本文整理汇总了Python中joblib.Parallel.reshape方法的典型用法代码示例。如果您正苦于以下问题：Python Parallel.reshape方法的具体用法？Python Parallel.reshape怎么用？Python Parallel.reshape使用的例子？那么恭喜您, 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joblib.Parallel的用法示例。
在下文中一共展示了Parallel.reshape方法的2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: fit
# 需要导入模块: from joblib import Parallel [as 别名]
# 或者: from joblib.Parallel import reshape [as 别名]
def fit(self, modality, ground_truth=None, cat=None):
"""Compute the images images.
Parameters
----------
modality : object of type TemporalModality
The modality object of interest.
ground-truth : object of type GTModality or None
The ground-truth of GTModality. If None, the whole data will be
considered.
cat : str or None
String corresponding at the ground-truth of interest. Cannot be
None if ground-truth is not None.
Return
------
self : object
Return self.
"""
super(HaralickExtraction, self).fit(modality=modality,
ground_truth=ground_truth,
cat=cat)
# Get the data and rescale as integers within the given levels
vol_haralick = ((modality.data_ - np.ndarray.min(modality.data_)) *
((self.levels -1) /
(np.ndarray.max(modality.data_) -
np.ndarray.min(modality.data_)))).astype(int)
# Extract the set of patches from the modality data
patches = extract_patches(vol_haralick, patch_shape=self.patch_size)
# Allocate the haralick maps, one for each feature that
# will be computed
nb_directions = 13
nb_features = 13
self.data_ = np.zeros((modality.data_.shape[0],
modality.data_.shape[1],
modality.data_.shape[2],
nb_directions,
nb_features))
# WE NEED TO PARALLELIZE THIS CODE
# # Extract Haralick feature for each patch
# # Define the shift to apply
if isinstance(self.patch_size, tuple):
y_shift = int(np.ceil((self.patch_size[0] - 1) / 2.))
x_shift = int(np.ceil((self.patch_size[1] - 1) / 2.))
z_shift = int(np.ceil((self.patch_size[2] - 1) / 2.))
elif isinstance(self.patch_size, int):
y_shift = int(np.ceil((self.patch_size - 1) / 2.))
x_shift = int(np.ceil((self.patch_size - 1) / 2.))
z_shift = int(np.ceil((self.patch_size - 1) / 2.))
# for y in range(patches.shape[0]):
# for x in range(patches.shape[1]):
# for z in range(patches.shape[2]):
# print 'Compute for the pixel at position {}{}{}'.format(
# y, x, z)
# # Compute the haralick features
# self.data_[y + y_shift,
# x + x_shift,
# z + z_shift, :] = haralick(
# patches[y, x, z, :],
# distance=self.distance)
# Create the list of indices to process
yy, xx, zz = np.meshgrid(range(patches.shape[0]),
range(patches.shape[1]),
range(patches.shape[2]))
# Linearize for fast processing
yy = yy.reshape(-1)
xx = xx.reshape(-1)
zz = zz.reshape(-1)
# Go for the parallel loop
haralick_features = Parallel(n_jobs=-1)(delayed(
_compute_haralick_features)(patches[y, x, z, :], self.distance)
for y, x, z in zip(yy, xx, zz))
# Convert to numpy array
haralick_features = np.array(haralick_features)
# Reshape the feature matrix
haralick_features = haralick_features.reshape((patches.shape[0],
patches.shape[1],
patches.shape[2],
nb_directions,
nb_features))
# Copy the feature into the object
self.data_[y_shift : -y_shift,
x_shift : -x_shift,
z_shift : -z_shift] = haralick_features
return self示例2: fit
# 需要导入模块: from joblib import Parallel [as 别名]
# 或者: from joblib.Parallel import reshape [as 别名]
def fit(self, modality, ground_truth=None, cat=None):
"""Compute the images images.
Parameters
----------
modality : object of type TemporalModality
The modality object of interest.
ground-truth : object of type GTModality or None
The ground-truth of GTModality. If None, the whole data will be
considered.
cat : str or None
String corresponding at the ground-truth of interest. Cannot be
None if ground-truth is not None.
Return
------
self : object
Return self.
"""
super(DCTExtraction, self).fit(modality=modality,
ground_truth=ground_truth,
cat=cat)
# Extract the set of patches from the modality data
patches = extract_patches(modality.data_, patch_shape=self.patch_size)
# Allocate the DCT maps, one for each feature that
# will be computed
nb_features = np.prod(self.patch_size)
self.data_ = np.zeros((modality.data_.shape[0],
modality.data_.shape[1],
modality.data_.shape[2],
nb_features))
# # Extract DCT feature for each patch
# # Define the shift to apply
if isinstance(self.patch_size, tuple):
y_shift = int(np.ceil((self.patch_size[0] - 1) / 2.))
x_shift = int(np.ceil((self.patch_size[1] - 1) / 2.))
z_shift = int(np.ceil((self.patch_size[2] - 1) / 2.))
elif isinstance(self.patch_size, int):
y_shift = int(np.ceil((self.patch_size - 1) / 2.))
x_shift = int(np.ceil((self.patch_size - 1) / 2.))
z_shift = int(np.ceil((self.patch_size - 1) / 2.))
# Create the list of indices to process
yy, xx, zz = np.meshgrid(range(patches.shape[0]),
range(patches.shape[1]),
range(patches.shape[2]))
# Linearize for fast processing
yy = yy.reshape(-1)
xx = xx.reshape(-1)
zz = zz.reshape(-1)
# Go for the parallel loop
dct_features = Parallel(n_jobs=-1)(delayed(
_compute_dct_features)(patches[y, x, z, :])
for y, x, z in zip(yy, xx, zz))
# Convert to numpy array
dct_features = np.array(dct_features)
# Reshape the feature matrix
dct_features = dct_features.reshape((patches.shape[0],
patches.shape[1],
patches.shape[2],
nb_features))
# Copy the feature into the object
self.data_[y_shift : -y_shift,
x_shift : -x_shift,
z_shift : -z_shift] = dct_features
return self