def getCar(): car = [] car.append( gene(0) ) car.append( gene(0) ) car.append( gene(0) ) car.append( gene(0) ) car.append( gene(0) ) return car
def calculate_ssConc_(self):
"""
This function calculates the steady state concentrations of both unspliced and spliced RNA in the given bin (cell type).
Note that this steady state concentration will be used to initilize U and S concentration of this bin (if it's a master bin) and its children (if any)
Half responses are also computed here by calling its function.
"""
for level in range(self.maxLevels_, -1, -1):
for binID in range(self.nBins_):
currGenes = self.level2verts_[level]
for g in currGenes:
if g[0].Type == 'MR':
currRate = self.graph_[g[0].ID]['rates'][binID]
self.binDict[binID][g[0].ID] = gene(
g[0].ID, 'MR', binID)
self.binDict[binID][g[0].ID].set_ss_conc_U(
np.true_divide(currRate,
self.decayVector_[g[0].ID]))
self.binDict[binID][g[0].ID].set_ss_conc_S(
self.ratioSp_[g[0].ID] * np.true_divide(
currRate, self.decayVector_[g[0].ID]))
else:
params = self.graph_[g[0].ID]['params']
currRate = 0
for interTuple in params:
meanExp = self.meanExpression[interTuple[0], binID]
currRate += np.abs(interTuple[1]) * self.hill_(
meanExp, interTuple[3], interTuple[2],
interTuple[1] < 0)
#if binID == 0 and g[0].ID == 0:
#print meanExp
#print interTuple[3]
#print interTuple[2]
#print interTuple[1]
#print self.hill_(meanExp, interTuple[3], interTuple[2], interTuple[1] < 0)
self.binDict[binID][g[0].ID] = gene(
g[0].ID, 'T', binID)
self.binDict[binID][g[0].ID].set_ss_conc_U(
np.true_divide(currRate,
self.decayVector_[g[0].ID]))
self.binDict[binID][g[0].ID].set_ss_conc_S(
self.ratioSp_[g[0].ID] * np.true_divide(
currRate, self.decayVector_[g[0].ID]))
# NOTE This is our assumption for dynamics simulations --> we estimate mean expression of g in b with steady state concentration of U_g in b
self.meanExpression[g[0].ID, binID] = self.binDict[binID][
g[0].ID].ss_U_
#if binID == 0 and g[0].ID == 0:
# print currRate
# print self.decayVector_[g[0].ID]
if level > 0:
self.calculate_half_response_(level - 1)
def find_levels_(self, graph):
"""
# This is a helper function that takes a graph and assigns layer to all
# verticies. It uses longest path layering algorithm from
# Hierarchical Graph Drawing by Healy and Nikolovself. A bottom-up
# approach is implemented to optimize simulator run-time. Layer zero is
# the last layer for which expression are simulated
# U: verticies with an assigned layer
# Z: vertizies assigned to a layer below the current layer
# V: set of all verticies (genes)
This also sets a dictionary that maps a level to a matrix (in form of python list)
of all genes in that level versus all bins
"""
U = set()
Z = set()
V = set(graph.keys())
currLayer = 0
self.level2verts_[currLayer] = []
idx = 0
while U != V:
currVerts = set(
filter(lambda v: set(graph[v]['targets']).issubset(Z), V - U))
for v in currVerts:
graph[v]['level'] = currLayer
U.add(v)
if {v}.issubset(self.master_regulators_idx_):
allBinList = [gene(v, 'MR', i) for i in range(self.nBins_)]
self.level2verts_[currLayer].append(allBinList)
self.gID_to_level_and_idx[v] = (currLayer, idx)
idx += 1
else:
allBinList = [gene(v, 'T', i) for i in range(self.nBins_)]
self.level2verts_[currLayer].append(allBinList)
self.gID_to_level_and_idx[v] = (currLayer, idx)
idx += 1
currLayer += 1
Z = Z.union(U)
self.level2verts_[currLayer] = []
idx = 0
self.level2verts_.pop(currLayer)
self.maxLevels_ = currLayer - 1
if not self.dyn_:
self.set_scIndices_()
def openGoalFile(self, filename=None, normalize=True):
## test=tkFileDialog
## print(test)
## print(type(test))
## print(dir(test))
## filename=test.askopenfilename()
# might want to replace it with:
# http://stackoverflow.com/questions/9319317/quick-and-easy-file-dialog-in-python
if filename==None:
# get the file name using a file dialog!
## t=Tk()
## t.withdraw()
filename=tkFileDialog.askopenfilename()
print(filename)
## t.quit()
self.goalGene=gene()
self.goalGene.normalized=normalize
self.goalGene.load(filename)
self.absoluteTruth=self.goalGene.solveODE()
self.goalGene.absoluteTruth=self.absoluteTruth
if self.analysableVariables==[]:
self.analysableVariables=scipy.array(range(len(self.goalGene.getVariableList())))
from config import *
from population import population
from chromosome import chromosome
from gene import gene
circle = [(39, 0),(51, 58), (30, 58), (10, 25), (69, 24), (20, 7), (66, 44),(59, 7),(14, 46)]
random_points = [(73, -94), (25, 73),(-98, 22),(-50, -43),(16, 46),(16, 13),(-27, -71),(-13, -23),(58, -81),(-75, 43),(95, -86),(-43, -70),(44, 45), (-19, -94),(-37, 45),(-59, 12),(33, -26),(-58, 85),(92, -97)]
random_points_2 = [(49, 19),(87, -69),(29, 45),(-50, 56),(-66, -33),(22, -33),(27, -72),(82, 61),(97, 33),(58, 18),(-9, -19),(50, 58),(-38, 67),(98, 3)]
if __name__ == '__main__':
# input is x PUT YOUR INPUT HERE
x = circle
#plotting initial
starting_population = population(x)
starting_chromosome = chromosome([starting_population])
starting_distance = starting_population._totaldistance()
starting_chromosome._plot_chromosome_tour()
#Applying Genetic Algorithm
g = gene(x)
#plotting best
final_best_population = g.overall_best
final_best_chromosome = chromosome([final_best_population])
final_best_distance = final_best_population._totaldistance()
final_best_chromosome._plot_chromosome_tour()
print('================== Results of Optimization ============================')
print('Initial Distance : ', starting_distance)
print('Optimized final Distance :', final_best_distance)
print(((starting_distance - final_best_distance)*100/starting_distance),'% distance reduced')
plt.show()
print('------------------Settings-------------------')
print('dirName : '+dirName)
print('originalGeneFile : '+originalGeneFile)
print('doLocalOptimize : '+str(doLocalOptimize))
print('uniqueNumer : '+str(uniqueNumber))
print('---------------------------------------------')
## # get the command line parameters
## dirname=sys.argv[0]
## originalGeneFile=sys.argv[1]
originalGene=gene()
originalGene.load(originalGeneFile)
goal=originalGene.solveODE()
originalGene.absoluteTruth=goal
# test fitness condition
if originalGene.fitness() != 0:
raise Exception('ERROR! this gene cannot predict its own ideal!!')
# create the new dir
import os
## os.mkdir(dirName) # didnt work
try:
os.makedirs(dirName) # so lets try
#variable used to assign gene start and finish points placeholder=0 #current gene name, used for transitioning between genes CG="" #reassign argv to not include 1st item (doesn't work for regex) sys.argv=sys.argv[1:] geneCount=0 for arg in sys.argv: #search file name for tree metadata #gene is group 1, run is group 2 searchTerm="Alltaxa_(\w+)_r(\d)" regexobj=re.search(searchTerm,arg) #if the file doesn't match the current gene, make a new one and add it to genelist if regexobj.group(1) != CG: treelist.read_from_path(arg,'nexus') genelist.append(gene.gene(regexobj.group(1),placeholder,len(treelist)-1)) geneCount+=1 #if the next file is still the same gene, then extend finish on the gene else: genelist[-1].setFinish(len(treelist)-1) treelist.read_from_path(arg,'nexus') #assign metadata to the trees added from the current file specified in argv x=0 for tree in treelist[placeholder:]: tree.name=regexobj.group(1)+"tree_"+str(x) x+=1 tree.gene=regexobj.group(1) tree.geneNum=geneCount tree.run=regexobj.group(2) tree.freqCount=1
import gene
from datetime import timedelta
class population(GA_population.GA_population):
""" implementation of GA_population
todo: reload old population
"""
def __init__(self):
pass
if __name__ == '__main__':
testgene = gene.gene()
testgene.load(
filename=
'E:\\Documents\\Stage\\python\\models\\3geneWithVarProduction.model')
testgene.absoluteTruth = testgene.solveODE() #fill it with and objective
print(testgene.fitness()) #should return perfect fitness!
test = population()
test.seed(testgene)
test.maxTime = timedelta(minutes=5)
test.maxGenerations = 100
test.evolve()
test.population[0].displayResults()
filename2 = 'E:\\Documents\\Stage\\python\\models\\testmodel.model'
simulator.initX()
print simulator.run(1000)
#print simulator.bookLimRun(100,opt.X)
elif first == 0:#reduction of Approximate Linear Programming
reduction = rALP.rALP(demander.reader)
reduction.construct()
reduction.addVar()
reduction.addOpt()
reduction.addConstr()
reduction.solve()
elif first == 2:#Gene Algorithm
decisionSolver = decisionRule.decisionRule()
decisionSolver.inputDemand(demander)
decisionSolver.x = {}
simulator = simulation.simulation(decisionSolver,demander)
realDemand = {}
for i in range(0,100):
realDemand[i] = demander.sim()
opt = gene.gene(decisionSolver,simulator)
opt.zeroX()
opt.setSTD(realDemand)
opt.evolve()
#simulator.run(1000)
print simulator.bookLimRun(100,opt.X)
print('------------------Settings-------------------')
print('dirName : '+dirName)
print('originalGeneFile : '+originalGeneFile)
print('doLocalOptimize : '+str(doLocalOptimize))
print('uniqueNumer : '+str(uniqueNumber))
print('---------------------------------------------')
## # get the command line parameters
## dirname=sys.argv[0]
## originalGeneFile=sys.argv[1]
originalGene=gene()
originalGene.load(originalGeneFile)
goal=originalGene.solveODE()
originalGene.absoluteTruth=goal
# test fitness condition
if originalGene.fitness() != 0:
raise Exception('ERROR! this gene cannot predict its own ideal!!')
# create the new dir
import os
## os.mkdir(dirName) # didnt work
try:
os.makedirs(dirName) # so lets try
class population(GA_population.GA_population):
""" implementation of GA_population
todo: reload old population
"""
def __init__(self):
pass
if __name__ == '__main__':
testgene=gene.gene()
testgene.load(filename='E:\\Documents\\Stage\\python\\models\\3geneWithVarProduction.model')
testgene.absoluteTruth=testgene.solveODE()#fill it with and objective
print(testgene.fitness())#should return perfect fitness!
test=population()
test.seed(testgene)
test.maxTime=timedelta(minutes=5)
test.maxGenerations=100
test.evolve()
test.population[0].displayResults()
filename2='E:\\Documents\\Stage\\python\\models\\testmodel.model'
test.population[0].save(filename2)
# wenn die anfangspopulation noch nicht fertig berechnet war:
gene.berechneFitness(population, epochen)
# dann die höchste generation holen, das +1 ist die durchlaufnummer.
durchlaufNr = 1
nextID = 0
for element in population:
if element.generation > durchlaufNr:
durchlaufNr = element.generation
if element.id > nextID:
nextID = element.id
nextID = durchlaufNr + num_population - 1
for i in range(0, nextID):
temp = gene.gene()
# Evolution
while (durchlaufNr <= anzahlDurchlaeufe):
sel1, sel2 = gene.selektiereEltern(population)
neuerKandidat = gene.rekombination(sel1, sel2, durchlaufNr)
neuerKandidat = gene.mutation(neuerKandidat)
# neues Individuum trainieren
model_neu = Modelbuilder(neuerKandidat)
model_neu.add_GenCode_To_Model()
myFitness = model_neu.finalize_Model(epochen)
# dann Fitness ermitteln
gene.berechneFitnessEinzel(neuerKandidat, myFitness)
def openFiles(self,filePaths=None):
""" open files and adds them to the population, can be called multiple times...."""
if self.absoluteTruth==None:
raise Exception('first load original gene!!')
if filePaths==None:
## t=Tk()
## t.withdraw()
filePaths=tkFileDialog.askopenfilenames().split()
print(filePaths)
## t.quit()
## print(type(filePaths))
## print(filePaths)
mi=self.goalGene.minParRangeList
ma=self.goalGene.maxParRangeList
nrOfMistFits=0
for i in range(len(filePaths)):
print('reading file '+str(i+1)+' of '+str(len(filePaths)))
filename=filePaths[i]
aGene=gene()
aGene.normalized=self.goalGene.normalized
aGene.load(filename)
aGene.absoluteTruth=self.absoluteTruth
# check if the individual is within the constraints
## variables=aGene.getVariableList()
## for i in range(len(mi)):
## if variables[i]>ma[i] or variables[i]<mi[i]: # if out of bounds
## # put that individual in the trash bin
## self.misfits.append(aGene)
## else:
## aGene.fitness()
## self.population.append(aGene)
## print('nr of corrupted individuals:'+str(len(self.misfits)))
isMisfit=False
params=aGene.getVariableList()
for i in range(len(mi)):
# make sure it stays withing model constraints/bounds
if params[i] < mi[i]:
print('correcting parameter from:' +str(params[i]) + ' to: '+ str(mi[i]))
params[i]=mi[i]
isMisfit=True
if params[i] > ma[i]:
print('correcting parameter from:' +str(params[i]) + ' to: '+ str(ma[i]))
params[i]=ma[i]
isMisfit=True
if isMisfit:
nrOfMistFits+=1
aGene.setVariableList(params)
aGene.fitness()
self.population.append(aGene)
print('nr of corrupted individuals:'+str(nrOfMistFits))
# sort it!
self.sort()
