uumami / mhar Goto Github PK

from mhar import walk
import numpy as np
from scipy.spatial import ConvexHull
import torch

n = torch.cuda.device_count()
print(n)

N=300
pts = np.random.rand(N+1, N)
hull = ConvexHull(pts)

ai = torch.from_numpy(hull.equations[:,:-1]).to(torch.float32).cuda()
bi = torch.from_numpy(-1 * hull.equations[:,-1]).unsqueeze(1).to(torch.float32).cuda()
x_0 = torch.from_numpy(np.mean(pts, axis=0)).unsqueeze(1).to(torch.float32).cuda()

X = walk(z=5, # Padding Parameter
     ai=ai, # Inequality matrix
     bi=bi, # Inequaliy restrictions
     ae=torch.empty(0), # Equality Matrix
     be=torch.empty(0), # Equality restriction
     x_0=x_0, # Inner Point of the polytope
     T=1, # Number of iid Iterations
     device='cuda', # Device to use cpu or cuda
     warm=0, # Number of iid iterations to burn before saving samples
     seed=None, # Automatic random seed
     thinning=None # Automatic thinning factor of O(n^3)
     )