[ecspy] r132 committed - Rolled back the observers.py file to a previous state before numpy was...

[ec...@googlecode.com]

Revision: 132
Author: aaron.lee.garrett
Date: Tue Feb 28 19:45:16 2012
Log: Rolled back the observers.py file to a previous state before
numpy was removed. It turns out that numpy was handling the multiobjective
fitness values very cleanly. That is much better than what I would have
done myself, so it has been put back in.
http://code.google.com/p/ecspy/source/detail?r=132

Modified:
/trunk/ecspy/observers.py

=======================================
--- /trunk/ecspy/observers.py Wed Feb 8 17:16:16 2012
+++ /trunk/ecspy/observers.py Tue Feb 28 19:45:16 2012
@@ -33,13 +33,13 @@
pass

-def stats_observer(population, num_generations, num_evaluations, args):
- """Print the statistics of the EC to the screen.
-
- This function displays the statistics of the evolutionary computation
+def screen_observer(population, num_generations, num_evaluations, args):
+ """Print the output of the EC to the screen.
+
+ This function displays the results of the evolutionary computation
to the screen. The output includes the generation number, the current
- number of evaluations, the maximum fitness, the minimum fitness,
- the average fitness, and the standard deviation.
+ number of evaluations, the average fitness, the maximum fitness, and
+ the full population.

.. Arguments:
population -- the population of Individuals
@@ -48,47 +48,24 @@
args -- a dictionary of keyword arguments

"""
-
+ import numpy
+
population = list(population)
population.sort(reverse=True)
worst_fit = population[-1].fitness
best_fit = population[0].fitness
-
- plen = len(population)
- if plen % 2 == 1:
- med_fit = population[(plen - 1) / 2].fitness
- else:
- med_fit = float(population[plen / 2 - 1].fitness + population[plen
/ 2].fitness) / 2
- avg_fit = sum([p.fitness for p in population]) / float(plen)
- std_fit = math.sqrt(sum([(p.fitness - avg_fit)**2 for p in
population]) / float(plen - 1))
-
+ med_fit = numpy.median([p.fitness for p in population])
+ avg_fit = numpy.mean([p.fitness for p in population])
+ std_fit = numpy.std([p.fitness for p in population], ddof=1)
+
print('Generation Evaluation Worst Best Median
Average Std Dev ')
print('---------- ---------- ---------- ---------- ----------
---------- ----------')
- print('{0:10} {1:10} {2:10.5} {3:10.5} {4:10.5} {5:10.5}
{6:10.5}\n'.format(num_generations, num_evaluations, worst_fit, best_fit,
med_fit, avg_fit, std_fit))
-
-
-def population_observer(population, num_generations, num_evaluations,
args):
- """Print the current population of the EC to the screen.
-
- This function displays the current population of the evolutionary
- computation to the screen in fitness-sorted order.
-
- .. Arguments:
- population -- the population of Individuals
- num_generations -- the number of elapsed generations
- num_evaluations -- the number of candidate solution evaluations
- args -- a dictionary of keyword arguments
-
- """
-
- population = list(population)
- population.sort(reverse=True)
-
print('----------------------------------------------------------------------------')
+ print('{0:10} {1:10} {2:10} {3:10} {4:10} {5:10}
{6:10}\n'.format(num_generations, num_evaluations, worst_fit, best_fit,
med_fit, avg_fit, std_fit))
print('Current Population:')
for ind in population:
print(str(ind))

print('----------------------------------------------------------------------------')
-
+

def file_observer(population, num_generations, num_evaluations, args):
"""Print the output of the EC to a file.
@@ -115,6 +92,9 @@
- *individuals_file* -- a file object (default: see text)

"""
+ # Import the necessary libraries here. Otherwise, they would have to be
+ # installed even if this function is not called.
+ import numpy

try:
statistics_file = args['statistics_file']
@@ -129,15 +109,9 @@
population.sort(reverse=True)
worst_fit = population[-1].fitness
best_fit = population[0].fitness
-
- plen = len(population)
- if plen % 2 == 1:
- med_fit = population[(plen - 1) / 2].fitness
- else:
- med_fit = float(population[plen / 2 - 1].fitness + population[plen
/ 2].fitness) / 2
- avg_fit = sum([p.fitness for p in population]) / float(plen)
- std_fit = math.sqrt(sum([(p.fitness - avg_fit)**2 for p in
population]) / float(plen - 1))
-
+ med_fit = numpy.median([p.fitness for p in population])
+ avg_fit = numpy.mean([p.fitness for p in population])
+ std_fit = numpy.std([p.fitness for p in population], ddof=1)
statistics_file.write('{0}, {1}, {2}, {3}, {4}, {5},
{6}\n'.format(num_generations, len(population), worst_fit, best_fit,
med_fit, avg_fit, std_fit))
for i, p in enumerate(population):
individuals_file.write('{0}, {1}, {2},
{3}\n'.format(num_generations, i, p.fitness, str(p.candidate)))