Python collections.Counter類代碼示例

    def update(self, *args, **kwargs):
        """D.update([E, ]**F) -> None.  Update D from dict/iterable E and F.
        If E present, does:  for k in E: D[k] = E[k]
        This is followed by: for k in F: D[k] = F[k]

        **Example:**

        >>> e = Exponents()
        >>> e.update(a=1)
        >>> e.update(dict(b=2), c=3)
        >>> e.update('1/d')
        >>> e # doctest: +SKIP
        Exponents({'a': 1, 'b': 2, 'c': 3, 'd': -1})

        .. testcleanup::
           >>> assert e == dict(a=1, b=2, c=3, d=-1)
        """
        try:
            # Assume args[0] is a string.
            arg = args[0].replace(' ', '')  # Remove spaces.
        except (IndexError, AttributeError):
            Counter.update(self, *args, **kwargs)
        else:
            if len(args) > 1:
                raise TypeError("update expected at most 1 arguments, got %i"
                                % len(args))
            Counter.update(self, Exponents.fromstr(arg), **kwargs)

    def __setitem__(self, item, val):
        if len(self) > self._max * 2:
            print("Purging")
            to_delete = self.most_common()[self._max:]
            for ii in to_delete:
                del self[ii]

        Counter.__setitem__(self, item, val)

 def __init__(self, _dict=None, **kwargs):
     if _dict is None:
         self.__init__(kwargs)
     else:
         if not all(isinstance(x, Rational) for x in _dict.values()):
             raise TypeError("powers of dimensions must be rational")
         Counter.__init__(self, _dict)
         self.clean()

 def __init__(self, n, *args, **kwargs):
     assert n>=1
     self._size = n
     self._worst = None
     Counter.__init__(self, *args, **kwargs)
     if len(self)>=n:
         self._worst = self.most_common(n)[-1]
         for k,v in self.most_common()[n:]:
             del self[k]

 def __setitem__(self, k, v):
     if k in self:
         assert self[k]==v
         return
     if self._worst is not None: # the beam is full
         if v<=self._worst[1]:
             return
         else:
             del self[self._worst[0]]
             
     Counter.__setitem__(self, k, v)
     assert self._size>=len(self)
     if self._size==len(self):
         self._worst = min(self.items(), key=lambda (k1,v1): v1)

def addFakeData(oData,oLabels,count=100,low=10):
	data = oData[:]
	labels = oLabels[:]
	for iafsa in range(count):
		c = Counter(chain(*labels))
		lc = Counter.most_common(c)
	
		dlc = {}
		for l in lc: dlc[l[0]] = l[1]
	
		#teze = [sum([ dlc[y]**2 for y in x])  for x in labels]
		teze = [sum([ dlc[y] for y in x])  for x in labels]
		teze = sorted([(y,x) for x,y in enumerate(teze)])
		tt = teze[:max(low*10,200)]
		shuffle(tt)
		duplicate = [x[1] for x in tt[:low]]
		dLabels = [labels[i][:] for i in duplicate]
		dData = [data[i][:] for i in duplicate]
		for ii in range(1):
			for i in range(len(duplicate)):
				labels.append(dLabels[i])
				data.append(dData[i])
	#shuflamo vrstice da niso vec lepo, pa poskrbimo da labele ostanejo 
	#pri svojem primeru
	sd = []
	[sd.append((data[i],labels[i])) for i in xrange(len(data))]
	shuffle(sd)
	ll = []
	dd = []
	for x,y in sd:
		dd.append(x)
		ll.append(y)
	return (dd, ll)

def wanabeknn(k=15):
	from collections import Counter
	ftrd = open("minidata/trainingData.csv")
	fted = open("minidata/testData.csv")
	flab = open("minidata/trainingLabels.csv")

	lab = [[int(j) for j in i.strip().split(",")]  for i in flab.readlines()]
	trd = [[int(j) for j in i.strip().split("\t")] for i in ftrd.readlines()]
	ted = [[int(j) for j in i.strip().split("\t")] for i in fted.readlines()]

	def dist(a,b): return sum([min(a[i], b[i]) for i in xrange(len(a))])

	rez = []
	for v in ted:
		print "hurej  %4d   %3d" % ( len(rez),len(rez[-1:]))
		t = []
		for trindex, train in enumerate(trd):
			t.append((dist(train, v), trindex))
		tt = sorted(t, reverse=True)
		ll = []
		for i in range(k): ll += lab[tt[i][1]]
		n = len(ll)
		for i in range(k/3): ll += lab[tt[i][1]]
		rez.append([x[0] for x in Counter.most_common(Counter(ll),n/k)])
		print rez
	cPickle.dump(rez, file("rezPickled/wnbknn%d.pickled" % k, "w"), -1)

    def __init__(self, max_len, N_max = 5):
        '''
        @summary: NGramSet Constructor

        @param max_len: 最優序列長度
        @param N_max: CCS2012 對應最大n-gram

        '''
        Counter.__init__(self)
        
        self.N_max = N_max
        self.max_len = max_len
        
        self.alphabet_size = 0    # 記錄項總數
        self.all_record_num = 0   # 記錄數據集中記錄總數
        self.TERM = 0             # 序列結束符

 def _most_preferred(self, alternatives):
     """Applies funcnamei from each trait to the alternatives and return the most preferred."""
     prefs = [y for y in [getattr(x, funcnamei)(alternatives) for x in self.traits] if y is not None]
     if not prefs:
         return None
     if len(prefs) == 1:
         return prefs[0]
     return Counter.most_common(Counter(prefs), 1)[0][0]

 def items(self, relative=False):
     """ Returns a list of (key, value)-tuples sorted by value, highest-first.
         With relative=True, the sum of values is 1.0.
     """
     a = Counter.most_common(self)
     if relative:
         n = sum(v for k, v in a) or 1.
         a = [(k, v / n) for v, k in a]
     return a

 def __len__(self):
     '''Returns the total number of members, excluding repeated elements.
     
     >>> m = multiset('abb')
     >>> len(m)
     2
     
     '''
     return Counter.__len__(self)

def most_common_viz(output_dir: str, ints: collections.Counter) -> None:
    df = pd.DataFrame(ints.most_common(), columns=["Integer", "Frequency"])

    with open(os.path.join(output_dir, 'index.html'), 'w') as fh:
        fh.write('<html><body>\n')
        fh.write('<h3>Most common integers:</h3>\n')
        fh.write(df.to_html(index=False))
        fh.write('</body></html>')

    with open(os.path.join(output_dir, 'index.tsv'), 'w') as fh:
        fh.write(df.to_csv(sep='\t', index=False))

    def __add__(self, other):
        """Add together, with recursion.  The basic idea is that the set of keys
        should be added together and then recurse the addition to the values where keys are shared
        otherwise just add the value.

        >>> m, m2 = Mdict('a'), Mdict('abb')
        >>> m + m2 == {'a':2, 'b':2}
        True
        >>> m + 1 == {None:1, 'a':1}
        True
        >>> m['a'] = m2  #now fractal
        >>> m == {'a': {'a':1, 'b':2}}
        True
        >>> m + m + 1 == {'a': {'a':2, 'b':4}, None: 1}
        True
        """
        try: # need to add (union) of keys, and then recurse into values of common keys
            return Mdict(Counter.__add__(self, other)) #Counter.__add__(self, other)
        except TypeError: #must've been an integer for other
            return Mdict(Counter.__add__(self, Counter({None: other})))

def removeLeastCommonData(oData, oLabels, least=5):
	data = oData[:]
	labels = oLabels[:]
	c = Counter(chain(*labels))
	lc = Counter.most_common(c)
	bb = sorted(list(Set([j for i,j in lc])))
	a = [x[0] for x in lc if x[1] < bb[5]]
	rem = [i for i,j in enumerate(labels) if len(Set(j).intersection(Set(a))) > 0 ]
	[labels.pop(x) for x in sorted(rem, reverse=True)]
	[data.pop(x) for x in sorted(rem, reverse=True)]
	return (data, labels)

def removeMostCommonData(oData, oLabels, count=20):
	data = oData[:]
	labels = oLabels[:]
	for iafsa in range(count):
		c = Counter(chain(*labels))
		lc = Counter.most_common(c)
		dlc = {}
		for l in lc: dlc[l[0]] = l[1]
		teze = [max([ dlc[y] for y in x])  for x in labels]
		teze = sorted([(y,x) for x,y in enumerate(teze)])
		rem = [x[1] for x in teze[-10:]]
		[labels.pop(x) for x in sorted(rem, reverse=True)]
		[data.pop(x) for x in sorted(rem, reverse=True)]
	return (data, labels)