def test_find_coupled_resource_sets_c():
"""
Test the _find_coupled_resource_sets method of the wrapped RL environment directly.
The method takes in a binary matrix denoting which resources are affected by which constraints
and returns a list of sets of resources the actions of which are made dependent by the
constraints. This is essentially noticing and interpreting the chaining effect of multiple
constraints on activities.
"""
# This test case tests the chaining effect. The first constraint links resources 0 and 1 then
# the second links resources 2 and 3 and the third links 1 and 2 which creates a chain such that
# resources 0, 1, 2 and 3 form one large dependent resource set
overlapping_resource_constraints_matrix = np.array([
[1, 0, 0],
[1, 0, 1],
[0, 1, 1],
[0, 1, 0]
]) # => Resource set is {0, 1, 2, 3}
# Run the method for the test case defined above
overlapping_resource_sets = RLControlledRandomWalk._find_coupled_resource_sets(
overlapping_resource_constraints_matrix
)
# Assert that the responses are as expected.
assert overlapping_resource_sets == [{0, 1, 2, 3}]
def test_find_coupled_resource_sets_a():
"""
Test the _find_coupled_resource_sets method of the wrapped RL environment directly.
The method takes in a binary matrix denoting which resources are affected by which constraints
and returns a list of sets of resources the actions of which are made dependent by the
constraints. This is essentially noticing and interpreting the chaining effect of multiple
constraints on activities.
"""
# Each resource has one activity and all are independent.
independent_resource_constraints_matrix = np.eye(4) # => resource sets are [{0}, {1}, {2}, {3}]
# Run the method for the test case defined above
independent_resource_sets = RLControlledRandomWalk._find_coupled_resource_sets(
independent_resource_constraints_matrix)
# Assert that the responses are as expected.
assert independent_resource_sets == [{0}, {1}, {2}, {3}]
def test_find_coupled_resource_sets_b():
"""
Test the _find_coupled_resource_sets method of the wrapped RL environment directly.
The method takes in a binary matrix denoting which resources are affected by which constraints
and returns a list of sets of resources the actions of which are made dependent by the
constraints. This is essentially noticing and interpreting the chaining effect of multiple
constraints on activities.
"""
# The constraints lead to resources 0 and 2, and 1 and 3 being coupled into two dependent sets.
simple_resource_constraints_matrix = np.array([
[1, 0],
[0, 1],
[1, 0],
[0, 1]
]) # => Resource sets are [{0, 2}, {1, 3}]
# Run the method for the test case defined above
simple_resource_sets = RLControlledRandomWalk._find_coupled_resource_sets(
simple_resource_constraints_matrix
)
# Assert that the responses are as expected.
assert simple_resource_sets == [{0, 2}, {1, 3}]