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本文整理汇总了Python中numpy.random.randint方法的典型用法代码示例。如果您正苦于以下问题：Python random.randint方法的具体用法？Python random.randint怎么用？Python random.randint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模块numpy.random的用法示例。
在下文中一共展示了random.randint方法的24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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: random_select
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def random_select(img_scales):
“””Randomly select an img_scale from given candidates.
Args:
img_scales (list[tuple]): Images scales for selection.
Returns:
(tuple, int): Returns a tuple “(img_scale, scale_dix)“,
where “img_scale“ is the selected image scale and
“scale_idx“ is the selected index in the given candidates.
“””
assert mmcv.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx
开发者ID:open-mmlab，项目名称:mmdetection，代码行数:18，
示例2: random_sample
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def random_sample(img_scales):
“””Randomly sample an img_scale when “multiscale_mode==’range’“.
Args:
img_scales (list[tuple]): Images scale range for sampling.
There must be two tuples in img_scales, which specify the lower
and uper bound of image scales.
Returns:
(tuple, None): Returns a tuple “(img_scale, None)“, where
“img_scale“ is sampled scale and None is just a placeholder
to be consistent with :func:`random_select`.
“””
assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(
min(img_scale_long),
max(img_scale_long) 1)
short_edge = np.random.randint(
min(img_scale_short),
max(img_scale_short) 1)
img_scale = (long_edge, short_edge)
return img_scale, None
开发者ID:open-mmlab，项目名称:mmdetection，代码行数:27，
示例3: sample
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def sample(self, batch_size):
“””
computes (x_t,u_t,x_{t 1}) pair
returns tuple of 3 ndarrays with shape
(batch,x_dim), (batch, u_dim), (batch, x_dim)
“””
if not self.initialized:
raise ValueError(“Dataset not loaded – call PlaneData.initialize() first.”)
traj=randint(0,num_t,size=batch_size) # which trajectory
tt=randint(0,T-1,size=batch_size) # time step t for each batch
X0=np.zeros((batch_size,x_dim))
U0=np.zeros((batch_size,u_dim),dtype=np.int)
X1=np.zeros((batch_size,x_dim))
for i in range(batch_size):
t=tt[i]
p=self.P[traj[i], t, :]
X0[i,:]=self.getX(traj[i],t)
X1[i,:]=self.getX(traj[i],t 1)
U0[i,:]=self.U[traj[i], t, :]
return (X0,U0,X1)
开发者ID:ericjang，项目名称:e2c，代码行数:22，
示例4: test_count_nonzero_axis_consistent
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_count_nonzero_axis_consistent(self):
# Check that the axis behaviour for valid axes in
# non-special cases is consistent (and therefore
# correct) by checking it against an integer array
# that is then casted to the generic object dtype
from itertools import combinations, permutations
axis = (0, 1, 2, 3)
size = (5, 5, 5, 5)
msg = “Mismatch for axis: %s”
rng = np.random.RandomState(1234)
m = rng.randint(-100, 100, size=size)
n = m.astype(object)
for length in range(len(axis)):
for combo in combinations(axis, length):
for perm in permutations(combo):
assert_equal(
np.count_nonzero(m, axis=perm),
np.count_nonzero(n, axis=perm),
err_msg=msg % (perm,))
开发者ID:Frank-qlu，项目名称:recruit，代码行数:24，
示例5: test_frame_negate
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_frame_negate(self):
expr = self.ex(‘-‘)
# float
lhs = DataFrame(randn(5, 2))
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
# int
lhs = DataFrame(randint(5, size=(5, 2)))
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
# bool doesn’t work with numexpr but works elsewhere
lhs = DataFrame(rand(5, 2) > 0.5)
if self.engine == ‘numexpr’:
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_frame_equal(expect, result)
开发者ID:Frank-qlu，项目名称:recruit，代码行数:26，
示例6: test_series_negate
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_series_negate(self):
expr = self.ex(‘-‘)
# float
lhs = Series(randn(5))
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# int
lhs = Series(randint(5, size=5))
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# bool doesn’t work with numexpr but works elsewhere
lhs = Series(rand(5) > 0.5)
if self.engine == ‘numexpr’:
with pytest.raises(NotImplementedError):
result = pd.eval(expr, engine=self.engine, parser=self.parser)
else:
expect = -lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
开发者ID:Frank-qlu，项目名称:recruit，代码行数:26，
示例7: test_series_pos
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_series_pos(self):
expr = self.ex(‘ ’)
# float
lhs = Series(randn(5))
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# int
lhs = Series(randint(5, size=5))
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
# bool doesn’t work with numexpr but works elsewhere
lhs = Series(rand(5) > 0.5)
expect = lhs
result = pd.eval(expr, engine=self.engine, parser=self.parser)
assert_series_equal(expect, result)
开发者ID:Frank-qlu，项目名称:recruit，代码行数:22，
示例8: test_identity_module
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_identity_module(self):
“”” identity module should preserve input “””
with IsolatedSession() as issn:
pred_input = tf.placeholder(tf.float32, [None, None])
final_output = tf.identity(pred_input, name=’output’)
gfn = issn.asGraphFunction([pred_input], [final_output])
for _ in range(10):
m, n = prng.randint(10, 1000, size=2)
mat = prng.randn(m, n).astype(np.float32)
with IsolatedSession() as issn:
feeds, fetches = issn.importGraphFunction(gfn)
mat_out = issn.run(fetches[0], {feeds[0]: mat})
self.assertTrue(np.all(mat_out == mat))
开发者ID:databricks，项目名称:spark-deep-learning，代码行数:18，
示例9: _sample_indices
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def _sample_indices(self, record):
“””
:param record: VideoRecord
:return: list
“””
if self.dense_sample: # i3d dense sample
sample_pos = max(1, 1 record.num_frames – 64)
t_stride = 64 // self.num_segments
start_idx = 0 if sample_pos == 1 else np.random.randint(0, sample_pos – 1)
offsets = [(idx * t_stride start_idx) % record.num_frames for idx in range(self.num_segments)]
return np.array(offsets) 1
else: # normal sample
average_duration = (record.num_frames – self.new_length 1) // self.num_segments
if average_duration > 0:
offsets = np.multiply(list(range(self.num_segments)), average_duration) randint(average_duration,
size=self.num_segments)
elif record.num_frames > self.num_segments:
offsets = np.sort(randint(record.num_frames – self.new_length 1, size=self.num_segments))
else:
offsets = np.zeros((self.num_segments,))
return offsets 1
开发者ID:CMU-CREATE-Lab，项目名称:deep-smoke-machine，代码行数:24，
示例10: resample
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def resample(self, size=None):
“””
Randomly sample a dataset from the estimated pdf.
Parameters
———-
size : int, optional
The number of samples to draw. If not provided, then the size is
the same as the underlying dataset.
Returns
——-
resample : (self.d, `size`) ndarray
The sampled dataset.
“””
if size is None:
size = self.n
norm = transpose(multivariate_normal(zeros((self.d,), float),
self.covariance, size=size))
indices = randint(0, self.n, size=size)
means = self.dataset[:, indices]
return means norm
开发者ID:ryfeus，项目名称:lambda-packs，代码行数:27，
示例11: _get_glyph
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def _get_glyph(gnum, height, width, shift_prob, shift_size):
if isinstance(gnum, list):
n = randint(*gnum)
else:
n = gnum
glyph = random_points_in_circle(
n, 0, 0, 0.5
)*array((width, height), ‘float’)
_spatial_sort(glyph)
if random()
shift = ((-1)**randint(0,2))*shift_size*height
glyph[:,1] = shift
if random()<0.5:
ii = randint(0,n-1,size=(1))
xy = glyph[ii,:]
glyph = row_stack((glyph, xy))
return glyph
开发者ID:inconvergent，项目名称:sand-glyphs，代码行数:23，
示例12: rand_shape_2d
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def rand_shape_2d(dim0=10, dim1=10):
return rnd.randint(1, dim0 1), rnd.randint(1, dim1 1)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:4，
示例13: rand_shape_3d
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def rand_shape_3d(dim0=10, dim1=10, dim2=10):
return rnd.randint(1, dim0 1), rnd.randint(1, dim1 1), rnd.randint(1, dim2 1)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:4，
示例14: rand_shape_nd
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def rand_shape_nd(num_dim, dim=10):
return tuple(rnd.randint(1, dim 1, size=num_dim))
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:4，
示例15: test_sparse_nd_slice
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_sparse_nd_slice():
shape = (rnd.randint(2, 10), rnd.randint(2, 10))
stype = ‘csr’
A, _ = rand_sparse_ndarray(shape, stype)
A2 = A.asnumpy()
start = rnd.randint(0, shape[0] – 1)
end = rnd.randint(start 1, shape[0])
assert same(A[start:end].asnumpy(), A2[start:end])
assert same(A[start – shape[0]:end].asnumpy(), A2[start:end])
assert same(A[start:].asnumpy(), A2[start:])
assert same(A[:end].asnumpy(), A2[:end])
ind = rnd.randint(-shape[0], shape[0] – 1)
assert same(A[ind].asnumpy(), A2[ind][np.newaxis, :])
start_col = rnd.randint(0, shape[1] – 1)
end_col = rnd.randint(start_col 1, shape[1])
result = mx.nd.slice(A, begin=(start, start_col), end=(end, end_col))
result_dense = mx.nd.slice(mx.nd.array(A2), begin=(start, start_col), end=(end, end_col))
assert same(result_dense.asnumpy(), result.asnumpy())
A = mx.nd.sparse.zeros(‘csr’, shape)
A2 = A.asnumpy()
assert same(A[start:end].asnumpy(), A2[start:end])
result = mx.nd.slice(A, begin=(start, start_col), end=(end, end_col))
result_dense = mx.nd.slice(mx.nd.array(A2), begin=(start, start_col), end=(end, end_col))
assert same(result_dense.asnumpy(), result.asnumpy())
def check_slice_nd_csr_fallback(shape):
stype = ‘csr’
A, _ = rand_sparse_ndarray(shape, stype)
A2 = A.asnumpy()
start = rnd.randint(0, shape[0] – 1)
end = rnd.randint(start 1, shape[0])
# non-trivial step should fallback to dense slice op
result = mx.nd.sparse.slice(A, begin=(start,), end=(end 1,), step=(2,))
result_dense = mx.nd.slice(mx.nd.array(A2), begin=(start,), end=(end 1,), step=(2,))
assert same(result_dense.asnumpy(), result.asnumpy())
shape = (rnd.randint(2, 10), rnd.randint(1, 10))
check_slice_nd_csr_fallback(shape)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:43，
示例16: test_sparse_nd_concat
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_sparse_nd_concat():
def check_concat(arrays):
ret = np.concatenate([arr.asnumpy() for arr in arrays], axis=0)
same(mx.nd.concat(*arrays, dim=0).asnumpy(), ret)
nds = []
zero_nds = []
ncols = rnd.randint(2, 10)
for i in range(3):
shape = (rnd.randint(2, 10), ncols)
A, _ = rand_sparse_ndarray(shape, ‘csr’)
nds.append(A)
zero_nds.append(mx.nd.zeros(shape).tostype(‘csr’))
check_concat(nds)
check_concat(zero_nds)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:16，
示例17: test_sparse_nd_binary_scalar_op
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_sparse_nd_binary_scalar_op():
N = 3
def check(fn, stype, out_stype=None):
for _ in range(N):
ndim = 2
shape = np.random.randint(1, 6, size=(ndim,))
npy = np.random.normal(0, 1, size=shape)
nd = mx.nd.array(npy).tostype(stype)
if out_stype is not None:
assert(nd.stype == out_stype)
assert_allclose(fn(npy), fn(nd).asnumpy(), rtol=1e-4, atol=1e-4)
stypes = [‘row_sparse’, ‘csr’]
for stype in stypes:
check(lambda x: 1 x, stype)
check(lambda x: 1 – x, stype)
check(lambda x: 1 * x, stype)
check(lambda x: 1 / x, stype)
check(lambda x: 2 ** x, stype)
check(lambda x: 1 > x, stype)
check(lambda x: 0.5 > x, stype)
check(lambda x: 0.5 < x, stype)
check(lambda x: 0.5 >= x, stype)
check(lambda x: 0.5 <= x, stype)
check(lambda x: 0.5 == x, stype)
check(lambda x: x / 2, stype, out_stype=stype)
check(lambda x: x 0, stype, out_stype=stype)
check(lambda x: x – 0, stype, out_stype=stype)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:30，
示例18: test_sparse_nd_binary_iop
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_sparse_nd_binary_iop():
N = 3
def check_binary(fn, stype):
for _ in range(N):
ndim = 2
oshape = np.random.randint(1, 6, size=(ndim,))
lshape = list(oshape)
rshape = list(oshape)
lhs = np.random.uniform(0, 1, size=lshape)
rhs = np.random.uniform(0, 1, size=rshape)
lhs_nd = mx.nd.array(lhs).tostype(stype)
rhs_nd = mx.nd.array(rhs).tostype(stype)
assert_allclose(fn(lhs, rhs),
fn(lhs_nd, rhs_nd).asnumpy(),
rtol=1e-4, atol=1e-4)
def inplace_add(x, y):
x = y
return x
def inplace_mul(x, y):
x *= y
return x
stypes = [‘csr’, ‘row_sparse’]
fns = [inplace_add, inplace_mul]
for stype in stypes:
for fn in fns:
check_binary(fn, stype)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:29，
示例19: test_sparse_nd_save_load
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def test_sparse_nd_save_load():
repeat = 1
stypes = [‘default’, ‘row_sparse’, ‘csr’]
stype_dict = {‘default’: NDArray, ‘row_sparse’: RowSparseNDArray, ‘csr’: CSRNDArray}
num_data = 20
densities = [0, 0.5]
fname = ‘tmp_list.bin’
for _ in range(repeat):
data_list1 = []
for i in range(num_data):
stype = stypes[np.random.randint(0, len(stypes))]
shape = rand_shape_2d(dim0=40, dim1=40)
density = densities[np.random.randint(0, len(densities))]
data_list1.append(rand_ndarray(shape, stype, density))
assert isinstance(data_list1[-1], stype_dict[stype])
mx.nd.save(fname, data_list1)
data_list2 = mx.nd.load(fname)
assert len(data_list1) == len(data_list2)
for x, y in zip(data_list1, data_list2):
assert same(x.asnumpy(), y.asnumpy())
data_map1 = {‘ndarray xx %s’ % i: x for i, x in enumerate(data_list1)}
mx.nd.save(fname, data_map1)
data_map2 = mx.nd.load(fname)
assert len(data_map1) == len(data_map2)
for k, x in data_map1.items():
y = data_map2[k]
assert same(x.asnumpy(), y.asnumpy())
os.remove(fname)
开发者ID:awslabs，项目名称:dynamic-training-with-apache-mxnet-on-aws，代码行数:32，
示例20: get_minibatch
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def get_minibatch(roidb, num_classes):
“””Given a roidb, construct a minibatch sampled from it.”””
num_images = len(roidb)
# Sample random scales to use for each image in this batch
random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),
size=num_images)
assert(cfg.TRAIN.BATCH_SIZE % num_images == 0),
‘num_images ({}) must divide BATCH_SIZE ({})’.
format(num_images, cfg.TRAIN.BATCH_SIZE)
# Get the input image blob, formatted for caffe
im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)
blobs = {‘data’: im_blob}
assert len(im_scales) == 1, “Single batch only”
assert len(roidb) == 1, “Single batch only”
# gt boxes: (x1, y1, x2, y2, cls)
if cfg.TRAIN.USE_ALL_GT:
# Include all ground truth boxes
gt_inds = np.where(roidb[0][‘gt_classes’] != 0)[0]
else:
# For the COCO ground truth boxes, exclude the ones that are ”iscrowd”
gt_inds = np.where(roidb[0][‘gt_classes’] != 0 & np.all(roidb[0][‘gt_overlaps’].toarray() > -1.0, axis=1))[0]
gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)
gt_boxes[:, 0:4] = roidb[0][‘boxes’][gt_inds, :] * im_scales[0]
gt_boxes[:, 4] = roidb[0][‘gt_classes’][gt_inds]
blobs[‘gt_boxes’] = gt_boxes
blobs[‘im_info’] = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]],
dtype=np.float32)
blobs[‘img_id’] = roidb[0][‘img_id’]
return blobs
开发者ID:guoruoqian，项目名称:cascade-rcnn_Pytorch，代码行数:38，
示例21: _sample_indices
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def _sample_indices(self, record):
“””
:param record: VideoRecord
:return: list
“””
average_duration = (record.num_frames – self.new_length 1) // self.num_segments
if average_duration > 0:
offsets = np.multiply(list(range(self.num_segments)), average_duration) randint(average_duration, size=self.num_segments)
elif record.num_frames > self.num_segments:
offsets = np.sort(randint(record.num_frames – self.new_length 1, size=self.num_segments))
else:
offsets = np.zeros((self.num_segments,))
return offsets 1
开发者ID:yjxiong，项目名称:tsn-pytorch，代码行数:17，
示例22: __call__
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
image[:, :, 1] *= random.uniform(self.lower, self.upper)
return image, boxes, labels
开发者ID:soo89，项目名称:CSD-SSD，代码行数:7，
示例23: compute_traj
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def compute_traj(self, max_dist=1):
# computes P,U data for single trajectory
# all P,U share the same environment obstacles.png
P=np.zeros((T,2),dtype=np.int) # r,c position
U=np.zeros((T,u_dim),dtype=np.int)
P[0,:]=[rw,randint(rw,w-rw)] # initial location
for t in range(1,T):
p=np.copy(P[t-1,:])
# dr direction
d=randint(-1,2) # direction
nsteps=randint(max_dist 1)
dr=d*nsteps # applied control
for i in range(nsteps):
p[0] =d
if self.is_colliding(p):
p[0]-=d
break
# dc direction
d=randint(-1,2) # direction
nsteps=randint(max_dist 1)
dc=d*nsteps # applied control
for i in range(nsteps):
p[1] =d
if self.is_colliding(p):
p[1]-=d # step back
break
P[t,:]=p
U[t,:]=[dr,dc]
return P,U
开发者ID:ericjang，项目名称:e2c，代码行数:31，
示例24: __call__
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import randint [as 别名]
def __call__(self, img, boxes, labels):
if random.randint(2):
return img, boxes, labels
h, w, c = img.shape
ratio = random.uniform(self.min_ratio, self.max_ratio)
expand_img = np.full((int(h * ratio), int(w * ratio), c),
self.mean).astype(img.dtype)
left = int(random.uniform(0, w * ratio – w))
top = int(random.uniform(0, h * ratio – h))
expand_img[top:top h, left:left w] = img
img = expand_img
boxes = np.tile((left, top), 2)
return img, boxes, labels
开发者ID:dingjiansw101，项目名称:AerialDetection，代码行数:16，
注：本文中的numpy.random.randint方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。
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