pymoto.Inverse
- class pymoto.Inverse
Calculate the inverse of a matrix \(\mathbf{B} = \mathbf{A}^{-1}\)
- Input Signal:
A(np.ndarray): Dense matrix:math:mathbf{A}
- Output Signal:
B (np.ndarray): The inverse of \(\mathbf{A}\) as dense matrix
- __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
Calculate sensitivities using backpropagation
Attributes
Get the number of input signals
Get the number of output signals
- 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