def __init__(self, initial_state, total_steps=1000):
"""
:initial_state: the initial graph partition. Must have a cut_edges updater
:total_steps: (defaults to 1000) the total number of steps that the random walk
should perform.
"""
if not initial_state['cut_edges']:
raise ValueError(
'BasicChain needs the Partition to have a cut_edges updater.')
if not initial_state['population']:
raise ValueError(
'BasicChain needs the Partition to have a population updater.')
population_constraint = within_percent_of_ideal_population(
initial_state, 0.01)
compactness_limit = L1_reciprocal_polsby_popper(initial_state)
compactness_constraint = UpperBound(L1_reciprocal_polsby_popper,
compactness_limit)
validator = Validator(default_constraints +
[population_constraint, compactness_constraint])
super().__init__(propose_random_flip,
validator,
always_accept,
initial_state,
total_steps=total_steps)
# This builds the partition object
initial_partition = Partition(graph, assignment, updaters)
# Choose which binary constraints to enforce
# Options are in validity.py
pop_limit = .2
population_constraint = within_percent_of_ideal_population(
initial_partition, pop_limit)
compactness_constraint_Lm1 = LowerBound(
L_minus_1_polsby_popper, L_minus_1_polsby_popper(initial_partition))
#edge_constraint = UpperBound(number_cut_edges, 850)
edge_constraint = UpperBound(number_cut_edges,
2 * number_cut_edges(initial_partition))
#perim_constraint = LowerBound(perimeter, 1)
#county_constraint = refuse_new_splits("County_Splits")
validator = Validator(
[single_flip_contiguous, population_constraint]
) #([edge_constraint])#Validator([single_flip_contiguous, population_constraint,
#compactness_constraint_Lm1,county_constraint])#edge_constraint])
# Names of validators for output
# Necessary since bounds don't have __name__'s
list_of_validators = [
single_flip_contiguous, within_percent_of_ideal_population,
L_minus_1_polsby_popper, number_cut_edges
]
updaters = {
**updaters,
**votes_updaters(election_columns[i], election_names[i])
}
# This builds the partition object
initial_partition = Partition(graph, assignment, updaters)
# Desired validators go here
# Can change constants and bounds
pop_limit = .01
population_constraint = within_percent_of_ideal_population(
initial_partition, pop_limit)
compactness_limit_L1 = 1.01 * L1_reciprocal_polsby_popper(initial_partition)
compactness_constraint_L1 = UpperBound(L1_reciprocal_polsby_popper,
compactness_limit_L1)
compactness_limit_Lm1 = .99 * L_minus_1_polsby_popper(initial_partition)
compactness_constraint_Lm1 = LowerBound(L_minus_1_polsby_popper,
compactness_limit_Lm1)
validator = Validator([
refuse_new_splits, no_vanishing_districts, single_flip_contiguous,
population_constraint, compactness_constraint_Lm1
])
# Names of validators for output
# Necessary since bounds don't have __name__'s
list_of_validators = [
refuse_new_splits, no_vanishing_districts, single_flip_contiguous,
within_percent_of_ideal_population, L_minus_1_polsby_popper
