fsodogandji

import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np

# Loading datasets'

fsodogandji

http://vermandel.fr/fr/dsge-dynare-model-matlab-codes/

http://www.chadfulton.com/posts/estimating_rbc.html

https://pythonhosted.org/pymaclab/#series-of-brief-tutorials

http://www.chadfulton.com/files/fulton_dismalpy_2015.pdf

fsodogandji

# Get all POST and GET requests using tshark

tshark -i en0 -f'port 80 and (tcp[((tcp[12:1] & 0xf0) >> 2):4] = 0x504F5354 or tcp[((tcp[12:1] & 0xf0) >> 2):4] = 0x47455420)' -w ~/out.ncap  

tcpdump -qns 0 -A -r /var/root/out.ncap | less

fsodogandji

"""
Implementations of:

    Probabilistic Matrix Factorization (PMF) [1],
    Bayesian PMF (BPMF) [2],
    Modified BPFM (mBPMF)

using `pymc3`. mBPMF is, to my knowledge, my own creation. It is an attempt
to circumvent the limitations of `pymc3` w/regards to the Wishart distribution:

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class StackedEnsembleClassifier(sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin):
    """
    Ensemble of classifiers. Each classifier must have predict_proba and the head classifier is trained
    to predict the output based on the individual classifier outputs. Stratified k-fold cross-validation
    is used to get probabilities on held-out data.
    """
    def __init__(self, classifiers, head_classifier, num_folds=3):
        self.classifiers = classifiers
        self.head_classifier = head_classifier
        self.num_folds = num_folds

fsodogandji

# -*- coding: utf-8 -*-
"""
Created on Mon Aug 22 00:36:48 2016

@author: park
"""

#%% train

import theano

fsodogandji

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

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