I hereby claim:
- I am jxcodetw on github.
- I am jxcode (https://keybase.io/jxcode) on keybase.
- I have a public key ASCgw1-M_ZrHroHTRAsmylxMFg9Gb7FGRaEbnGpkpYxngAo
To claim this, I am signing this object:
import torch | |
import torch.optim as optim | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from openTSNE import TSNE | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.manifold import SpectralEmbedding | |
from scipy.sparse import save_npz, load_npz | |
from functools import partial |
import torch | |
import torch.optim as optim | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from umap.umap_ import fuzzy_simplicial_set, find_ab_params | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.manifold import SpectralEmbedding | |
from scipy.sparse import save_npz, load_npz | |
import random |
import torch | |
import torch.optim as optim | |
import torch.nn.functional as F | |
from openTSNE import TSNE | |
from umap.umap_ import fuzzy_simplicial_set, find_ab_params | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.manifold import SpectralEmbedding | |
from scipy.sparse import save_npz, load_npz | |
import random |
import torch | |
import torch.optim as optim | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from openTSNE import TSNE | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.manifold import SpectralEmbedding | |
from scipy.sparse import save_npz, load_npz | |
import random |
{ | |
"lkunrate": [ | |
{ | |
"startDate": "2002-03-11", | |
"endDate": "2002-08-26", | |
"light": 1, | |
}, | |
{ | |
"startDate": "2002-09-02", | |
"endDate": "2002-12-02", |
# draw a bunch of samples | |
def random_walk(png, Gs, cx, cy, cw, ch, step, seeds): | |
print(png) | |
latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds) | |
# images = Gs.run(latents, None, **synthesis_kwargs) # [seed, y, x, rgb] | |
idx = 0 | |
for idxx, (l1, l2) in enumerate(zip(latents, latents[1:])): | |
print(idxx, '/', len(latents)) | |
latent = np.stack([l1 * (1-alpha) + l2 * alpha for alpha in np.linspace(0, 1, step)]) |
import torch | |
import torch.nn as nn | |
from tqdm import tqdm | |
device = torch.device('cuda') | |
# 5629MiB / 11178MiB | |
# NVIDIA-SMI 410.78 Driver Version: 410.78 CUDA Version: 10.0 | |
torch.backends.cudnn.benchmark = True |
I hereby claim:
To claim this, I am signing this object:
<?xml version="1.0" encoding="UTF-8" ?> | |
<!DOCTYPE html> | |
<html b:version='2' expr:dir='data:blog.languageDirection' xmlns='http://www.w3.org/1999/xhtml' xmlns:b='http://www.google.com/2005/gml/b' xmlns:data='http://www.google.com/2005/gml/data' xmlns:expr='http://www.google.com/2005/gml/expr'> | |
<head> | |
<meta content='width=device-width, initial-scale=1' name='viewport'/> | |
<title><data:view.title.escaped/></title> | |
<b:include data='blog' name='all-head-content'/> | |
<!-- CSS STARTS --> | |
<b:skin><![CDATA[ | |
body, pre{ font-size: 1rem; font-style: normal; font-family: Menlo,Monaco,Lucida Console,Liberation Mono,DejaVu Sans Mono,Bitstream Vera Sans Mono,Courier New,monospace,serif; text-rendering: geometricPrecision; background: #002b36; color: #93a1a1; line-height: 1.4rem;}a { color: #ff9800; text-decoration: none; border-bottom: 1px solid #ff2e88;}a[href*="://"]:after { content: " " url("data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20class='i-external'%20viewBox='0%200 |
#include <stdio.h> | |
#include <math.h> | |
#define MAX 100 | |
double M[MAX][MAX]; // matrix | |
bool DR[MAX]; // row that has been deleted | |
int row, col; // maxrow, maxcol | |
int sub; // col to be substituted |