本文整理匯總了Python中numpy.random.binomial方法的典型用法代碼示例。如果您正苦於以下問題：Python random.binomial方法的具體用法？Python random.binomial怎麽用？Python random.binomial使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.random的用法示例。
在下文中一共展示了random.binomial方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_binomial
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_binomial(self):
n = [1]
p = [0.5]
bad_n = [-1]
bad_p_one = [-1]
bad_p_two = [1.5]
binom = np.random.binomial
desired = np.array([1, 1, 1])
self.setSeed()
actual = binom(n * 3, p)
assert_array_equal(actual, desired)
assert_raises(ValueError, binom, bad_n * 3, p)
assert_raises(ValueError, binom, n * 3, bad_p_one)
assert_raises(ValueError, binom, n * 3, bad_p_two)
self.setSeed()
actual = binom(n, p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, binom, bad_n, p * 3)
assert_raises(ValueError, binom, n, bad_p_one * 3)
assert_raises(ValueError, binom, n, bad_p_two * 3) 示例2: get_example_data
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def get_example_data(*, sparse=False):
# create test object
adata = AnnData(np.multiply(binomial(1, 0.15, (100, 20)), negative_binomial(2, 0.25, (100, 20))))
# adapt marker_genes for cluster (so as to have some form of reasonable input
adata.X[0:10, 0:5] = np.multiply(binomial(1, 0.9, (10, 5)), negative_binomial(1, 0.5, (10, 5)))
# The following construction is inefficient, but makes sure that the same data is used in the sparse case
if sparse:
adata.X = sp.csr_matrix(adata.X)
# Create cluster according to groups
adata.obs['true_groups'] = pd.Categorical(np.concatenate((
np.zeros((10,), dtype=int),
np.ones((90,), dtype=int),
)))
return adata 示例3: sample_binomial_frag_len
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def sample_binomial_frag_len(frag_mean=200, frag_variance=100):
"""
Sample a fragment length from a binomial distribution parameterized with a
mean and variance.
If frag_variance > frag_mean, use a Negative-Binomial distribution.
"""
assert(abs(frag_mean - frag_variance) > 1)
if frag_variance < frag_mean:
p = 1 - (frag_variance/float(frag_mean))
# N = mu/(1-(sigma^2/mu))
n = float(frag_mean) / (1 - (float(frag_variance)/float(frag_mean)))
return binomial(n, p)
else:
r = -1 * (power(frag_mean, 2)/float(frag_mean - frag_variance))
p = frag_mean / float(frag_variance)
print "Sampling frag_mean=",frag_mean, " frag_variance=", frag_variance
print "r: ",r, " p: ", p
return negative_binomial(r, p) 示例4: test_n_zero
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_n_zero(self):
# Tests the corner case of n == 0 for the binomial distribution.
# binomial(0, p) should be zero for any p in [0, 1].
# This test addresses issue #3480.
zeros = np.zeros(2, dtype='int')
for p in [0, .5, 1]:
assert_(random.binomial(0, p) == 0)
assert_array_equal(random.binomial(zeros, p), zeros) 示例5: test_p_is_nan
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan) 示例6: test_negative_binomial
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_negative_binomial(self):
# Ensure that the negative binomial results take floating point
# arguments without truncation.
self.prng.negative_binomial(0.5, 0.5) 示例7: test_n_zero
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_n_zero(self):
# Tests the corner case of n == 0 for the binomial distribution.
# binomial(0, p) should be zero for any p in [0, 1].
# This test addresses issue #3480.
zeros = np.zeros(2, dtype='int')
for p in [0, .5, 1]:
assert_(random.binomial(0, p) == 0)
np.testing.assert_array_equal(random.binomial(zeros, p), zeros) 示例8: test_binomial
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_binomial(self):
np.random.seed(self.seed)
actual = np.random.binomial(100.123, .456, size=(3, 2))
desired = np.array([[37, 43],
[42, 48],
[46, 45]])
np.testing.assert_array_equal(actual, desired) 示例9: flip
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def flip(p):
return random.binomial(1, p) 示例10: test_negative_binomial
# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import binomial [as 別名]
def test_negative_binomial(self):
""" Ensure that the negative binomial results take floating point
arguments without truncation.
"""
self.prng.negative_binomial(0.5, 0.5)