tigercontrol.models package

core

Model

control

tigercontrol.models.control.ControlModel() Description: class for implementing algorithms with enforced modularity
tigercontrol.models.control.KalmanFilter() Description: Kalman Filter adjusts measurements of a signal based on prior states and knowledge of intrinsic equations of the system.
tigercontrol.models.control.ODEShootingMethod() Description: Implements the shooting method to solve second order boundary value problems with conditions y(0) = a and y(L) = b.
tigercontrol.models.control.LQR() Description: Computes optimal set of actions using the Linear Quadratic Regulator algorithm.
tigercontrol.models.control.MPPI() Description: Implements Model Predictive Path Integral Control to compute optimal control sequence.
tigercontrol.models.control.CartPoleNN() Description: Simple multi-layer perceptron policy, no internal state
tigercontrol.models.control.ILQR() Description: Computes optimal set of actions using the Linear Quadratic Regulator algorithm.

time_series

tigercontrol.models.time_series.TimeSeriesModel() Description: class for implementing algorithms with enforced modularity
tigercontrol.models.time_series.AutoRegressor() Description: Implements the equivalent of an AR(p) model - predicts a linear combination of the previous p observed values in a time-series
tigercontrol.models.time_series.LastValue() Description: Predicts the last value in the time series, i.e.
tigercontrol.models.time_series.PredictZero() Description: Predicts the next value in the time series to be 0, i.e.
tigercontrol.models.time_series.RNN() Description: Produces outputs from a randomly initialized recurrent neural network.
tigercontrol.models.time_series.LSTM() Description: Produces outputs from a randomly initialized LSTM neural network.
tigercontrol.models.time_series.LeastSquares() Description: Implements online least squares.

optimizers

tigercontrol.models.optimizers.Optimizer([…]) Description: Core class for model optimizers
tigercontrol.models.optimizers.Adagrad([…]) Description: Ordinary Gradient Descent optimizer.
tigercontrol.models.optimizers.Adam([pred, …]) Description: Ordinary Gradient Descent optimizer.
tigercontrol.models.optimizers.ONS([pred, …]) Online newton step algorithm.
tigercontrol.models.optimizers.SGD([pred, …]) Description: Stochastic Gradient Descent optimizer.
tigercontrol.models.optimizers.OGD([pred, …]) Description: Ordinary Gradient Descent optimizer.
tigercontrol.models.optimizers.mse(y_pred, …) Description: mean-square-error loss :param y_pred: value predicted by model :param y_true: ground truth value :param eps: some scalar
tigercontrol.models.optimizers.cross_entropy(…) Description: cross entropy loss, y_pred is equivalent to logits and y_true to labels :param y_pred: value predicted by model :param y_true: ground truth value :param eps: some scalar

boosting

tigercontrol.models.boosting.SimpleBoost() Description: Implements the equivalent of an AR(p) model - predicts a linear combination of the previous p observed values in a time-series