from model.model import Model
from rbm.entity import Entity, EntityParams, TrainingParams
from rbm.matrix import Mat, np

class DeepModel(Model):
	def __init__(self, name: str, work_dir: str = '.'):
		super().__init__(name, work_dir)
		self.unit1 = Entity((1024, 333), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
		self.unit2 = Entity((333, 64), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
		self.unit3 = Entity((64, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
		self.unit4 = Entity((128, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))

	def forward(self, x: Mat):
		x = self.unit1.forward(x)
		x = self.unit2.forward(x)
		x = self.unit3.forward(x)
		x = self.unit4.forward(x)
		return x

	def backward(self, x: Mat):
		x = self.unit4.reconstruct(x)
		x = self.unit3.reconstruct(x)
		x = self.unit2.reconstruct(x)
		x = self.unit1.reconstruct(x)
		return x

def test_deep_model():
	model = DeepModel("TestModel_pytest", "results")
	model.unit1.training_params.num_epochs = 100
	model.unit2.training_params.num_epochs = 100
	model.unit3.training_params.num_epochs = 100
	model.unit4.training_params.num_epochs = 100
	model.init(0.1)
	batch = (np.random.rand(32, 1024) > 0.5).astype(np.float64)
	model.train(batch)
	errors = [float(np.mean((model.backward(model.forward(x)) - x) ** 2)) for x in batch]
	assert sum(errors) / len(errors) < 0.5, "Deep model reconstruction MSE too high"


def main():
	model = DeepModel("TestModel", "results")
	model.init(0.1)
	model.load()
	batch = (np.random.rand(64, 1024) > 0.5).astype(np.float64)
	model.train(batch)
	model.save()
	for index, inp in enumerate(batch):
		out = model.backward(model.forward(inp))[0]
		print(f"- Pattern {index} -------------------------")
		print(f"Input  : {inp}")
		print(f"Output : {(out > 0.9).astype(np.float64)}")
		print(f"Error  : {np.mean((out - inp)**2):0.3f}")


if __name__ == "__main__":
	main()