def gaussian_sampling_valid_demo(u=None,
cov=None,
num_sample=1000,
num_class=2,
batch=2):
if u is None:
u = torch.tensor([[0, 0], [4, 4]])
if cov is None:
cov = torch.tensor([[[1, 0], [0, 1]], [[1, 0.5], [0.5, 1]]])
sample = torch.randn(num_sample, num_class)
sample = sample.unsqueeze(0).expand(batch, -1, -1)
l = torch.linalg.cholesky(cov).unsqueeze(1).expand(-1, num_sample, -1, -1)
sample = torch.matmul(l, sample.unsqueeze(-1)).squeeze(-1) + u.unsqueeze(1)
sample = sample.reshape(-1, num_class)
x = sample[:num_sample, :]
y = sample[num_sample:, :]
print(torch.mean(x, dim=0), torch.cov(x.T))
print(torch.mean(y, dim=0), torch.cov(y.T))
def compute_covariance_matrix(walks):
diffs = []
for walk in walks:
x_pre = walk[:-1, :]
x_post = walk[1:, :]
diff = x_post - x_pre
diffs.append(diff)
diffs_cat = torch.cat(diffs, axis=0)
cov = torch.cov(diffs_cat.T)
return cov
def cov(*args, **kwargs):
return torch.cov(*args, **kwargs)
# Author: Jintao Huang # Email: [email protected] # Date: import torch from dev.torch import cov # In[0]: x = torch.randn(10, 20) print(torch.allclose(torch.cov(x), cov(x))) print(torch.allclose(torch.cov(x, correction=0), cov(x, correction=0))) print() """Out[0] True True """
def calculate_ditribution(input):
mu = torch.mean(input)
sigma = torch.cov(input)
return mu, sigma
