pymoto.SoftMinMax

class pymoto.SoftMinMax(alpha=1.0, scaling: AggScaling = None, active_set: AggActiveSet = None)

Soft maximum/minimum function aggregation

\(S_a(x_1, x_2, \dotsc, x_n) = \frac{\sum_i (x_i \exp(a x_i))}{\sum_i (\exp(a x_i))}\)

When using as maximum, it underestimates the maximum It is exact however when \(x_1=x_2=\dotsc=x_n`\)

__init__(alpha=1.0, scaling: AggScaling = None, active_set: AggActiveSet = None)

Initialize soft min/max aggregation module

Parameters:

alpha (float, optional) – Scaling factor of the soft function. Approximate maximum for alpha>0 and minimum for alpha<0. Defaults to 1.0.
scaling (pymoto.AggScaling, optional) – Scaling strategy to improve approximation
active_set (pymoto.AggActiveSet, optional) – Active set strategy to improve approximation

Methods

`__init__`([alpha, scaling, active_set])	Initialize soft min/max aggregation module
`aggregation_derivative`(x)	" Calculates df(x) / dx
`aggregation_function`(x)	Calculates f(x)
`connect`(sig_in[, sig_out])	Connect without automatic adding to a function network
`get_input_sensitivities`([as_list])
`get_input_states`([as_list])
`get_output_sensitivities`([as_list])
`get_output_states`([as_list])
`reset`()	Reset the state of the sensitivities (they are set to zero or to None)
`response`()	Calculate the response from sig_in and output this to sig_out
`sensitivity`()	Calculate sensitivities using backpropagation

Attributes

`n_in`	Get the number of input signals
`n_out`	Get the number of output signals
`sig_in`
`sig_out`

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None): Connect without automatic adding to a function network

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset(): Reset the state of the sensitivities (they are set to zero or to None)

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in