Python numpy.random.binomial() Examples
The following are 30
code examples of numpy.random.binomial().
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Example #1
| Source File: test_random.py From vnpy_crypto with MIT License | 6 votes |
|
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)
Example #2
| Source File: test_random.py From recruit with Apache License 2.0 | 6 votes |
|
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)
Example #3
| Source File: test_random.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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)
Example #4
| Source File: test_random.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
|
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)
Example #5
| Source File: read_simulator.py From altanalyze with Apache License 2.0 | 6 votes |
|
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)
Example #6
| Source File: test_random.py From coffeegrindsize with MIT License | 6 votes |
|
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)
Example #7
| Source File: test_random.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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)
Example #8
| Source File: test_random.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
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)
Example #9
| Source File: test_rank_genes_groups.py From scanpy with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
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
Example #10
| Source File: test_random.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
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)
Example #11
| Source File: test_random.py From mxnet-lambda with Apache License 2.0 | 6 votes |
|
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)
Example #12
| Source File: test_random.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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)
Example #13
| Source File: read_simulator.py From rmats2sashimiplot with GNU General Public License v2.0 | 6 votes |
|
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)
Example #14
| Source File: test_random.py From pySINDy with MIT License | 6 votes |
|
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)
Example #15
| Source File: test_random.py From coffeegrindsize with MIT License | 5 votes |
|
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]])
assert_array_equal(actual, desired)
Example #16
| Source File: test_random.py From coffeegrindsize with MIT License | 5 votes |
|
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)
Example #17
| Source File: test_random.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
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)
Example #18
| Source File: test_random.py From ImageFusion with MIT License | 5 votes |
|
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)
Example #19
| Source File: test_random.py From ImageFusion with MIT License | 5 votes |
|
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)
Example #20
| Source File: test_random.py From ImageFusion with MIT License | 5 votes |
|
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan)
Example #21
| Source File: test_random.py From ImageFusion with MIT License | 5 votes |
|
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)
Example #22
| Source File: test_random.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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]])
assert_array_equal(actual, desired)
Example #23
| Source File: test_random.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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)
Example #24
| Source File: test_random.py From keras-lambda with MIT License | 5 votes |
|
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)
Example #25
| Source File: test_random.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan)
Example #26
| Source File: test_random.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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)
Example #27
| Source File: test_random.py From keras-lambda with MIT License | 5 votes |
|
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan)
Example #28
| Source File: simulate_counts.py From WASP with Apache License 2.0 | 5 votes |
|
def simulate_BB(tot, mean_p, sigma):
a = mean_p * (1/sigma**2 - 1)
b = (1-mean_p) * (1/sigma**2 - 1)
p = beta(a, b)
counts = binomial(tot, p)
#sys.stderr.write("%f %f %i\n"%(mean_p,p,counts))
return counts, (tot-counts)
Example #29
| Source File: test_random.py From keras-lambda with MIT License | 5 votes |
|
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)
Example #30
| Source File: test_random.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan)
