python中uniform(a、b)_Python stats.uniform方法代码示例

本文整理汇总了Python中scipy.stats.uniform方法的典型用法代码示例。如果您正苦于以下问题：Python stats.uniform方法的具体用法？Python stats.uniform怎么用？Python stats.uniform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scipy.stats的用法示例。

在下文中一共展示了stats.uniform方法的29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: likelihood

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def likelihood(parameter_vector):

parameter_vector = 10**np.array(parameter_vector)

#Solve ODE system given parameter vector

yout = odeint(odefunc, y0, tspan, args=(parameter_vector,))

cout = yout[:, 2]

#Calculate log probability contribution given simulated experimental values.

logp_ctotal = np.sum(like_ctot.logpdf(cout))

#If simulation failed due to integrator errors, return a log probability of -inf.

if np.isnan(logp_ctotal):

logp_ctotal = -np.inf

return logp_ctotal

# Add vector of rate parameters to be sampled as unobserved random variables in DREAM with uniform priors.

示例2: test_param_sampler

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def test_param_sampler():

# test basic properties of param sampler

param_distributions = {"kernel": ["rbf", "linear"],

"C": uniform(0, 1)}

sampler = ParameterSampler(param_distributions=param_distributions,

n_iter=10, random_state=0)

samples = [x for x in sampler]

assert_equal(len(samples), 10)

for sample in samples:

assert sample["kernel"] in ["rbf", "linear"]

assert 0 <= sample["C"] <= 1

# test that repeated calls yield identical parameters

param_distributions = {"C": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}

sampler = ParameterSampler(param_distributions=param_distributions,

n_iter=3, random_state=0)

assert_equal([x for x in sampler], [x for x in sampler])

if sp_version >= (0, 16):

param_distributions = {"C": uniform(0, 1)}

sampler = ParameterSampler(param_distributions=param_distributions,

n_iter=10, random_state=0)

assert_equal([x for x in sampler], [x for x in sampler])

开发者ID:PacktPublishing，项目名称:Mastering-Elasticsearch-7.0，代码行数:25，

示例3: setUp_configure

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def setUp_configure(self):

from scipy import stats

self.dist = distributions.Uniform

self.scipy_dist = stats.uniform

self.test_targets = set([

'batch_shape', 'cdf', 'entropy', 'event_shape', 'icdf', 'log_prob',

'mean', 'sample', 'stddev', 'support', 'variance'])

if self.use_loc_scale:

loc = numpy.random.uniform(

-10, 0, self.shape).astype(numpy.float32)

scale = numpy.random.uniform(

0, 10, self.shape).astype(numpy.float32)

self.params = {'loc': loc, 'scale': scale}

self.scipy_params = {'loc': loc, 'scale': scale}

else:

low = numpy.random.uniform(

-10, 0, self.shape).astype(numpy.float32)

high = numpy.random.uniform(

low, low + 10, self.shape).astype(numpy.float32)

self.params = {'low': low, 'high': high}

self.scipy_params = {'loc': low, 'scale': high-low}

self.support = '[low, high]'

开发者ID:chainer，项目名称:chainer，代码行数:27，

示例4: _construct_generator_obj

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def _construct_generator_obj(self, C_tv_range, C_group_l1_range,

logspace=True):

generators = []

if len(C_tv_range) == 2:

if logspace:

generators.append(Log10UniformGenerator(*C_tv_range))

else:

generators.append(uniform(C_tv_range))

else:

generators.append(null_generator)

if len(C_group_l1_range) == 2:

if logspace:

generators.append(Log10UniformGenerator(*C_group_l1_range))

else:

generators.append(uniform(C_group_l1_range))

else:

generators.append(null_generator)

return generators

# Properties #

开发者ID:X-DataInitiative，项目名称:tick，代码行数:24，

示例5: test_frozen_dirichlet

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def test_frozen_dirichlet(self):

np.random.seed(2846)

n = np.random.randint(1, 32)

alpha = np.random.uniform(10e-10, 100, n)

d = dirichlet(alpha)

assert_equal(d.var(), dirichlet.var(alpha))

assert_equal(d.mean(), dirichlet.mean(alpha))

assert_equal(d.entropy(), dirichlet.entropy(alpha))

num_tests = 10

for i in range(num_tests):

x = np.random.uniform(10e-10, 100, n)

x /= np.sum(x)

assert_equal(d.pdf(x[:-1]), dirichlet.pdf(x[:-1], alpha))

assert_equal(d.logpdf(x[:-1]), dirichlet.logpdf(x[:-1], alpha))

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:19，

示例6: test_pairwise_distances

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def test_pairwise_distances(self):

# Test that the distribution of pairwise distances is close to correct.

np.random.seed(514)

def random_ortho(dim):

u, _s, v = np.linalg.svd(np.random.normal(size=(dim, dim)))

return np.dot(u, v)

for dim in range(2, 6):

def generate_test_statistics(rvs, N=1000, eps=1e-10):

stats = np.array([

np.sum((rvs(dim=dim) - rvs(dim=dim))**2)

for _ in range(N)

])

# Add a bit of noise to account for numeric accuracy.

stats += np.random.uniform(-eps, eps, size=stats.shape)

return stats

expected = generate_test_statistics(random_ortho)

actual = generate_test_statistics(scipy.stats.ortho_group.rvs)

_D, p = scipy.stats.ks_2samp(expected, actual)

assert_array_less(.05, p)

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:26，

示例7: test_haar

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def test_haar(self):

# Test that the eigenvalues, which lie on the unit circle in

# the complex plane, are uncorrelated.

# Generate samples

dim = 5

samples = 1000 # Not too many, or the test takes too long

np.random.seed(514) # Note that the test is sensitive to seed too

xs = unitary_group.rvs(dim, size=samples)

# The angles "x" of the eigenvalues should be uniformly distributed

# Overall this seems to be a necessary but weak test of the distribution.

eigs = np.vstack(scipy.linalg.eigvals(x) for x in xs)

x = np.arctan2(eigs.imag, eigs.real)

res = kstest(x.ravel(), uniform(-np.pi, 2*np.pi).cdf)

assert_(res.pvalue > 0.05)

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:18，

示例8: test_randomizedsearchcv_best_estimator

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def test_randomizedsearchcv_best_estimator(u1_ml100k):

"""Ensure that the best estimator is the one that gives the best score (by

re-running it)"""

param_distributions = {'n_epochs': [5], 'lr_all': uniform(0.002, 0.003),

'reg_all': uniform(0.04, 0.02), 'n_factors': [1],

'init_std_dev': [0]}

rs = RandomizedSearchCV(SVD, param_distributions, measures=['mae'],

cv=PredefinedKFold(), joblib_verbose=100)

rs.fit(u1_ml100k)

best_estimator = rs.best_estimator['mae']

# recompute MAE of best_estimator

mae = cross_validate(best_estimator, u1_ml100k, measures=['MAE'],

cv=PredefinedKFold())['test_mae']

assert mae == rs.best_score['mae']

开发者ID:NicolasHug，项目名称:Surprise，代码行数:19，

示例9: testUniformPDF

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def testUniformPDF(self):

with self.test_session():

a = tf.constant([-3.0] * 5 + [15.0])

b = tf.constant([11.0] * 5 + [20.0])

uniform = tf.contrib.distributions.Uniform(a=a, b=b)

a_v = -3.0

b_v = 11.0

x = np.array([-10.5, 4.0, 0.0, 10.99, 11.3, 17.0], dtype=np.float32)

def _expected_pdf():

pdf = np.zeros_like(x) + 1.0 / (b_v - a_v)

pdf[x > b_v] = 0.0

pdf[x < a_v] = 0.0

pdf[5] = 1.0 / (20.0 - 15.0)

return pdf

expected_pdf = _expected_pdf()

pdf = uniform.pdf(x)

self.assertAllClose(expected_pdf, pdf.eval())

log_pdf = uniform.log_pdf(x)

self.assertAllClose(np.log(expected_pdf), log_pdf.eval())

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:26，

示例10: testUniformCDF

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def testUniformCDF(self):

with self.test_session():

batch_size = 6

a = tf.constant([1.0] * batch_size)

b = tf.constant([11.0] * batch_size)

a_v = 1.0

b_v = 11.0

x = np.array([-2.5, 2.5, 4.0, 0.0, 10.99, 12.0], dtype=np.float32)

uniform = tf.contrib.distributions.Uniform(a=a, b=b)

def _expected_cdf():

cdf = (x - a_v) / (b_v - a_v)

cdf[x >= b_v] = 1

cdf[x < a_v] = 0

return cdf

cdf = uniform.cdf(x)

self.assertAllClose(_expected_cdf(), cdf.eval())

log_cdf = uniform.log_cdf(x)

self.assertAllClose(np.log(_expected_cdf()), log_cdf.eval())

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:24，

示例11: testUniformSample

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def testUniformSample(self):

with self.test_session():

a = tf.constant([3.0, 4.0])

b = tf.constant(13.0)

a1_v = 3.0

a2_v = 4.0

b_v = 13.0

n = tf.constant(100000)

uniform = tf.contrib.distributions.Uniform(a=a, b=b)

samples = uniform.sample(n, seed=137)

sample_values = samples.eval()

self.assertEqual(sample_values.shape, (100000, 2))

self.assertAllClose(sample_values[::, 0].mean(), (b_v + a1_v) / 2,

atol=1e-2)

self.assertAllClose(sample_values[::, 1].mean(), (b_v + a2_v) / 2,

atol=1e-2)

self.assertFalse(np.any(sample_values[::, 0] < a1_v) or np.any(

sample_values >= b_v))

self.assertFalse(np.any(sample_values[::, 1] < a2_v) or np.any(

sample_values >= b_v))

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:23，

示例12: testUniformNans

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def testUniformNans(self):

with self.test_session():

a = 10.0

b = [11.0, 100.0]

uniform = tf.contrib.distributions.Uniform(a=a, b=b)

no_nans = tf.constant(1.0)

nans = tf.constant(0.0) / tf.constant(0.0)

self.assertTrue(tf.is_nan(nans).eval())

with_nans = tf.stack([no_nans, nans])

pdf = uniform.pdf(with_nans)

is_nan = tf.is_nan(pdf).eval()

self.assertFalse(is_nan[0])

self.assertTrue(is_nan[1])

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:18，

示例13: testUniformSampleWithShape

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def testUniformSampleWithShape(self):

with self.test_session():

a = 10.0

b = [11.0, 20.0]

uniform = tf.contrib.distributions.Uniform(a, b)

pdf = uniform.pdf(uniform.sample((2, 3)))

# pylint: disable=bad-continuation

expected_pdf = [

[[1.0, 0.1],

[1.0, 0.1],

[1.0, 0.1]],

[[1.0, 0.1],

[1.0, 0.1],

[1.0, 0.1]],

]

# pylint: enable=bad-continuation

self.assertAllClose(expected_pdf, pdf.eval())

pdf = uniform.pdf(uniform.sample())

expected_pdf = [1.0, 0.1]

self.assertAllClose(expected_pdf, pdf.eval())

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:24，

示例14: __init__

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def __init__(self, lower, upper):

self.lower = lower

self.upper = upper

self.bounds = np.array([-1.0, 1.0])

if (self.lower is None) or (self.upper is None):

print('One or more bounds not specified. Assuming [0, 1].')

self.lower = 0.0

self.upper = 1.0

self.mean = 0.5 * (self.upper + self.lower)

self.variance = 1.0/12.0 * (self.upper - self.lower)**2

self.x_range_for_pdf = np.linspace(self.lower, self.upper, RECURRENCE_PDF_SAMPLES)

self.parent = uniform(loc=(self.lower), scale=(self.upper-self.lower))

self.skewness = 0.0

self.shape_parameter_A = 0.

self.shape_parameter_B = 0.

开发者ID:Effective-Quadratures，项目名称:Effective-Quadratures，代码行数:18，

示例15: get_cdf

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def get_cdf(self, points=None):

"""

A uniform cumulative density function.

:param points:

Matrix of points which have to be evaluated

:param double lower:

Lower bound of the support of the uniform distribution.

:param double upper:

Upper bound of the support of the uniform distribution.

:return:

An array of N equidistant values over the support of the distribution.

:return:

Cumulative density values along the support of the uniform distribution.

"""

if points is not None:

return self.parent.cdf(points)

else:

raise ValueError( 'Please digit an input for getCDF method')

开发者ID:Effective-Quadratures，项目名称:Effective-Quadratures，代码行数:20，

示例16: get_pdf

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def get_pdf(self, points=None):

"""

A uniform probability distribution.

:param points:

Matrix of points which have to be evaluated

:param double lower:

Lower bound of the support of the uniform distribution.

:param double upper:

Upper bound of the support of the uniform distribution.

:return:

An array of N equidistant values over the support of the distribution.

:return:

Probability density values along the support of the uniform distribution.

"""

if points is not None:

return self.parent.pdf(points)

else:

raise ValueError( 'Please digit an input for get_pdf method')

开发者ID:Effective-Quadratures，项目名称:Effective-Quadratures，代码行数:20，

示例17: __init__

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def __init__(self, outputs=None, inputs=None, low=0, high=1, rng=None):

assert not inputs

if rng is None:

rng = gu.gen_rng(0)

if outputs is None:

outputs = [0]

self.rng = rng

self.low = low

self.high = high

self.outputs = outputs

self.inputs = []

self.uniform = uniform(loc=self.low, scale=self.high-self.low)

开发者ID:probcomp，项目名称:cgpm，代码行数:14，

示例18: simulate

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def simulate(self, rowid, targets, constraints=None, inputs=None, N=None):

assert not constraints

assert targets == self.outputs

x = self.rng.uniform(low=self.low, high=self.high)

return {self.outputs[0]: x}

开发者ID:probcomp，项目名称:cgpm，代码行数:7，

示例19: logpdf

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def logpdf(self, rowid, targets, constraints=None, inputs=None):

assert not constraints

assert not inputs

assert targets.keys() == self.outputs

x = targets[self.outputs[0]]

return self.uniform.logpdf(x)

开发者ID:probcomp，项目名称:cgpm，代码行数:8，

示例20: __init__

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def __init__(self, outputs=None, inputs=None, noise=None, rng=None):

if rng is None:

rng = gu.gen_rng(1)

if outputs is None:

outputs = [0]

if inputs is None:

inputs = [1]

if noise is None:

noise = .1

self.rng = rng

self.outputs = outputs

self.inputs = inputs

self.noise = noise

self.uniform = uniform(scale=self.noise)

开发者ID:probcomp，项目名称:cgpm，代码行数:16，

示例21: simulate

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def simulate(self, rowid, targets, constraints=None, inputs=None, N=None):

assert targets == self.outputs

assert inputs.keys() == self.inputs

assert not constraints

x = inputs[self.inputs[0]]

noise = self.rng.uniform(high=self.noise)

if np.cos(x) < 0:

y = np.cos(x) + noise

else:

y = np.cos(x) - noise

return {self.outputs[0]: y}

开发者ID:probcomp，项目名称:cgpm，代码行数:13，

示例22: __init__

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def __init__(self, outputs=None, inputs=None, noise=None, rng=None):

if rng is None:

rng = gu.gen_rng(1)

if outputs is None:

outputs = [0]

if inputs is None:

inputs = [1]

if noise is None:

noise = .1

self.rng = rng

self.outputs = outputs

self.inputs = inputs

self.noise = noise

self.uniform = uniform(loc=-self.noise, scale=2*self.noise)

开发者ID:probcomp，项目名称:cgpm，代码行数:16，

示例23: simulate

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def simulate(self, rowid, targets, constraints=None, inputs=None, N=None):

assert targets == self.outputs

assert inputs.keys() == self.inputs

assert not constraints

x = inputs[self.inputs[0]]

u = self.rng.rand()

noise = self.rng.uniform(low=-self.noise, high=self.noise)

if u < .5:

y = x**2 + noise

else:

y = -(x**2 + noise)

return {self.outputs[0]: y}

开发者ID:probcomp，项目名称:cgpm，代码行数:14，

示例24: logpdf

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def logpdf(self, rowid, targets, constraints=None, inputs=None):

assert targets.keys() == self.outputs

assert inputs.keys() == self.inputs

assert not constraints

x = inputs[self.inputs[0]]

y = targets[self.outputs[0]]

return logsumexp([

np.log(.5)+self.uniform.logpdf(y-x**2),

np.log(.5)+self.uniform.logpdf(-y-x**2)

])

开发者ID:probcomp，项目名称:cgpm，代码行数:12，

示例25: __init__

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def __init__(self, nobs=50, x=None, distr_x=None, distr_noise=None):

if distr_x is None:

from scipy import stats

distr_x = stats.uniform

self.s_noise = 0.15

self.func = fg1eu

super(self.__class__, self).__init__(nobs=nobs, x=x,

distr_x=distr_x,

distr_noise=distr_noise)

开发者ID:birforce，项目名称:vnpy_crypto，代码行数:11，

示例26: likelihood

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def likelihood(parameter_vector):

param_dict = {pname: pvalue for pname, pvalue in zip(pysb_sampled_parameter_names, parameter_vector)}

for pname, pvalue in param_dict.items():

#Change model parameter values to current location in parameter space

model.parameters[pname].value = 10**(pvalue)

#Simulate experimentally measured Ctotal values.

solver.run()

#Calculate log probability contribution from simulated experimental values.

logp_ctotal = np.sum(like_ctot.logpdf(solver.yobs['C_total']))

#If model simulation failed due to integrator errors, return a log probability of -inf.

if np.isnan(logp_ctotal):

logp_ctotal = -np.inf

return logp_ctotal

# Add vector of PySB rate parameters to be sampled as unobserved random variables to DREAM with uniform priors.

开发者ID:LoLab-VU，项目名称:PyDREAM，代码行数:28，

示例27: multidmodel_uniform

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# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def multidmodel_uniform():

"""Multidimensional model with uniform priors."""

lower = np.array([-5, -9, 5, 3])

upper = np.array([10, 2, 7, 8])

range = upper-lower

x = SampledParam(uniform, loc=lower, scale=range)

like =simple_likelihood

return [x], like

开发者ID:LoLab-VU，项目名称:PyDREAM，代码行数:13，

示例28: parallel_params

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​

# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def parallel_params(log_dir, niter=10000, seed=123456789):

"""

Create the parameters for a parallel run.

@param log_dir: The directory to store the results in.

@param niter: The number of iterations to perform.

@param seed: The seed for the random number generators.

@return: Returns a tuple containing the parameters.

"""

static_params = {

'ninputs': 784,

'trim': 1e-4,

'disable_boost': True,

'seed': seed,

'pct_active': None,

'random_permanence': True,

'pwindow': 0.5,

'global_inhibition': True,

'syn_th': 0.5,

'pinc': 0.001,

'pdec': 0.001,

'nepochs': 10

}

dynamic_params = {

'ncolumns': randint(500, 3500),

'nactive': uniform(0.5, 0.35), # As a % of the number of columns

'nsynapses': randint(25, 784),

'seg_th': uniform(0, 0.2), # As a % of the number of synapses

'log_dir': log_dir

}

# Build the parameter generator

gen = ParamGenerator(dynamic_params, niter, 1, 784)

params = {key:gen for key in dynamic_params}

return static_params, params

开发者ID:tehtechguy，项目名称:mHTM，代码行数:42，

示例29: check_initializer_statistics

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​

# 需要导入模块: from scipy import stats [as 别名]

# 或者: from scipy.stats import uniform [as 别名]

def check_initializer_statistics(self, backend_config, n):

from scipy import stats

xp = backend_config.xp

ws = numpy.empty((n,) + self.shape, dtype=self.dtype)

ws = backend_config.get_array(ws)

for i in range(n):

initializer = self.target(**self.target_kwargs)

initializer(xp.squeeze(ws[i:i+1], axis=0))

fan = self.fan_option or default_fan.get(self.target)

expected_max = self.scale or default_scale.get(self.target) or 1.

expected_max *= default_coeff.get(self.target) or 1.

if fan is not None:

if fan == 'fan_in':

expected_max *= math.sqrt(1. / self.fans[0])

elif fan == 'fan_out':

expected_max *= math.sqrt(1. / self.fans[1])

elif fan == 'fan_avg':

expected_max *= math.sqrt(2. / sum(self.fans))

else:

assert False

sampless = cuda.to_cpu(ws.reshape(n, -1).T)

alpha = 0.01 / len(sampless)

for samples in sampless:

_, p = stats.kstest(

samples,

stats.uniform(-expected_max, 2*expected_max).cdf

)

assert p >= alpha

开发者ID:chainer，项目名称:chainer，代码行数:33，

注：本文中的scipy.stats.uniform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。