def create_one_train_data(input1, input2):
data = dict(in1=input1, in2=input2)
q = copy(data)
program_set = AdditionProgramSet()
addition_env = AdditionEnv(FIELD_ROW, FIELD_WIDTH, FIELD_DEPTH)
teacher = AdditionTeacher(program_set)
npi_runner = NPIRunner(teacher, recording=True)
npi_runner.verbose = True
addition_env.reset()
run_npi(addition_env, npi_runner, program_set.ADD, data)
step_list = {"q": q, "steps": npi_runner.step_list}
return step_list
def create_one_train_data(input1, input2):
data = dict(in1=input1, in2=input2)
q = copy(data)
program_set = AdditionProgramSet()
addition_env = AdditionEnv(FIELD_ROW, FIELD_WIDTH, FIELD_DEPTH)
teacher = AdditionTeacher(program_set)
npi_runner = NPIRunner(teacher, recording=True)
npi_runner.verbose = True
addition_env.reset()
run_npi(addition_env, npi_runner, program_set.ADD, data)
step_list = {"q": q, "steps": npi_runner.step_list}
return step_list
def create_train_data():
program_set = AdditionProgramSet()
addition_env = AdditionEnv(FIELD_ROW, FIELD_WIDTH, FIELD_DEPTH)
num = 10
questions = create_questions(num)
teacher = AdditionTeacher(program_set)
npi_runner = NPIRunner(teacher, recording=True)
npi_runner.verbose = True
steps_list = []
for data in questions:
addition_env.reset()
q = copy(data)
run_npi(addition_env, npi_runner, program_set.ADD, data)
steps_list.append({"q": q, "steps": npi_runner.step_list})
return steps_list
def create_train_data():
program_set = AdditionProgramSet()
addition_env = AdditionEnv(FIELD_ROW, FIELD_WIDTH, FIELD_DEPTH)
num = 10
questions = create_questions(num)
teacher = AdditionTeacher(program_set)
npi_runner = NPIRunner(teacher, recording=True)
npi_runner.verbose = True
steps_list = []
for data in questions:
addition_env.reset()
q = copy(data)
run_npi(addition_env, npi_runner, program_set.ADD, data)
steps_list.append({"q": q, "steps": npi_runner.step_list})
return steps_list
