Python Examples of scipy.optimize.minimize (2023)

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Example #1

Source File: conftest.pyFrom NiBetaSerieswith MIT License8votesPython Examples of scipy.optimize.minimize (2)Python Examples of scipy.optimize.minimize (3)
def betaseries_file(tmpdir_factory, deriv_betaseries_fname=deriv_betaseries_fname): bfile = tmpdir_factory.mktemp("beta").ensure(deriv_betaseries_fname) np.random.seed(3) num_trials = 40 tgt_corr = 0.1 bs1 = np.random.rand(num_trials) # create another betaseries with a target correlation bs2 = minimize(lambda x: abs(tgt_corr - pearsonr(bs1, x)[0]), np.random.rand(num_trials)).x # two identical beta series bs_data = np.array([[[bs1, bs2]]]) # the nifti image bs_img = nib.Nifti1Image(bs_data, np.eye(4)) bs_img.to_filename(str(bfile)) return bfile 

Example #2

Source File: construct_portoflio.pyFrom Risk_Budgetingwith GNU General Public License v3.07votesPython Examples of scipy.optimize.minimize (4)Python Examples of scipy.optimize.minimize (5)
def rb_p_weights(asset_rets, rb):# number of ARP seriesnum_arp = asset_rets.shape[1]# covariance matrix of asset returnsp_cov = asset_rets.cov()# initial weightsw0 = 1.0 * np.ones((num_arp, 1)) / num_arp# constraintscons = ({'type': 'eq', 'fun': cons_sum_weight}, {'type': 'ineq', 'fun': cons_long_only_weight})# portfolio optimisationreturn minimize(obj_fun, w0, args=(p_cov, rb), method='SLSQP', constraints=cons) 

Example #3

Source File: test_optimize.pyFrom revrandwith Apache License 2.07votesPython Examples of scipy.optimize.minimize (6)Python Examples of scipy.optimize.minimize (7)
def test_structured_params(make_quadratic, make_random): random = make_random a, b, c, data, _ = make_quadratic w0 = [Parameter(random.randn(2), Bound()), Parameter(random.randn(1), Bound()) ] qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj) assert_opt = lambda Eab, Ec: \ np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0) nmin = structured_minimizer(minimize) res = nmin(qobj_struc, w0, args=(data,), jac=True, method='L-BFGS-B') assert_opt(*res.x) nsgd = structured_sgd(sgd) res = nsgd(qobj_struc, w0, data, eval_obj=True, random_state=make_random) assert_opt(*res.x) qf_struc = lambda w12, w3, data: q_struc(w12, w3, data, qfun) qg_struc = lambda w12, w3, data: q_struc(w12, w3, data, qgrad) res = nmin(qf_struc, w0, args=(data,), jac=qg_struc, method='L-BFGS-B') assert_opt(*res.x) 

Example #4

Source File: test_optimize.pyFrom Computablewith MIT License7votesPython Examples of scipy.optimize.minimize (8)Python Examples of scipy.optimize.minimize (9)
def test_minimize_l_bfgs_b_ftol(self): # Check that the `ftol` parameter in l_bfgs_b works as expected v0 = None for tol in [1e-1, 1e-4, 1e-7, 1e-10]: opts = {'disp': False, 'maxiter': self.maxiter, 'ftol': tol} sol = optimize.minimize(self.func, self.startparams, method='L-BFGS-B', jac=self.grad, options=opts) v = self.func(sol.x) if v0 is None: v0 = v else: assert_(v < v0) assert_allclose(v, self.func(self.solution), rtol=tol) 

Example #5

Source File: fisheye.pyFrom DualFisheyewith MIT License6votesPython Examples of scipy.optimize.minimize (10)Python Examples of scipy.optimize.minimize (11)
def optimize(self, psize=256, wt_pixel=1000, wt_blank=1000): # Precalculate raster-order XYZ coordinates at given resolution. [xyz, rows, cols] = self._get_equirectangular_raster(psize) # Scoring function gives bonus points per overlapping pixel. score = lambda svec: self._score(svec, xyz, wt_pixel, wt_blank) # Multivariable optimization using gradient-descent or similar. # https://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html svec0 = self._get_state_vector() final = minimize(score, svec0, method='Nelder-Mead', options={'xtol':1e-4, 'disp':True}) # Store final lens parameters. self._set_state_vector(final.x) # Render combined panorama in equirectangular projection mode. # See also: https://en.wikipedia.org/wiki/Equirectangular_projection 

Example #6

Source File: iGAN_predict.pyFrom iGANwith MIT License6votesPython Examples of scipy.optimize.minimize (12)Python Examples of scipy.optimize.minimize (13)
(Video) Intro to Scipy Optimization: Minimize Method
def invert_bfgs(gen_model, invert_model, ftr_model, im, z_predict=None, npx=64): _f, z = invert_model nz = gen_model.nz if z_predict is None: z_predict = np_rng.uniform(-1., 1., size=(1, nz)) else: z_predict = floatX(z_predict) z_predict = np.arctanh(z_predict) im_t = gen_model.transform(im) ftr = ftr_model(im_t) prob = optimize.minimize(f_bfgs, z_predict, args=(_f, im_t, ftr), tol=1e-6, jac=True, method='L-BFGS-B', options={'maxiter': 200}) print('n_iters = %3d, f = %.3f' % (prob.nit, prob.fun)) z_opt = prob.x z_opt_n = floatX(z_opt[np.newaxis, :]) [f_opt, g, gx] = _f(z_opt_n, im_t, ftr) gx = gen_model.inverse_transform(gx, npx=npx) z_opt = np.tanh(z_opt) return gx, z_opt, f_opt 

Example #7

Source File: sampler.pyFrom phoenicswith Apache License 2.06votesPython Examples of scipy.optimize.minimize (14)Python Examples of scipy.optimize.minimize (15)
def _proposal_optimization_thread(self, batch_index, return_dict = None):print('starting process for ', batch_index)# prepare penalty functiondef penalty(x):num, den = self.penalty_contributions(x)return (num + self.lambda_values[batch_index]) / denoptimized = []#for sample in self.proposals:#if np.random.uniform() < 0.5:#optimized.append(sample)#continue#res = minimize(penalty, sample, method = 'L-BFGS-B', options = {'maxiter': 25})### FIXME#if np.any(res.x < self.var_lows) or np.any(res.x > self.var_highs):#optimized.append(sample)#else:#optimized.append(res.x)for sample in self.proposals:# set some entries to zero!set_to_zero = self._gen_set_to_zero_vector(sample)nulls = np.where(set_to_zero == 0)[0]opt = self.local_opt.optimize(penalty, sample * set_to_zero, max_iter = 10, ignore = nulls)optimized.append(opt)optimized = np.array(optimized)optimized[:, self._ints] = np.around(optimized[:, self._ints])optimized[:, self._cats] = np.around(optimized[:, self._cats])print('finished process for ', batch_index)if return_dict.__class__.__name__ == 'DictProxy':return_dict[batch_index] = optimizedelse:return optimized 

Example #8

Source File: test_optimize.pyFrom revrandwith Apache License 2.06votesPython Examples of scipy.optimize.minimize (16)Python Examples of scipy.optimize.minimize (17)
def test_unbounded(make_quadratic, make_random): random = make_random a, b, c, data, _ = make_quadratic w0 = random.randn(3) assert_opt = lambda Ea, Eb, Ec: \ np.allclose((a, b, c), (Ea, Eb, Ec), atol=1e-3, rtol=0) for updater in [SGDUpdater, AdaDelta, AdaGrad, Momentum, Adam]: res = sgd(qobj, w0, data, eval_obj=True, updater=updater(), random_state=make_random) assert_opt(*res['x']) res = minimize(qobj, w0, args=(data,), jac=True, method='L-BFGS-B') assert_opt(*res['x']) res = minimize(qfun, w0, args=(data,), jac=qgrad, method='L-BFGS-B') assert_opt(*res['x']) res = minimize(qfun, w0, args=(data), jac=False, method=None) assert_opt(*res['x']) 

Example #9

Source File: test_optimize.pyFrom revrandwith Apache License 2.06votesPython Examples of scipy.optimize.minimize (18)Python Examples of scipy.optimize.minimize (19)
def test_bounded(make_quadratic, make_random): random = make_random a, b, c, data, bounds = make_quadratic w0 = np.concatenate((random.randn(2), [1.5])) res = minimize(qobj, w0, args=(data,), jac=True, bounds=bounds, method='L-BFGS-B') Ea_bfgs, Eb_bfgs, Ec_bfgs = res['x'] res = sgd(qobj, w0, data, bounds=bounds, eval_obj=True, random_state=random) Ea_sgd, Eb_sgd, Ec_sgd = res['x'] assert np.allclose((Ea_bfgs, Eb_bfgs, Ec_bfgs), (Ea_sgd, Eb_sgd, Ec_sgd), atol=5e-2, rtol=0) 

Example #10

Source File: test_optimize.pyFrom revrandwith Apache License 2.06votesPython Examples of scipy.optimize.minimize (20)Python Examples of scipy.optimize.minimize (21)
def test_log_params(make_quadratic, make_random): random = make_random a, b, c, data, _ = make_quadratic w0 = np.abs(random.randn(3)) bounds = [Positive(), Bound(), Positive()] assert_opt = lambda Ea, Eb, Ec: \ np.allclose((a, b, c), (Ea, Eb, Ec), atol=1e-3, rtol=0) nmin = logtrick_minimizer(minimize) res = nmin(qobj, w0, args=(data,), jac=True, method='L-BFGS-B', bounds=bounds) assert_opt(*res.x) nsgd = logtrick_sgd(sgd) res = nsgd(qobj, w0, data, eval_obj=True, bounds=bounds, random_state=make_random) assert_opt(*res.x) nmin = logtrick_minimizer(minimize) res = nmin(qfun, w0, args=(data,), jac=qgrad, method='L-BFGS-B', bounds=bounds) assert_opt(*res.x) 

Example #11

Source File: test_optimize.pyFrom revrandwith Apache License 2.06votesPython Examples of scipy.optimize.minimize (22)Python Examples of scipy.optimize.minimize (23)
def test_logstruc_params(make_quadratic, make_random): random = make_random a, b, c, data, _ = make_quadratic w0 = [Parameter(random.gamma(2, size=(2,)), Positive()), Parameter(random.randn(), Bound()) ] qobj_struc = lambda w12, w3, data: q_struc(w12, w3, data, qobj) assert_opt = lambda Eab, Ec: \ np.allclose((a, b, c), (Eab[0], Eab[1], Ec), atol=1e-3, rtol=0) nmin = structured_minimizer(logtrick_minimizer(minimize)) res = nmin(qobj_struc, w0, args=(data,), jac=True, method='L-BFGS-B') assert_opt(*res.x) nsgd = structured_sgd(logtrick_sgd(sgd)) res = nsgd(qobj_struc, w0, data, eval_obj=True, random_state=make_random) assert_opt(*res.x) qf_struc = lambda w12, w3, data: q_struc(w12, w3, data, qfun) qg_struc = lambda w12, w3, data: q_struc(w12, w3, data, qgrad) res = nmin(qf_struc, w0, args=(data,), jac=qg_struc, method='L-BFGS-B') assert_opt(*res.x) 

Example #12

Source File: recastlib.pyFrom nevergradwith MIT License6votesPython Examples of scipy.optimize.minimize (24)Python Examples of scipy.optimize.minimize (25)
(Video) Python Tutorial: Learn Scipy - Optimization (scipy.optimize) in 13 Minutes
def _optimization_function(self, objective_function: Callable[[base.ArrayLike], float]) -> base.ArrayLike: # pylint:disable=unused-argument budget = np.inf if self.budget is None else self.budget best_res = np.inf best_x: np.ndarray = self.current_bests["average"].x # np.zeros(self.dimension) if self.initial_guess is not None: best_x = np.array(self.initial_guess, copy=True) # copy, just to make sure it is not modified remaining = budget - self._num_ask while remaining > 0: # try to restart if budget is not elapsed options: Dict[str, int] = {} if self.budget is None else {"maxiter": remaining} res = scipyoptimize.minimize( objective_function, best_x if not self.random_restart else self._rng.normal(0.0, 1.0, self.dimension), method=self.method, options=options, tol=0, ) if res.fun < best_res: best_res = res.fun best_x = res.x remaining = budget - self._num_ask return best_x 

Example #13

Source File: env.pyFrom fragilewith MIT License6votesPython Examples of scipy.optimize.minimize (26)Python Examples of scipy.optimize.minimize (27)
def __init__(self, function: Function, bounds=None, *args, **kwargs): """ Initialize a :class:`Minimizer`. Args: function: :class:`Function` that will be minimized. bounds: :class:`Bounds` defining the domain of the minimization \ process. If it is ``None`` the :class:`Function` :class:`Bounds` \ will be used. *args: Passed to ``scipy.optimize.minimize``. **kwargs: Passed to ``scipy.optimize.minimize``. """ self.env = function self.function = function.function self.bounds = self.env.bounds if bounds is None else bounds self.args = args self.kwargs = kwargs 

Example #14

Source File: env.pyFrom fragilewith MIT License6votesPython Examples of scipy.optimize.minimize (28)Python Examples of scipy.optimize.minimize (29)
def minimize(self, x: numpy.ndarray): """ Apply ``scipy.optimize.minimize`` to a single point. Args: x: Array representing a single point of the function to be minimized. Returns: Optimization result object returned by ``scipy.optimize.minimize``. """ def _optimize(_x): try: _x = _x.reshape((1,) + _x.shape) y = self.function(_x) except (ZeroDivisionError, RuntimeError): y = numpy.inf return y bounds = ScipyBounds( ub=self.bounds.high if self.bounds is not None else None, lb=self.bounds.low if self.bounds is not None else None, ) return minimize(_optimize, x, bounds=bounds, *self.args, **self.kwargs) 

Example #15

Source File: env.pyFrom fragilewith MIT License6votesPython Examples of scipy.optimize.minimize (30)Python Examples of scipy.optimize.minimize (31)
def minimize_point(self, x: numpy.ndarray) -> Tuple[numpy.ndarray, Scalar]: """ Minimize the target function passing one starting point. Args: x: Array representing a single point of the function to be minimized. Returns: Tuple containing a numpy array representing the best solution found, \ and the numerical value of the function at that point. """ optim_result = self.minimize(x) point = optim_result["x"] reward = float(optim_result["fun"]) return point, reward 

Example #16

Source File: test_anneal.pyFrom Computablewith MIT License6votesPython Examples of scipy.optimize.minimize (32)Python Examples of scipy.optimize.minimize (33)
def anneal_schedule(self, schedule='fast', use_wrapper=False): """ Call anneal algorithm using specified schedule """ n = 0 # index of test function if use_wrapper: opts = {'upper': self.upper[n], 'lower': self.lower[n], 'ftol': 1e-3, 'maxiter': self.maxiter, 'schedule': schedule, 'disp': False} res = minimize(self.fun[n], self.x0[n], method='anneal', options=opts) x, retval = res['x'], res['status'] else: x, retval = anneal(self.fun[n], self.x0[n], full_output=False, upper=self.upper[n], lower=self.lower[n], feps=1e-3, maxiter=self.maxiter, schedule=schedule, disp=False) assert_almost_equal(x, self.sol[n], 2) return retval 

Example #17

Source File: test_optimize.pyFrom Computablewith MIT License6votesPython Examples of scipy.optimize.minimize (34)Python Examples of scipy.optimize.minimize (35)
def test_minimize(self): """Tests for the minimize wrapper.""" self.setUp() self.test_bfgs(True) self.setUp() self.test_bfgs_infinite(True) self.setUp() self.test_cg(True) self.setUp() self.test_ncg(True) self.setUp() self.test_ncg_hess(True) self.setUp() self.test_ncg_hessp(True) self.setUp() self.test_neldermead(True) self.setUp() self.test_powell(True) 

Example #18

Source File: test_optimize.pyFrom Computablewith MIT License6votesPython Examples of scipy.optimize.minimize (36)Python Examples of scipy.optimize.minimize (37)
(Video) Using scipy optimize minimize
def test_minimize_tol_parameter(self): # Check that the minimize() tol= argument does something def func(z): x, y = z return x**2*y**2 + x**4 + 1 def dfunc(z): x, y = z return np.array([2*x*y**2 + 4*x**3, 2*x**2*y]) for method in ['nelder-mead', 'powell', 'cg', 'bfgs', 'newton-cg', 'anneal', 'l-bfgs-b', 'tnc', 'cobyla', 'slsqp']: if method in ('nelder-mead', 'powell', 'anneal', 'cobyla'): jac = None else: jac = dfunc sol1 = optimize.minimize(func, [1,1], jac=jac, tol=1e-10, method=method) sol2 = optimize.minimize(func, [1,1], jac=jac, tol=1.0, method=method) assert_(func(sol1.x) < func(sol2.x), "%s: %s vs. %s" % (method, func(sol1.x), func(sol2.x))) 

Example #19

Source File: 4_multi_classification.pyFrom deep-learning-notewith MIT License5votesPython Examples of scipy.optimize.minimize (38)Python Examples of scipy.optimize.minimize (39)
def one_vs_all(X, y, num_labels, learning_rate): rows = X.shape[0] params = X.shape[1] # k X (n + 1) array for the parameters of each of the k classifiers all_theta = np.zeros((num_labels, params + 1)) # insert a column of ones at the beginning for the intercept term X = np.insert(X, 0, values=np.ones(rows), axis=1) # labels are 1-indexed instead of 0-indexed for i in range(1, num_labels + 1): theta = np.zeros(params + 1) y_i = np.array([1 if label == i else 0 for label in y]) y_i = np.reshape(y_i, (rows, 1)) # minimize the objective function fmin = minimize(fun=cost, x0=theta, args=(X, y_i, learning_rate), method='TNC', jac=gradient) all_theta[i-1,:] = fmin.x return all_theta 

Example #20

Source File: 6_bias_variance.pyFrom deep-learning-notewith MIT License5votesPython Examples of scipy.optimize.minimize (40)Python Examples of scipy.optimize.minimize (41)
def linear_regression_np(X, y, l=1): """linear regression args: X: feature matrix, (m, n+1) # with incercept x0=1 y: target vector, (m, ) l: lambda constant for regularization return: trained parameters """ # init theta theta = np.ones(X.shape[1]) # train it res = opt.minimize(fun=regularized_cost, x0=theta, args=(X, y, l), method='TNC', jac=regularized_gradient, options={'disp': True}) return res 

Example #21

Source File: core.pyFrom neuropythywith GNU Affero General Public License v3.05votesPython Examples of scipy.optimize.minimize (42)Python Examples of scipy.optimize.minimize (43)
def minimize(self, x0, **kwargs): ''' pf.minimize(x0) minimizes the given potential function starting at the given point x0; any additional options are passed along to scipy.optimize.minimize. ''' x0 = np.asarray(x0) kwargs = pimms.merge({'jac':self.jac(), 'method':'CG'}, kwargs) res = spopt.minimize(self.fun(), x0.flatten(), **kwargs) res.x = np.reshape(res.x, x0.shape) return res 

Example #22

Source File: core.pyFrom neuropythywith GNU Affero General Public License v3.05votesPython Examples of scipy.optimize.minimize (44)Python Examples of scipy.optimize.minimize (45)
def argmin(self, x0, **kwargs): ''' pf.argmin(x0) is equivalent to pf.minimize(x0).x. ''' return self.minimize(x0, **kwargs).x 

Example #23

Source File: core.pyFrom neuropythywith GNU Affero General Public License v3.05votesPython Examples of scipy.optimize.minimize (46)Python Examples of scipy.optimize.minimize (47)
def maximize(self, x0, **kwargs): ''' pf.maximize(x0) is equivalent to (-pf).minimize(x0). ''' return (-self).minimize(x0, **kwargs) 

Example #24

Source File: core.pyFrom neuropythywith GNU Affero General Public License v3.05votesPython Examples of scipy.optimize.minimize (48)Python Examples of scipy.optimize.minimize (49)
(Video) scipy.optimize.minimize Python
def curve_intersection(c1, c2, grid=16): ''' curve_intersect(c1, c2) yields the parametric distances (t1, t2) such that c1(t1) == c2(t2). The optional parameter grid may specify the number of grid-points to use in the initial search for a start-point (default: 16). ''' from scipy.optimize import minimize from neuropythy.geometry import segment_intersection_2D if c1.coordinates.shape[1] > c2.coordinates.shape[1]: (t1,t2) = curve_intersection(c2, c1, grid=grid) return (t2,t1) # before doing a search, see if there are literal exact intersections of the segments x1s = c1.coordinates.T x2s = c2.coordinates for (ts,te,xs,xe) in zip(c1.t[:-1], c1.t[1:], x1s[:-1], x1s[1:]): pts = segment_intersection_2D((xs,xe), (x2s[:,:-1], x2s[:,1:])) ii = np.where(np.isfinite(pts[0]))[0] if len(ii) > 0: ii = ii[0] def f(t): return np.sum((c1(t[0]) - c2(t[1]))**2) t01 = 0.5*(ts + te) t02 = 0.5*(c2.t[ii] + c2.t[ii+1]) (t1,t2) = minimize(f, (t01, t02)).x return (t1,t2) if pimms.is_vector(grid): (ts1,ts2) = [c.t[0] + (c.t[-1] - c.t[0])*grid for c in (c1,c2)] else: (ts1,ts2) = [np.linspace(c.t[0], c.t[-1], grid) for c in (c1,c2)] (pts1,pts2) = [c(ts) for (c,ts) in zip([c1,c2],[ts1,ts2])] ds = np.sqrt([np.sum((pts2.T - pp)**2, axis=1) for pp in pts1.T]) (ii,jj) = np.unravel_index(np.argmin(ds), ds.shape) (t01,t02) = (ts1[ii], ts2[jj]) ttt = [] def f(t): return np.sum((c1(t[0]) - c2(t[1]))**2) (t1,t2) = minimize(f, (t01, t02)).x return (t1,t2) 

Example #25

Source File: methods.pyFrom Localizationwith MIT License5votesPython Examples of scipy.optimize.minimize (50)Python Examples of scipy.optimize.minimize (51)
def lse(cA, mode='2D', cons=True): l = len(cA) r = [w.r for w in cA] c = [w.c for w in cA] S = sum(r) W = [(S - w) / ((l - 1) * S) for w in r] p0 = gx.point(0, 0, 0) # Initialized point for i in range(l): p0 = p0 + W[i] * c[i] if mode == '2D' or mode == 'Earth1': x0 = num.array([p0.x, p0.y]) elif mode == '3D': x0 = num.array([p0.x, p0.y, p0.z]) else: raise cornerCases('Mode not supported:' + mode) if mode == 'Earth1': fg1 = 1 else: fg1 = 0 if cons: print('GC-LSE geolocating...') if not is_disjoint(cA, fg=fg1): cL = [] for q in range(l): def ff(x, q=q): return r[q] - Norm(x, c[q].std(), mode=mode) cL.append(ff) res = fmin_cobyla(sum_error, x0, cL, args=(c, r, mode), consargs=(), rhoend=1e-5) ans = res else: raise cornerCases('Disjoint') else: print('LSE Geolocating...') res = minimize(sum_error, x0, args=(c, r, mode), method='BFGS') ans = res.x return gx.point(ans) 

Example #26

Source File: ext_car.pyFrom feetswith MIT License5votesPython Examples of scipy.optimize.minimize (52)Python Examples of scipy.optimize.minimize (53)
def _calculate_CAR(self, time, magnitude, error, minimize_method): magnitude = magnitude.copy() time = time.copy() error = error.copy() ** 2 x0 = [10, 0.5] bnds = ((0, 100), (0, 100)) with warnings.catch_warnings(): warnings.filterwarnings("ignore") res = minimize( _car_like, x0, args=(time, magnitude, error), method=minimize_method, bounds=bnds, ) sigma, tau = res.x[0], res.x[1] return sigma, tau 

Example #27

Source File: operators.pyFrom proxalgswith MIT License5votesPython Examples of scipy.optimize.minimize (54)Python Examples of scipy.optimize.minimize (55)
def poissreg(x0, rho, x, y): """ Proximal operator for Poisson regression Computes the proximal operator of the negative log-likelihood loss assumping a Poisson noise distribution. Parameters ---------- x0 : array_like The starting or initial point used in the proximal update step rho : float Momentum parameter for the proximal step (larger value -> stays closer to x0) x : (n, k) array_like A design matrix consisting of n examples of k-dimensional features (or input). y : (n,) array_like A vector containing the responses (outupt) to the n features given in x. Returns ------- theta : array_like The parameter vector found after running the proximal update step """ # objective and gradient n = float(x.shape[0]) f = lambda w: np.mean(np.exp(x.dot(w)) - y * x.dot(w)) df = lambda w: (x.T.dot(np.exp(x.dot(w))) - x.T.dot(y)) / n # minimize via BFGS return bfgs(x0, rho, f, df) 

Example #28

Source File: estimation.pyFrom pylogitwith BSD 3-Clause "New" or "Revised" License5votesPython Examples of scipy.optimize.minimize (56)Python Examples of scipy.optimize.minimize (57)
def calc_neg_log_likelihood_and_neg_gradient(self, params): """ Calculates and returns the negative of the log-likelihood and the negative of the gradient. This function is used as the objective function in scipy.optimize.minimize. """ neg_log_likelihood = -1 * self.convenience_calc_log_likelihood(params) neg_gradient = -1 * self.convenience_calc_gradient(params) if self.constrained_pos is not None: neg_gradient[self.constrained_pos] = 0 return neg_log_likelihood, neg_gradient 

Example #29

Source File: bam_cov.pyFrom basenjiwith Apache License 2.05votesPython Examples of scipy.optimize.minimize (58)Python Examples of scipy.optimize.minimize (59)
def set_clips(self, coverage): """ Hash indexes to clip at various thresholds. Must run this before running clip_multi, which will use self.multi_clip_indexes. The objective is to estimate coverage conservatively w/ clip_max and smoothing before asking whether the raw coverage count is compelling. In: coverage (np.array): Pre-clipped genome coverage. Out: self.adaptive_t (int->float): Clip values mapped to coverage thresholds above which to apply them. self.multi_clip_indexes (int->np.array): Clip values mapped to genomic indexes to clip. """ # choose clip thresholds if len(self.adaptive_t) == 0: for clip_value in range(2, self.clip_max + 1): # aiming for .01 cumulative density above the threshold. # decreasing the density increases the thresholds. cdf_matcher = lambda u: (self.adaptive_cdf - (1-poisson.cdf(clip_value, u)))**2 self.adaptive_t[clip_value] = minimize(cdf_matcher, clip_value)['x'][0] # take indexes with coverage between this clip threshold and the next self.multi_clip_indexes = {} for clip_value in range(2, self.clip_max): mci = np.where((coverage > self.adaptive_t[clip_value]) & (coverage <= self.adaptive_t[clip_value + 1]))[0] if len(mci) > 0: self.multi_clip_indexes[clip_value] = mci print('Sites clipped to %d: %d' % (clip_value, len(mci))) # set the last clip_value mci = np.where(coverage > self.adaptive_t[self.clip_max])[0] if len(mci) > 0: self.multi_clip_indexes[self.clip_max] = mci print('Sites clipped to %d: %d' % (self.clip_max, len(mci))) 

Example #30

Source File: density_weighted_uncertainty_sampling.pyFrom libactwith BSD 2-Clause "Simplified" License5votesPython Examples of scipy.optimize.minimize (60)Python Examples of scipy.optimize.minimize (61)
(Video) Solve Optimization Problems in Python Using SciPy minimize() Function
def train(self, X, y): d = np.shape(self.centers)[1] w = np.zeros((d+1, 1)) # TODO Use more sophistic optimization methods result = minimize(lambda _w: self._likelihood(_w, X, y), w.reshape(-1), method='CG') w = result.x.reshape(-1, 1) self.w_ = w 

FAQs

What does SciPy optimize minimize? ›

SciPy optimize provides functions for minimizing (or maximizing) objective functions, possibly subject to constraints. It includes solvers for nonlinear problems (with support for both local and global optimization algorithms), linear programing, constrained and nonlinear least-squares, root finding, and curve fitting.

What is the purpose of SciPy in Python? ›

SciPy is a scientific computation library that uses NumPy underneath. SciPy stands for Scientific Python. It provides more utility functions for optimization, stats and signal processing. Like NumPy, SciPy is open source so we can use it freely.

How do you optimize search in Python? ›

We can perform a line search manually in Python using the line_search() function. It supports univariate optimization, as well as multivariate optimization problems.

Is SciPy optimize multithreaded? ›

NumPy/SciPy's functions are usually optimized for multithreading. Did you look at your CPU utilization to confirm that only one core is being used while the simulation is being ran? Otherwise you have nothing to gain from running multiple instances.

How do you minimize an objective function? ›

To minimize the objective function, we find the vertices of the feasibility region. These vertices are (0, 24), (8, 12), (15, 5) and (25, 0). To minimize cholesterol, we will substitute these points in the objective function to see which point gives us the smallest value.

What are the five steps in solving optimization problems? ›

Five Steps to Solve Optimization Problems

It is: visualize the problem, define the problem, write an equation for it, find the minimum or maximum for the problem (usually the derivatives or end-points) and answer the question.

What are the steps in solving optimization problems? ›

To solve an optimization problem, begin by drawing a picture and introducing variables. Find an equation relating the variables. Find a function of one variable to describe the quantity that is to be minimized or maximized. Look for critical points to locate local extrema.

What is optimization explain with example? ›

: an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible. specifically : the mathematical procedures (such as finding the maximum of a function) involved in this.

What is code optimization with example? ›

Code optimization is a program modification strategy that endeavours to enhance the intermediate code, so a program utilises the least potential memory, minimises its CPU time and offers high speed.

What are the key features in SciPy? ›

SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Where is SciPy used in Python? ›

SciPy is an open-source Python library which is used to solve scientific and mathematical problems. It is built on the NumPy extension and allows the user to manipulate and visualize data with a wide range of high-level commands.

Why multithreading is not good in Python? ›

Python doesn't support multi-threading because Python on the Cpython interpreter does not support true multi-core execution via multithreading. However, Python does have a threading library. The GIL does not prevent threading.

Is SciPy faster than NumPy? ›

NumPy is written in C and so has a faster computational speed. SciPy is written in Python and so has a slower execution speed but vast functionality.

How many threads can Python handle? ›

Generally, Python only uses one thread to execute the set of written statements. This means that in python only one thread will be executed at a time.

How do you overcome time complexity in Python? ›

You can easily omit declaration of perfect squares, count and total_length, as they aren't needed, as explained further. This will reduce both Time and Space complexities of your code. Also, you can use Fast IO, in order to speed up INPUTS and OUTPUTS This is done by using 'stdin. readline', and 'stdout.

How do you make Python wait for 1 minute? ›

If you've got a Python program and you want to make it wait, you can use a simple function like this one: time. sleep(x) where x is the number of seconds that you want your program to wait.

How do you sleep 2 minutes in Python? ›

Adding a Python sleep() Call With time.sleep()

The time module has a function sleep() that you can use to suspend execution of the calling thread for however many seconds you specify. If you run this code in your console, then you should experience a delay before you can enter a new statement in the REPL.

How do you minimize a function with two variables? ›

We want to minimize L, which is a function of 2 variables. To do this, we solve ∂L/∂x = 0 and ∂L/∂y = 0.

What are the Minimize options? ›

In all versions of Windows, you can minimize windows only using the keyboard by pressing Alt + spacebar . And in the window properties drop-down menu, press N to minimize the window.

What is minimize Optimizer? ›

Processing gradients before applying them

Calling minimize() takes care of both computing the gradients and applying them to the variables. If you want to process the gradients before applying them you can instead use the optimizer in three steps: Compute the gradients with tf. GradientTape .

What does it mean to minimize a function? ›

Minimizing a function means finding the value of variable ( say x ) for which the function (say f ) has minimum value .

What does enable optimizations do? ›

The Optimize option enables or disables optimizations performed by the compiler to make your output file smaller, faster, and more efficient. The Optimize option is enabled by default for a Release build configuration. It is off by default for a Debug build configuration.

What does -- Enable optimizations do? ›

Enable Optimization (-O)

Enables maximum performance at run time by carrying out the minimum run-time checks.

How do you minimize a function in Python? ›

To minimize the function we can use "scipy. optimize. minimize" function and further there are some methods we can use to minimize the function. Build a Chatbot in Python from Scratch!

When should I use optimizers? ›

If you have a site with shading, or simply want a more efficient system, then power optimizers are a great option for you. If you have a south facing roof, with low chance of cloudy days or shading, the additional cost may be unwarranted (although, this is mandatory with a SolarEdge inverter).

Which Optimizer is best? ›

Adam is the best optimizers. If one wants to train the neural network in less time and more efficiently than Adam is the optimizer. For sparse data use the optimizers with dynamic learning rate.

What is an example of minimizing? ›

Example Sentences

The company will work to minimize costs. I don't want to minimize the contributions he has made to the company. During the interview, she minimized her weaknesses and emphasized her strengths. Please minimize all open windows.

How do you minimize two variables? ›

We want to minimize L, which is a function of 2 variables. To do this, we solve ∂L/∂x = 0 and ∂L/∂y = 0.

What does enable optimizations do Python? ›

If you want a release build with all optimizations active (LTO, PGO, etc), please run ./configure --enable-optimizations . what does --enable-optimizations do? This used to add about 30 minutes to a compile of CPython, but as of Python3. 8, it now runs a small subset of the regression tests for profiling.

What kind of optimizations do compilers do? ›

Compiler optimization is generally implemented using a sequence of optimizing transformations, algorithms which take a program and transform it to produce a semantically equivalent output program that uses fewer resources or executes faster.

Should I use network optimization? ›

Network optimization is critical for the end-user experience, cutting down business costs, and improving employee productivity. For instance, when the network has latency, a website or online application can take a longer amount of time to load.

What is configure in Python? ›

The Python Configuration can be used to build a customized Python which behaves as the regular Python. For example, environment variables and command line arguments are used to configure Python. The Isolated Configuration can be used to embed Python into an application. It isolates Python from the system.

How to compile Python code? ›

You can also automatically compile all Python files using the compileall module. You can do it from the shell prompt by running compileall.py and providing the path of the directory containing the Python files to compile: monty@python:~/python$ python -m compileall .

Videos

1. SciPy Beginner's Guide for Optimization
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2. Unconstrained Optimization using SciPy.optimize.minimize package Part I
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3. lec08a - minimization using scipy.optimize
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4. Python Optimization Example Snowball Rolling with Scipy Minimize
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5. Python Scipy Optimization Example: Constrained Box Volume
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6. Solving Constrained Optimization problems with SciPy.optimize
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