我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用scipy.optimize.brent()。
def _yj_normmax(x, brack=(-2, 2)): """Compute optimal YJ transform parameter for input data. Parameters ---------- x : array_like Input array. brack : 2-tuple The starting interval for a downhill bracket search """ # Use MLE to compute the optimal YJ parameter def _mle_opt(i, brck): def _eval_mle(lmb, data): # Function to minimize return -_yj_llf(data, lmb) return optimize.brent(_eval_mle, brack=brck, args=(i,)) return _mle_opt(x, brack) # _mle(x, brack)
def train_nullmodel(self, y, K, S=None, U=None): self.ldeltamin += self.scale self.ldeltamax += self.scale if S is None or U is None: S, U = linalg.eigh(K) Uy = scipy.dot(U.T, y) # grid search nllgrid = scipy.ones(self.numintervals + 1) * scipy.inf ldeltagrid = scipy.arange(self.numintervals + 1) / (self.numintervals * 1.0) * (self.ldeltamax - self.ldeltamin) + self.ldeltamin for i in scipy.arange(self.numintervals + 1): nllgrid[i] = nLLeval(ldeltagrid[i], Uy, S) nllmin = nllgrid.min() ldeltaopt_glob = ldeltagrid[nllgrid.argmin()] for i in scipy.arange(self.numintervals - 1) + 1: if (nllgrid[i] < nllgrid[i - 1] and nllgrid[i] < nllgrid[i + 1]): ldeltaopt, nllopt, iter, funcalls = opt.brent(nLLeval, (Uy, S), (ldeltagrid[i - 1], ldeltagrid[i], ldeltagrid[i + 1]), full_output=True) if nllopt < nllmin: nllmin = nllopt ldeltaopt_glob = ldeltaopt return S, U, ldeltaopt_glob, None
def nullModel(self, y, K, S=None, U=None, numintervals=500, ldeltamin=-5, ldeltamax=5, scale=0, p=1): ldeltamin += scale ldeltamax += scale if S is None or U is None: S, U = linalg.eigh(K) Uy = scipy.dot(U.T, y) # grid search nllgrid = scipy.ones(numintervals + 1) * scipy.inf ldeltagrid = scipy.arange(numintervals + 1) / (numintervals * 1.0) * (ldeltamax - ldeltamin) + ldeltamin for i in scipy.arange(numintervals + 1): nllgrid[i] = nLLeval(ldeltagrid[i], Uy, S) # the method is in helpingMethods nllmin = nllgrid.min() ldeltaopt_glob = ldeltagrid[nllgrid.argmin()] for i in scipy.arange(numintervals - 1) + 1: if (nllgrid[i] < nllgrid[i - 1] and nllgrid[i] < nllgrid[i + 1]): ldeltaopt, nllopt, iter, funcalls = opt.brent(nLLeval, (Uy, S), (ldeltagrid[i - 1], ldeltagrid[i], ldeltagrid[i + 1]), full_output=True) if nllopt < nllmin: nllmin = nllopt ldeltaopt_glob = ldeltaopt return S, U, ldeltaopt_glob
