本文整理匯總了Python中numpy.float_power方法的典型用法代碼示例。如果您正苦於以下問題:Python numpy.float_power方法的具體用法?Python numpy.float_power怎麽用?Python numpy.float_power使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy
的用法示例。
在下文中一共展示了numpy.float_power方法的11個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _draw_samples
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _draw_samples(self, size, random_state):
seed = random_state.randint(0, 10**6, 1)[0]
samples = self.other_param.draw_samples(size, random_state=ia.new_random_state(seed))
elementwise = self.elementwise and not isinstance(self.val, Deterministic)
if elementwise:
exponents = self.val.draw_samples(size, random_state=ia.new_random_state(seed+1))
else:
exponents = self.val.draw_sample(random_state=ia.new_random_state(seed+1))
# without this we get int results in the case of
# Power(<int>, <stochastic float param>)
samples, exponents = both_np_float_if_one_is_float(samples, exponents)
samples_dtype = samples.dtype
# float_power requires numpy>=1.12
#result = np.float_power(samples, exponents)
# TODO why was float32 type here replaced with complex number
# formulation?
result = np.power(samples.astype(np.complex), exponents).real
if result.dtype != samples_dtype:
result = result.astype(samples_dtype)
return result
示例2: estimate
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def estimate(self, query, logged_ranking, new_ranking, logged_value):
exactMatch=numpy.absolute(new_ranking-logged_ranking).sum() == 0
currentValue=0.0
if exactMatch:
numAllowedDocs=self.loggingPolicy.dataset.docsPerQuery[query]
validDocs=logged_ranking.size
invPropensity=None
if self.loggingPolicy.allowRepetitions:
invPropensity=numpy.float_power(numAllowedDocs, validDocs)
else:
invPropensity=numpy.prod(range(numAllowedDocs+1-validDocs, numAllowedDocs+1), dtype=numpy.float64)
currentValue=logged_value*invPropensity
self.updateRunningAverage(currentValue)
return self.runningMean
示例3: pick_move
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def pick_move(self, game, side):
possible_moves = game.possible_moves(side)
if len(possible_moves) == 0:
possible_moves.append((-1,-1))
monte_prob = self.monte_carlo(game, side)
if self.train:
self.temp_state.append((self.preprocess_input(game.board, side), np.divide(monte_prob, np.sum(monte_prob))))
monte_prob = np.float_power(monte_prob, 1/self.tau)
monte_prob = np.divide(monte_prob, np.sum(monte_prob))
r = random()
for i, move in enumerate(possible_moves):
r -= monte_prob[Othello.move_id(move)]
if r <= 0:
return move
return possible_moves[-1]
示例4: plot_saliency
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def plot_saliency(raw_img, image_var, img_embedding_var, caption_var):
dis = (caption_var.squeeze() * img_embedding_var.squeeze()).sum()
dis.backward(retain_graph=True)
grad = image_var.grad.data.cpu().squeeze().numpy().transpose((1, 2, 0))
grad = normalize_grad(grad, stat=True)
grad = imresize((grad * 255).astype('uint8'), (raw_img.height, raw_img.width)) / 255
grad = normalize_grad(grad.mean(axis=-1, keepdims=True).repeat(3, axis=-1))
grad = np.float_power(grad, args.grad_power)
np_img = np.array(raw_img)
masked_img = np_img * grad
final = np.hstack([np_img, masked_img.astype('uint8'), (grad * 255).astype('uint8')])
return Image.fromarray(final.astype('uint8'))
示例5: _fractal_dfa_fluctuation
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _fractal_dfa_fluctuation(segments, trends, multifractal=False, q=2):
detrended = segments - trends
if multifractal is True:
var = np.var(detrended, axis=1)
fluctuation = np.float_power(np.mean(np.float_power(var, q / 2), axis=1) / 2, 1 / q.T)
fluctuation = np.mean(fluctuation) # Average over qs (not sure of that!)
else:
# Compute Root Mean Square (RMS)
fluctuation = np.sum(detrended ** 2, axis=1) / detrended.shape[1]
fluctuation = np.sqrt(np.sum(fluctuation) / len(fluctuation))
return fluctuation
示例6: _fractal_mfdfa_q
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _fractal_mfdfa_q(q=2):
# TODO: Add log calculator for q ≈ 0
# Fractal powers as floats
q = np.asarray_chkfinite(q, dtype=np.float)
# Ensure q≈0 is removed, since it does not converge. Limit set at |q| < 0.1
q = q[(q < -0.1) + (q > 0.1)]
# Reshape q to perform np.float_power
q = q.reshape(-1, 1)
return q
示例7: test_type_conversion
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def test_type_conversion(self):
arg_type = '?bhilBHILefdgFDG'
res_type = 'ddddddddddddgDDG'
for dtin, dtout in zip(arg_type, res_type):
msg = "dtin: %s, dtout: %s" % (dtin, dtout)
arg = np.ones(1, dtype=dtin)
res = np.float_power(arg, arg)
assert_(res.dtype.name == np.dtype(dtout).name, msg)
示例8: _argcheck
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _argcheck(self, h, k):
condlist = [np.logical_and(h > 0, k > 0),
np.logical_and(h > 0, k == 0),
np.logical_and(h > 0, k < 0),
np.logical_and(h <= 0, k > 0),
np.logical_and(h <= 0, k == 0),
np.logical_and(h <= 0, k < 0)]
def f0(h, k):
return (1.0 - float_power(h, -k))/k
def f1(h, k):
return np.log(h)
def f3(h, k):
a = np.empty(np.shape(h))
a[:] = -np.inf
return a
def f5(h, k):
return 1.0/k
self.a = _lazyselect(condlist,
[f0, f1, f0, f3, f3, f5],
[h, k],
default=np.nan)
def f0(h, k):
return 1.0/k
def f1(h, k):
a = np.empty(np.shape(h))
a[:] = np.inf
return a
self.b = _lazyselect(condlist,
[f0, f1, f1, f0, f1, f1],
[h, k],
default=np.nan)
return h == h
示例9: float_power
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def float_power(x1, x2):
return power(x1, x2)
示例10: _draw_samples
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _draw_samples(self, size, random_state):
rngs = random_state.duplicate(2)
samples = self.other_param.draw_samples(size, random_state=rngs[0])
elementwise = (
self.elementwise
and not isinstance(self.val, Deterministic))
if elementwise:
exponents = self.val.draw_samples(size, random_state=rngs[1])
else:
exponents = self.val.draw_sample(random_state=rngs[1])
# without this we get int results in the case of
# Power(<int>, <stochastic float param>)
samples, exponents = both_np_float_if_one_is_float(samples, exponents)
samples_dtype = samples.dtype
# TODO switch to this as numpy>=1.15 is now a requirement
# float_power requires numpy>=1.12
# result = np.float_power(samples, exponents)
# TODO why was float32 type here replaced with complex number
# formulation?
result = np.power(samples.astype(np.complex), exponents).real
if result.dtype != samples_dtype:
result = result.astype(samples_dtype)
return result
示例11: _draw_samples
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import float_power [as 別名]
def _draw_samples(self, size, random_state):
seed = random_state.randint(0, 10**6, 1)[0]
samples = self.other_param.draw_samples(
size,
random_state=eu.new_random_state(seed))
elementwise = self.elementwise and not isinstance(self.val, Deterministic)
if elementwise:
exponents = self.val.draw_samples(
size,
random_state=eu.new_random_state(seed+1))
else:
exponents = self.val.draw_sample(
random_state=eu.new_random_state(seed+1))
# without this we get int results in the case of
# Power(<int>, <stochastic float param>)
samples, exponents = both_np_float_if_one_is_float(samples, exponents)
samples_dtype = samples.dtype
# float_power requires numpy>=1.12
#result = np.float_power(samples, exponents)
# TODO why was float32 type here replaced with complex number
# formulation?
result = np.power(samples.astype(np.complex), exponents).real
if result.dtype != samples_dtype:
result = result.astype(samples_dtype)
return result