Python numpy.random() Examples

The following are 30 code examples of numpy.random(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function

Example #1

Source File: utils.py From contextualbandits with BSD 2-Clause "Simplified" License

6 votes

def partial_fit(self, X, y, classes=None):
        if self.partial_method == "gamma":
            w_all = -np.log(self
                            .random_state
                            .random(size=(X.shape[0], self.nsamples))
                            .clip(min=1e-12, max=None))
            appear_times = None
            rng = None
        elif self.partial_method == "poisson":
            w_all = None
            appear_times = self.random_state.poisson(1, size = (X.shape[0], self.nsamples))
            rng = np.arange(X.shape[0])
        else:
            raise ValueError(_unexpected_err_msg)
        Parallel(n_jobs=self.njobs, verbose=0, require="sharedmem")\
                (delayed(self._partial_fit_single)\
                    (sample, w_all, appear_times, rng, X, y) \
                        for sample in range(self.nsamples))

Example #2

Source File: viz.py From evolution-strategies-starter with MIT License

6 votes

def main(env_id, policy_file, record, stochastic, extra_kwargs):
    import gym
    from gym import wrappers
    import tensorflow as tf
    from es_distributed.policies import MujocoPolicy
    import numpy as np

    env = gym.make(env_id)
    if record:
        import uuid
        env = wrappers.Monitor(env, '/tmp/' + str(uuid.uuid4()), force=True)

    if extra_kwargs:
        import json
        extra_kwargs = json.loads(extra_kwargs)

    with tf.Session():
        pi = MujocoPolicy.Load(policy_file, extra_kwargs=extra_kwargs)
        while True:
            rews, t = pi.rollout(env, render=True, random_stream=np.random if stochastic else None)
            print('return={:.4f} len={}'.format(rews.sum(), t))

            if record:
                env.close()
                return

Example #3

Source File: myImageTransformations.py From Attention-Gated-Networks with MIT License

6 votes

def elastic_transform(image, alpha=1000, sigma=30, spline_order=1, mode='nearest', random_state=np.random):
    """Elastic deformation of image as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.
    """
    assert image.ndim == 3
    shape = image.shape[:2]

    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1),
                         sigma, mode="constant", cval=0) * alpha
    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1),
                         sigma, mode="constant", cval=0) * alpha

    x, y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')
    indices = [np.reshape(x + dx, (-1, 1)), np.reshape(y + dy, (-1, 1))]
    result = np.empty_like(image)
    for i in range(image.shape[2]):
        result[:, :, i] = map_coordinates(
            image[:, :, i], indices, order=spline_order, mode=mode).reshape(shape)
    return result

Example #4

Source File: test_common.py From recruit with Apache License 2.0

6 votes

def test_random_state():
    import numpy.random as npr
    # Check with seed
    state = com.random_state(5)
    assert state.uniform() == npr.RandomState(5).uniform()

    # Check with random state object
    state2 = npr.RandomState(10)
    assert com.random_state(state2).uniform() == npr.RandomState(10).uniform()

    # check with no arg random state
    assert com.random_state() is np.random

    # Error for floats or strings
    with pytest.raises(ValueError):
        com.random_state('test')

    with pytest.raises(ValueError):
        com.random_state(5.5)

Example #5

Source File: testing.py From recruit with Apache License 2.0

6 votes

def _create_missing_idx(nrows, ncols, density, random_state=None):
    if random_state is None:
        random_state = np.random
    else:
        random_state = np.random.RandomState(random_state)

    # below is cribbed from scipy.sparse
    size = int(np.round((1 - density) * nrows * ncols))
    # generate a few more to ensure unique values
    min_rows = 5
    fac = 1.02
    extra_size = min(size + min_rows, fac * size)

    def _gen_unique_rand(rng, _extra_size):
        ind = rng.rand(int(_extra_size))
        return np.unique(np.floor(ind * nrows * ncols))[:size]

    ind = _gen_unique_rand(random_state, extra_size)
    while ind.size < size:
        extra_size *= 1.05
        ind = _gen_unique_rand(random_state, extra_size)

    j = np.floor(ind * 1. / nrows).astype(int)
    i = (ind - j * nrows).astype(int)
    return i.tolist(), j.tolist()

Example #6

Source File: dataset.py From rankeval with Mozilla Public License 2.0

6 votes

def _check_random_state(seed):
        """
        Turn seed into a np.random.RandomState instance (took for sklearn)

        Parameters
        ----------
        seed : None | int | instance of RandomState
            If seed is None, return the RandomState singleton used by np.random.
            If seed is an int, return a new RandomState instance seeded with it.
            If seed is already a RandomState instance, return it.
            Otherwise raise ValueError.
        """
        if seed is None or seed is np.random:
            return np.random.mtrand._rand
        if isinstance(seed, (numbers.Integral, np.integer)):
            return np.random.RandomState(seed)
        if isinstance(seed, np.random.RandomState):
            return seed
        raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                         ' instance' % seed)

Example #7

Source File: utils.py From mars with Apache License 2.0

6 votes

def check_random_state(seed):
    """
    Turn seed into a mt.random.RandomState instance

    :param seed:
        If seed is None, return the RandomState singleton used by mt.random.
        If seed is an int, return a new RandomState instance seeded with seed.
        If seed is already a RandomState instance, return it.
        Otherwise raise ValueError.
    :return:
    """
    from . import random as mtrand
    from numpy import random as np_mtrand

    if seed is None or seed is mtrand or seed is np_mtrand:
        return mtrand._random_state
    if isinstance(seed, (Integral, np.integer)):
        return mtrand.RandomState(seed)
    if isinstance(seed, np.random.RandomState):
        return mtrand.RandomState.from_numpy(seed)
    if isinstance(seed, mtrand.RandomState):
        return seed
    raise ValueError('%r cannot be used to seed a mt.random.RandomState'
                     ' instance' % seed)

Example #8

Source File: Dataloader.py From Text_Segmentation_Image_Inpainting with GNU General Public License v3.0

6 votes

def __init__(self, image_folder, max_images=False, image_size=(512, 512), add_random_masks=False):
        super(ImageInpaintingData, self).__init__()

        if isinstance(image_folder, str):
            self.images = glob.glob(os.path.join(image_folder, "clean/*"))
        else:
            self.images = list(chain.from_iterable([glob.glob(os.path.join(i, "clean/*")) for i in image_folder]))
        assert len(self.images) > 0

        if max_images:
            self.images = random.choices(self.images, k=max_images)
        print(f"Find {len(self.images)} images.")

        self.img_size = image_size

        self.transformer = Compose([RandomGrayscale(p=0.4),
                                    # ColorJitter(brightness=0.2, contrast=0.2, saturation=0, hue=0),
                                    ToTensor(),
                                    # Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
                                    ])
        self.add_random_masks = add_random_masks

Example #9

Source File: Dataloader.py From Text_Segmentation_Image_Inpainting with GNU General Public License v3.0

6 votes

def random_masks(pil_img, size=512, offset=10):
    draw = ImageDraw.Draw(pil_img)
    # draw liens
    # can't use np.random because its not forkable under PyTorch's dataloader with multiprocessing
    reps = random.randint(1, 5)

    for i in range(reps):
        cords = np.array(random.choices(range(offset, size), k=4)).reshape(2, 2)
        cords[1] = np.clip(cords[1], a_min=cords[0] - 75, a_max=cords[0] + 75)

        width = random.randint(15, 20)
        draw.line(cords.reshape(-1).tolist(), width=width, fill=255)
    # # draw circles
    reps = random.randint(1, 5)
    for i in range(reps):
        cords = np.array(random.choices(range(offset, size - offset), k=2))
        cords.sort()
        ex = np.array(random.choices(range(20, 70), k=2)) + cords
        ex = np.clip(ex, a_min=offset, a_max=size - offset)
        draw.ellipse(np.concatenate([cords, ex]).tolist(), fill=255)
    return pil_img

Example #10

Source File: Dataloader.py From Text_Segmentation_Image_Inpainting with GNU General Public License v3.0

6 votes

def __init__(self, image_folder, max_images=False, image_size=(512, 512), add_random_masks=False):
        super(TestDataset, self).__init__()
        if isinstance(image_folder, str):
            self.images = glob.glob(os.path.join(image_folder, "clean/*"))
        else:
            self.images = list(chain.from_iterable([glob.glob(os.path.join(i, "clean/*")) for i in image_folder]))
        assert len(self.images) > 0

        if max_images:
            self.images = random.choices(self.images, k=max_images)
        print(f"Find {len(self.images)} images.")

        self.img_size = image_size

        self.transformer = Compose([  # RandomGrayscale(p=0.4),
            # ColorJitter(brightness=0.2, contrast=0.2, saturation=0, hue=0),
            ToTensor(),
            # Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        self.add_random_masks = add_random_masks

Example #11

Source File: task.py From NiaPy with MIT License

6 votes

def repair(self, x, rnd=rand):
        r"""Repair solution and put the solution in the random position inside of the bounds of problem.

        Arguments:
                x (numpy.ndarray): Solution to check and repair if needed.
                rnd (mtrand.RandomState): Random number generator.

        Returns:
                numpy.ndarray: Fixed solution.

        See Also:
                * :func:`NiaPy.util.limitRepair`
                * :func:`NiaPy.util.limitInversRepair`
                * :func:`NiaPy.util.wangRepair`
                * :func:`NiaPy.util.randRepair`
                * :func:`NiaPy.util.reflectRepair`

        """

        return self.frepair(x, self.Lower, self.Upper, rnd=rnd)

Example #12

Source File: hc.py From NiaPy with MIT License

6 votes

def Neighborhood(x, delta, task, rnd=rand):
	r"""Get neighbours of point.

	Args:
		x numpy.ndarray: Point.
		delta (float): Standard deviation.
		task (Task): Optimization task.
		rnd (Optional[mtrand.RandomState]): Random generator.

	Returns:
		Tuple[numpy.ndarray, float]:
			1. New solution.
			2. New solutions function/fitness value.
	"""
	X = x + rnd.normal(0, delta, task.D)
	X = task.repair(X, rnd)
	Xfit = task.eval(X)
	return X, Xfit

Example #13