I have a input file which are all floating point numbers to 4 decimal place.

i.e. 13359    0.0000    0.0000    0.0001    0.0001    0.0002`    0.0003    0.0007    ... 

(the first is the id). My class uses the loadVectorsFromFile method which multiplies it by 10000 and then int() these numbers. On top of that, I also loop through each vector to ensure that there are no negative values inside. However, when I perform _hclustering, I am continually seeing the error, "Linkage Z contains negative values".

I seriously think this is a bug because:

1. I checked my values,
2. the values are no where small enough or big enough to approach the limits of the floating point numbers and
3. the formula that I used to derive the values in the file uses absolute value (my input is DEFINITELY right).

Can someone enligten me as to why I am seeing this weird error? What is going on that is causing this negative distance error?

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def loadVectorsFromFile(self, limit, loc, assertAllPositive=True, inflate=True):
    """Inflate to prevent "negative" distance, we use 4 decimal points, so *10000
    """
    vectors = {}
    self.winfo("Each vector is set to have %d limit in length" % limit)
    with open( loc ) as inf:
        for line in filter(None, inf.read().split('\n')):
            l = line.split('\t')
            if limit:
                scores = map(float, l[1:limit+1])
            else:
                scores = map(float, l[1:])

            if inflate:        
                vectors[ l[0]] = map( lambda x: int(x*10000), scores)     #int might save space
            else:
                vectors[ l[0]] = scores                           

    if assertAllPositive:
        #Assert that it has no negative value
        for dirID, l in vectors.iteritems():
            if reduce(operator.or_, map( lambda x: x < 0, l)):
                self.werror( "Vector %s has negative values!" % dirID)
    return vectors

def main( self, inputDir, outputDir, limit=0,
        inFname="data.vectors.all", mappingFname='all.id.features.group.intermediate'):
    """
    Loads vector from a file and start clustering
    INPUT
        vectors is { featureID: tfidfVector (list), }
    """
    IDFeatureDic = loadIdFeatureGroupDicFromIntermediate( pjoin(self.configDir, mappingFname))
    if not os.path.exists(outputDir):
        os.makedirs(outputDir)

    vectors = self.loadVectorsFromFile( limit, pjoin( inputDir, inFname))
    for threshold in map( lambda x:float(x)/30, range(20,30)):
        clusters = self._hclustering(threshold, vectors)
        if clusters:
            outputLoc = pjoin(outputDir, "threshold.%s.result" % str(threshold))
            with open(outputLoc, 'w') as outf:
                for clusterNo, cluster in clusters.iteritems():
                    outf.write('%s\n' % str(clusterNo))
                    for featureID in cluster:
                        feature, group = IDFeatureDic[featureID]
                        outline = "%s\t%s\n" % (feature, group)
                        outf.write(outline.encode('utf-8'))
                    outf.write("\n")
        else:
            continue

def _hclustering(self, threshold, vectors):
    """function which you should call to vary the threshold
    vectors:    { featureID:    [ tfidf scores, tfidf score, .. ]
    """
    clusters = defaultdict(list)
    if len(vectors) > 1:
        try:
            results = hierarchy.fclusterdata( vectors.values(), threshold, metric='cosine')
        except ValueError, e:
            self.werror("_hclustering: %s" % str(e))
            return False

        for i, featureID in enumerate( vectors.keys()):