.rankedPairs import RankedPairs
+from prefpy.profile import Profile
+
+if __name__ == '__main__' :
+
+	data=Profile({},[])
+
+	#data.importPreflibFile("4candpartialorder.txt")
+	data.importPreflibFile("input1.txt")
+	
+	rankpairMech=RankedPairs()
+	myList = [(4, 'a', 'b'), (3, 'b', 'c'), (3, 'c', 'd'), (2, 'd', 'b'), (2, 'c', 'a'), (4, 'd', 'a')]
+	myEdgePrefList = [('a', 'b'), ('a', 'c'), ('b', 'c'), ('c', 'b'), ('b', 'a'), ('c', 'a'), ]
+	#print(rankpairMech.getWinners(edges = myList))
+	#print(rankpairMech.getWinners(prof = data, edgePrefList = myEdgePrefList))
+	print(rankpairMech.getWinners(prof = data))
+	#print(rankpairMech.getOneWinner(prof = data))
\ No newline at end of file
diff --git a/prefpy/profile.py b/prefpy/profile.py
index 34951f5..b4b1d8b 100644
--- a/prefpy/profile.py
+++ b/prefpy/profile.py
@@ -2,7 +2,7 @@
 Author: Kevin J. Hwang
 """
 import copy
-import io
+from . import io
 import itertools
 import math
 import json
diff --git a/prefpy/rankedPairs.py b/prefpy/rankedPairs.py
new file mode 100644
index 0000000..4949a21
--- /dev/null
+++ b/prefpy/rankedPairs.py
@@ -0,0 +1,215 @@
+'''
+Authors: Bilal Salam, Levin Huang, Lucky Cho
+'''
+import io
+import math
+import itertools
+from prefpy.profile import Profile
+from prefpy.preference import Preference
+from prefpy.mechanism import Mechanism
+import networkx as nx
+import matplotlib.pyplot as plt
+import copy
+
+def getWinningNodes(netxGraph):
+	#function to find the winning nodes given a networkx graph
+	topRanks = []
+	for i in netxGraph.nodes():
+		if netxGraph.in_edges(i) == []:
+			topRanks.append(i)
+	return topRanks
+
+class branchedGraph():
+	def __init__(self, rL, DG):
+		self.remainingList = rL
+		self.DG = DG
+		
+	def isDone(self):
+	#function to check if the graph is done
+		if len(self.remainingList) ==0:
+			return True
+		else:
+			return False
+	
+	#currentWinners - list of string names of known winners
+	#edgePrefList - [(node1, node2), (node2, node3)] list of edges sorted in decreasing order of preference, each tuple is an edge 
+	def getNextEdge(self, currentWinners, edgePrefList = None):
+	#function to progress through each 
+		#list of graphs to branch to
+		branchedGraphList = [] 
+		branchedEdges, branchedRemEdges = self.findTies(edgePrefList)
+		#iterate through each branch of the graph
+		for i in range(len(branchedEdges)):
+			tmpGraph = self.DG.copy()
+			tmpEdge = branchedEdges[i]
+			#add the edge
+			self.addOneWayEdge(tmpGraph, tmpEdge)
+			#pruning check to see if we have already found the possible winners
+			if not set(getWinningNodes(tmpGraph)).issubset(set(currentWinners)):
+				tmpBranchedGraph = branchedGraph(branchedRemEdges[i], tmpGraph)
+				#add a new branched graph to the branchedGraphList
+				branchedGraphList.append( tmpBranchedGraph)
+		return branchedGraphList
+		
+	def addOneWayEdge(self, graph, edge):
+	#function to check whether or not we want to add the edge in the case of the graph already having an opposite edge
+		if not graph.has_edge(edge[2], edge[1]):
+			graph.add_edge(edge[1], edge[2], weight=edge[0])
+			try:
+				nx.find_cycle(graph)
+				graph.remove_edge(edge[1], edge[2])
+			except:
+				pass
+
+	#edgePrefList - [(node1, node2), (node2, node3)] list of edges sorted in decreasing order of preference, each tuple is an edge 
+	def findTies(self, edgePrefList = None):
+	#function to find which edges are tied
+		branchedRemEdges = [] #list of lists
+		edges = self.remainingList 
+		tmpList = [] #holds list of tied edges to branch out to
+		tmpRemEdges = []
+		
+		if len(edges) > 0: #we know that we don't care about negative edge weights
+			tmpWeight = edges[0][0]
+			#loop through the list of edges
+			for i in range(len(edges)):
+				if edges[i][0] == tmpWeight: #we find tied edge that we want to use
+					tmpList.append(copy.copy(edges[i])) #add the edge we are using to the list of edges to extend G`
+		if edgePrefList is not None:
+			if len(tmpList) > 0:
+				tmpList = self.getBestEdge(tmpList, edgePrefList)
+		for i in range(len(edges)):
+			tmpRemEdges = copy.copy(edges) #deep copy edge list
+			del tmpRemEdges[i] #remove tied edge from the remaining list
+			branchedRemEdges.append(tmpRemEdges)
+		return tmpList, branchedRemEdges
+	
+	def getBestEdge(self, edgeList, edgePrefList):
+		bestIndex = None
+		edgeListIndex = 0
+		for i in range(0, len(edgeList)):
+			edge = edgeList[i]
+			try:
+				tmpBestIndex = edgePrefList.index((edge[1], edge[2]))
+				if bestIndex is None or bestIndex > tmpBestIndex:
+					bestIndex = tmpBestIndex
+					edgeListIndex = i
+			except ValueError:
+				print('edge does not exist in preference list, user did not give preference over all edges')
+		return [edgeList[edgeListIndex]]
+				
+			
+	
+	
+
+class RankedPairs(Mechanism):
+#Calculates winners using the RankedPairs voting mechanism
+
+	#returns edges from the wmg sorted by weights
+	def getSortedEdges(self,prof):
+		#initialize the wmg from the given profile
+		wmg = prof.getWmg()
+		# empty array to hold the edges
+		edges = []
+		#add edges to the array
+		for cand1 in prof.candMap.keys():
+			for cand2 in prof.candMap.keys():
+				if cand1 is not cand2:
+					edges.append((wmg[cand1][cand2], prof.candMap[cand1], prof.candMap[cand2]))
+		#sort the edges
+		edges = sorted(edges, key=lambda weight: weight[0], reverse = True) 
+		return edges
+		
+	def drawGraph(self, DG):
+		#function to draw the given networkx graph.
+		plt.figure()
+		nodeLayout=nx.spring_layout(DG)
+		nx.draw_networkx(DG,pos=nodeLayout,arrows=True)
+		labels = nx.get_edge_attributes(DG, 'weight')
+		nx.draw_networkx_edge_labels(DG, pos=nodeLayout, edge_labels=labels)
+		#plt.show()
+
+	def getOneWinner(self, prof = None, edgePrefList = None):
+	#function to get one winner
+		edges = self.getSortedEdges(prof)
+		DG=self.initDG(edges)#get the networkx Directed Graph from the set of edges
+		winners = [] #list to hold the winners
+		doneList = [] #list of the graphs that have been completed
+		newBranchedGraph = branchedGraph(edges, DG) #create the first branched graph
+		graphs = newBranchedGraph.getNextEdge(winners, edgePrefList) #try to get the next edge from the new branched graph
+		
+		while(len(graphs) != 0):
+		#Iterating through graphs, appending to tmp graphs list such that we arent modifying the list we are iterating over
+			tmpGraphs = []
+			graphs = graphs[0:1]
+			graph=graphs.pop(0) #we've finished looking at this graph
+			tmpNextEdgeList = graph.getNextEdge(winners, edgePrefList) #look at the next edge of the graph
+			if tmpNextEdgeList == [] and graph.isDone(): #we know that the graph is complete if it satisfies these conditions
+				doneList.append(graph)
+				winners += getWinningNodes(graph.DG) #add the nodes returned by the getWinningNodes call
+			else:
+				graphs=tmpNextEdgeList+graphs
+		print(len(doneList)) #debug statement to see how many graphs needed to be computed
+		#loop to output each completed graph
+		for winner in doneList:
+			self.drawGraph(winner.DG)
+		plt.show()
+		return set(winners)
+		
+	def initDG(self, edges):
+	#function to intialize the directed graph
+		nodeSet=set()
+		DG = nx.DiGraph()
+		for i in range(len(edges)):
+			nodeSet.add(edges[i][1])
+		DG.add_nodes_from(nodeSet)
+		return DG
+
+
+	def getRanking(self, profile):
+
+		raise NotImplementedError
+
+	def getWinners(self, prof=None, edgePrefList=None):
+		"""
+		prof Profile
+		edgePrefList- list of tuples representing a preference over wmg edges
+		primary method to calculate multiple winners
+		returns the set of winning nodes and then the linear orders justifying each
+	    winner in the form of networkx objects
+
+		"""
+
+		edges = self.getSortedEdges(prof)
+		
+		DG=self.initDG(edges)#get the networkx Directed Graph from the set of edges
+		winners = [] #list to hold the winners
+		doneList = [] #list of the graphs that have been completed
+		newBranchedGraph = branchedGraph(edges, DG) #create the first branched graph
+		graphs = newBranchedGraph.getNextEdge(winners, edgePrefList) #try to get the next edge from the new branched graph
+		
+		while(len(graphs) != 0):
+		#Iterating through graphs, appending to tmp graphs list such that we arent modifying the list we are iterating over
+			tmpGraphs = []
+			graph=graphs.pop(0) #we've finished looking at this graph
+			tmpNextEdgeList = graph.getNextEdge(winners, edgePrefList) #look at the next edge of the graph
+			if tmpNextEdgeList == [] and graph.isDone(): #we know that the graph is complete if it satisfies these conditions
+				doneList.append(graph)
+				winners += getWinningNodes(graph.DG) #add the nodes returned by the getWinningNodes call
+			else:
+				graphs=tmpNextEdgeList+graphs
+		#print(len(doneList)) #debug statement to see how many graphs needed to be computed
+		'''
+		#loop to output each completed graph
+		for winner in doneList:
+			self.drawGraph(winner.DG)
+		plt.show()'''
+		
+		return set(winners), [graph.DG for graph in doneList]
+		#returns the set of winning nodes and then the linear orders justifying each winner in the form of networkx objects
+		#uncomment the above loop for a visual representation
+
+
+
+
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..cc0ed78
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+matplotlib==1.5.3
+networkx==1.11
+numpy==1.11.2
+scipy==0.18.1
+six==1.10.0