g = d['G']
t = d['T']
res = ''
if (a >= c and a >= g and a >= t):
res = 'A'
elif (c >= a and c >= g and c >= t):
res = 'C'
elif (g >= a and g >= c and g >= t):
res = 'G'
else:
res = 'T'
return res
new = ""
counts = [{'A':0, 'C':0, 'G':0, 'T':0} for i in xrange(len(strands[0]))]
for strand in strands:
for j in xrange(len(strand)):
c = strand[j]
counts[j][c] += 1
for i in xrange(len(strand)):
new += maxChar(counts[i])
return new
dnaFuncs = ks.kMeansInfo(distance, centroids)
data = ['ACGT', 'CCTT', 'TCGT', 'CGTT', 'ACGG', 'ATGT']
obj = ks.kMeansCluster(dnaFuncs, data, 2, .01)
print obj.findClusters()
import math
import kmeansSequential as ks
def distance(p1, p2):
return math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
def centroids(points):
x_sum = 0.0
y_sum = 0.0
for (x,y) in points:
x_sum += x
y_sum += y
final_x = x_sum / len(points)
final_y = y_sum / len(points)
return (final_x, final_y)
#points gives data in the form [(x1,y1), (x2,y2), ....]
pointFuncs = ks.kMeansInfo(distance, centroids)
data = [(-1,-1),(1,1),(-1,1),(1,-1),(2,-2),(-2,2),(2,2),(-2,-2)]
obj = ks.kMeansCluster(pointFuncs, data, 4, .01)
print obj.findClusters()
