本文整理汇总了Python中urbansim.datasets.household_dataset.HouseholdDataset.set_values_of_one_attribute方法的典型用法代码示例。如果您正苦于以下问题：Python HouseholdDataset.set_values_of_one_attribute方法的具体用法？Python HouseholdDataset.set_values_of_one_attribute怎么用？Python HouseholdDataset.set_values_of_one_attribute使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类urbansim.datasets.household_dataset.HouseholdDataset的用法示例。
在下文中一共展示了HouseholdDataset.set_values_of_one_attribute方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: run_ALCM
# 需要导入模块: from urbansim.datasets.household_dataset import HouseholdDataset [as 别名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import set_values_of_one_attribute [as 别名]
def run_ALCM(niter):
nhhs = 100
ngcs = 10
ngcs_attr = ngcs/2
ngcs_noattr = ngcs - ngcs_attr
hh_grid_ids = array(nhhs*[-1])
storage = StorageFactory().get_storage('dict_storage')
households_table_name = 'households'
storage.write_table(
table_name = households_table_name,
table_data = {
'household_id': arange(nhhs)+1,
'grid_id': hh_grid_ids
}
)
gridcells_table_name = 'gridcells'
storage.write_table(
table_name = gridcells_table_name,
table_data = {
'grid_id': arange(ngcs)+1,
'cost':array(ngcs_attr*[100]+ngcs_noattr*[1000])
}
)
households = HouseholdDataset(in_storage=storage, in_table_name=households_table_name)
gridcells = GridcellDataset(in_storage=storage, in_table_name=gridcells_table_name)
# create coefficients and specification
coefficients = Coefficients(names=('costcoef', ), values=(-0.001,))
specification = EquationSpecification(variables=('gridcell.cost', ), coefficients=('costcoef', ))
logger.be_quiet()
result = zeros((niter,ngcs))
for iter in range(niter):
hlcm = HouseholdLocationChoiceModelCreator().get_model(location_set=gridcells, compute_capacity_flag=False,
choices = 'opus_core.random_choices_from_index',
sampler=None,
#sample_size_locations = 30
)
hlcm.run(specification, coefficients, agent_set=households, debuglevel=1,
chunk_specification={'nchunks':1})
# get results
gridcells.compute_variables(['urbansim.gridcell.number_of_households'],
resources=Resources({'household':households}))
result_more_attractive = gridcells.get_attribute_by_id('number_of_households', arange(ngcs_attr)+1)
result_less_attractive = gridcells.get_attribute_by_id('number_of_households', arange(ngcs_attr+1, ngcs+1))
households.set_values_of_one_attribute(attribute='grid_id', values=hh_grid_ids)
gridcells.delete_one_attribute('number_of_households')
result[iter,:] = concatenate((result_more_attractive, result_less_attractive))
#print result #, result_more_attractive.sum(), result_less_attractive.sum()
return result示例2: HouseholdLocationChoiceModelCreator
# 需要导入模块: from urbansim.datasets.household_dataset import HouseholdDataset [as 别名]
# 或者: from urbansim.datasets.household_dataset.HouseholdDataset import set_values_of_one_attribute [as 别名]
results = hlcm.run(specification, coef, agents)
hlcm.upc_sequence.plot_choice_histograms(
capacity=locations.get_attribute("vacant_units"))
hlcm.upc_sequence.show_plots()
hlcm2 = HouseholdLocationChoiceModelCreator().get_model(
location_set = locations,
sampler=None,
utilities="opus_core.linear_utilities",
probabilities="opus_core.mnl_probabilities",
choices="urbansim.lottery_choices",
compute_capacity_flag=True,
run_config=Resources({"capacity_string":"gridcell.vacant_units"}))
agents.set_values_of_one_attribute("location", -1*ones(agents.size()))
agents.get_attribute("location")
results = hlcm2.run(specification, coefficients, agents)
agents.get_attribute("location")
hlcm2.upc_sequence.plot_choice_histograms(
capacity=locations.get_attribute("vacant_units"))
hlcm2.upc_sequence.show_plots()
coef, results = hlcm2.estimate(specification, agents)
#HLCM on PSRC
# households from PSRC
agents_psrc = HouseholdDataset(in_storage = StorageFactory().get_storage('flt_storage',
storage_location = "/home/hana/bandera/urbansim/data/GPSRC"),
in_table_name = "hh")
agents_psrc.summary()