for i in range(data_size):
for j in range(input_size):
input_bit = (i >> j) & 1
data[i, j] = float(input_bit)
target[i] = float(random.randint(0, 1))
return (data, target.view(-1, 1))
def create_case_data(self, case):
input_size = min(3 + case, 7)
data, target = self.create_data(input_size, case)
return sm.CaseData(case, Limits(), (data, target),
(data, target)).set_description(
"{} inputs".format(input_size))
class Config:
def __init__(self):
self.max_samples = 1000
def get_data_provider(self):
return DataProvider()
def get_solution(self):
return Solution()
# If you want to run specific case, put number here
BestSol = {"steps": 10000, "LR": 0, "HS": 0}
sm.SolutionManager(Config()).run(case_number=-1)
#print("Best parameters: LR = {}, HS = {}, solves in {} steps".format(BestSol["LR"],BestSol["HS"],BestSol["steps"]))
train_data = self.select_data(self.train_data, digits)
test_data = self.select_data(self.test_data, digits)
return sm.CaseData(
case, Limits(), train_data,
test_data).set_description(description).set_output_size(10)
class Config:
def __init__(self):
self.max_samples = 1000
def get_data_provider(self):
return DataProvider()
def get_solution(self):
return Solution()
# If you want to run specific case, put number here
BestSol = {"time": 10000, "steps": 10000, "LR": 0, "HS": 0}
DoGridSearch = False
if DoGridSearch:
MyCaseNumber = 10
else:
MyCaseNumber = -1
sm.SolutionManager(Config()).run(MyCaseNumber)
if DoGridSearch:
print("Best parameters: LR = {}, HS = {}, solves in {} s and {} steps".
format(BestSol["LR"], BestSol["HS"], BestSol["time"],
BestSol["steps"]))
