def test_sample(self):
m = np.array([0, 0])
cov = np.eye(2)
distribution = MultiStudentT(m, cov, 4)
samples = distribution.sample(10000000)
expected_mean = np.array([0., 0.])
expected_var = np.array([1.9978, 2.0005])
diff_mean = np.abs(samples.mean(axis=0) - expected_mean)
diff_var = np.abs(samples.var(axis=0) - expected_var)
self.assertLess(diff_mean.sum(), 2e-2)
self.assertLess(diff_var.sum(), 2e-2)
def infer_parameters():
# define observation for true parameters mean=170, std=15
y_obs = [
160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812,
140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934,
176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886,
166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379,
170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047,
178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686,
146.67058471763457, 179.41946565658628, 238.02751620619537,
206.22458790620766, 220.89530574344568, 221.04082532837026,
142.25301427453394, 261.37656571434275, 171.63761180867033,
210.28121820385866, 237.29130237612236, 175.75558340169619,
224.54340549862235, 197.42448680731226, 165.88273684581381,
166.55094082844519, 229.54308602661584, 222.99844054358519,
185.30223966014586, 152.69149367593846, 206.94372818527413,
256.35498655339154, 165.43140916577741, 250.19273595481803,
148.87781549665536, 223.05547559193792, 230.03418198709608,
146.13611923127021, 138.24716809523139, 179.26755740864527,
141.21704876815426, 170.89587081800852, 222.96391329259626,
188.27229523693822, 202.67075179617672, 211.75963110985992,
217.45423324370509
]
# define prior
from abcpy.distributions import Uniform
prior = Uniform([150, 5], [200, 25])
# define the model
model = Gaussian(prior)
# define statistics
from abcpy.statistics import Identity
statistics_calculator = Identity(degree=2, cross=False)
# define distance
from abcpy.distances import LogReg
distance_calculator = LogReg(statistics_calculator)
# define kernel
from abcpy.distributions import MultiStudentT
mean, cov, df = np.array([.0, .0]), np.eye(2), 3.
kernel = MultiStudentT(mean, cov, df)
# define backend
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC(model, distance_calculator, kernel, backend)
# sample from scheme
T, n_sample, n_samples_per_param = 3, 250, 10
eps_arr = np.array([.75])
epsilon_percentile = 10
journal = sampler.sample(y_obs, T, eps_arr, n_sample, n_samples_per_param,
epsilon_percentile)
return journal
def test_pdf(self):
m = np.array([0, 0])
cov = np.eye(2)
distribution = MultiStudentT(m, cov, 1)
self.assertLess(abs(distribution.pdf([0., 0.]) - 0.15915), 1e-5)
cov = np.array([[2, 0], [0, 2]])
distribution = MultiStudentT(m, cov, 1)
self.assertLess(abs(distribution.pdf([0., 0.]) - 0.079577), 1e-5)
self.assertLess(abs(distribution.pdf([1., 1.]) - 0.028135), 1e-5)