class FeatureStats(object):
    def __init__(self,word, ld, net):
        f = net.getConvFunction()
        inds = ld.word2inds(word)
        windows = []
        for i in range(len(inds) + 1 - net.filter_height):
            windows.append(inds[i:i+net.filter_height])
        windows = map(lambda x: ld.inds2word(x), windows)
        features = f([[inds]])
        shape = features.shape
        features = features.reshape((shape[1],shape[2])).transpose()
        self.data = zip(windows,features)

    def thresholdStats(self, threshold):
        res = []
        for pair in self.data:
            l = len(filter(lambda x: x >= threshold, pair[1]))
            res.append((pair[0],l))
        return res

    def normStats(self):
        res = []
        for pair in self.data:
            m = np.linalg.norm(pair[1])
            res.append((pair[0],m))
        return res

    def maxStats(self):
        ngramms
        res = {i:0 for i in map(lambda x: x[0], self.data)}
        for i in xrange(len(self.data)):
            l = lambda x: x[1][i]
            cur_data = self.data
            cur_data.sort(key=l, reverse=True)
            res[cur_data[0][0]] += 1
        return [res[i] for i in ]


    def printStats(self, metrics='threshold',threshold=None, ordered=True, mask="%s - %d"):
        if metrics == 'threshold':
            pairs = self.thresholdStats(threshold)
        elif metrics == 'norm':
            pairs = self.normStats()
        if ordered:
            pairs.sort(key=(lambda x: x[1]), reverse=True)
        for i in pairs:
            print mask % (i[0],i[1])




def test(word):
    print
    print
    print word
    print
    print "Максимальные нграммы по количеству фильтров прошедших порог 0.03"
    FeatureStats(word, letterDict, network).printStats(metrics='threshold', threshold=0.04)
    print
    print "Максимсальные нграммы по норме"
    FeatureStats(word, letterDict, network).printStats(metrics='norm', mask="%s - %.3f")
    print
    print