Python matrix using min function in k-means -

i trying learn k-means book"machine learning in action" now. using code given book in ipython notebook, outcome

matrix([[<map object @ 0x0000000008832c88>]], dtype=object) 

happened after input locmat = mat(loaddataset("user1.txt")) , min(locmat[:,0]).

what meaning of outcome? why not exact value 3.245555? code showed below, thank in advanced!

def loaddataset(filename):          datamat = []                  fr = open(filename)     line in fr.readlines():     curline = line.strip().split('\t')     fltline = map(float,curline) #map elements float()     datamat.append(fltline)     return datamat  def disteclud(veca, vecb):     return sqrt(sum(power(veca - vecb, 2))) #la.norm(veca-vecb)  def randcent(dataset, k):     n = shape(dataset)[1]     centroids = mat(zeros((k,n)))     j in range(n):         minj = min(dataset[:,j])          rangej = float(max(dataset[:,j]) - minj)         centroids[:,j] = mat(minj + rangej * random.rand(k,1))     return centroids 

this happening because you're using python 3, map returns generator instead of list.

you need use fltline = list(map(float, curline)) or fltline = [float(x) x in curline] make sure result list, things work expected.

since assume you're using numpy here, can use genfromtxt function load data file:

>>> import numpy np >>> np.genfromtxt(filename) array([[ 1.,  2.,  3.],        [ 4.,  5.,  6.]])