本文整理汇总了Python中cupy.sqrt方法的典型用法代码示例。如果您正苦于以下问题:Python cupy.sqrt方法的具体用法?Python cupy.sqrt怎么用?Python cupy.sqrt使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cupy
的用法示例。
在下文中一共展示了cupy.sqrt方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: standard_deviation
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def standard_deviation(input, labels=None, index=None):
"""Calculates the standard deviation of the values of an n-D image array,
optionally at specified sub-regions.
Args:
input (cupy.ndarray): Nd-image data to process.
labels (cupy.ndarray or None): Labels defining sub-regions in `input`.
If not None, must be same shape as `input`.
index (cupy.ndarray or None): `labels` to include in output. If None
(default), all values where `labels` is non-zero are used.
Returns:
standard_deviation (cupy.ndarray): standard deviation of values, for
each sub-region if `labels` and `index` are specified.
.. seealso:: :func:`scipy.ndimage.standard_deviation`
"""
return cupy.sqrt(variance(input, labels, index))
示例2: test_elementwise_trinary
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def test_elementwise_trinary(self):
desc_a = cutensor.create_tensor_descriptor(self.a, ct.OP_SQRT)
desc_b = cutensor.create_tensor_descriptor(self.b, ct.OP_TANH)
desc_c = cutensor.create_tensor_descriptor(self.c, ct.OP_COS)
d = cutensor.elementwise_trinary(
self.alpha, self.a, desc_a, self.mode_a,
self.beta, self.b, desc_b, self.mode_b,
self.gamma, self.c, desc_c, self.mode_c,
op_AB=ct.OP_ADD, op_ABC=ct.OP_MUL
)
testing.assert_allclose(
(self.alpha * cupy.sqrt(self.a_transposed) +
self.beta * cupy.tanh(self.b_transposed)) *
self.gamma * cupy.cos(self.c),
d,
rtol=1e-6, atol=1e-6
)
示例3: hfft
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def hfft(a, n=None, axis=-1, norm=None):
"""Compute the FFT of a signal that has Hermitian symmetry.
Args:
a (cupy.ndarray): Array to be transform.
n (None or int): Length of the transformed axis of the output. For
``n`` output points, ``n//2+1`` input points are necessary. If
``n`` is not given, it is determined from the length of the input
along the axis specified by ``axis``.
axis (int): Axis over which to compute the FFT.
norm (None or ``"ortho"``): Keyword to specify the normalization mode.
Returns:
cupy.ndarray:
The transformed array which shape is specified by ``n`` and type
will convert to complex if the input is other. If ``n`` is not
given, the length of the transformed axis is ``2*(m-1)`` where `m`
is the length of the transformed axis of the input.
.. seealso:: :func:`numpy.fft.hfft`
"""
a = irfft(a.conj(), n, axis)
return a * (a.shape[axis] if norm is None else
cupy.sqrt(a.shape[axis], dtype=a.dtype))
示例4: extract_features
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def extract_features(vgg16, images, masks=None):
mean = cp.array([103.939, 116.779, 123.68], 'float32') # BGR
images = images[:, ::-1] * 255 - mean[None, :, None, None]
features = vgg16(images, layers=['conv1_2', 'conv2_2', 'conv3_3', 'conv4_3']).values()
if masks is None:
masks = cp.ones((images.shape[0], images.shape[2], images.shape[3]), 'float32')
else:
masks = masks.data
style_features = []
for f in features:
scale = masks.shape[-1] / f.shape[-1]
m = cf.average_pooling_2d(masks[:, None, :, :], scale, scale).data
dim = f.shape[1]
m = m.reshape((m.shape[0], -1))
f2 = f.transpose((0, 2, 3, 1))
f2 = f2.reshape((f2.shape[0], -1, f2.shape[-1]))
f2 *= cp.sqrt(m)[:, :, None]
f2 = cf.batch_matmul(f2.transpose((0, 2, 1)), f2)
f2 /= dim * m.sum(axis=1)[:, None, None]
style_features.append(f2)
return style_features
示例5: corrcoef
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def corrcoef(a, y=None, rowvar=True, bias=None, ddof=None):
"""Returns the Pearson product-moment correlation coefficients of an array.
Args:
a (cupy.ndarray): Array to compute the Pearson product-moment
correlation coefficients.
y (cupy.ndarray): An additional set of variables and observations.
rowvar (bool): If ``True``, then each row represents a variable, with
observations in the columns. Otherwise, the relationship is
transposed.
bias (None): Has no effect, do not use.
ddof (None): Has no effect, do not use.
Returns:
cupy.ndarray: The Pearson product-moment correlation coefficients of
the input array.
.. seealso:: :func:`numpy.corrcoef`
"""
if bias is not None or ddof is not None:
warnings.warn('bias and ddof have no effect and are deprecated',
DeprecationWarning)
out = cov(a, y, rowvar)
try:
d = cupy.diag(out)
except ValueError:
return out / out
stddev = cupy.sqrt(d.real)
out /= stddev[:, None]
out /= stddev[None, :]
cupy.clip(out.real, -1, 1, out=out.real)
if cupy.iscomplexobj(out):
cupy.clip(out.imag, -1, 1, out=out.imag)
return out
示例6: __init__
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def __init__(self, alpha, fl=400, fftl=512):
super(SpectralLoss, self).__init__()
self.alpha = alpha[:,fl//2-1:-fl//2]
self.fl = fl
self.fftl = fftl
self.norm = cupy.sqrt(self.fftl, dtype=cupy.float32)
示例7: test_22
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def test_22(self):
N = 32
M = 4
Nd = 8
D = cp.random.randn(Nd, Nd, M)
D /= cp.sqrt(cp.sum(D**2, axis=(0, 1)))
X0 = cp.zeros((N, N, M))
xr = cp.random.randn(N, N, M)
xp = cp.abs(xr) > 3
X0[xp] = cp.random.randn(X0[xp].size)
S = cp.sum(fftconv(D, X0), axis=2)
lmbda = 1e-3
opt = cbpdn.ConvBPDN.Options(
{'Verbose': False, 'MaxMainIter': 500, 'RelStopTol': 1e-5,
'rho': 5e-1, 'AutoRho': {'Enabled': False}})
bp = cbpdn.ConvBPDN(D, S, lmbda, opt)
Xp = bp.solve()
epsilon = cp.linalg.norm(bp.reconstruct(Xp).squeeze() - S)
opt = cbpdn.ConvMinL1InL2Ball.Options(
{'Verbose': False, 'MaxMainIter': 500, 'RelStopTol': 1e-5,
'rho': 2e2, 'RelaxParam': 1.0, 'AutoRho': {'Enabled': False}})
bc = cbpdn.ConvMinL1InL2Ball(D, S, epsilon=epsilon, opt=opt)
Xc = bc.solve()
assert cp.linalg.norm(Xp - Xc) / cp.linalg.norm(Xp) < 1e-3
assert cp.abs(cp.linalg.norm(Xp.ravel(), 1) -
cp.linalg.norm(Xc.ravel(), 1)) < 1e-3
示例8: root_mean_square_err
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def root_mean_square_err(expect, correct):
return cupy.sqrt(mean_squared_error(expect, correct))
# Matrix operations
示例9: _exec_fft
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def _exec_fft(a, direction, value_type, norm, axis, overwrite_x,
out_size=None, out=None, plan=None):
fft_type = _convert_fft_type(a.dtype, value_type)
if axis % a.ndim != a.ndim - 1:
a = a.swapaxes(axis, -1)
if a.base is not None or not a.flags.c_contiguous:
a = a.copy()
if out_size is None:
out_size = a.shape[-1]
batch = a.size // a.shape[-1]
curr_plan = cufft.get_current_plan()
if curr_plan is not None:
if plan is None:
plan = curr_plan
else:
raise RuntimeError('Use the cuFFT plan either as a context manager'
' or as an argument.')
if plan is None:
devices = None if not config.use_multi_gpus else config._devices
plan = cufft.Plan1d(out_size, fft_type, batch, devices=devices)
else:
# check plan validity
if not isinstance(plan, cufft.Plan1d):
raise ValueError('expected plan to have type cufft.Plan1d')
if fft_type != plan.fft_type:
raise ValueError('cuFFT plan dtype mismatch.')
if out_size != plan.nx:
raise ValueError('Target array size does not match the plan.',
out_size, plan.nx)
if batch != plan.batch:
raise ValueError('Batch size does not match the plan.')
if config.use_multi_gpus != plan._use_multi_gpus:
raise ValueError('Unclear if multiple GPUs are to be used or not.')
if overwrite_x and value_type == 'C2C':
out = a
elif out is not None:
# verify that out has the expected shape and dtype
plan.check_output_array(a, out)
else:
out = plan.get_output_array(a)
plan.fft(a, out, direction)
sz = out.shape[-1]
if fft_type == cufft.CUFFT_R2C or fft_type == cufft.CUFFT_D2Z:
sz = a.shape[-1]
if norm is None:
if direction == cufft.CUFFT_INVERSE:
out /= sz
else:
out /= math.sqrt(sz)
if axis % a.ndim != a.ndim - 1:
out = out.swapaxes(axis, -1)
return out
示例10: mass2_gpu
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def mass2_gpu(ts, query):
"""
Compute the distance profile for the given query over the given time
series. This require cupy to be installed.
Parameters
----------
ts : array_like
The array to create a rolling window on.
query : array_like
The query.
Returns
-------
An array of distances.
Raises
------
ValueError
If ts is not a list or np.array.
If query is not a list or np.array.
If ts or query is not one dimensional.
"""
def moving_mean_std_gpu(a, w):
s = cp.concatenate([cp.array([0]), cp.cumsum(a)])
sSq = cp.concatenate([cp.array([0]), cp.cumsum(a ** 2)])
segSum = s[w:] - s[:-w]
segSumSq = sSq[w:] -sSq[:-w]
movmean = segSum / w
movstd = cp.sqrt(segSumSq / w - (segSum / w) ** 2)
return (movmean, movstd)
x = cp.asarray(ts)
y = cp.asarray(query)
n = x.size
m = y.size
meany = cp.mean(y)
sigmay = cp.std(y)
meanx, sigmax = moving_mean_std_gpu(x, m)
meanx = cp.concatenate([cp.ones(n - meanx.size), meanx])
sigmax = cp.concatenate([cp.zeros(n - sigmax.size), sigmax])
y = cp.concatenate((cp.flip(y, axis=0), cp.zeros(n - m)))
X = cp.fft.fft(x)
Y = cp.fft.fft(y)
Z = X * Y
z = cp.fft.ifft(Z)
dist = 2 * (m - (z[m - 1:n] - m * meanx[m - 1:n] * meany) /
(sigmax[m - 1:n] * sigmay))
dist = cp.sqrt(dist)
return cp.asnumpy(dist)
示例11: mass2
# 需要导入模块: import cupy [as 别名]
# 或者: from cupy import sqrt [as 别名]
def mass2(ts, query):
"""
Compute the distance profile for the given query over the given time
series. Optionally, the correlation coefficient can be returned.
Parameters
----------
ts : array_like
The array to create a rolling window on.
query : array_like
The query.
Returns
-------
An array of distances.
Raises
------
ValueError
If ts is not a list or np.array.
If query is not a list or np.array.
If ts or query is not one dimensional.
"""
ts, query = mtscore.precheck_series_and_query(ts, query)
n = len(ts)
m = len(query)
x = ts
y = query
meany = np.mean(y)
sigmay = np.std(y)
meanx = mtscore.moving_average(x, m)
meanx = np.append(np.ones([1, len(x) - len(meanx)]), meanx)
sigmax = mtscore.moving_std(x, m)
sigmax = np.append(np.zeros([1, len(x) - len(sigmax)]), sigmax)
y = np.append(np.flip(y), np.zeros([1, n - m]))
X = np.fft.fft(x)
Y = np.fft.fft(y)
Y.resize(X.shape)
Z = X * Y
z = np.fft.ifft(Z)
dist = 2 * (m - (z[m - 1:n] - m * meanx[m - 1:n] * meany) /
(sigmax[m - 1:n] * sigmay))
dist = np.sqrt(dist)
return dist