Tag: Python

Python

GLib GString wrapper for Python

I’ve done a little wrapper of GLib/GString functionality using Python ctypes. GString of GLib is an amazing and very stable work done by GLib team, the core used by GTK+ and many other libraries. The wrapper I’ve done works in Windows and Linux, but the performance results seems more interesting for Windows, however, the GString functionality is very interesting for any platform. The project is hosted here in Google project hosting.

Here is some examples of the wrapper:

>>> from GString import GString
>>> obj1 = GString("test")
>>> obj1

>>> print obj1
test
>>> obj1+obj1

>>> obj1+=obj1
>>> obj1

>>> obj1.truncate(5)

>>> obj1.insert(2, "xx")

>>> obj1.assign("test")

>>> obj1.erase(2,2)

>>> obj1.assign("12345")
>>> for i in xrange(len(obj1)):
...    print obj1[i]
...
1
2
3
4
5

I hope you enjoyed the wrapper, you can find performance comparisons in the project home site.

by Christian S. Perone
java, Python

Jinja2 in a Java JSP

This is a simple trick possible using Jython; to call jinja2 template engine under JSP we instantiate the PythonInterpreter class of Jython, set some parameters in the interpreter and then call jinja2 to render a template and write to the Java “out” object.

To install Jython, just download the last stable version Jython 2.5 and then install it as “Standalone” version; the installer will create a simple jython.jar file under the installation directory, copy this package to your Java web project under the \WEB-INF\lib or where you put your web application libraries.

Later, copy the jinja2 module to the \build\classes.

Create a simple template under the WebContent\templates\template.html with contents below:

This is just a Jinja2 template test !

Parameter "p" = {{ request.getParameter("p") }}

Getting session attribute: {{ session.getAttribute("session_attribute") }}

Iterating over Java array:

    
{% for user in users %}
	
  • {{ user }}
    • 
{% endfor %}

And now create a file called index.jsp in the root of the WebContent, with the contents:

<%@ page language="java" contentType="text/html; charset=ISO-8859-1"
    pageEncoding="ISO-8859-1"%>
<%@page import="org.python.util.PythonInterpreter" %>




Jinja2 Template under JSP


<%
	PythonInterpreter py = new PythonInterpreter();

	String[] users = {"User Number One", "User Number Two"};
	session.setAttribute("session_attribute", "Testing Session");
	
	py.set("out",     out);
	py.set("request", request);
	py.set("session", session);
	py.set("users",   users);
	py.set("context_path", application.getRealPath("/") + "templates");
		
	py.exec("from jinja2 import Environment, FileSystemLoader");
	py.exec("env = Environment(loader=FileSystemLoader(context_path))");
	py.exec("template = env.get_template('template.html')");
	py.exec("out.write(template.render(locals()))");
%>

The structure will be like this:

structure

And the output will be something like this:

out

by Christian S. Perone
genetic programming, News, Pyevolve, Python, Time Waste

Approximating Pi number using Genetic Programming

pi

As many (or very few in the real life haha) people know, today is the Pi Approximation Day ! So it’s time to make a contribution to celebrate this funny day =)

My contribution is to use Python and Pyevolve to approximate Pi number using Genetic Programming approach. I’ve created the functions gp_add(+), gp_sub(-), gp_div(/), gp_mul(*) and gp_sqrt (square root) to use as non-terminals of the GP. The fitness function is very simple too, it simple returns the absolute difference between the Python math.pi and the evaluated individual. I’ve used also a population size of 1k individuals with max tree depth of 8 and the random ephemeral constants as random integers. The best approximation I’ve got while running the GP for about 8 minutes (40 generations) was 3.1416185511, best for 3 digits, you can improve it and let it run for more time to get better approximations.

Here is the formulae I’ve got with the GP (click to enlarge):

tree_pi

And here is the output of the script:

Best (0): 3.1577998365
        Error: 0.0162071829
Best (10): 3.1417973679
        Error: 0.0002047143
Best (20): 3.1417973679
        Error: 0.0002047143
Best (30): 3.1417973679
        Error: 0.0002047143
Best (40): 3.1416185511
        Error: 0.0000258975

- GenomeBase
        Score:                   0.000026
        Fitness:                 15751.020831

        Params:          {'max_depth': 8, 'method': 'ramped'}

        Slot [Evaluator] (Count: 1)
        Slot [Initializator] (Count: 1)
                Name: GTreeGPInitializator - Weight: 0.50
                Doc: This initializator accepts the follow parameters:

   *max_depth*
      The max depth of the tree

   *method*
      The method, accepts "grow" or "full"

   .. versionadded:: 0.6
      The *GTreeGPInitializator* function.

        Slot [Mutator] (Count: 1)
                Name: GTreeGPMutatorSubtree - Weight: 0.50
                Doc:  The mutator of GTreeGP, Subtree Mutator

   .. versionadded:: 0.6
      The *GTreeGPMutatorSubtree* function

        Slot [Crossover] (Count: 1)
                Name: GTreeGPCrossoverSinglePoint - Weight: 0.50

- GTree
        Height:                 8
        Nodes:                  21

GTreeNodeBase [Childs=1] - [gp_sqrt]
  GTreeNodeBase [Childs=2] - [gp_div]
    GTreeNodeBase [Childs=2] - [gp_add]
      GTreeNodeBase [Childs=0] - [26]
      GTreeNodeBase [Childs=2] - [gp_div]
        GTreeNodeBase [Childs=2] - [gp_mul]
          GTreeNodeBase [Childs=2] - [gp_add]
            GTreeNodeBase [Childs=2] - [gp_sub]
              GTreeNodeBase [Childs=0] - [34]
              GTreeNodeBase [Childs=2] - [gp_sub]
                GTreeNodeBase [Childs=0] - [44]
                GTreeNodeBase [Childs=0] - [1]
            GTreeNodeBase [Childs=2] - [gp_mul]
              GTreeNodeBase [Childs=0] - [49]
              GTreeNodeBase [Childs=0] - [43]
          GTreeNodeBase [Childs=1] - [gp_sqrt]
            GTreeNodeBase [Childs=0] - [18]
        GTreeNodeBase [Childs=0] - [16]
    GTreeNodeBase [Childs=2] - [gp_add]
      GTreeNodeBase [Childs=0] - [24]
      GTreeNodeBase [Childs=0] - [35]

- GTreeGP
        Expression: gp_sqrt(gp_div(gp_add(26,
gp_div(gp_mul(gp_add(gp_sub(34,
gp_sub(44, 1)), gp_mul(49, 43)), gp_sqrt(18)),
16)), gp_add(24, 35)))

And finally, here is the source code:

from __future__ import division
from pyevolve import *
import math

def gp_add(a, b): return a+b
def gp_sub(a, b): return a-b
def gp_div(a, b): return 1 if b==0 else a/b
def gp_mul(a, b): return a*b
def gp_sqrt(a):   return math.sqrt(abs(a))

def eval_func(chromosome):
   code_comp = chromosome.getCompiledCode()
   ret = eval(code_comp)
   return abs(math.pi - ret)

def step_callback(engine):
   gen = engine.getCurrentGeneration()
   if gen % 10 == 0:
      best = engine.bestIndividual()
      best_pi = eval(best.getCompiledCode())
      print "Best (%d): %.10f" % (gen, best_pi)
      print "\tError: %.10f" % (abs(math.pi - best_pi))

   return False

def main_run():
   genome = GTree.GTreeGP()

   genome.setParams(max_depth=8, method="ramped")
   genome.evaluator += eval_func

   ga = GSimpleGA.GSimpleGA(genome)
   ga.setParams(gp_terminals       = ['ephemeral:random.randint(1, 50)'],
                gp_function_prefix = "gp")

   ga.setMinimax(Consts.minimaxType["minimize"])
   ga.setGenerations(50000)
   ga.setCrossoverRate(1.0)
   ga.setMutationRate(0.09)
   ga.setPopulationSize(1000)
   ga.stepCallback.set(step_callback)

   ga.evolve()
   best = ga.bestIndividual()
   best.writeDotImage("tree_pi.png")

   print best

if __name__ == "__main__":
   main_run()

If you are interested why today is the Pi Approximation day, see some resources:

Little Cartoon

Some Background History

Some Pi Approximations

by Christian S. Perone
genetic programming, Pyevolve, Python

Genetic Programming and Flex layouts

To show how Genetic Programming of Pyevolve can be flexible, I’ve done a simple example using Adobe Flex and Pyevolve, the example is just to show how to evolve some kind of Flex layouts, I’ve not implemented the fitness function, this example will just create a random Flex layout using MXML. So, here is the Pyevolve code of the example:

import random
from pyevolve import *

def gp_hbox(x, y):
   return "%s %s" % (x,y)

def gp_vbox(x, y):
   return "%s %s" % (x,y)

def gp_panel(x, y):
   return "%s %s" % (x,y)

def eval_func(chromosome):
   code_comp = chromosome.getCompiledCode()

   for a in xrange(0, 5):
      for b in xrange(0, 5):
         evaluated     = eval(code_comp)
   return random.randint(1,100)

def main_run():
   genome = GTree.GTreeGP()
   genome.setParams(max_depth=5, method="ramped")
   genome.evaluator += eval_func

   ga = GSimpleGA.GSimpleGA(genome)

   button     = repr("<mx:Button label='Button'/>")
   label      = repr("<mx:Label text='Label'/>")
   text_input = repr("<mx:TextInput width='50'/>")

   ga.setParams(gp_terminals       = [button, label, text_input],
                gp_function_prefix = "gp")
   ga.setMinimax(Consts.minimaxType["minimize"])
   ga.evolve(freq_stats=5)
   print ga.bestIndividual()

if __name__ == "__main__":
   main_run()

As you can see, I’ve created the layout tags like HBox, VBox and Panel as functions of GP and the Button, Labe, TextInput as terminals of the GP, the result is very funny, it’s just a random layout, but you can use your imagination to create some nice and interesting fitness functions.

Here is the SWF generated from a random individual of the population:

I hope you enjoyed =)

by Christian S. Perone
genetic programming, Pyevolve, Python

Genetic Programming meets Python

I’m proud to announce that the new versions of Pyevolve will have Genetic Programming support; after some time fighting with these evil syntax trees, I think I have a very easy and flexible implementation of GP in Python. I was tired to see people giving up and trying to learn how to implement a simple GP using the hermetic libraries for C/C++ and Java (unfortunatelly I’m a Java web developer hehe).

The implementation is still under some tests and optimization, but it’s working nice, here is some details about it:

The implementation has been done in pure Python, so we still have many bonus from this, but unfortunatelly we lost some performance.

The GP core is very very flexible, because it compiles the GP Trees in Python bytecodes to speed the execution of the function. So, you can use even Python objects as terminals, or any possible Python expression. Any Python function can be used too, and you can use all power of Python to create those functions, which will be automatic detected by the framework using the name prefix =)

As you can see in the source-code, you don’t need to bind variables when calling the syntax tree of the individual, you simple use the “getCompiledCode” method which returns the Python compiled function ready to be executed.

Here is a source-code example:

from pyevolve import *
import math

error_accum = Util.ErrorAccumulator()

# This is the functions used by the GP core,
# Pyevolve will automatically detect them
# and the they number of arguments
def gp_add(a, b): return a+b
def gp_sub(a, b): return a-b
def gp_mul(a, b): return a*b
def gp_sqrt(a):   return math.sqrt(abs(a))

def eval_func(chromosome):
   global error_accum
   error_accum.reset()
   code_comp = chromosome.getCompiledCode()

   for a in xrange(0, 5):
      for b in xrange(0, 5):
         # The eval will execute a pre-compiled syntax tree
         # as a Python expression, and will automatically use
         # the "a" and "b" variables (the terminals defined)
         evaluated     = eval(code_comp)
         target        = math.sqrt((a*a)+(b*b))
         error_accum += (target, evaluated)
   return error_accum.getRMSE()

def main_run():
   genome = GTree.GTreeGP()
   genome.setParams(max_depth=5, method="ramped")
   genome.evaluator.set(eval_func)

   ga = GSimpleGA.GSimpleGA(genome)
   # This method will catch and use every function that
   # begins with "gp", but you can also add them manually.
   # The terminals are Python variables, you can use the
   # ephemeral random consts too, using ephemeral:random.randint(0,2)
   # for example.
   ga.setParams(gp_terminals       = ['a', 'b'],
                gp_function_prefix = "gp")
   # You can even use a function call as terminal, like "func()"
   # and Pyevolve will use the result of the call as terminal
   ga.setMinimax(Consts.minimaxType["minimize"])
   ga.setGenerations(1000)
   ga.setMutationRate(0.08)
   ga.setCrossoverRate(1.0)
   ga.setPopulationSize(2000)
   ga.evolve(freq_stats=5)

   print ga.bestIndividual()

if __name__ == "__main__":
   main_run()

I’m very happy and testing the possibilities of this GP implementation in Python.

And of course, everything in Pyevolve can be visualized any time you want (click to enlarge):

ramped_small

ramped_big

The visualization is very flexible too, if you use Python decorators to set how functions will be graphical represented, you can have many interesting visualization patterns. If I change the function “gp_add” to:

@GTree.gpdec(representation="+", color="red")
def gp_add(a, b): return a+b

We’ll got the follow visualization (click to enlarge):

full

I hope you enjoyed it, I’m currently fixing some bugs, implementing new features, docs and preparing the next release of Pyevolve, which will take some time yet =)

by Christian S. Perone
Genetic Algorithms, Pyevolve, pys60, Python

TSP on Nokia N73 using PyS60 and Pyevolve

As I promised on the post about Pyevolve on N73, I’ve ported the TSP problem to run on the cellphone. The script will draw the best individual of every generation on the screen using the PyS60 Canvas API. The quality of the video is very low, I’m using another cellphone with VGA only recording.

When I have time, I’ll release the source-code here in the same post. The TSP code is the same used in PSP, but ported to use the Canvas API of PyS60.

I hope you enjoy.

UPDATE 24/04: the source-code can be downloaded here.

by Christian S. Perone
Genetic Algorithms, genetic programming, News, Pyevolve, Python, Time Waste

Genetic Algorithms on cellphones ! Pyevolve on Nokia N73 (Symbian + PyS60)

Hello ! This is the 2nd post related to the Pyevolve on portable devices, the first was in the Sony PSP here and the PoC of Pyevolve solving the TSP problem with a graphical output of best individuals on the Sony PSP screen. Now, it’s time to go further and run Pyevolve into the most portable device used by us, the cellphone.

Using the new version of the PyS60, the release 1.9.1, which comes with the new amazing 2.5.1 Python core, I’ve executed the Pyevolve with no problem, and I was very surprised by the performance of the GA on the Nokia N73, the GA I’ve ran is the minimization of one of the De Jogn’s test suite functions, the Sphere function, the function is very simple and I’ve used 5 real variables between the interval of [-5.12, 5.12], the Gaussian Real Mutator and the Single Point Crossover of the Pyevolve framework. Also I’ve set a population size of 80 individuals and the mutation rate of 2% and crossover rate of 90%.

After 18 generations (about 8 seconds), the GA ended with the best score of 0.0, representing the optimal minimization of the De Jong’s Sphere function.

Follow some screenshots of the adventure (click on the pictures to enlarge):

How to install Pyevolve on the PyS60 ?

1) First, you need to install the PyS60 for your Symbian platform. Here is the installation manual. The order of the installation is: Python Runtime for S60, Python Script Shell and PIPS Library.

2) Then, you must create a directory on your Memory Card, inside the “Python” directory, named “Lib”, and inside this directory, you copy the “pyevolve” folder. The absolute folder structure will be like this:

MemoryCard:\Python\Lib\pyevolve

Some features of the framework will not work, like the some DB Adapters, however, the GA core is working really good. I’ve used the Pyevolve subversion release r157, but it should works with the 0.5 release too. I’ve not finished the documentation of the new release, I’m working on some features yet.

Here is the source code I’ve used to minimize the De Jong’s Sphere function:

import e32
print "Loading Pyevolve modules...",
e32.ao_yield()

from pyevolve import G1DList, GSimpleGA
from pyevolve import Initializators, Mutators, Consts

print " done !"
e32.ao_yield()

def sphere(xlist):
   n = len(xlist)
   total = 0
   for i in range(n):
      total += (xlist[i]**2)
   return total

def ga_callback(ga_engine):
   gen = ga_engine.getCurrentGeneration()
   best = ga.bestIndividual()
   print "Generation %d - Best Score: %.2f" % (gen, best.score)
   e32.ao_yield()

   return False

if __name__ == "__main__":

   genome = G1DList.G1DList(5)
   genome.setParams(rangemin=-5.12, rangemax=5.13, bestRawScore=0.00, roundDecimal=2)
   genome.initializator.set(Initializators.G1DListInitializatorReal)
   genome.mutator.set(Mutators.G1DListMutatorRealGaussian)

   genome.evaluator.set(sphere)

   ga = GSimpleGA.GSimpleGA(genome)
   ga.setMinimax(Consts.minimaxType["minimize"])
   ga.setGenerations(100)
   ga.setMutationRate(0.02)
   ga.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
   ga.stepCallback.set(ga_callback)

   ga.evolve()

   best = ga.bestIndividual()
   print "\nBest individual score: %.2f" % (best.score,)

You can note the use of the module “e32” of the PyS60, this is used to process pending events, so we can follow the statistics of current generation while it evolves.

I hope you enjoyed this work, the next step is to port the TSP problem to cellphone =)

Some time ago I’ve asked Guido van Rossum on the Google Moderator about the future of Python for mobile phones (aka PyS60), and here is the full answer from him:

I’m hopeful, but concerned that Java has cornered this market. For example, the Android development kit is extremely slick but only supports Java at the moment. There’s no doubt about which is the dominant app development language on most mobile platforms, including S60 and anything Symbian-based. In the long run I expect Python to just happen on mobile devices, as increases in disk space will allow the set of pre-installed tools to grow. In the mean time I see a bigger role for Python server-side, for example there are iPhone apps backed by services written in Python running on App Engine (and probably also apps backed by Python running on other server platforms).
Guido van Rossum, San Francisco Bay Area

Well, I think that with this new version of PyS60, with the 2.5.1 core, the Python on mobiles can be very useful and productive. Recently, Nokia have signed a loan agreement with the European Investment Bank (EIB) to the tune of €500 million ($623.9 million). According to Reuters, the five-year loan will be used in part to “finance software research and development (R&D) projects Nokia is undertaking during 2009-2011 to make Symbian-based smartphones more competitive.” So I’m with great expectations with this new investment on Symbian smartphones and with the future possibilities of the PyS60.

by Christian S. Perone

I'm starting a new course "Machine Learning: Foundations and Engineering" for 2024.

Register Interest