def test_exception_invalid_distribution_function():
"""
Ensure an exception is thrown if the provided function does not return
the expected data type.
"""
def invalid_distribution_function():
return [1, 2, 3]
with pytest.raises(TypeError):
invalid_sampler = RandomInput(invalid_distribution_function)
invalid_sampler.draw_samples(5)
def test_extra_distribution_function_parameters():
"""
Test ability to specify optional distribution function parameters.
"""
np.random.seed(1)
normal_sampler = RandomInput(np.random.normal, loc=1.)
sample = normal_sampler.draw_samples(100)
assert isinstance(sample, np.ndarray)
assert sample.shape == (100, 1)
assert np.abs(np.mean(sample) - 1.) < .2
def test_distribution_exception_if_size_parameter_not_accepted():
"""
Ensure an exception occurs if the distribution function does not accept
a size parameter.
"""
def invalid_distribution_function():
return np.zeros(5)
invalid_input = \
RandomInput(distribution_function=invalid_distribution_function)
with pytest.raises(TypeError):
invalid_input.draw_samples(10)
def test_init_invalid_input():
"""
Ensure an exception is raised if an object that is not a function is
provided.
"""
with pytest.raises(TypeError):
RandomInput(1)
def beta_distribution_input():
"""
Creates a RandomInput object that produces samples from a
beta distribution.
"""
np.random.seed(1)
def beta_distribution(shift, scale, alpha, beta, size):
return shift + scale * np.random.beta(alpha, beta, size)
return RandomInput(distribution_function=beta_distribution,
shift=1.0, scale=2.5, alpha=3., beta=2.)
def test_setting_random_seed():
"""
Test setting of random seed by taking samples, resetting seed, then drawing
again to see if the samples match.
"""
random_input = RandomInput(np.random.uniform, random_seed=1)
num_samples = 5
samples1 = np.zeros(num_samples)
samples2 = np.zeros(num_samples)
for i in range(num_samples):
samples1[i] = random_input.draw_samples(1)
random_input.reset_sampling()
for i in range(num_samples):
samples2[i] = random_input.draw_samples(1)
assert np.array_equal(samples1, samples2)
def random_input():
"""
Creates a RandomInput object that produces samples from a
uniform distribution.
"""
return RandomInput()
on level 0 and level 1. This provides the reference solution for the test:
"test_hard_coded_spring_mass_random_input"
'''
# Step 1 - Define random variable for spring stiffness:
# Need to provide a sampleable function to create RandomInput instance in MLMCPy
np.random.seed(1)
def beta_distribution(shift, scale, alpha, beta, size):
return shift + scale * np.random.beta(alpha, beta, size)
stiffness_distribution = RandomInput(distribution_function=beta_distribution,
shift=1.0,
scale=2.5,
alpha=3.,
beta=2.)
# Step 2 - Initialize spring-mass models. Here using three levels with MLMC.
# defined by different time steps
model_level1 = SpringMassModel(mass=1.5, time_step=1.0, cost=1.0)
model_level2 = SpringMassModel(mass=1.5, time_step=0.1, cost=10.0)
# Step 3 - Initialize MLMC & predict max displacement to specified error.
#These numbers were generated for epsilon=0.1
N0 = 1113
N1 = 34
#Level 0
in files as inputs.
p
'''
# Step 1 - Define random variable for spring stiffness:
# Need to provide a sampleable function to create RandomInput instance in MLMCPy
def beta_distribution(shift, scale, alpha, beta, size):
return shift + scale * np.random.beta(alpha, beta, size)
np.random.seed(1)
stiffness_distribution = RandomInput(distribution_function=beta_distribution,
shift=1.0,
scale=2.5,
alpha=3.,
beta=2.,
random_seed=1)
# Step 2: Run standard Monte Carlo to generate a reference solution and target
# precision
num_samples = 5000
model = SpringMassModel(mass=1.5, time_step=0.01)
input_samples = stiffness_distribution.draw_samples(num_samples)
output_samples_mc = np.zeros(num_samples)
start_mc = timeit.default_timer()
for i, sample in enumerate(input_samples):
output_samples_mc[i] = model.evaluate([sample])
