p.py

def file(name, flag = False):
    f = open(name)
    if flag == 'w':
        return f
    return f.readlines()

def readfile(filename):
    lines = [line for line in file(filename)]
    print(len(lines))
  # 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)

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 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]