ssqpy.solvers.search

search ¶

Submodule containing classes related to various types of parameter
searching methods.

DynamicsParamGradientDescent(dynamics, min_data_size=100, armijo_beta=0.5, armijo_c1=0.0001)

addData(x, u, x_next)

Adds a new data point
Returns: whether the parameters were updated

clearData()

Clears all previously added data

setMinimumDataSize(min_data_size)

Sets the minimum required data size in order to perform an update

DynamicsParamGaussNewton(dynamics, window_size=100, armijo_beta=0.5, armijo_c1=0.0001)

addData(x, u, x_next)

Adds a new data point
Returns: whether the parameters were updated

clearData()

Clears window of all previously added data

setWindowSize(window_size, prune_last=False)

Sets the window size to the given value
Parameters:

window_size: The size to resize the window to
prune_last: Whether to remove the most recent data points, in case the window currently holds more data than the provided size; if false, an algorithm is used to instead prune the most similar data points has no effect if the provided size is larger than the data currently held