cobra/sampling/hr_sampler.py
def step(sampler, x, delta, fraction=None, tries=0):
    """Sample a new feasible point from the point `x` in direction `delta`."""

    prob = sampler.problem
    valid = ((np.abs(delta) > sampler.feasibility_tol) &
             np.logical_not(prob.variable_fixed))

    # permissible alphas for staying in variable bounds
    valphas = ((1.0 - sampler.bounds_tol) * prob.variable_bounds -
               x)[:, valid]
    valphas = (valphas / delta[valid]).flatten()

    if prob.bounds.shape[0] > 0:
        # permissible alphas for staying in constraint bounds
        ineqs = prob.inequalities.dot(delta)
        valid = np.abs(ineqs) > sampler.feasibility_tol
        balphas = ((1.0 - sampler.bounds_tol) * prob.bounds -
                   prob.inequalities.dot(x))[:, valid]
        balphas = (balphas / ineqs[valid]).flatten()

        # combined alphas
        alphas = np.hstack([valphas, balphas])
    else:
        alphas = valphas
    pos_alphas = alphas[alphas > 0.0]
    neg_alphas = alphas[alphas <= 0.0]
    alpha_range = np.array([neg_alphas.max() if len(neg_alphas) > 0 else 0,
                            pos_alphas.min() if len(pos_alphas) > 0 else 0])

    if fraction:
        alpha = alpha_range[0] + fraction * (alpha_range[1] - alpha_range[0])
    else:
        alpha = np.random.uniform(alpha_range[0], alpha_range[1])

    p = x + alpha * delta

    # Numerical instabilities may cause bounds invalidation
    # reset sampler and sample from one of the original warmup directions
    # if that occurs. Also reset if we got stuck.
    if (np.any(sampler._bounds_dist(p) < -sampler.bounds_tol) or
            np.abs(np.abs(alpha_range).max() * delta).max() <
            sampler.bounds_tol):
        if tries > MAX_TRIES:
            raise RuntimeError("Can not escape sampling region, model seems"
                               " numerically unstable :( Reporting the "
                               "model to "
                               "https://github.com/opencobra/cobrapy/issues "
                               "will help us to fix this :)")
        LOGGER.info("found bounds infeasibility in sample, "
                    "resetting to center")
        newdir = sampler.warmup[np.random.randint(sampler.n_warmup)]
        sampler.retries += 1

        return step(sampler, sampler.center, newdir - sampler.center, None,
                    tries + 1)
    return p
def step(sampler, x, delta, fraction=None, tries=0):
    """Sample a new feasible point from the point `x` in direction `delta`."""

    prob = sampler.problem
    valid = ((np.abs(delta) > sampler.feasibility_tol) &
             np.logical_not(prob.variable_fixed))

    # permissible alphas for staying in variable bounds
    valphas = ((1.0 - sampler.bounds_tol) * prob.variable_bounds -
               x)[:, valid]
    valphas = (valphas / delta[valid]).flatten()

    if prob.bounds.shape[0] > 0:
        # permissible alphas for staying in constraint bounds
        ineqs = prob.inequalities.dot(delta)
        valid = np.abs(ineqs) > sampler.feasibility_tol
        balphas = ((1.0 - sampler.bounds_tol) * prob.bounds -
                   prob.inequalities.dot(x))[:, valid]
        balphas = (balphas / ineqs[valid]).flatten()

        # combined alphas
        alphas = np.hstack([valphas, balphas])
    else:
        alphas = valphas
    pos_alphas = alphas[alphas > 0.0]
    neg_alphas = alphas[alphas <= 0.0]
    alpha_range = np.array([neg_alphas.max() if len(neg_alphas) > 0 else 0,
                            pos_alphas.min() if len(pos_alphas) > 0 else 0])

    if fraction:
        alpha = alpha_range[0] + fraction * (alpha_range[1] - alpha_range[0])
    else:
        alpha = np.random.uniform(alpha_range[0], alpha_range[1])

    p = x + alpha * delta

    # Numerical instabilities may cause bounds invalidation
    # reset sampler and sample from one of the original warmup directions
    # if that occurs. Also reset if we got stuck.
    if (np.any(sampler._bounds_dist(p) < -sampler.bounds_tol) or
            np.abs(np.abs(alpha_range).max() * delta).max() <
            sampler.bounds_tol):
        if tries > MAX_TRIES:
            raise RuntimeError("Can not escape sampling region, model seems"
                               " numerically unstable :( Reporting the "
                               "model to "
                               "https://github.com/opencobra/cobrapy/issues "
                               "will help us to fix this :)")
        LOGGER.info("found bounds infeasibility in sample, "
                    "resetting to center")
        newdir = sampler.warmup[np.random.randint(sampler.n_warmup)]
        sampler.retries += 1

        return step(sampler, sampler.center, newdir - sampler.center, None,
                    tries + 1)
    return p
Sample a new feasible point from the point `x` in direction `delta`.