def calc_chosenalternative(self, data):
"""
The method returns the chosen alternaitve among the count choices
for the choice variable under consideration
Inputs:
data - DataArray object
"""
probabilities = DataArray(self.calc_probabilities(data), self.specification.choices)
prob_model = AbstractProbabilityModel(probabilities, self.specification.seed)
return prob_model.selected_choice()
def calc_chosenalternative(self, data, choiceset=None, seed=1):
"""
The method returns the chosen alternaitve among the count choices
for the choice variable under consideration
Inputs:
data - DataArray object
"""
probabilities = DataArray(self.calc_probabilities(data),
self.specification.choices)
prob_model = AbstractProbabilityModel(probabilities, seed)
return prob_model.selected_choice()
def sample_choices(self, data, destLocSetInd, zoneLabels, count,
sampleVarName, seed):
destLocSetInd = destLocSetInd[:, 1:]
#print 'number of choices - ', destLocSetInd.shape
#raw_input()
ti = time.time()
for i in range(count):
destLocSetIndSum = destLocSetInd.sum(-1)
#print 'Number of choices', destLocSetIndSum
probLocSet = (destLocSetInd.transpose() /
destLocSetIndSum).transpose()
zeroChoices = destLocSetIndSum.mask
#print 'zero choices', zeroChoices
if (~zeroChoices).sum() == 0:
continue
#probLocSet = probLocSet[zeroChoices,:]
#print probLocSet.shape, len(zoneLabels), 'SHAPES -- <<'
probDataArray = DataArray(probLocSet, zoneLabels)
# seed is the count of the sampled destination starting with 1
probModel = AbstractProbabilityModel(probDataArray,
self.projectSeed + seed + i)
res = probModel.selected_choice()
# Assigning the destination
# We subtract -1 from the results that were returned because the
# abstract probability model returns results indexed at 1
# actual location id = choice returned - 1
colName = '%s%s' % (sampleVarName, i + 1)
nonZeroRows = where(res.data <> 0)
#print 'SELECTED LOCATIONS FOR COUNT - ', i+1
#print res.data[:,0]
#actualLocIds = res.data[nonZeroRows]
#actualLocIds[nonZeroRows] -= 1
#print actualLocIds
data.setcolumn(colName, res.data)
#print data.columns([colName]).data[:,0]
#raw_input()
# Retrieving the row indices
dataCol = data.columns([colName]).data
rowIndices = array(xrange(dataCol.shape[0]), int)
#rowIndices = 0
colIndices = res.data.astype(int)
destLocSetInd.mask[rowIndices, colIndices - 1] = True
def calc_chosenalternative(self, data, choiceset=None, seed=1):
"""
The method returns the selected choice among the available
alternatives.
Inputs:
data = DataArray object
choiceset = DataArray object
"""
if choiceset is None:
choiceset = DataArray()
probabilities = self.calc_probabilities(data, choiceset)
prob_model = AbstractProbabilityModel(probabilities, seed)
return prob_model.selected_choice()
def sample_choices(self, data, destLocSetInd, zoneLabels, count, sampleVarName, seed):
destLocSetInd = destLocSetInd[:,1:]
#print 'number of choices - ', destLocSetInd.shape
#raw_input()
ti = time.time()
for i in range(count):
destLocSetIndSum = destLocSetInd.sum(-1)
#print 'Number of choices', destLocSetIndSum
probLocSet = (destLocSetInd.transpose()/destLocSetIndSum).transpose()
zeroChoices = destLocSetIndSum.mask
#print 'zero choices', zeroChoices
if (~zeroChoices).sum() == 0:
continue
#probLocSet = probLocSet[zeroChoices,:]
#print probLocSet.shape, len(zoneLabels), 'SHAPES -- <<'
probDataArray = DataArray(probLocSet, zoneLabels)
# seed is the count of the sampled destination starting with 1
probModel = AbstractProbabilityModel(probDataArray, self.projectSeed+seed+i)
res = probModel.selected_choice()
# Assigning the destination
# We subtract -1 from the results that were returned because the
# abstract probability model returns results indexed at 1
# actual location id = choice returned - 1
colName = '%s%s' %(sampleVarName, i+1)
nonZeroRows = where(res.data <> 0)
#print 'SELECTED LOCATIONS FOR COUNT - ', i+1
#print res.data[:,0]
#actualLocIds = res.data[nonZeroRows]
#actualLocIds[nonZeroRows] -= 1
#print actualLocIds
data.setcolumn(colName, res.data)
#print data.columns([colName]).data[:,0]
#raw_input()
# Retrieving the row indices
dataCol = data.columns([colName]).data
rowIndices = array(xrange(dataCol.shape[0]), int)
#rowIndices = 0
colIndices = res.data.astype(int)
destLocSetInd.mask[rowIndices, colIndices-1] = True
def calc_chosenalternative(self, data, choiceset=None, seed=1):
"""
The method returns the selected choice among the available
alternatives.
Inputs:
data = DataArray object
"""
#ti = time.time()
probabilities = DataArray(self.calc_probabilities(data),
self.specification.choices)
#print "\t\t\tprobabilities calculated in %.4f" %(time.time()-ti)
prob_model = AbstractProbabilityModel(probabilities, seed)
#ti = time.time()
choice = prob_model.selected_choice()
#print "\t\t\tsimulated completed in %.4f" %(time.time()-ti)
return choice
def calc_chosenalternative(self, data, choiceset=None, seed=1):
"""
The method returns the selected choice among the available
alternatives.
Inputs:
data = DataArray object
"""
#ti = time.time()
probabilities = DataArray(self.calc_probabilities(data),
self.specification.choices)
#print "\t\t\tprobabilities calculated in %.4f" %(time.time()-ti)
prob_model = AbstractProbabilityModel(probabilities, seed)
#ti = time.time()
choice = prob_model.selected_choice()
#print "\t\t\tsimulated completed in %.4f" %(time.time()-ti)
return choice
def calc_chosenalternative(self, data, choiceset=None, seed=1):
probabilities = DataArray(self.calc_probabilities(data),
self.specification.choices)
prob_model = AbstractProbabilityModel(probabilities, seed)
return prob_model.selected_choice()
def calc_chosenalternative(self, data, choiceset=None, seed=1):
probabilities = DataArray(self.calc_probabilities(data),
self.specification.choices)
prob_model = AbstractProbabilityModel(probabilities, seed)
return prob_model.selected_choice()
