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How to use the torchdiffeq._impl.interp._interp_fit function in torchdiffeq

To help you get started, we’ve selected a few torchdiffeq examples, based on popular ways it is used in public projects.

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rtqichen / torchdiffeq / torchdiffeq / _impl / adaptive_heun.py View on Github

def _interp_fit_adaptive_heun(y0, y1, k, dt, tableau=_ADAPTIVE_HEUN_TABLEAU):
    """Fit an interpolating polynomial to the results of a Runge-Kutta step."""
    dt = dt.type_as(y0[0])
    y_mid = tuple(y0_ + _scaled_dot_product(dt, AH_C_MID, k_) for y0_, k_ in zip(y0, k))
    f0 = tuple(k_[0] for k_ in k)
    f1 = tuple(k_[-1] for k_ in k)
    return _interp_fit(y0, y1, y_mid, f0, f1, dt)

uncbiag / easyreg / torchdiffeq / _impl / dopri5.py View on Github

def _interp_fit_dopri5(y0, y1, k, dt, tableau=_DORMAND_PRINCE_SHAMPINE_TABLEAU):
    """Fit an interpolating polynomial to the results of a Runge-Kutta step."""
    dt = dt.type_as(y0[0])
    y_mid = tuple(y0_ + _scaled_dot_product(dt, DPS_C_MID, k_) for y0_, k_ in zip(y0, k))
    f0 = tuple(k_[0] for k_ in k)
    f1 = tuple(k_[-1] for k_ in k)
    return _interp_fit(y0, y1, y_mid, f0, f1, dt)