import math
import operator
from scipy import stats
from utils import getTweets
w1 = 0.3
w2 = 0.3
w3 = 0.3


def kullback_leibler(distr_a, distr_b):
    return stats.entropy(distr_a, distr_b)


def logistic_increasing_func(x):
    return 1 / (1 + math.exp(x))


def coverage_score(tweet):
    return 0


def significance_score(tweet):
    return 0


def diversity_score(tweet):
    return 0


def overall_score(tweet):
    return w1 * (1 - logistic_increasing_func(coverage_score(tweet))
        ) + w2 * logistic_increasing_func(significance_score(tweet)
        ) + w3 * logistic_increasing_func(diversity_score(tweet))


tweets = getTweets('baltimore')
selected = {}
scores = {}
for id, tweet in tweets.items():
    score = overall_score(tweet.text)
    scores[id] = score
sorted_scores = sorted(scores.items(), key=operator.itemgetter(1))
sorted_scores = dict(sorted_scores[0:49])

d4814dc755ff0f3a8e82427659a7e4dfeaa84bcd
<mask token>


def kullback_leibler(distr_a, distr_b):
    return stats.entropy(distr_a, distr_b)


def logistic_increasing_func(x):
    return 1 / (1 + math.exp(x))


def coverage_score(tweet):
    return 0


def significance_score(tweet):
    return 0


def diversity_score(tweet):
    return 0


<mask token>

<mask token>


def kullback_leibler(distr_a, distr_b):
    return stats.entropy(distr_a, distr_b)


def logistic_increasing_func(x):
    return 1 / (1 + math.exp(x))


def coverage_score(tweet):
    return 0


def significance_score(tweet):
    return 0


def diversity_score(tweet):
    return 0


def overall_score(tweet):
    return w1 * (1 - logistic_increasing_func(coverage_score(tweet))
        ) + w2 * logistic_increasing_func(significance_score(tweet)
        ) + w3 * logistic_increasing_func(diversity_score(tweet))


<mask token>
for id, tweet in tweets.items():
    score = overall_score(tweet.text)
    scores[id] = score
<mask token>

<mask token>
w1 = 0.3
w2 = 0.3
w3 = 0.3


def kullback_leibler(distr_a, distr_b):
    return stats.entropy(distr_a, distr_b)


def logistic_increasing_func(x):
    return 1 / (1 + math.exp(x))


def coverage_score(tweet):
    return 0


def significance_score(tweet):
    return 0


def diversity_score(tweet):
    return 0


def overall_score(tweet):
    return w1 * (1 - logistic_increasing_func(coverage_score(tweet))
        ) + w2 * logistic_increasing_func(significance_score(tweet)
        ) + w3 * logistic_increasing_func(diversity_score(tweet))


tweets = getTweets('baltimore')
selected = {}
scores = {}
for id, tweet in tweets.items():
    score = overall_score(tweet.text)
    scores[id] = score
sorted_scores = sorted(scores.items(), key=operator.itemgetter(1))
sorted_scores = dict(sorted_scores[0:49])

import math
import operator
from scipy import stats
from utils import getTweets
w1 = 0.3
w2 = 0.3
w3 = 0.3


def kullback_leibler(distr_a, distr_b):
    return stats.entropy(distr_a, distr_b)


def logistic_increasing_func(x):
    return 1 / (1 + math.exp(x))


def coverage_score(tweet):
    return 0


def significance_score(tweet):
    return 0


def diversity_score(tweet):
    return 0


def overall_score(tweet):
    return w1 * (1 - logistic_increasing_func(coverage_score(tweet))
        ) + w2 * logistic_increasing_func(significance_score(tweet)
        ) + w3 * logistic_increasing_func(diversity_score(tweet))


tweets = getTweets('baltimore')
selected = {}
scores = {}
for id, tweet in tweets.items():
    score = overall_score(tweet.text)
    scores[id] = score
sorted_scores = sorted(scores.items(), key=operator.itemgetter(1))
sorted_scores = dict(sorted_scores[0:49])
