def convert_for_step_return(step_values: tuple) -> StepOutput:
if len(step_values) == 2:
return StepOutput(PG_CONTINUE, step_values[0],
IntegerArguments(step_values[1]))
else:
return StepOutput(step_values[0], step_values[1],
IntegerArguments(step_values[2]))
def convert_for_step_return(step_values: tuple) -> StepOutput:
if len(step_values) == 3:
# this is the StepOutput on the end of primitive program, with the first item specifying PG_RETURN
return StepOutput(step_values[0], step_values[1],
IntegerArguments(step_values[2]))
else:
return StepOutput(PG_CONTINUE, step_values[0],
IntegerArguments(step_values[1]))
def step(self, env_observation: np.ndarray, pg: Program, arguments: IntegerArguments) -> StepOutput:
x = self.convert_input(StepInput(env_observation, pg, arguments))
results = self.model.predict(x, batch_size=1) # if batch_size==1, returns single row
r, pg_one_hot, arg_values = results[0], results[1], results[2:]
program = self.program_set.get(pg_one_hot.argmax())
ret = StepOutput(r, program, IntegerArguments(values=np.stack(arg_values)))
return ret
def run_npi(addition_env, npi_runner, program, data):
data['expect'] = data['in1'] + data['in2']
addition_env.setup_problem(data['in1'], data['in2'])
npi_runner.reset()
npi_runner.display_env(addition_env, force=True)
npi_runner.npi_program_interface(addition_env, program, IntegerArguments())
data['result'] = addition_env.get_output()
data['correct'] = data['result'] == data['expect']
def run_npi(addition_env, npi_runner, program, data):
data['expect'] = data['in1'] + data['in2']
addition_env.setup_problem(data['in1'], data['in2'])
npi_runner.reset()
npi_runner.npi_program_interface(addition_env, program,
IntegerArguments(ARG_NUM, ARG_DEPTH))
data['result'] = addition_env.get_output()
data['correct'] = data['result'] == data['expect']
def run_npi(bubblesort_env, npi_runner, program, data):
data['expect'] = sorted(data['raw'])
bubblesort_env.setup_problem(data['raw'])
npi_runner.reset()
npi_runner.display_env(bubblesort_env, force=True)
npi_runner.npi_program_interface(bubblesort_env, program,
IntegerArguments())
data['result'] = bubblesort_env.get_output()
data['correct'] = data['result'] == data['expect']
def run_npi(multiplication_env, npi_runner, program, data):
data['expect'] = data['mul1'] * data['mul2']
multiplication_env.setup_problem(data['mul1'], data['mul2'])
npi_runner.reset()
# TODO bug here: display_env
npi_runner.display_env(multiplication_env, force=True)
npi_runner.npi_program_interface(multiplication_env, program,
IntegerArguments())
data['result'] = multiplication_env.get_output()
data['correct'] = data['result'] == data['expect']
def convert_output(self, p_out: StepOutput):
y = [np.array((p_out.r, ))]
weights = [[1.]]
if p_out.program:
arg_values = p_out.arguments.values
arg_num = len(p_out.program.args or [])
y += [p_out.program.to_one_hot(PROGRAM_VEC_SIZE)]
weights += [[1.]]
else:
arg_values = IntegerArguments().values
arg_num = 0
y += [np.zeros((PROGRAM_VEC_SIZE, ))]
weights += [[1e-10]]
for v in arg_values: # split by each args
y += [v]
weights += [[1.]] * arg_num + [[1e-10]] * (len(arg_values) - arg_num)
weights = [np.array(w) for w in weights]
return [yy.reshape((self.batch_size, -1)) for yy in y], weights
def convert_for_step_return(step_values):
if len(step_values) == 2:
return StepOutput(PG_CONTINUE, step_values[0], IntegerArguments(ARG_NUM, ARG_DEPTH, step_values[1]))
else:
return StepOutput(step_values[0], step_values[1], IntegerArguments(ARG_NUM, ARG_DEPTH, step_values[2]))
