## Run via scan task because this gives only best values in function
## evaluations, rather than the periodic function evaluations as well
print '\n\n'
print report_name
print '\n'
PyCoTools.pycopi.Scan(copasi_file,
ScanType='repeat',
Run=mode,
NumberOfSteps=1,
ReportName=report,
ReportType='parameter_estimation')
#data.shape[0]*0.01)
if __name__ == '__main__':
C = FilePaths.Celliere2011Example()
if os.path.isdir(C.PEData_dir):
PEData_path = C.PEData_dir
elif os.path.isdir(C.PEData_dir) != True:
if os.path.isfile(C.PEData_file):
PEData_path = C.PEData_file
if os.path.isdir(PEData_path) != True:
raise TypeError
data = PyCoTools.PEAnalysis.ParsePEData(PEData_path).data
print data
# if sys.platform=='win32':
# '''
Author:
Ciaran Welsh
Date:
12/03/2017
"""
import PyCoTools
import os
import pandas
import numpy
from shutil import copy
import FilePaths
## Instantiate class contianing file paths for Kholodenko example
K = FilePaths.KholodenkoExample()
## Set up a parameter estimation
'''
First instantiate ParameterEstimation class
with chosen keyword arguments. define the method variables within the PE class using the key word arguments
'''
PE = PyCoTools.pycopi.ParameterEstimation(
K.kholodenko_model, #model
K.noisy_timecourse_report, #experimental data
Method='GeneticAlgorithm', #use a quick global algorithm
NumberOfGenerations=300, #set Generation Number and population size
PopulationSize=150,
SwarmSize=200,
IterationLimit=3000,
# senseval-3 : /home/kanghj/ims_0.9.2/EnglishLS.train/EnglishLS.train.xml
# /home/kanghj/ims_0.9.2/EnglishLS.train/EnglishLS.train.key
# todo : add directory mode for xml file and key file
_config = {
'xml_file': FilePaths.SENSEVAL_3_XML,
'key_file': FilePaths.SENSEVAL_3_KEY,
'dtd' : '/home/kanghj/ims_0.9.2/corpora/english-lex-sample/train/lexical-sample.dtd',
'feature_extractor': context_common_words,
'output_directory': 'common_words/'
}
if __name__ == '__main__':
FilePaths.make_dir(_config['output_directory'] )
word_to_common_contexual_words = _config['feature_extractor']((_config['xml_file'], _config['dtd']), _config['key_file'])
# make a file for each word
for word, context_common_words in word_to_common_contexual_words.iteritems():
with codecs.open(_config['output_directory'] + word, 'w+') as feature_file:
for context_word in context_common_words:
try:
feature_file.write(context_word.decode('utf8') + '\n')
except UnicodeEncodeError as e:
pass # ignore because it probably doesn't matter
print 'All completed for '