yoyolicoris

🌞 Morning   202 commits  ██████▍░░░░░░░░░░░░░░  30.7%
🌆 Daytime   251 commits  ████████░░░░░░░░░░░░░  38.1%
🌃 Evening    96 commits  ███░░░░░░░░░░░░░░░░░░  14.6%
🌙 Night     109 commits  ███▍░░░░░░░░░░░░░░░░░  16.6%

yoyolicoris

The code is adapted from https://github.com/Anthony-Tatowicz/docker-ethminer.

Make sure you have 1) Docker and 2) Nvidia runtime installed then you are good to go.

yoyolicoris

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchaudio.functional import lfilter as torch_lfilter
from torch.autograd import Function, gradcheck


class lfilter(Function):

    @staticmethod

yoyolicoris

import numpy as np
import networkx as nx
from scipy.spatial import Delaunay

def W(x):    
    return (x + np.pi) % (2 * np.pi) - np.pi

def mcf_sparse(x, y, psi, capacity=None):
    points = np.vstack((x, y)).T
    num_points = points.shape[0]

yoyolicoris

import numpy as np
import networkx as nx

def W(x):    
    return (x + np.pi) % (2 * np.pi) - np.pi

def mcf(x: np.ndarray, capacity=None):
    assert x.ndim == 2, "Input x should be a 2d array!"

    # construct index for each node