本文整理匯總了Python中numpy.zeroes方法的典型用法代碼示例。如果您正苦於以下問題:Python numpy.zeroes方法的具體用法?Python numpy.zeroes怎麽用?Python numpy.zeroes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.zeroes方法的5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: batch_update
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import zeroes [as 別名]
def batch_update(self, mini_batch, eta, n, regularization=L2):
""" Update the network's weights and biases by applying gradient
descent using backpropagation to a single mini batch. """
nabla_b = [np.zeroes(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
for x, y in mini_batch:
delta_nabla_b, delta_nabla_w = self.back_propogation(x, y)
nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
self.biases = [b-(eta/len(mini_batch))*nb for b, nb in zip(self.biases, nabla_b)]
if regularization == L2:
self.weights = [(1-eta*(self.l2/n))*w-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]
elif regularization == L1:
self.weights = [w - eta*self.l1*np.sign(w)/n-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]
示例2: _make_test_folds
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import zeroes [as 別名]
def _make_test_folds(self, X, y=None):
rng = check_random_state(self.random_state)
y = np.asarray(y)
type_of_target_y = type_of_target(y)
allowed_target_types = ('binary', 'multiclass')
if type_of_target_y not in allowed_target_types:
raise ValueError(
'Supported target types are: {}. Got {!r} instead.'.format(
allowed_target_types, type_of_target_y))
y = column_or_1d(y)
n_samples = y.shape[0]
unique_y, y_inversed = np.unique(y, return_inverse=True)
y_counts = np.bincount(y_inversed)
min_groups = np.min(y_counts)
if np.all(self.n_splits > y_counts):
raise ValueError("n_splits=%d cannot be greater than the"
" number of members in each class."
% (self.n_splits))
if self.n_splits > min_groups:
warnings.warn(("The least populated class in y has only %d"
" members, which is too few. The minimum"
" number of members in any class cannot"
" be less than n_splits=%d."
% (min_groups, self.n_splits)), Warning)
# pre-assign each sample to a test fold index using individual KFold
# splitting strategies for each class so as to respect the balance of
# classes
# NOTE: Passing the data corresponding to ith class say X[y==class_i]
# will break when the data is not 100% stratifiable for all classes.
# So we pass np.zeroes(max(c, n_splits)) as data to the KFold
per_cls_cvs = [
KFold(self.n_splits, shuffle=self.shuffle,
random_state=rng).split(np.zeros(max(count, self.n_splits)))
for count in y_counts]
test_folds = np.zeros(n_samples, dtype=np.int)
for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
for cls, (_, test_split) in zip(unique_y, per_cls_splits):
cls_test_folds = test_folds[y == cls]
# the test split can be too big because we used
# KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
# stratifiable for all the classes
# (we use a warning instead of raising an exception)
# If this is the case, let's trim it:
test_split = test_split[test_split < len(cls_test_folds)]
cls_test_folds[test_split] = test_fold_indices
test_folds[y == cls] = cls_test_folds
return test_folds
示例3: _make_test_folds
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import zeroes [as 別名]
def _make_test_folds(self, X, y=None):
rng = self.random_state
y = np.asarray(y)
n_samples = y.shape[0]
unique_y, y_inversed = np.unique(y, return_inverse=True)
y_counts = np.bincount(y_inversed)
min_groups = np.min(y_counts)
if np.all(self.n_splits > y_counts):
raise ValueError("n_splits=%d cannot be greater than the"
" number of members in each class."
% (self.n_splits))
if self.n_splits > min_groups:
warnings.warn(("The least populated class in y has only %d"
" members, which is too few. The minimum"
" number of members in any class cannot"
" be less than n_splits=%d."
% (min_groups, self.n_splits)), Warning)
# pre-assign each sample to a test fold index using individual KFold
# splitting strategies for each class so as to respect the balance of
# classes
# NOTE: Passing the data corresponding to ith class say X[y==class_i]
# will break when the data is not 100% stratifiable for all classes.
# So we pass np.zeroes(max(c, n_splits)) as data to the KFold
per_cls_cvs = [
KFold(self.n_splits, shuffle=self.shuffle,
random_state=rng).split(np.zeros(max(count, self.n_splits)))
for count in y_counts]
test_folds = np.zeros(n_samples, dtype=np.int)
for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
for cls, (_, test_split) in zip(unique_y, per_cls_splits):
cls_test_folds = test_folds[y == cls]
# the test split can be too big because we used
# KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
# stratifiable for all the classes
# (we use a warning instead of raising an exception)
# If this is the case, let's trim it:
test_split = test_split[test_split < len(cls_test_folds)]
cls_test_folds[test_split] = test_fold_indices
test_folds[y == cls] = cls_test_folds
return test_folds
示例4: zscore
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import zeroes [as 別名]
def zscore(a, axis=0, ddof=0):
""" A static method to return the normalised version of series.
This mirrors the scipy implementation
with a small difference - rather than allowing /0, the function
returns output = np.zeroes(len(input)).
This is to allow for sensible processing of candidate
shapelets/comparison subseries that are a straight
line. Original version:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats
.zscore.html
Parameters
----------
a : array_like
An array like object containing the sample data.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array a.
ddof : int, optional
Degrees of freedom correction in the calculation of the standard
deviation. Default is 0.
Returns
-------
zscore : array_like
The z-scores, standardized by mean and standard deviation of
input array a.
"""
zscored = np.empty(a.shape)
for i, j in enumerate(a):
# j = np.asanyarray(j)
sstd = j.std(axis=axis, ddof=ddof)
# special case - if shapelet is a straight line (i.e. no
# variance), zscore ver should be np.zeros(len(a))
if sstd == 0:
zscored[i] = np.zeros(len(j))
else:
mns = j.mean(axis=axis)
if axis and mns.ndim < j.ndim:
zscored[i] = ((j - np.expand_dims(mns, axis=axis)) /
np.expand_dims(sstd, axis=axis))
else:
zscored[i] = (j - mns) / sstd
return zscored
示例5: _make_test_folds
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import zeroes [as 別名]
def _make_test_folds(self, X, y=None):
rng = self.random_state
y = np.asarray(y)
type_of_target_y = type_of_target(y)
allowed_target_types = ('binary', 'multiclass')
if type_of_target_y not in allowed_target_types:
raise ValueError(
'Supported target types are: {}. Got {!r} instead.'.format(
allowed_target_types, type_of_target_y))
y = column_or_1d(y)
n_samples = y.shape[0]
unique_y, y_inversed = np.unique(y, return_inverse=True)
y_counts = np.bincount(y_inversed)
min_groups = np.min(y_counts)
if np.all(self.n_splits > y_counts):
raise ValueError("n_splits=%d cannot be greater than the"
" number of members in each class."
% (self.n_splits))
if self.n_splits > min_groups:
warnings.warn(("The least populated class in y has only %d"
" members, which is too few. The minimum"
" number of members in any class cannot"
" be less than n_splits=%d."
% (min_groups, self.n_splits)), Warning)
# pre-assign each sample to a test fold index using individual KFold
# splitting strategies for each class so as to respect the balance of
# classes
# NOTE: Passing the data corresponding to ith class say X[y==class_i]
# will break when the data is not 100% stratifiable for all classes.
# So we pass np.zeroes(max(c, n_splits)) as data to the KFold
per_cls_cvs = [
KFold(self.n_splits, shuffle=self.shuffle,
random_state=rng).split(np.zeros(max(count, self.n_splits)))
for count in y_counts]
test_folds = np.zeros(n_samples, dtype=np.int)
for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
for cls, (_, test_split) in zip(unique_y, per_cls_splits):
cls_test_folds = test_folds[y == cls]
# the test split can be too big because we used
# KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
# stratifiable for all the classes
# (we use a warning instead of raising an exception)
# If this is the case, let's trim it:
test_split = test_split[test_split < len(cls_test_folds)]
cls_test_folds[test_split] = test_fold_indices
test_folds[y == cls] = cls_test_folds
return test_folds