import os.path as osp
import re
import string
from math import log

import gensim.downloader as api
import numpy as np
from gensim.models.keyedvectors import KeyedVectors
from nltk.corpus import stopwords

dbig = np.dtype('>f8')
try:
    model = KeyedVectors.load_word2vec_format("glove-twitter-100.txt", binary=False)
except FileNotFoundError:
    print('File not found, attempting to download')
    model = api.load("glove-twitter-100")

num_features = model.wv.vector_size
stopwords = set(stopwords.words('english'))

DATA_PATH = osp.join(osp.dirname(osp.dirname(
    osp.dirname(osp.realpath(__file__)))), osp.join('data'))


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:
    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words))))
                              for i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        # Infer location of spaces in hashtags
        text = text.lower()
        text = re.sub(r'[^\w\s]', '', text)

        def best_match(i):
            # Find the best match for the first i characters
            # assuming costs has been built for the first (i-1) characters
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i],
                                               9e999), k + 1) for k, c in candidates)

        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)

        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k

        return list(reversed(out))

bedd2de2a04589a8d288e5c013f6b2709b474051
<mask token>


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in
            stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:

    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words)))) for
            i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        text = text.lower()
        text = re.sub('[^\\w\\s]', '', text)

        def best_match(i):
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i], 1e1000), 
                k + 1) for k, c in candidates)
        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)
        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k
        return list(reversed(out))

<mask token>
try:
    model = KeyedVectors.load_word2vec_format('glove-twitter-100.txt',
        binary=False)
except FileNotFoundError:
    print('File not found, attempting to download')
    model = api.load('glove-twitter-100')
<mask token>


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in
            stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:

    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words)))) for
            i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        text = text.lower()
        text = re.sub('[^\\w\\s]', '', text)

        def best_match(i):
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i], 1e1000), 
                k + 1) for k, c in candidates)
        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)
        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k
        return list(reversed(out))

<mask token>
dbig = np.dtype('>f8')
try:
    model = KeyedVectors.load_word2vec_format('glove-twitter-100.txt',
        binary=False)
except FileNotFoundError:
    print('File not found, attempting to download')
    model = api.load('glove-twitter-100')
num_features = model.wv.vector_size
stopwords = set(stopwords.words('english'))
DATA_PATH = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(
    __file__)))), osp.join('data'))


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in
            stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:

    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words)))) for
            i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        text = text.lower()
        text = re.sub('[^\\w\\s]', '', text)

        def best_match(i):
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i], 1e1000), 
                k + 1) for k, c in candidates)
        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)
        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k
        return list(reversed(out))

import os.path as osp
import re
import string
from math import log
import gensim.downloader as api
import numpy as np
from gensim.models.keyedvectors import KeyedVectors
from nltk.corpus import stopwords
dbig = np.dtype('>f8')
try:
    model = KeyedVectors.load_word2vec_format('glove-twitter-100.txt',
        binary=False)
except FileNotFoundError:
    print('File not found, attempting to download')
    model = api.load('glove-twitter-100')
num_features = model.wv.vector_size
stopwords = set(stopwords.words('english'))
DATA_PATH = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(
    __file__)))), osp.join('data'))


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in
            stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:

    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words)))) for
            i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        text = text.lower()
        text = re.sub('[^\\w\\s]', '', text)

        def best_match(i):
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i], 1e1000), 
                k + 1) for k, c in candidates)
        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)
        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k
        return list(reversed(out))

import os.path as osp
import re
import string
from math import log

import gensim.downloader as api
import numpy as np
from gensim.models.keyedvectors import KeyedVectors
from nltk.corpus import stopwords

dbig = np.dtype('>f8')
try:
    model = KeyedVectors.load_word2vec_format("glove-twitter-100.txt", binary=False)
except FileNotFoundError:
    print('File not found, attempting to download')
    model = api.load("glove-twitter-100")

num_features = model.wv.vector_size
stopwords = set(stopwords.words('english'))

DATA_PATH = osp.join(osp.dirname(osp.dirname(
    osp.dirname(osp.realpath(__file__)))), osp.join('data'))


def get_embedding(sentence, ignore_stopwords=True):
    sentence = re.sub('[' + string.punctuation + ']', '', sentence)
    words = sentence.lower().split()
    feature_vec = np.zeros((num_features,), dtype='float32')
    n_words = 0
    for word in words:
        if word in model.vocab and (not ignore_stopwords or word not in stopwords):
            n_words += 1
            feature_vec = np.add(feature_vec, model[word])
    if n_words > 0:
        feature_vec = np.divide(feature_vec, n_words)
    else:
        feature_vec[0] += 0.001
    feature_vec = list(feature_vec)
    return [round(float(elem), 3) for elem in feature_vec]


class WordSplitter:
    def __init__(self):
        file = osp.join(DATA_PATH, 'wordsfreq_wiki2.txt')
        with open(file) as f:
            self._words = f.read().split()
        self._wordcost = dict((k, log((i + 1) * log(len(self._words))))
                              for i, k in enumerate(self._words))
        self._maxword = max(len(x) for x in self._words)

    def infer_spaces(self, text):
        # Infer location of spaces in hashtags
        text = text.lower()
        text = re.sub(r'[^\w\s]', '', text)

        def best_match(i):
            # Find the best match for the first i characters
            # assuming costs has been built for the first (i-1) characters
            candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
            return min((c + self._wordcost.get(text[i - k - 1:i],
                                               9e999), k + 1) for k, c in candidates)

        cost = [0]
        for i in range(1, len(text) + 1):
            cur_cost, k = best_match(i)
            cost.append(cur_cost)

        out = []
        i = len(text)
        while i > 0:
            cur_cost, k = best_match(i)
            assert cur_cost == cost[i]
            out.append(text[i - k:i])
            i -= k

        return list(reversed(out))
