Here are the examples of the python api numpy.random.randint taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
9085 Examples
5
Source : test_binaural.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_drr():
for _ in range(10):
steepness = numpy.random.randint(1, 3)
duration = numpy.random.uniform(2.0, 8.0)
decay_resolution = 10
decay_curve = [numpy.exp(-i * steepness) for i in range(decay_resolution)]
sound = slab.Binaural.whitenoise(kind='dichotic', duration=duration, samplerate=44100)
impulse = sound.envelope(apply_envelope=decay_curve)
winlength = numpy.random.uniform(0.0001, 0.01)
if numpy.random.randint(0, 2):
winlength = max(2, impulse.in_samples(winlength, impulse.samplerate))
if numpy.random.randint(0, 2):
drr = impulse.drr(winlength=winlength)
else:
drr = impulse.drr()
assert 0 > drr > -100
assert isinstance(drr, float)
5
Source : test_psychoacoustics.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_sequence_from_trials():
for i in range(100): # generate from integers
start = numpy.random.randint(1, 100)
stop = start+numpy.random.randint(1, 10)
trials = [numpy.random.randint(start, stop) for i in range(100)]
sequence = slab.Trialsequence(trials=trials)
assert all(numpy.unique(sequence.trials) == numpy.array(range(1, sequence.n_conditions+1)))
trials = ["a", "x", "x", "z", "a", "a", "a", "z"]
sequence = slab.Trialsequence(trials=trials)
assert all(numpy.unique(sequence.trials) == numpy.array(range(1, sequence.n_conditions + 1)))
sounds = [slab.Sound.pinknoise(), slab.Sound.whitenoise()]
trials = [random.choice(sounds) for i in range(50)]
sequence = slab.Trialsequence(trials=trials)
assert all(numpy.unique(sequence.trials) == numpy.array(range(1, sequence.n_conditions + 1)))
def test_sequence():
5
Source : test_signal.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_signal_generation():
# (numpy.ndarray | object | list)
for _ in range(100):
n_samples = numpy.random.randint(100, 10000)
n_channels = numpy.random.randint(1, 10)
samplerate = numpy.random.randint(10, 1000)
sig = slab.Signal(numpy.random.randn(n_samples, n_channels), samplerate)
assert sig.n_channels == n_channels
assert sig.n_samples == n_samples
assert sig.samplerate == samplerate
assert len(sig.times) == len(sig.data)
numpy.testing.assert_almost_equal(sig.times.max()*samplerate, n_samples, decimal=-1)
def test_arithmetics():
5
Source : test_signal.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_arithmetics():
for _ in range(100):
n_samples = numpy.random.randint(100, 10000)
n_channels = numpy.random.randint(1, 10)
samplerate = numpy.random.randint(10, 1000)
sig = slab.Signal(numpy.random.randn(n_samples, n_channels), samplerate=samplerate)
numpy.testing.assert_equal((sig+sig).data, (sig*2).data)
numpy.testing.assert_equal((sig-sig).data, numpy.zeros([n_samples, n_channels]))
numpy.testing.assert_equal((sig*sig).data, sig**2)
def test_samplerate():
5
Source : test_signal.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_channels():
for _ in range(100):
n_samples = numpy.random.randint(100, 10000)
n_channels = numpy.random.randint(1, 10)
samplerate = numpy.random.randint(10, 1000)
sig = slab.Signal(numpy.random.randn(n_samples, n_channels), samplerate=samplerate)
for i, ch in enumerate(sig.channels()):
numpy.testing.assert_equal(sig.channel(i).data, ch.data)
def test_resize():
5
Source : test_signal.py
with MIT License
from DrMarc
with MIT License
from DrMarc
def test_trim():
for _ in range(100):
n_samples = numpy.random.randint(100, 10000)
n_channels = numpy.random.randint(1, 10)
samplerate = numpy.random.randint(10, 1000)
sig = slab.Signal(numpy.random.randn(n_samples, n_channels), samplerate=samplerate)
start, stop = numpy.sort(numpy.random.randint(0, n_samples+1, 2))
if start == stop:
start -= 1
if numpy.random.rand() < 0.5:
trimmed = sig.trim(start=start, stop=stop)
else:
trimmed = sig.trim(start=start/samplerate, stop=stop/samplerate)
assert numpy.abs(trimmed.n_samples - (stop-start)) < = 1
with pytest.raises(ValueError): # testing start not preceding stop case
trimmed = sig.trim(start=stop, stop=start)
def test_resample():
3
Source : room_agent.py
with MIT License
from 011235813
with MIT License
from 011235813
def reset(self, randomize=False):
if randomize:
self.position = np.random.randint(3)
else:
self.position = 1
self.total_given = np.zeros(self.n_agents - 1)
3
Source : room_symmetric_centralized.py
with MIT License
from 011235813
with MIT License
from 011235813
def reset(self):
self.solved = False
self.state = (np.random.randint(3, size=self._n_agents) if
self.randomize else np.ones(self._n_agents, dtype=int))
self.steps = 0
obs = self.get_obs()
return [obs]
3
Source : facenet.py
with MIT License
from 1024210879
with MIT License
from 1024210879
def crop(image, random_crop, image_size):
if image.shape[1]>image_size:
sz1 = int(image.shape[1]//2)
sz2 = int(image_size//2)
if random_crop:
diff = sz1-sz2
(h, v) = (np.random.randint(-diff, diff+1), np.random.randint(-diff, diff+1))
else:
(h, v) = (0,0)
image = image[(sz1-sz2+v):(sz1+sz2+v),(sz1-sz2+h):(sz1+sz2+h),:]
return image
def flip(image, random_flip):
3
Source : 生成数据并增强.py
with Apache License 2.0
from 1044197988
with Apache License 2.0
from 1044197988
def add_noise(img):
for i in range(200): #添加点噪声
temp_x = np.random.randint(0,img.shape[0])
temp_y = np.random.randint(0,img.shape[1])
img[temp_x][temp_y] = 255
return img
#添加数据
def data_augment(xb,yb):
3
Source : data_augment.py
with Apache License 2.0
from 1adrianb
with Apache License 2.0
from 1adrianb
def _apply_blended(self, img, mixing_weights, m):
# This is my first crack and implementing a slightly faster mixed augmentation. Instead
# of accumulating the mix for each chain in a Numpy array and then blending with original,
# it recomputes the blending coefficients and applies one PIL image blend per chain.
# TODO the results appear in the right ballpark but they differ by more than rounding.
img_orig = img.copy()
ws = self._calc_blended_weights(mixing_weights, m)
for w in ws:
depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
ops = np.random.choice(self.ops, depth, replace=True)
img_aug = img_orig # no ops are in-place, deep copy not necessary
for op in ops:
img_aug = op(img_aug)
img = Image.blend(img, img_aug, w)
return img
def _apply_basic(self, img, mixing_weights, m):
3
Source : transforms.py
with MIT License
from 2han9x1a0release
with MIT License
from 2han9x1a0release
def random_crop(im, size, pad_size=0):
"""Performs random crop (CHW format)."""
if pad_size > 0:
im = zero_pad(im=im, pad_size=pad_size)
h, w = im.shape[1:]
y = np.random.randint(0, h - size)
x = np.random.randint(0, w - size)
im_crop = im[:, y : (y + size), x : (x + size)]
assert im_crop.shape[1:] == (size, size)
return im_crop
def scale(size, im):
3
Source : addnoise.py
with MIT License
from 3dperceptionlab
with MIT License
from 3dperceptionlab
def __call__(self, sample):
ff_noise_ = np.random.randint(-self.ff_noise, high=self.ff_noise, size=sample.x[:, 0].shape)
mf_noise_ = np.random.randint(-self.mf_noise, high=self.mf_noise, size=sample.x[:, 1].shape)
th_noise_ = np.random.randint(-self.th_noise, high=self.th_noise, size=sample.x[:, 2].shape)
noise_ = np.array([ff_noise_, mf_noise_, th_noise_]).T
augmented_batch_ = deepcopy(sample)
augmented_batch_.x = augmented_batch_.x + torch.from_numpy(noise_).type(torch.FloatTensor)
return augmented_batch_
def __repr__(self):
3
Source : test_PlotDataItem.py
with MIT License
from 3fon3fonov
with MIT License
from 3fon3fonov
def test_bool():
truths = np.random.randint(0, 2, size=(100,)).astype(bool)
pdi = pg.PlotDataItem(truths)
bounds = pdi.dataBounds(1)
assert isinstance(bounds[0], np.uint8)
assert isinstance(bounds[1], np.uint8)
xdata, ydata = pdi.getData()
assert ydata.dtype == np.uint8
def test_fft():
3
Source : train.py
with Apache License 2.0
from 5agado
with Apache License 2.0
from 5agado
def get_training_data(images, batch_size, config, warp_mult_factor=1):
warped_images = []
target_images = []
indexes = np.random.randint(len(images), size=batch_size)
for index in indexes:
image = images[index]
image = FaceGenerator.random_transform(image, **config['random_transform'])
warped_img, target_img = FaceGenerator.random_warp(image, mult_f=warp_mult_factor)
warped_images.append(warped_img)
target_images.append(target_img)
return np.array(warped_images), np.array(target_images)
def main(_):
3
Source : imagenet.py
with GNU General Public License v3.0
from 82magnolia
with GNU General Public License v3.0
from 82magnolia
def random_shift_events(event_tensor, max_shift=20, resolution=(224, 224)):
H, W = resolution
x_shift, y_shift = np.random.randint(-max_shift, max_shift + 1, size=(2,))
event_tensor[:, 0] += x_shift
event_tensor[:, 1] += y_shift
valid_events = (event_tensor[:, 0] >= 0) & (event_tensor[:, 0] < W) & (event_tensor[:, 1] >= 0) & (event_tensor[:, 1] < H)
event_tensor = event_tensor[valid_events]
return event_tensor
def random_flip_events_along_x(event_tensor, resolution=(224, 224), p=0.5):
3
Source : square_crop.py
with MIT License
from 921kiyo
with MIT License
from 921kiyo
def random_rot(image):
angles = [0, 90, 180, 270]
i = np.random.randint(0,3)
angle = angles[i]
return rotate(image, angle)
for (dirpath,_,filenames) in os.walk(src_folder):
3
Source : transformations.py
with Apache License 2.0
from 94mia
with Apache License 2.0
from 94mia
def __call__(self, sample):
image, label = sample['image'], sample['label']
h, w = image.shape[:2]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[top: top + new_h, left: left + new_w, :]
label = label[top: top + new_h, left: left + new_w]
sample['image'], sample['label'] = image, label
return sample
class RandomResizedCrop(object):
3
Source : strided_window_data.py
with MIT License
from 95616ARG
with MIT License
from 95616ARG
def test_out_shape():
"""Tests that the out_* methods work correctly.
"""
in_shape = (32, 64, np.random.randint(1, 128))
out_channels = np.random.randint(1, 128)
window_shape = (4, 2)
strides = (3, 1)
pad = (1, 3)
window_data = StridedWindowData(in_shape, window_shape, strides,
pad, out_channels)
# After padding, height is 34.
# [0 - 4), [3 - 7), [6 - 10), ..., [30 - 34)
assert window_data.out_height() == 11
# After padding, width is 70.
# [0 - 2), [1 - 3), [2 - 4), ..., [68 - 70)
assert window_data.out_width() == 69
assert window_data.out_shape() == (11, 69, out_channels)
def test_serialize():
3
Source : Evaluation.py
with MIT License
from aaronworry
with MIT License
from aaronworry
def initial():
k = np.random.randint(0, 3)
if k == 0:
s = env.initialUp()
elif k == 1:
s = env.initialDown()
else:
s = env.initialOn()
return s
def evalWithHardCode():
3
Source : trainAndEvalDDPG.py
with MIT License
from aaronworry
with MIT License
from aaronworry
def initial():
k = np.random.randint(0, 2)
if k == 0:
s = env.initialOn()
else:
s = env.initialDown()
return s
def train():
3
Source : trainAndEvalStepUp.py
with MIT License
from aaronworry
with MIT License
from aaronworry
def initial():
k = np.random.randint(0, 2)
if k == 0:
s = env.initialOn()
else:
s = env.initialUp()
return s
def train():
3
Source : trainQ.py
with MIT License
from aaronworry
with MIT License
from aaronworry
def initial():
tt = np.random.randint(0, 3)
if tt == 0:
s = env.initialUp()
elif tt == 1:
s = env.initialDown()
else:
s = env.initialOn()
return s
def train():
3
Source : ant_soft_actor_critic.py
with MIT License
from abbyvansoest
with MIT License
from abbyvansoest
def sample_batch(self, batch_size=32):
idxs = np.random.randint(0, self.size, size=batch_size)
return dict(obs1=self.obs1_buf[idxs],
obs2=self.obs2_buf[idxs],
acts=self.acts_buf[idxs],
rews=self.rews_buf[idxs],
done=self.done_buf[idxs])
"""
3
Source : utils.py
with MIT License
from abduallahmohamed
with MIT License
from abduallahmohamed
def data_generator(T, mem_length, b_size):
"""
Generate data for the copying memory task
:param T: The total blank time length
:param mem_length: The length of the memory to be recalled
:param b_size: The batch size
:return: Input and target data tensor
"""
seq = torch.from_numpy(np.random.randint(1, 9, size=(b_size, mem_length))).float()
zeros = torch.zeros((b_size, T))
marker = 9 * torch.ones((b_size, mem_length + 1))
placeholders = torch.zeros((b_size, mem_length))
x = torch.cat((seq, zeros[:, :-1], marker), 1)
y = torch.cat((placeholders, zeros, seq), 1).long()
x, y = Variable(x), Variable(y)
return x, y
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels=None):
if random.randint(2):
image[:, :, 1] *= random.uniform(self.lower, self.upper)
return image, labels
class RandomHue(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels=None):
if random.randint(2):
image[:, :, 0] += random.uniform(-self.delta, self.delta)
image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
return image, labels
class SwapChannels(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels=None):
if random.randint(2):
swap = self.perms[random.randint(len(self.perms))]
shuffle = SwapChannels(swap) # shuffle channels
image = shuffle(image)
return image, labels
class ConvertColor(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels=None):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
image *= alpha
return image, labels
class RandomBrightness(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels=None):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
image += delta
return image, labels
class ToCV2Image(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels):
im = image.copy()
im, labels = self.rand_brightness(im, labels)
if random.randint(2):
distort = Compose(self.pd[:-1])
else:
distort = Compose(self.pd[1:])
im, labels = distort(im, labels)
return self.rand_light_noise(im, labels)
class SubtractMeans(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, labels):
_, width, _ = image.shape
if random.randint(2):
image = image[:, ::-1]
labels = labels[:, ::-1]
return image, labels
class SegNetAugmentation(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
image[:, :, 1] *= random.uniform(self.lower, self.upper)
return image, boxes, labels
class RandomHue(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
image[:, :, 0] += random.uniform(-self.delta, self.delta)
image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
return image, boxes, labels
class RandomLightingNoise(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
swap = self.perms[random.randint(len(self.perms))]
shuffle = SwapChannels(swap) # shuffle channels
image = shuffle(image)
return image, boxes, labels
class ConvertColor(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
image *= alpha
return image, boxes, labels
class RandomBrightness(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
image += delta
return image, boxes, labels
class ToCV2Image(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes, classes):
_, width, _ = image.shape
if random.randint(2):
image = image[:, ::-1]
boxes = boxes.copy()
boxes[:, 0::2] = width - boxes[:, 2::-2]
return image, boxes, classes
class SwapChannels(object):
3
Source : augmentations.py
with MIT License
from abeja-inc
with MIT License
from abeja-inc
def __call__(self, image, boxes, labels):
im = image.copy()
im, boxes, labels = self.rand_brightness(im, boxes, labels)
if random.randint(2):
distort = Compose(self.pd[:-1])
else:
distort = Compose(self.pd[1:])
im, boxes, labels = distort(im, boxes, labels)
return self.rand_light_noise(im, boxes, labels)
class SSDAugmentation(object):
3
Source : contrib.py
with GNU General Public License v3.0
from acamero
with GNU General Public License v3.0
from acamero
def next_solution(self):
solution = op.Solution(self.targets,
['architecture'])
num_layers = np.random.randint(low=self.min_layers,
high=(self.max_layers + 1))
layers = np.random.randint(low=self.min_neurons,
high=(self.max_neurons + 1),
size=num_layers)
look_back = np.random.randint(low=self.min_look_back,
high=(self.max_look_back + 1),
size=1)
solution.set_encoded('architecture',
np.concatenate((look_back, layers)).tolist())
return solution
def validate_solution(self,
3
Source : ea.py
with GNU General Public License v3.0
from acamero
with GNU General Public License v3.0
from acamero
def binaryTournament(population):
""" Binary tournament. Selects two solutions from the population
and compare them. Returns the fittest one.
"""
positions = np.random.randint(low=0,
high=len(population),
size=2)
if population[positions[0]].comparedTo(
population[positions[1]]) > 0:
return deepcopy(population[positions[0]])
else:
return deepcopy(population[positions[1]])
def elitistPlusReplacement(population,
3
Source : algorithms.py
with GNU General Public License v3.0
from acamero
with GNU General Public License v3.0
from acamero
def _init_individual(self, clazz):
solution = list()
# First, we define the architecture (how many layers and neurons per layer)
ranges = [(self.config.min_neurons, self.config.max_neurons+1)] * np.random.randint(self.config.min_layers, high=self.config.max_layers+1)
layers = [self.layer_in] + [np.random.randint(*p) for p in ranges] + [self.layer_out]
solution.append(layers)
# Then, the look back
solution.append( np.random.randint(self.config.min_look_back, self.config.max_look_back+1 ) )
solution.extend( self._generate_weights(layers) )
return clazz(solution)
def _validate_individual(self, individual):
3
Source : algorithms.py
with GNU General Public License v3.0
from acamero
with GNU General Public License v3.0
from acamero
def _init_individual(self, clazz):
# range [min,max)
ranges = [(0,100), (1, self.config.max_look_back+1)]
ranges = ranges + [(1, self.config.max_neurons+1)] * np.random.randint(1, high=self.config.max_layers+1)
return clazz(np.random.randint(*p) for p in ranges)
def _validate_individual(self, individual):
3
Source : test_image.py
with Apache License 2.0
from Accenture
with Apache License 2.0
from Accenture
def run_around_tests(tmp_path):
"""Create a temp image for test"""
rand_img = np.random.randint(0, 255, (3, 3, 3), dtype='uint8')
Image.fromarray(rand_img).save(os.path.join(tmp_path, _STUB_IMG_FNAME))
yield
def test_read_image_bgr(tmp_path):
3
Source : encoding.py
with MIT License
from ace19-dev
with MIT License
from ace19-dev
def encoding_op(func, inp, grad, name=None, victim_op='IdentityN'):
# Need to generate a unique name to avoid duplicates.
rnd_name = 'my_gradient' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradient_override_map({victim_op: rnd_name}):
return func(inp, name=name)
def encoding(X, C, S):
3
Source : multipie_3d.py
with Apache License 2.0
from achao2013
with Apache License 2.0
from achao2013
def __call__(self, image):
h, w = image.shape[:2]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
cropped_image = image[top:top+new_h, left:left+new_w]
return cropped_image, [left,top,left+new_w,top+new_h]
3
Source : transforms.py
with Apache License 2.0
from achao2013
with Apache License 2.0
from achao2013
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 deep3dmap.core.utils.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
@staticmethod
3
Source : transforms.py
with Apache License 2.0
from achao2013
with Apache License 2.0
from achao2013
def get_indexes(self, dataset):
"""Call function to collect indexes.
Args:
dataset (:obj:`MultiImageMixDataset`): The dataset.
Returns:
list: indexes.
"""
indexs = [random.randint(0, len(dataset)) for _ in range(3)]
return indexs
def _mosaic_transform(self, results):
3
Source : transforms.py
with Apache License 2.0
from achao2013
with Apache License 2.0
from achao2013
def get_indexes(self, dataset):
"""Call function to collect indexes.
Args:
dataset (:obj:`MultiImageMixDataset`): The dataset.
Returns:
list: indexes.
"""
for i in range(self.max_iters):
index = random.randint(0, len(dataset))
gt_bboxes_i = dataset.get_ann_info(index)['bboxes']
if len(gt_bboxes_i) != 0:
break
return index
def _mixup_transform(self, results):
3
Source : test_utils.py
with Apache License 2.0
from achao2013
with Apache License 2.0
from achao2013
def test_flip_is_label(self):
# Generate the points
heatmaps = torch.from_numpy(np.random.randint(1, high=250, size=(68, 64, 64)).astype('float32'))
flipped_heatmaps = flip(flip(heatmaps.clone(), is_label=True), is_label=True)
assert np.allclose(heatmaps.numpy(), flipped_heatmaps.numpy())
def test_flip_is_image(self):
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