import numpy as np

class SpectralEdgeController:
    """Multiplicative edge controller for h'=tanh(g W h + input)."""
    def __init__(self, target=0.9, alpha=0.25, g_min=1e-3, g_max=3.0):
        self.target = float(target)
        self.alpha = float(alpha)
        self.g_min, self.g_max = float(g_min), float(g_max)
        self.g = 1.0

    @staticmethod
    def edge_estimate(W, derivative, iterations=5, rng=None):
        # Singular edge of D W is robust when the nonlinear local Jacobian is nonsymmetric.
        rng = np.random.default_rng() if rng is None else rng
        v = rng.normal(size=W.shape[0]); v /= np.linalg.norm(v)
        for _ in range(iterations):
            u = derivative * (W @ v)
            nu = np.linalg.norm(u)
            if nu == 0: return 0.0
            u /= nu
            v = W.T @ (derivative * u)
            nv = np.linalg.norm(v)
            if nv == 0: return 0.0
            v /= nv
        return float(np.linalg.norm(derivative * (W @ v)))

    def update(self, edge):
        self.g *= np.exp(self.alpha * (self.target - self.g * edge))
        self.g = float(np.clip(self.g, self.g_min, self.g_max))
        return self.g