I 'm working on machine learning.
Here is my class
class bicluster:
def __init__(
self,
vec,
left=None,
right=None,
distance=0.0,
id=None,
):
self.left = left
self.rAnd i use list comprehension as below:
clust = [bicluster(rows[i], id=i) for i in range(len(rows))]
When i run program i got this error
"TypeError: object of type 'bicluster' has no len()"
How can i fix it ?
Not enough code to diagnose. It seems you are trying to iterate over a sequence of instances, but currently rows is just a single instance of bicluster.
Here is file cluster.py
from PIL import Image, ImageDraw
from math import sqrt
import random
def readfile(filename):
lines = [line for line in open(filename)]
# First line is the column titles
colnames = lines[0].strip().split('\t')[1:]
rownames = []
data = []
for line in lines[1:]:
p = line.strip().split('\t')
# First column in each row is the rowname
rownames.append(p[0])
# The data for this row is the remainder of the row
data.append([float(x) for x in p[1:]])
return (rownames, colnames, data)
def pearson(v1, v2):
# Simple sums
sum1 = sum(v1)
sum2 = sum(v2)
# Sums of the squares
sum1Sq = sum([pow(v, 2) for v in v1])
sum2Sq = sum([pow(v, 2) for v in v2])
# Sum of the products
pSum = sum([v1[i] * v2[i] for i in range(len(v1))])
# Calculate r (Pearson score)
num = pSum - sum1 * sum2 / len(v1)
den = sqrt((sum1Sq - pow(sum1, 2) / len(v1)) * (sum2Sq - pow(sum2, 2)
/ len(v1)))
if den == 0:
return 0
return 1.0 - num / den
class bicluster:
def __init__(
self,
vec,
left=None,
right=None,
distance=0.0,
id=None,
):
self.left = left
self.right = right
self.vec = vec
self.id = id
self.distance = distance
'''
def __len__(self):
return len(self.vec)
def __getitem__(self, index):
return self.vec[index]
def __setitem__(self,vec, left = None, right = None, distance = 0.0, id = None):
self.vec = vec
self.left = left
self.right = right
self.vec = vec
self.id = id
self.distance = distance
'''
def hcluster(rows, distance=pearson):
distances = {}
currentclustid = -1
# Clusters are initially just the rows
clust = [bicluster(rows[i], id=i) for i in range(len(rows))]
while len(clust) > 1:
lowestpair = (0, 1)
closest = distance(clust[0].vec, clust[1].vec)
# loop through every pair looking for the smallest distance
for i in range(len(clust)):
for j in range(i + 1, len(clust)):
# distances is the cache of distance calculations
if (clust[i].id, clust[j].id) not in distances:
distances[(clust[i].id, clust[j].id)] = \
distance(clust[i].vec, clust[j].vec)
d = distances[(clust[i].id, clust[j].id)]
if d < closest:
closest = d
lowestpair = (i, j)
# calculate the average of the two clusters
mergevec = [(clust[lowestpair[0]].vec[i] + clust[lowestpair[1]].vec[i])
/ 2.0 for i in range(len(clust[0].vec))]
# create the new cluster
newcluster = bicluster(mergevec, left=clust[lowestpair[0]],
right=clust[lowestpair[1]], distance=closest,
id=currentclustid)
# cluster ids that weren't in the original set are negative
currentclustid -= 1
del clust[lowestpair[1]]
del clust[lowestpair[0]]
clust.append(newcluster)
return clust[0]
def printclust(clust, labels=None, n=0):
# indent to make a hierarchy layout
for i in range(n):
print (' '),
if clust.id < 0:
# negative id means that this is branch
print ('-')
else:
# positive id means that this is an endpoint
if labels == None:
print (clust.id)
else:
print (labels[clust.id])
# now print the right and left branches
if clust.left != None:
printclust(clust.left, labels=labels, n=n + 1)
if clust.right != None:
printclust(clust.right, labels=labels, n=n + 1)