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 object of type ... has no len()
#1
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.r
And 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 ?
Quote
#2
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.
Quote
#3
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)
Quote
#4
Well, that still doesn't include where you call hcluster.  All I can tell you is you are passing hcluster a single bicluster instance rather than a sequence of them.

Also I really think you should read this:
https://python-forum.io/Thread-Basic-Nev...n-sequence
metulburr likes this post
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