Python numpy.random() Examples

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)) 

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 

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 

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) 

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() 

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) 

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) 

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 

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 

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 

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) 

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 

def Elitism(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Select the best of all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		Tuple[numpy.ndarray, float]:
			1. New position of individual
			2. New positions fitness/function value
	"""
	xn = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
	xn_f = apply_along_axis(task.eval, 1, xn)
	ib = argmin(xn_f)
	return xn[ib], xn_f[ib] 

def Sequential(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Sequentialy combines all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		tuple[numpy.ndarray, float]:
			1. new position
			2. new positions function/fitness value
	"""
	xn = task.repair(MP_S(MP_P(MP_C(x, F, CR, MP_c, rnd), xpb, CR, MP_p, rnd), xr, xb, CR, MP_s, rnd), rnd=rnd)
	return xn, task.eval(xn) 

def Crossover(x, xpb, xb, xr, MP_c, MP_s, MP_p, F, CR, task, rnd=rand):
	r"""Create a crossover over all three strategies.

	Args:
		x (numpy.ndarray): individual position.
		xpb (numpy.ndarray): individuals best position.
		xb (numpy.ndarray): current best position.
		xr (numpy.ndarray): random individual.
		MP_c (float): Fickleness index value.
		MP_s (float): External irregularity index value.
		MP_p (float): Internal irregularity index value.
		F (float): scale factor.
		CR (float): crossover factor.
		task (Task): optimization task.
		rnd (mtrand.randomstate): random number generator.

	Returns:
		Tuple[numpy.ndarray, float]:
			1. new position
			2. new positions function/fitness value
	"""
	xns = [task.repair(MP_C(x, F, CR, MP_c, rnd), rnd=rnd), task.repair(MP_S(x, xr, xb, CR, MP_s, rnd), rnd=rnd), task.repair(MP_P(x, xpb, CR, MP_p, rnd), rnd=rnd)]
	x = asarray([xns[rnd.randint(len(xns))][i] if rnd.rand() < CR else x[i] for i in range(len(x))])
	return x, task.eval(x) 

def MP_C(x, F, CR, MP, rnd=rand):
	r"""Get bew position based on fickleness.

	Args:
		x (numpy.ndarray): Current individuals position.
		F (float): Scale factor.
		CR (float): Crossover probability.
		MP (float): Fickleness index value
		rnd (mtrand.RandomState): Random number generator

	Returns:
		numpy.ndarray: New position
	"""
	if MP < 0.5:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = x[b[0]:b[1]] + F * rnd.normal(0, 1, b[1] - b[0])
		return x
	return asarray([x[i] + F * rnd.normal(0, 1) if rnd.rand() < CR else x[i] for i in range(len(x))]) 

def MP_S(x, xr, xb, CR, MP, rnd=rand):
	r"""Get new position based on external irregularity.

	Args:
		x (numpy.ndarray): Current individuals position.
		xr (numpy.ndarray): Random individuals position.
		xb (numpy.ndarray): Global best individuals position.
		CR (float): Crossover probability.
		MP (float): External irregularity index.
		rnd (mtrand.RandomState): Random number generator.

	Returns:
		numpy.ndarray: New position.
	"""
	if MP < 0.25:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = xb[b[0]:b[1]]
		return x
	elif MP < 0.5: return asarray([xb[i] if rnd.rand() < CR else x[i] for i in range(len(x))])
	elif MP < 0.75:
		b = sort(rnd.choice(len(x), 2, replace=False))
		x[b[0]:b[1]] = xr[b[0]:b[1]]
		return x
	return asarray([xr[i] if rnd.rand() < CR else x[i] for i in range(len(x))]) 

def nextX(self, cb, E_init, S_init, task, rnd=rand):
		r"""Apply function nextX on Camel.

		This method/function move this Camel to new position in search space.

		Args:
			cb (Camel): Best Camel in population.
			E_init (float): Starting endurance of camel.
			S_init (float): Starting supply of camel.
			task (Task): Optimization task.
			rnd (Optional[mtrand.RandomState]): Random number generator.
		"""
		delta = -1 + rnd.rand() * 2
		self.x = self.x_past + delta * (1 - (self.E / E_init)) * exp(1 - self.S / S_init) * (cb - self.x_past)
		if not task.isFeasible(self.x): self.x = self.x_past
		else: self.f = task.eval(self.x) 

def SexualCrossoverSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop) // 2): pop[i] = asarray([pop[i, d] if rnd.rand() < p else pop[i * 2, d] for d in range(task.D)])
	return pop, apply_along_axis(task.eval, 1, pop) 

def BroodingSimple(pop, p, task, rnd=rand, **kwargs):
	r"""Brooding or internal sexual reproduction of corals.

	Args:
		pop (numpy.ndarray): Current population.
		p (float): Probability in range [0, 1].
		task (Task): Optimization task.
		rnd (mtrand.RandomState): Random generator.
		**kwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, numpy.ndarray]:
			1. New population.
			2. New population function/fitness values.
	"""
	for i in range(len(pop)): pop[i] = task.repair(asarray([pop[i, d] if rnd.rand() < p else task.Lower[d] + task.bRange[d] * rnd.rand() for d in range(task.D)]), rnd=rnd)
	return pop, apply_along_axis(task.eval, 1, pop) 

def runIteration(self, task, c, fpop, xb, fxb, **dparams):
		r"""Core function of EvolutionStrategyML algorithm.

		Args:
			task (Task): Optimization task.
			c (numpy.ndarray): Current population.
			fpop (numpy.ndarray): Current population fitness/function values.
			xb (numpy.ndarray): Global best individual.
			fxb (float): Global best individuals fitness/function value.
			**dparams Dict[str, Any]: Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
				1. New population.
				2. New populations fitness/function values.
				3. New global best solution.
				4. New global best solutions fitness/objective value.
				5. Additional arguments.
		"""
		cn = objects2array([IndividualES(x=self.mutateRand(c, task), task=task, rand=self.Rand) for _ in range(self.lam)])
		c = self.newPop(cn)
		fc = asarray([x.f for x in c])
		xb, fxb = self.getBest(c, fc, xb, fxb)
		return c, fc, xb, fxb, {} 

def popDecrement(self, pop, task):
		r"""Decrement population.

		Args:
			pop (numpy.ndarray): Current population.
			task (Task): Optimization task.

		Returns:
			numpy.ndarray[Individual]: Decreased population.
		"""
		deltapop = int(round(max(1, self.NP * self.deltaPopC(task.Iters))))
		if len(pop) - deltapop <= 0: return pop
		ni = self.Rand.choice(len(pop), deltapop, replace=False)
		npop = []
		for i, e in enumerate(pop):
			if i not in ni: npop.append(e)
			elif self.rand() >= self.omega: npop.append(e)
		return objects2array(npop) 

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

    If seed is None (or np.random), return the RandomState singleton used
    by np.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.
    """
    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) 

def rvs(self, alpha, size=1, random_state=None):
        """
        Draw random samples from a Dirichlet distribution.

        Parameters
        ----------
        %(_dirichlet_doc_default_callparams)s
        size : int, optional
            Number of samples to draw (default 1).
        %(_doc_random_state)s

        Returns
        -------
        rvs : ndarray or scalar
            Random variates of size (`size`, `N`), where `N` is the
            dimension of the random variable.

        """
        alpha = _dirichlet_check_parameters(alpha)
        random_state = self._get_random_state(random_state)
        return random_state.dirichlet(alpha, size=size) 

def random_seed(seed=None):
    """
    Runs a code block with a new seed for np, mx and python's random.

    Parameters
    ----------

    seed : the seed to pass to np.random, mx.random and python's random.

    To impose rng determinism, invoke e.g. as in:

    with random_seed(1234):
        ...

    To impose rng non-determinism, invoke as in:

    with random_seed():
        ...

    Upon conclusion of the block, the rng's are returned to
    a state that is a function of their pre-block state, so
    any prior non-determinism is preserved.

    """

    try:
        next_seed = np.random.randint(0, np.iinfo(np.int32).max)
        if seed is None:
            np.random.seed()
            seed = np.random.randint(0, np.iinfo(np.int32).max)
        logger = default_logger()
        logger.debug('Setting np, mx and python random seeds = %s', seed)
        np.random.seed(seed)
        mx.random.seed(seed)
        random.seed(seed)
        yield
    finally:
        # Reinstate prior state of np.random and other generators
        np.random.seed(next_seed)
        mx.random.seed(next_seed)
        random.seed(next_seed) 

def random_seed(seed=None):
    """
    Runs a code block with a new seed for np, mx and python's random.

    Parameters
    ----------

    seed : the seed to pass to np.random, mx.random and python's random.

    To impose rng determinism, invoke e.g. as in:

    with random_seed(1234):
        ...

    To impose rng non-determinism, invoke as in:

    with random_seed():
        ...

    Upon conclusion of the block, the rng's are returned to
    a state that is a function of their pre-block state, so
    any prior non-determinism is preserved.

    """

    try:
        next_seed = np.random.randint(0, np.iinfo(np.int32).max)
        if seed is None:
            np.random.seed()
            seed = np.random.randint(0, np.iinfo(np.int32).max)
        logger = default_logger()
        logger.debug('Setting np, mx and python random seeds = %s', seed)
        np.random.seed(seed)
        mx.random.seed(seed)
        random.seed(seed)
        yield
    finally:
        # Reinstate prior state of np.random and other generators
        np.random.seed(next_seed)
        mx.random.seed(next_seed)
        random.seed(next_seed) 

def _check_random_state(random_state):
    if random_state is None:
        return np.random.Generator(np.random.MT19937())
    if isinstance(random_state, np.random.Generator):
        return random_state
    elif isinstance(random_state, np.random.RandomState) or (random_state == np.random):
        random_state = int(random_state.randint(np.iinfo(np.int32).max) + 1)
    if isinstance(random_state, float):
        random_state = int(random_state)
    assert random_state > 0
    return np.random.Generator(np.random.MT19937(seed = random_state)) 

def _gen_random(self, X):
        return self.random_state.random(size = X.shape[0]) 

def _score_rnd(self, X):
        if not self.ts_byrow:
            chosen_sample = self.random_state.integers(self.nsamples)
            return self._get_score(chosen_sample, X)
        else:
            pred = self._pred_by_sample(X)
            if not self.ts_weighted:
                return pred[np.arange(X.shape[0]),
                            self.random_state.integers(self.nsamples, size=X.shape[0])]
            else:
                w = self.random_state.random(size = (X.shape[0], self.nsamples))
                w[:] /= w.sum(axis=0, keepdims=True)
                return np.einsum("ij,ij->i", w, pred) 

def _spawn_arm(self, fitted_classifier = None, n_w_rew = 0, n_wo_rew = 0,
                   buffer_X = None, buffer_y = None):
        self.n += 1
        self.rng_arm.append(self.random_state if (self.random_state == np.random) else \
                            _check_random_state(
                                self.random_state.integers(np.iinfo(np.int32).max) + 1))
        if self.smooth is not None:
            self.counters = np.c_[self.counters, np.array([n_w_rew + n_wo_rew]).reshape((1, 1)).astype(self.counters.dtype)]
        if (self.force_counters) or (self.thr > 0 and not self.force_fit):
            new_beta_col = np.array([0 if (n_w_rew + n_wo_rew) < self.thr else 1, self.alpha + n_w_rew, self.beta + n_wo_rew]).reshape((3, 1)).astype(self.beta_counters.dtype)
            self.beta_counters = np.c_[self.beta_counters, new_beta_col]
        if fitted_classifier is not None:
            if 'predict_proba' not in dir(fitted_classifier):
                fitted_classifier = _convert_decision_function_w_sigmoid(fitted_classifier)
            if partialfit:
                fitted_classifier = _add_method_predict_robust(fitted_classifier)
            self.algos.append(fitted_classifier)
        else:
            if self.force_fit or self.partialfit:
                if self.base is None:
                    raise ValueError("Must provide a classifier when initializing with different classifiers per arm.")
                self.algos.append( deepcopy(self.base) )
            else:
                if (self.force_counters) or (self.thr > 0 and not self.force_fit):
                    self.algos.append(_BetaPredictor(self.beta_counters[:, -1][1],
                                                     self.beta_counters[:, -1][2],
                                                     self.rng_arm[-1]))
                else:
                    self.algos.append(_ZeroPredictor())
        if (self.buffer is not None):
            self.buffer.append(_RefitBuffer(self.refit_buffer, self.deep_copy,
                                            self.rng_arm[-1]))
            if (buffer_X is not None):
                self.buffer[-1].add_obs(bufferX, buffer_y)