Governments programs are destructive for new entrepreneurs

I reached my limits of bureaucracy. Enough is enough. My conclusion is that governments programs are destructive to new entrepreneurs. Let's face it, it is simply incompatible, entrepreneurs are looking for efficiency and government can only produce bureaucracy and inefficient process that are killing wealth creation.

Many entrepreneurs are trapped by government programs where they expect help and finally end up losing precious time, getting ridiculous support for massive paperwork if not excluded by incoherent rules. Entrepreneurs are here to create wealth not justifying bureaucracy existence.

I am tire of hearing bullshit like:

• we are here to help entrepreneurs

Stop saying it and do it.

• but you have to be profitable, have clients, make more then 200K, been incorporated since, have a product, have N employees, have to been able to pay your new employee to get the support, not this, not that, this, but only in 3 months, sorry in 6 months, it needs to go through the committee of the committee of the board and can't tell you when they might decide but it will required another meeting to decide, you have to come to our office, we need this form and this proof and letter of this and details of this....like if entrepreneur time is free.

If we were respecting your criteria, we wouldn't need your support. Again we aren't looking for advices but only financial supports and efficient process, we know what we have to do. The current system seems to subsidising establish corporations, corporation without financial constraints and most important, for a second time, justifying bureaucracy existence.

Here is in a nutshell what entrepreneurs need:

• Real founding support, light and fast decision process...not advices nor endless decisions and paperwork process of months and please stop pretending you can help if you know you can't.

Worst thing is that it is sending a so bad signal to the new entrepreneurs, you should be an bureaucrat official, you will make more money, you will have a pension plan, less stress, no performance evaluation from people you are suppose to help...

Don't worry entrepreneurs can't go that way but can kill this system slowly. Without entrepreneurs, this system cannot sustain. I hope the new majority Conservative Party of Canada will make drastic cuts in this bureaucracy (their 10 billions cuts hasn't been unveiled). Officials aren't untouchable, don't shit in your plate rules applies to everyone. The effect might take longer but it will come and new entrepreneurs will help the momentum but what is bad is that founds might been cuts but no untouchable government jobs. Stop wasting time on their salaries and invest in entrepreneurs directly.

Government programs are making more damaged then good for new entrepreneurs by making them loose too much time and energy and encourage indirectly outsourcing which overall is extremely bad for the economy. Hopefully Canada has natural resources....but it isn't an excuse to tolerate destructive economy programs.

http://www.mdeie.gouv.qc.ca/fileadmin/contenu/documents_soutien/apropos/strategies/entrepreneuriat/renouvellement_entrepreneuriat.pdf (see page 24)

http://www.radio-canada.ca/nouvelles/Economie/2011/05/04/006-indice-entrepreneurial-quebec2011.shtml

Letters order isn't important for our mind: a simple appengine service to play with this idea

I got several time an email about cool visual illusions and enjoyed a lot the one on the fact that our mind doesn't care about letter order, the only important thing is that the first and last letter be in the right place (refer as a Cambridge study). You might have already seen this:

Olny srmat poelpe can raed tihs. I cdnuolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg. The phaonmneal pweor of the hmuan mnid, aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Amzanig huh? yaeh and I awlyas tghuhot slpeling was ipmorantt! if you can raed tihs psas it on!!

I decided to create a service to play with the idea on google appengine.

You can try it now with your own text:

http://nonhumanreadable.appspot.com/

How to create standalone python apps?

You might have to run your applications in your customer infrastructure but you might not want to give your recipes (python source code) so here are the alternatives depending on your OS:

• windows = py2exe
• mac = py2app
• linux = pyinstaller (freeze doesn't work->compile errors*)

On linux, pyinstaller works quite well but you have to generate it on the same distribution.

Here are the steps:

1. download latest version
2. python Configure.py
3. python Makespec.py /path/to/yourscript.py
4. python Build.py /path/to/yourscript.spec
5. start app: yourscript/dist/yourscript/yourscript(binary executable)

(*) Freeze instructions:

1. svn checkout http://svn.python.org/projects/python/trunk/Tools/freeze/
2. python freeze/freeze.py yourscript.py
3. make

simple multivariate classifier example using python & numpy

I was wondering how long it could take to write a multivariate classifier in python.

With python and numpy it isn't long. We simply need to be able to compute the covariance matrix, the determinant and to inverse a matrix (covariance matrix). Even if the matrix is singular, which mean it can't inverse it, you can compute the pseudo-inverse (Moore-Penrose) easily (i.e.: numpy.linalg.pinv).

As expected, assuming too much about the data lead to poor classification.

You can find a simple python program of 75 lines here.

Dimensionality reduction; a simple PCA example using python

Dimensionality reduction is a powerful approach to reduce inputs size, reduce training time and visualize data.

As an example, you can use PCA(Principal Component Analysis) or ICA (independent component analysis) or LLE (Locally Linear Embedding).

to see class grouping. You can try it on your data easily with python in a couple of lines.

import mdp

pca = mdp.pca(ds.data)

pylab.title("PCA")

pylab.plot(pca[:,0], pca[:,1], '.')

The figure presents the PCA dimensionally reduction applied on a digit dataset. You can find the source code here to see you to do a PCA, ICA or LLE using python. Unfortunately, ICA doesn't work on our dataset because it doesn't converge.

PDF watermarking service using pdfrw on google appengine

If you are looking to watermark a pdf, you can use this simple appengine service:

http://pdfwatermarker.appspot.com/

This service use pdfrw (a PDF file manipulation library written by Paul Gauvin) and reportlab. pdfrw is much faster then pypdf for watermarking.

Summary of machine learning libs available in python

Here is a summary of all python related machine learning libraries in python (inspired by Similar or Related Projects of PyMVPA, lisa mailing list and personal notes).

• pybrain: PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym". see features. key feature : ecurrent networks (RNN), including Long Short-Term Memory (LSTM) architectures
• mlpy:Machine Learning PYthon (mlpy) is a high-performance Python library for predictive modeling. mlpy makes extensive use of NumPy to provide fast N-dimensional array manipulation and easy integration of C code. The GNU Scientific Library ( GSL) is also required. It provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification, regression and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. Key feature: feature selection
• scikit.learn: scikits.learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). Key distinct features: lasso, nearest neighbor, isomap, various metrics, mean shift, cross validation, LDA, HMMs
• opencv (machine learning): Normal Bayes Classifier, K Nearest Neighbors, SVM, Decision Trees, Boosting, Random Trees, Expectation-Maximization, Neural Networks
• Shogun: A Large Scale Machine Learning Toolbox Comprehensive machine learning toolbox with bindings to various programming languages. PyMVPA can optionally use implementations of Support Vector Machines from Shogun. Large scale kernel learning (mostly svms). this wraps other libraries such as libsvm (well-established) and others that get state of the art performance or are good for extremely large datasets, etc.
• PyMVPA (Multivariate Pattern Analysis in Python): PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known asdecoding or MVPA analysis.

• pylearn (build on top of theano), under V2 construction. New version of plean (c++).
• Theano: (deep learning) Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
• jml: Jeremy's Machine Learning library (C++), include a python interface: Basic classifiers (perceptrons, decision trees, etc) plus ensemble methods (boosting, bagging). Very highly optimized to work with thousands of features and millions of examples. GPGPU support under development. Code derived from this library is extensively used in a commercial computational linguistics application, so it has gone through its paces.
• 3dsvm: AFNI plugin to apply support vector machine classifiers to fMRI data.
• Elefant: Efficient Learning, Large-scale Inference, and Optimization Toolkit. Multi-purpose open source library for machine learning.
• MDP Python data processing framework. MDP provides various algorithms. PyMVPA makes use of MDP’s PCA and ICA implementations. interesting features: ica, LLE
• MVPA Toolbox: Matlab-based toolbox to facilitate multi-voxel pattern analysis of fMRI neuroimaging data.
• NiPy: Project with growing functionality to analyze brain imaging data. NiPy is heavily connected to SciPy and lots of functionality developed within NiPy becomes part of SciPy.
• OpenMEEG: Software package for low-frequency bio-electromagnetism including the EEG/MEG forward and inverse problems. OpenMEEG includes Python bindings.
• Orange: Powerful general-purpose data mining software. Orange also has Python bindings.
• PyMGH/PyFSIO: Python IO library to for FreeSurfer’s .mgh data format.
• PyML: PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods.
• PyNIfTI: Read and write NIfTI images from within Python. PyMVPA uses PyNIfTI to access MRI datasets.
• milk: k-means, svm's with arbitrary python types for kernel arguments. Pythonic interface to libSVM. Stepwise Discriminant Analysis for feature selection. K-means clustering. odels can be pickled and unpickled.
• mlboost: Machine Learning Boost Library (python; includes flayers wrapper); minimal version of sourceforge mlboost project. Specialized on features extraction and visualization.

to watch:

• http://torch5.sourceforge.net/ Torch5 is the official successor of Torch3, and is now developed at NEC Laboratories America andGoogle Labs. No python wrapper yet.
• pylearn v2; new version of pylearn from LISA lab of udm.