Scipy 负距离？什么？

Question

我有一个输入文件，里面包含了小数点后四位的浮点数：

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

（第一个是ID）。我的类使用了一个叫做 loadVectorsFromFile 的方法，这个方法会把这些数字乘以10000，然后用 int() 转换成整数。此外，我还会遍历每个向量，确保里面没有负值。但是，当我进行 _hclustering 操作时，我总是看到一个错误提示，内容是 "LinkageZ包含负值"。

我真的觉得这可能是个bug，因为：

我检查过我的数值，
这些数值绝对没有小到或者大到接近浮点数的限制，
我用来计算文件中数值的公式使用了绝对值（我的输入绝对是正确的）。

有没有人能告诉我，为什么我会看到这个奇怪的错误？到底发生了什么，导致这个负距离的错误？

=====

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()):

浮点数整数转换数据处理数值计算错误调试绝对值负值检查计算公式

Scipy 负距离？什么？

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