Distributions

Sample from probability distributions not yet included in torch.distributions.

from ml4gw.distributions import PowerLaw, Cosine, UniformComovingVolume
import matplotlib.pyplot as plt

# Initialize distributions
power_law = PowerLaw(
   minimum=4,
   maximum=100,
   index=-3,
)
cosine = Cosine()
ucv = UniformComovingVolume(
   minimum=0,
   maximum=2,
   distance_type="redshift",
)

# Sample from distributions
samples_power_law = power_law.sample((10000,))
samples_cosine = cosine.sample((10000,))
samples_ucv = ucv.sample((10000,))

# Plot samples
plt.figure(figsize=(12, 4))

plt.subplot(1, 3, 1)
plt.hist(samples_power_law, bins=50)
plt.title("PowerLaw")

plt.subplot(1, 3, 2)
plt.hist(samples_cosine, bins=50)
plt.title("Cosine")

plt.subplot(1, 3, 3)
plt.hist(samples_ucv, bins=50)
plt.title("UniformComovingVolume")

plt.tight_layout()
plt.show()