def sample(self, N, rule):
"""Sample from parameter distribution.
**Parameters**
**N:** [int] Number of samples.
**rule:** [str] Sampling rule (e.g. "L" for latin hypercube sampling).
"""
if not self._model: self._loadmodel()
return J(*[i.distribution
for i in self._params]).sample(N, rule).transpose()
def sample(self, N, rule, uniform=False):
"""
Return N samples from parameter distribution at given rule.
Parameters
----------
N : int
Number of samples.
rule : str
Sampling rule.
uniform=False : bool, optional
Whether to assume a joint uniform distribution across parameter bounds.
Otherwise, sample from a joint distribution of all parameters.
Notes
-----
Use the following ``rule`` flag for sampling:
+-------+-------------------------------------------------+
| key | Description |
+=======+=================================================+
| ``C`` | Roots of the first order Chebyshev polynomials. |
+-------+-------------------------------------------------+
| ``NC``| Chebyshev nodes adjusted to ensure nested. |
+-------+-------------------------------------------------+
| ``K`` | Korobov lattice. |
+-------+-------------------------------------------------+
| ``R`` | Classical (Pseudo-)Random samples. |
+-------+-------------------------------------------------+
| ``RG``| Regular spaced grid. |
+-------+-------------------------------------------------+
| ``NG``| Nested regular spaced grid. |
+-------+-------------------------------------------------+
| ``L`` | Latin hypercube samples. |
+-------+-------------------------------------------------+
| ``S`` | Sobol low-discrepancy sequence. |
+-------+-------------------------------------------------+
| ``H`` | Halton low-discrepancy sequence. |
+-------+-------------------------------------------------+
| ``M`` | Hammersley low-discrepancy sequence. |
+-------+-------------------------------------------------+
"""
if not self._update: self._load_parameters()
parameters = self._parameters
if uniform:
distributions = [Uniform(*i.bounds) for i in parameters]
else:
distributions = [i.distribution for i in parameters]
return J(*distributions).sample(N, rule).transpose()
def sample(self, N, rule):
"""Return N samples from parameter distribution at given rule.
Parameters
----------
N : int
Number of samples.
rule : str
Sampling rule.
Notes
-----
Use the following ``rule`` flag for sampling:
+-------+-------------------------------------------------+
| key | Description |
+=======+=================================================+
| ``C`` | Roots of the first order Chebyshev polynomials. |
+-------+-------------------------------------------------+
| ``NC``| Chebyshev nodes adjusted to ensure nested. |
+-------+-------------------------------------------------+
| ``K`` | Korobov lattice. |
+-------+-------------------------------------------------+
| ``R`` | Classical (Pseudo-)Random samples. |
+-------+-------------------------------------------------+
| ``RG``| Regular spaced grid. |
+-------+-------------------------------------------------+
| ``NG``| Nested regular spaced grid. |
+-------+-------------------------------------------------+
| ``L`` | Latin hypercube samples. |
+-------+-------------------------------------------------+
| ``S`` | Sobol low-discrepancy sequence. |
+-------+-------------------------------------------------+
| ``H`` | Halton low-discrepancy sequence. |
+-------+-------------------------------------------------+
| ``M`` | Hammersley low-discrepancy sequence. |
+-------+-------------------------------------------------+
"""
if not self._update: self._loadparams()
return J(*[i.distribution
for i in self._params]).sample(N, rule).transpose()
"""
Created: Fri Jul 26 09:57:16 2019
@author: Christopher Albert <*****@*****.**>
"""
import time
from chaospy import Normal, J, generate_quadrature
params = [Normal(mu=0.999, sigma=0.0052),
Normal(mu=27.1, sigma=17.0),
Normal(mu=0.318, sigma=0.1),
Normal(mu=0.015, sigma=0.0087),
Normal(mu=0.0817, sigma=0.0077),
Normal(mu=1.309, sigma=0.086),
Normal(mu=2.19, sigma=0.22)]
dist = J(*params)
#%%
t = time.time()
nodes, weights = generate_quadrature(4, dist, rule='G', sparse=True)
print('time elapsed: {} s'.format(time.time() - t))
pickle.dump(approx, filehandler)
#%%
indata = np.load('indata.npy')
outdata = np.load('outdata.npy')
with open('pce.pickle', 'rb') as filehandler:
approx = pickle.load(filehandler)
params = OrderedDict()
data = np.genfromtxt('params_1998.txt', dtype=None, encoding=None)
for param in data:
if(param[1] == 'Normal'):
params[param[0]] = Normal(param[2], param[3])
#%%
distribution = J(*params.values())
#%%
F0 = E(approx, distribution)
dF = Std(approx, distribution)
#%%
F0[0] = F0[1]
dF[0] = dF[1]
plt.plot(np.arange(len(F0))-38, F0, 'k')
plt.fill_between(np.arange(len(F0))-38,F0-dF, F0+dF, color='k', alpha=0.3)
plt.ylim(-100,200)
plt.xlabel('t')
plt.ylabel('f(t)')
plt.show()