diff --git a/greedyInsertion.py b/greedyInsertion.py
deleted file mode 100755
index 0b3e421..0000000
--- a/greedyInsertion.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/local/bin/python -O
-
-from random import shuffle
-from metric import GivenDistances
-from numpy import median
-from numpy import isnan
-
-'''
-	This method inserts each point at its optimal location in a given order.
-	The first two points are free since there is no context.
-	m - The data of the problem in a Metric object
-	w - The weight vector
-	o - The order to insert points
-	verbose - Print the results
-	greedyInsertion returns the path that it finds as a list of points from the Metric
-    Originally developed by Dan Schellhas
-    Modified on 8-30-2016 by Craig Zirbel, adding pathLength
-    Modified on 8-30-2016 by Craig Zirbel, adding orderWithPathLengthFromDistanceMatrix
-'''
-
-def pathLength(m, path):
-  score = 0
-  for i in range(0, len(path)-1):
-    score += m.d(path[i], path[i+1])
-  return score
-
-def testScore(m, path, depth = False):
-	if depth == False:
-		depth = len(path)
-	testScore = 0
-	for i in range(0, len(path)):
-		for j in range(i+1, min(i+depth+1, len(path))):
-			testScore += m.d(path[i], path[j])/abs(j-i)
-	return testScore
-
-def orderWithPathLengthFromDistanceMatrix(distances,numReps = 1,scanForNan = False):
-
-  if scanForNan:
-    values = []
-    for i in range(0,len(distances)):
-      for j in range(0,len(distances[0])):
-        if not isnan(distances[i,j]):
-          values.append(distances[i,j])
-
-    m = 0
-    if len(values) > 0:
-      m = median(values)
-      for i in range(0,len(distances)):
-        if isnan(distances[i,i]):
-          distances[i,i] = 0
-        for j in range(0,len(distances[0])):
-          if isnan(distances[i,j]):
-            distances[i,j] = m
-
-  dataset = GivenDistances(distances)
-
-  bestPathLength = float("inf")
-  bestOrder = range(0,len(distances))
-
-  for i in range(0,numReps):
-    order = greedyInsertion(dataset,depth=1)
-    newPathLength = pathLength(dataset,order[0])
-    if newPathLength < bestPathLength:
-      bestPathLength = newPathLength
-      bestOrder = order[0]
-
-  return bestOrder, bestPathLength, distances
-
-def greedyInsertion(m, w=False, o=[], depth=False, verbose=False):
-  if w == False:
-    w = []
-    for i in range(1,len(m.points)+1):
-      if i <= depth:
-        w.append(1/(i*1.0))
-      else:
-        w.append(0)
-  if len(o) == 0:
-  	o = m.points
-  	shuffle(o)
-  if depth == False:
-    depth = len(o)
-  path = o[:2]
-  score = w[0] * m.d(path[0], path[1])
-
-  for p in range(2, len(o)):
-    distances = []
-    for point in path:
-      distances.append(m.d(o[p], point))
-    for position in range(0, len(path)+1):
-      myScore = 0
-      l = min(len(path), depth)
-      s = max(0, position - l)
-      e = min(len(path), position + l)
-      for i in range(s, e):
-        d = int(abs(i+.5-position))
-        myScore += distances[i] * w[d]
-      if position == 0:
-        tempScore = score + myScore
-        bestPosition = 0
-        bestScore = tempScore
-        hmm = [o[p]] + path
-      else:
-        tempScore += myScore - myOldScore
-        for i in range(max(0,s-1), position-1):
-          tempScore += m.d(path[i], path[position - 1]) * (w[abs(position - 1 - i) - 1] - w[abs(position - 1 - i)])
-        for i in range(position, e):
-          tempScore += m.d(path[i], path[position - 1]) * (w[abs(position - 1 - i)] - w[abs(position - 1 - i) - 1])
-
-      if tempScore < bestScore:
-        bestScore = tempScore
-        bestPosition = position
-
-      myOldScore = myScore
-    path.insert(bestPosition, o[p])
-    score = bestScore
-  return path, score
\ No newline at end of file