def loss_weights(N):
if FUNC_TYPE == 0: # exponential spacing
return anytime_loss.at_func(N, func=lambda x: 2**x)
elif FUNC_TYPE == 1: # square spacing
return anytime_loss.at_func(N, func=lambda x: x**2)
elif FUNC_TYPE == 2: #optimal at ?
return anytime_loss.optimal_at(N, OPTIMAL_AT)
elif FUNC_TYPE == 3: #exponential weights
return anytime_loss.exponential_weights(N, base=EXP_BASE)
elif FUNC_TYPE == 4: #constant weights
return anytime_loss.constant_weights(N)
elif FUNC_TYPE == 5: # sieve with stack
return anytime_loss.stack_loss_weights(N, NUM_UNITS_PER_STACK)
elif FUNC_TYPE == 6: # linear
return anytime_loss.linear(N, a=0.25, b=1.0)
elif FUNC_TYPE == 7: # half constant, half optimal at -1
return anytime_loss.half_constant_half_optimal(N, -1)
elif FUNC_TYPE == 8: # quater constant, half optimal
return anytime_loss.quater_constant_half_optimal(N)
elif FUNC_TYPE == 9:
return anytime_loss.stack_loss_weights(N, NUM_UNITS_PER_STACK,
anytime_loss.eann_sieve)
else:
raise NameError('func type must be either 0: exponential or 1: square\
or 2: optimal at --opt_at, or 3: exponential weight with base --base'
)
def loss_weights(N):
if FUNC_TYPE == 0: # exponential spacing
return anytime_loss.at_func(N, func=lambda x: 2**x)
elif FUNC_TYPE == 1: # square spacing
return anytime_loss.at_func(N, func=lambda x: x**2)
else:
raise NameError('func type must be either 0: exponential or 1: square')