class Execution:
def __init__(self):
self.metadata = {}
self.dataset = {}
self.model = {}
self.metrics = {}
self.data = None
self.map = None
def open(self, path):
txt = codecs.open(path, 'r', encoding='utf-8').read()
data = json.loads(txt)
self.metadata = data["metadata"]
self.dataset = data["dataset"]
self.model = data["model"]
self.metrics = data["metrics"]
def save(self, path):
data = {
"metadata": self.metadata,
"dataset": self.dataset,
"model": self.model,
"metrics": self.metrics
}
json.dump(data,
codecs.open(os.path.join(path,
self.metadata["name"] + ".json"),
'w',
encoding='utf-8'),
indent=2)
def load_dataset(self):
if self.dataset["type"] == "Square":
self.data = uniform(1000, 2)
elif self.dataset["type"] == "Shape":
self.data = shape(1000, "cat-silhouette.png")
elif self.dataset["type"] == "Cube":
self.data = uniform(1000, 3)
elif self.dataset["type"] == "Colors":
self.data = pixels_colors(1000, "Elijah.png")
elif self.dataset["type"] == "Digits":
self.data = spoken_digits("FSDD", 1000)
elif self.dataset["type"] == "Image":
self.data = mosaic_image("Elijah.png", [10, 10])
else:
print("Error : No dataset type specified !")
def run(self):
np.random.seed(self.metadata["seed"])
if self.data is None:
self.load_dataset()
parameters = Parameters({
"alpha":
Variable(start=0.6, end=0.05, nb_steps=self.model["nb_epochs"]),
"sigma":
Variable(start=0.5, end=0.001, nb_steps=self.model["nb_epochs"]),
"data":
self.data,
"neurons_nbr": (self.model["width"], self.model["height"]),
"epochs_nbr":
self.model["nb_epochs"],
"topology":
self.model["topology"],
"bmu_search":
self.model["bmu_search"]
})
if self.model["bmu_search"] == "Parallel":
self.map = ParallelSOM(parameters)
self.map.run_parallel()
else:
self.map = SOM(parameters)
self.map.run()
def compute_metrics(self):
self.metrics = self.map.compute_metrics()
def full_simulation(self, path):
self.run()
self.compute_metrics()
self.save(path)
print("Simulation", self.metadata["name"], "ended")
