Here are the examples of the python api numpy.random.exponential taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
56 Examples
3
View Complete Implementation : distribution.py
Copyright MIT License
Author : acsicuib
Copyright MIT License
Author : acsicuib
def next(self):
if not self.started:
self.started = True
return self.start
else:
return int(np.random.exponential(self.lambd, size=1)[0])
3
View Complete Implementation : gillespie_base.py
Copyright Apache License 2.0
Author : amzn
Copyright Apache License 2.0
Author : amzn
def _draw_next_event(self, state: np.ndarray) -> Tuple[float, float]:
"""Draws which event of infection or recovery happens next"""
# Compute current rates and the sum thereof
rates = self._get_current_rates(state)
sum_of_rates = rates.sum()
# draw timestep from exponential distribution
dt = np.random.exponential(1.0 / sum_of_rates)
# draw occurring event according to current rates
event = np.random.choice(np.asarray([i for i in range(len(self.initial_state))], dtype=int),
p=rates / sum_of_rates)
return event, dt
3
View Complete Implementation : test_matfuncs.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
@pytest.mark.slow
@pytest.mark.skip(reason='this test is deliberately slow')
def test_medium_matrix(self):
# profile this to see the speed difference
n = 1000
A = np.random.exponential(size=(n, n))
E = np.random.exponential(size=(n, n))
sps_expm, sps_frechet = expm_frechet(
A, E, method='SPS')
blockEnlarge_expm, blockEnlarge_frechet = expm_frechet(
A, E, method='blockEnlarge')
astert_allclose(sps_expm, blockEnlarge_expm)
astert_allclose(sps_frechet, blockEnlarge_frechet)
3
View Complete Implementation : test_imputer_iterators.py
Copyright Apache License 2.0
Author : awslabs
Copyright Apache License 2.0
Author : awslabs
def test_iter_decoder_df():
# draw skewed brands
brands = [{feature_col: brand} for brand in
list(map(lambda e: str(int(e)), np.random.exponential(scale=1, size=1000)))]
brand_df = pd.DataFrame(brands)
it = ImputerIterDf(brand_df,
data_columns=[SequentialEncoder(feature_col, max_tokens=10, seq_len=2)],
label_columns=[CategoricalEncoder(feature_col, max_tokens=100)],
batch_size=2)
decoded = it.decode(next(it).label)
np.testing.astert_array_equal(decoded[0], brand_df[feature_col].head(it.batch_size).values)
3
View Complete Implementation : test_imputer_iterators.py
Copyright Apache License 2.0
Author : awslabs
Copyright Apache License 2.0
Author : awslabs
def test_iter_padding_offset():
col = 'brand'
df = pd.DataFrame(
[{col: brand} for brand in
list(map(lambda e: str(int(e)), np.random.exponential(scale=1, size=36)))]
)
df_train = df.sample(frac=0.5)
it = ImputerIterDf(
df_train,
data_columns=[BowEncoder(col)],
label_columns=[CategoricalEncoder(col, max_tokens=5)],
batch_size=32
)
astert it.start_padding_idx == df_train.shape[0]
3
View Complete Implementation : brownian_meander.py
Copyright MIT License
Author : crflynn
Copyright MIT License
Author : crflynn
def _sample_brownian_meander_at(self, times, b=None):
"""Generate a Brownian meander realization.
Williams, 1970, or Imhof, 1984.
"""
if b is None:
b = np.sqrt(2 * times[-1] * np.random.exponential())
else:
self._check_nonnegative_number(b, "Right endpoint")
bridge_1 = self._sample_brownian_bridge_at(times)
bridge_2 = self._sample_brownian_bridge_at(times)
bridge_3 = self._sample_brownian_bridge_at(times)
return np.sqrt(
(b * times / times[-1] + bridge_1) ** 2 +
bridge_2 ** 2 + bridge_3 ** 2
)
3
View Complete Implementation : causal_mechanisms.py
Copyright MIT License
Author : Diviyan-Kalainathan
Copyright MIT License
Author : Diviyan-Kalainathan
def __init__(self, ncauses, points, noise_function, d=4, noise_coeff=.4):
"""Init the mechanism."""
super(SigmoidAM_Mechanism, self).__init__()
self.n_causes = ncauses
self.points = points
self.a = np.random.exponential(1/4) + 1
ber = bernoulli.rvs(0.5)
self.b = ber * np.random.uniform(-2, -0.5) + (1-ber)*np.random.uniform(0.5, 2)
self.c = np.random.uniform(-2, 2)
self.noise = noise_coeff * noise_function(points)
3
View Complete Implementation : causal_mechanisms.py
Copyright MIT License
Author : Diviyan-Kalainathan
Copyright MIT License
Author : Diviyan-Kalainathan
def __init__(self, ncauses, points, noise_function, d=4, noise_coeff=.4):
"""Init the mechanism."""
super(SigmoidMix_Mechanism, self).__init__()
self.n_causes = ncauses
self.points = points
self.a = np.random.exponential(1/4) + 1
ber = bernoulli.rvs(0.5)
self.b = ber * np.random.uniform(-2, -0.5) + (1-ber)*np.random.uniform(0.5, 2)
self.c = np.random.uniform(-2, 2)
self.noise = noise_coeff * noise_function(points)
3
View Complete Implementation : test_matfuncs.py
Copyright Apache License 2.0
Author : dnanexus
Copyright Apache License 2.0
Author : dnanexus
@decorators.slow
@decorators.skipif(True, 'this test is deliberately slow')
def test_medium_matrix(self):
# profile this to see the speed difference
n = 1000
A = np.random.exponential(size=(n, n))
E = np.random.exponential(size=(n, n))
sps_expm, sps_frechet = expm_frechet(
A, E, method='SPS')
blockEnlarge_expm, blockEnlarge_frechet = expm_frechet(
A, E, method='blockEnlarge')
astert_allclose(sps_expm, blockEnlarge_expm)
astert_allclose(sps_frechet, blockEnlarge_frechet)
3
View Complete Implementation : model.py
Copyright MIT License
Author : EliseJ
Copyright MIT License
Author : EliseJ
def make_mock(self, param, var):
'''
Input:
param: variable to generate either exponential data or Normal data with variance = var
'''
if self.name == "exp":
b = 1/param[0]
return np.random.exponential(b,self.num)
if self.name == "normal":
if isinstance(var,float):
sigm = np.diag(np.ones(len(param))*var)
elif len(var)==len(param):
sigm = np.diag(var)
else:
sigm = var.reshape(len(param),len(param)) #covariance matrix
return np.random.multivariate_normal(param,sigm,self.num)