spicyramen

/*
 * qualy.c by Gonzalo Gasca -- 2013-06-01 -- v.1.0
 *
 * Copyright (c) 2013 Gonzalo Gasca
 *
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License
 * as published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.

spicyramen

__author__ = 'gogasca'
#!/usr/bin/python
import websocket
import thread
import time
import ssl


register = "REGISTER sip:open-ims.test SIP/2.0\r\n" +\
	"Test-Header: 0\r\n" + \

spicyramen

from nltk.tokenize import word_tokenize
import pickle
import pprint
import json

"""

(heads, descs, keywords) = ([headline], [description], )

"""

spicyramen

"""Honeypot classifier. Based on https://www.kaggle.com/mrklees/applying-keras-scikit-learn-to-titanic"""

import pandas as pd
import numpy as np

from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout

from sklearn.model_selection import train_test_split

spicyramen

"""Extract important information from AppAnnie via API."""

import pandas as pd

from absl import app
from absl import flags
from absl import logging
from bs4 import BeautifulSoup as BS
from collections import namedtuple
from retrying import retry

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from keras.models import Sequential
from keras.layers import Dense
import numpy

seed = 7
numpy.random.seed(seed)

# Cargar el dataset de los indios Pima.
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")

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model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

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model.fit(X, Y, nb_epoch=150, batch_size=10)

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scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))