pymoto.AddMatrix

class pymoto.AddMatrix

Compute linear combination of sparse matrices

\(Y = \sum_i a_i \mathbf{A}_i\)

Any number of matrices can be added, as long as the input signals are in the form [scalar, matrix, scalar, matrix, …].

Input signals:

• a_1: Scalar

• A_1: Sparse matrix

• a_2 (optional): Second scalar

• A_2 (optional): Second matrix … pairs of further scalar and matrices

Output signal:

Y: Linear combination of matrices

__init__()

Methods

__init__()
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

get_input_sensitivities(as_list=False)

get_input_states(as_list=False)

get_output_sensitivities(as_list=False)

get_output_states(as_list=False)

property n_in: int

Get the number of input signals

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)

response()

Calculate the response from sig_in and output this to sig_out

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

sig_in: List = None

sig_out: List = None