def test_basicExampleWithoutCycles(self):
genes = {
1: (1, 6),
2: (2, 6),
3: (1, 7),
4: (3, 7),
5: (6, 4),
6: (7, 4),
7: (2, 5),
8: (7, 5),
}
storage_genome = StorageGenome()
analysis_result = AnalysisResult()
storage_genome.inputs['input_1'] = 1
storage_genome.inputs['input_2'] = 2
storage_genome.inputs['input_3'] = 3
storage_genome.outputs['output_1'] = 4
storage_genome.outputs['output_2'] = 5
storage_genome.genes[1] = (False, float(Fraction(2, 10)))
storage_genome.genes[2] = (False, float(Fraction(7, 10)))
storage_genome.genes[3] = (False, float(Fraction(1, 10)))
storage_genome.genes[4] = (False, float(Fraction(3, 10)))
storage_genome.genes[5] = (False, float(Fraction(6, 10)))
storage_genome.genes[6] = (False, float(Fraction(8, 10)))
storage_genome.genes[7] = (False, float(Fraction(9, 10)))
storage_genome.genes[8] = (False, float(Fraction(5, 10)))
analysis_result.topologically_sorted_nodes = [3, 2, 1, 7, 5, 6, 4]
analysis_result.topologically_sorted_cycle_nodes = []
storage_genome.analysis_result = analysis_result
mock_gene_repository = MagicMock()
mock_gene_repository.get_node_labels_by_gene_id =\
lambda node_id: genes[node_id]
gen = SimulationGenome(
mock_gene_repository,
storage_genome
)
result = gen.calculate_step({
'input_1': float(Fraction(5, 10)),
'input_2': float(Fraction(5, 10)),
'input_3': float(Fraction(5, 10))
}) # type: Dict[str, float]
self.assertEqual(float(Fraction(43, 100)), result['output_1'])
self.assertEqual(float(Fraction(55, 100)), result['output_2'])
def decode_AnalysisResult(document: dict) -> AnalysisResult:
"""
decodes dictionary in an AnalysisResult
:param document: dictionary to decode
:return: AnalysisResult
"""
try:
if document['_type'] == 'AnalysisResult':
document.pop('_type')
document["gene_closes_cycle_map"] = {
ObjectId(gene_id_as_str): boolean_flag
for gene_id_as_str, boolean_flag
in document["gene_closes_cycle_map"].items()
}
result = AnalysisResult()
result.__dict__ = document
return result
except KeyError:
return None
def __init__(
self,
genome: 'StorageGenome' = None,
inputs: List[str] = None,
outputs: List[str] = None
):
"""
:param genome: If genome is given, its inputs are copied
(except for id)
:param inputs: A list of strings, representing the inputs nodes' labels
"""
self._id = ObjectId()
self.is_alive = True if genome is None else genome.is_alive
self.fitness = 0 if genome is None else genome.fitness # type: float
self.inputs = {} if genome is None else deepcopy(genome.inputs) # type: Dict[str, int]
self.outputs = {} if genome is None else deepcopy(genome.outputs) # type: Dict[str, int]
self.genes = {} if genome is None else deepcopy(genome.genes) # type: Dict[int, Tuple[bool, Fraction]]
self.analysis_result = AnalysisResult() if genome is None \
else AnalysisResult(genome.analysis_result)
self.cluster = ObjectId() if genome is None else genome.cluster
if inputs and outputs:
self._init_with_nodes(inputs, outputs)
class StorageGenome(object):
"""
A data structure for storing genome information in a
compact way.
Attributes:
genome_id (_id):
The Genome's ID, unique in the population.
inputs:
A list of the ids of the input nodes. There has to be at least
one gene per input that is connected to it. Represented as a list
of tuples of the form (Input_Label, Node-ID).
outputs:
A list of the ids of the output nodes. There has to be at least
one gene per output that is connected to it. Represented as a list
of tuples of the form (Output_Label, Node-ID).
genes:
A list of all genes, that make up the genome. Presented as a tu-
ple of Gene-ID, a boolean that is true, if the gene is disabled
and a Fraction that stores the weight of the gene.
analysis_result:
A result object that is generated when analyzing the genome.
This is empty per default.
cluster:
The cluster to which the Genome belongs.
"""
def __init__(
self,
genome: 'StorageGenome' = None,
inputs: List[str] = None,
outputs: List[str] = None
):
"""
:param genome: If genome is given, its inputs are copied
(except for id)
:param inputs: A list of strings, representing the inputs nodes' labels
"""
self._id = ObjectId()
self.is_alive = True if genome is None else genome.is_alive
self.fitness = 0 if genome is None else genome.fitness # type: float
self.inputs = {} if genome is None else deepcopy(genome.inputs) # type: Dict[str, int]
self.outputs = {} if genome is None else deepcopy(genome.outputs) # type: Dict[str, int]
self.genes = {} if genome is None else deepcopy(genome.genes) # type: Dict[int, Tuple[bool, Fraction]]
self.analysis_result = AnalysisResult() if genome is None \
else AnalysisResult(genome.analysis_result)
self.cluster = ObjectId() if genome is None else genome.cluster
if inputs and outputs:
self._init_with_nodes(inputs, outputs)
def _init_with_nodes(self, inputs: List[str], outputs: List[str]):
node_id = 0
for input_label in inputs:
self.inputs[input_label] = node_id
node_id += 1
for output_label in outputs:
self.outputs[output_label] = node_id
node_id += 1
def __eq__(self, obj: 'StorageGenome'):
if not self._id.__eq__(obj._id) \
or not self.is_alive.__eq__(obj.is_alive) \
or not self.inputs.__eq__(obj.inputs) \
or not self.outputs.__eq__(obj.outputs) \
or not self.genes.__eq__(obj.genes) \
or not self.analysis_result.__eq__(obj.analysis_result) \
or not self.fitness.__eq__(obj.fitness) \
or not self.cluster.__eq__(obj.cluster):
return False
return True
@property
def genome_id(self):
return self._id
