Skip to main content

UNKNOWN

Project description

Installation
=======

Ubuntu Linux (or other Debian-based distro)
-----------------------------------------------------------
Open a terminal and change directory to the location of this file.

Run the install_dependencies script:

$ sh install_dependencies.sh

Windows
------------
Best to install the python(x,y) bundle (http://code.google.com/p/pythonxy/).

Afterwards all the packages mentioned in the install_dependencies.sh file can be installed via pip. For example to install xlrd one would issue the following command inside Command Prompt:

$ pip install xlrd

Running
=======
Set the desired preferences in the configuration file:

./common/conf.py

Write down the crisis/normal years in the XLS file:

./odabir_uzoraka.xls

Position yourself inside the irb.foc.forecaster folder (pwd output just to show an example of the correct path):

$ cd irb.foc.forecaster
$ pwd
/media/Data/Drazen/Dropbox/dev/eclipse/w2/irb.foc.forecaster

Run the Python interpreter with the entry script run.py as an argument:

$ python run.py


Lay of the code
===============

forcaster - main module, use it to start the program

common
------
|- conf - configuration file with all the preferences
\- exceptions - all the custom exceptions are defined here

model - contains the data structures
-----
|- country - code and list of indicators
\- indicator - internal representation: list of dates, list of values

sources - represents the data sources available online.
-------
\- wb - extracts data from the World Bank

ai - classes regarding pattern recognition, train and test building etc.
--
|- input - parses XLS files to get crisis and normal period years
|- output - writes the dataset into a text file in a subgroup-discovery-friendly format
|- samples_set - the representation of the train and test datasets that can build samples based on the crisis/normal years input and indicators and countries specified in the conf file; fetches the data live from the World Bank API
|- preprocessor - processes the samples to extract useful features (min, max, slope...)
\- metadata - column labels and data type marks used when writing the dataset

tests - unit tests for individual modules
-----

Project details


Release history Release notifications

This version
History Node

0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
foc_forecaster-0.1-py2.7.egg (41.7 kB) Copy SHA256 hash SHA256 Egg 2.7 Mar 16, 2012

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page