def visualize(coin_results):
num_heads = coin_results.count("head")
num_tails = coin_results.count("tail")
heads_percentage = num_heads / (num_heads + num_tails)
tails_percentage = num_tails / (num_heads + num_tails)
return elice_utils.visualize_boxplot(
"Coin Flip: %d times" % (num_heads + num_tails),
[heads_percentage, tails_percentage], ["heads (%)", "tails (%)"])
def visualize(coin_results):
num_heads = coin_results.count("head")
num_tails = coin_results.count("tail")
heads_percentage = num_heads / (num_heads + num_tails)
tails_percentage = num_tails / (num_heads + num_tails)
return elice_utils.visualize_boxplot("Coin Flip: %d times" % (num_heads + num_tails),
[heads_percentage, tails_percentage],
["heads (%)", "tails (%)"])
def main():
training1_sentences = read_text_data('./txt_sentoken/pos/')
training2_sentences = read_text_data('./txt_sentoken/neg/')
testing_sentence = input()
alpha = 0.1
prob1 = 0.5
prob2 = 0.5
prob_pair = naive_bayes(training1_sentences, training2_sentences, testing_sentence, alpha, prob1, prob2)
plot_title = testing_sentence
if len(plot_title) > 50: plot_title = plot_title[:50] + "..."
print(elice_utils.visualize_boxplot(plot_title,
list(prob_pair),
['Positive', 'Negative']))
def main():
training1_sentences = read_text_data('./txt_sentoken/pos/')
training2_sentences = read_text_data('./txt_sentoken/neg/')
testing_sentence = input()
alpha = 0.1
prob1 = 0.5
prob2 = 0.5
prob_pair = naive_bayes(training1_sentences, training2_sentences,
testing_sentence, alpha, prob1, prob2)
plot_title = testing_sentence
if len(plot_title) > 50: plot_title = plot_title[:50] + "..."
print(
elice_utils.visualize_boxplot(plot_title, list(prob_pair),
['Positive', 'Negative']))