Python numpy.fmax方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_reduce(self):
        dflt = np.typecodes['AllFloat']
        dint = np.typecodes['AllInteger']
        seq1 = np.arange(11)
        seq2 = seq1[::-1]
        func = np.fmax.reduce
        for dt in dint:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
        for dt in dflt:
            tmp1 = seq1.astype(dt)
            tmp2 = seq2.astype(dt)
            assert_equal(func(tmp1), 10)
            assert_equal(func(tmp2), 10)
            tmp1[::2] = np.nan
            tmp2[::2] = np.nan
            assert_equal(func(tmp1), 9)
            assert_equal(func(tmp2), 9) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def clip_to_window(boxlist, window):
  """Clip bounding boxes to a window.

  This op clips input bounding boxes (represented by bounding box
  corners) to a window, optionally filtering out boxes that do not
  overlap at all with the window.

  Args:
    boxlist: BoxList holding M_in boxes
    window: a numpy array of shape [4] representing the
            [y_min, x_min, y_max, x_max] window to which the op
            should clip boxes.

  Returns:
    a BoxList holding M_out boxes where M_out <= M_in
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  win_y_min = window[0]
  win_x_min = window[1]
  win_y_max = window[2]
  win_x_max = window[3]
  y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min)
  y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min)
  x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min)
  x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min)
  clipped = np_box_list.BoxList(
      np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped]))
  clipped = _copy_extra_fields(clipped, boxlist)
  areas = area(clipped)
  nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)),
                                    [-1]).astype(np.int32)
  return gather(clipped, nonzero_area_indices) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
        npa, npb = as_numpy(a), as_numpy(b)
        self.assertTrue(
                np.allclose(npa, npb, atol=atol),
                'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
        ) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def MTS_LS3v1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, phi=3, BONUS1=10, BONUS2=1, rnd=rand, **ukwargs):
	r"""Multiple trajectory local search three version one.

	Args:
		Xk (numpy.ndarray): Current solution.
		Xk_fit (float): Current solutions fitness/function value.
		Xb (numpy.ndarray): Global best solution.
		Xb_fit (float): Global best solutions fitness/function value.
		improve (bool): Has the solution been improved.
		SR (numpy.ndarray): Search range.
		task (Task): Optimization task.
		phi (int): Number of new generated positions.
		BONUS1 (int): Bonus reward for improving global best solution.
		BONUS2 (int): Bonus reward for improving solution.
		rnd (mtrand.RandomState): Random number generator.
		**ukwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray]:
			1. New solution.
			2. New solutions fitness/function value.
			3. Global best if found else old global best.
			4. Global bests function/fitness value.
			5. If solution has improved.
			6. Search range.
	"""
	grade, Disp = 0.0, task.bRange / 10
	while True in (Disp > 1e-3):
		Xn = apply_along_axis(task.repair, 1, asarray([rnd.permutation(Xk) + Disp * rnd.uniform(-1, 1, len(Xk)) for _ in range(phi)]), rnd)
		Xn_f = apply_along_axis(task.eval, 1, Xn)
		iBetter, iBetterBest = argwhere(Xn_f < Xk_fit), argwhere(Xn_f < Xb_fit)
		grade += len(iBetterBest) * BONUS1 + (len(iBetter) - len(iBetterBest)) * BONUS2
		if len(Xn_f[iBetterBest]) > 0:
			ib, improve = argmin(Xn_f[iBetterBest]), True
			Xb, Xb_fit, Xk, Xk_fit = Xn[iBetterBest][ib][0].copy(), Xn_f[iBetterBest][ib][0], Xn[iBetterBest][ib][0].copy(), Xn_f[iBetterBest][ib][0]
		elif len(Xn_f[iBetter]) > 0:
			ib, improve = argmin(Xn_f[iBetter]), True
			Xk, Xk_fit = Xn[iBetter][ib][0].copy(), Xn_f[iBetter][ib][0]
		Su, Sl = fmin(task.Upper, Xk + 2 * Disp), fmax(task.Lower, Xk - 2 * Disp)
		Disp = (Su - Sl) / 10
	return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def CovarianceMaatrixAdaptionEvolutionStrategyF(task, epsilon=1e-20, rnd=rand):
	lam, alpha_mu, hs, sigma0 = (4 + round(3 * log(task.D))) * 10, 2, 0, 0.3 * task.bcRange()
	mu = int(round(lam / 2))
	w = log(mu + 0.5) - log(range(1, mu + 1))
	w = w / sum(w)
	mueff = 1 / sum(w ** 2)
	cs = (mueff + 2) / (task.D + mueff + 5)
	ds = 1 + cs + 2 * max(sqrt((mueff - 1) / (task.D + 1)) - 1, 0)
	ENN = sqrt(task.D) * (1 - 1 / (4 * task.D) + 1 / (21 * task.D ** 2))
	cc, c1 = (4 + mueff / task.D) / (4 + task.D + 2 * mueff / task.D), 2 / ((task.D + 1.3) ** 2 + mueff)
	cmu, hth = min(1 - c1, alpha_mu * (mueff - 2 + 1 / mueff) / ((task.D + 2) ** 2 + alpha_mu * mueff / 2)), (1.4 + 2 / (task.D + 1)) * ENN
	ps, pc, C, sigma, M = full(task.D, 0.0), full(task.D, 0.0), eye(task.D), sigma0, full(task.D, 0.0)
	x = rnd.uniform(task.bcLower(), task.bcUpper())
	x_f = task.eval(x)
	while not task.stopCondI():
		pop_step = asarray([rnd.multivariate_normal(full(task.D, 0.0), C) for _ in range(int(lam))])
		pop = asarray([task.repair(x + sigma * ps, rnd) for ps in pop_step])
		pop_f = apply_along_axis(task.eval, 1, pop)
		isort = argsort(pop_f)
		pop, pop_f, pop_step = pop[isort[:mu]], pop_f[isort[:mu]], pop_step[isort[:mu]]
		if pop_f[0] < x_f: x, x_f = pop[0], pop_f[0]
		M = sum(w * pop_step.T, axis=1)
		ps = solve(chol(C).conj() + epsilon, ((1 - cs) * ps + sqrt(cs * (2 - cs) * mueff) * M + epsilon).T)[0].T
		sigma *= exp(cs / ds * (norm(ps) / ENN - 1)) ** 0.3
		ifix = where(sigma == inf)
		if any(ifix): sigma[ifix] = sigma0
		if norm(ps) / sqrt(1 - (1 - cs) ** (2 * (task.Iters + 1))) < hth: hs = 1
		else: hs = 0
		delta = (1 - hs) * cc * (2 - cc)
		pc = (1 - cc) * pc + hs * sqrt(cc * (2 - cc) * mueff) * M
		C = (1 - c1 - cmu) * C + c1 * (tile(pc, [len(pc), 1]) * tile(pc.reshape([len(pc), 1]), [1, len(pc)]) + delta * C)
		for i in range(mu): C += cmu * w[i] * tile(pop_step[i], [len(pop_step[i]), 1]) * tile(pop_step[i].reshape([len(pop_step[i]), 1]), [1, len(pop_step[i])])
		E, V = eig(C)
		if any(E < epsilon):
			E = fmax(E, 0)
			C = lstsq(V.T, dot(V, diag(E)).T, rcond=None)[0].T
	return x, x_f 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def MutationUros(pop, ic, mr, task, rnd=rand):
	r"""Mutation method made by Uros Mlakar.

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual.
		mr (float): Mutation rate.
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.

	Returns:
		numpy.ndarray: New genotype.
	"""
	return fmin(fmax(rnd.normal(pop[ic], mr * task.bRange), task.Lower), task.Upper) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def calcLuciferin(self, L, GS_f):
		r"""TODO.

		Args:
			L:
			GS_f:

		Returns:

		"""
		return fmax(0, (1 - self.rho) * L + self.gamma * GS_f) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import fmax [as 别名]
def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)