| """Perlin noise implementation.""" | |
| # Licensed under ISC | |
| from itertools import product | |
| import math | |
| import random | |
| def smoothstep(t): | |
| """Smooth curve with a zero derivative at 0 and 1, making it useful for | |
| interpolating. |
| // 1. Go to https://twitter.com/i/likes | |
| // 2. Keep scrolling to the bottom repeatedly until all your favs are loaded. | |
| // 3. Run this in your console (open in chrome by View > Developer > JavaScript Console) | |
| // Notes: this may take a while if you have a lot of favs/likes | |
| // you can only access your most recent ~2000 likes. | |
| // inspired by https://gist.github.com/JamieMason/7580315 | |
| $('.ProfileTweet-actionButtonUndo').click() |
| // Include this at the very top of both your main and window processes, so that | |
| // it loads as soon as possible. | |
| // | |
| // Why does this work? The node.js module system calls fs.realpathSync a _lot_ | |
| // to load stuff, which in turn, has to call fs.lstatSync a _lot_. While you | |
| // generally can't cache stat() results because they change behind your back | |
| // (i.e. another program opens a file, then you stat it, the stats change), | |
| // caching it for a very short period of time is :ok: :gem:. These effects are | |
| // especially apparent on Windows, where stat() is far more expensive - stat() | |
| // calls often take more time in the perf graph than actually evaluating the |
| from batcountry import BatCountry | |
| import numpy as np | |
| from PIL import Image | |
| from glob import glob | |
| import os | |
| import random | |
| CAFFE_ROOT = '../caffe' | |
| INPUT_PATH = 'input.jpg' |
| import torch.utils.data | |
| from torchvision import datasets, transforms | |
| class PartialDataset(torch.utils.data.Dataset): | |
| def __init__(self, parent_ds, offset, length): | |
| self.parent_ds = parent_ds | |
| self.offset = offset | |
| self.length = length | |
| assert len(parent_ds)>=offset+length, Exception("Parent Dataset not long enough") | |
| super(PartialDataset, self).__init__() |
| import argparse | |
| from collections import Counter | |
| import csv | |
| import os | |
| import torch | |
| from torch.autograd import Variable | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import torch.utils.data as data | |
| import tarfile |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.nn.parameter import Parameter | |
| def debug(debug_open, x, layername): | |
| if debug_open: | |
| print x.size(), 'after', layername | |
| class PVANet(nn.Module): |
| """ | |
| DQN in PyTorch | |
| """ | |
| import argparse | |
| import torch | |
| import torch.nn | |
| import numpy as np | |
| import random | |
| import gym |
| WEBGL // p5 WEBGL rendering mode. | |
| createCanvas(w, h, renderer) // Creates a 3D canvas (if renderer is WEBGL). | |
| // Primitives | |
| plane(width, height) // Creates a plane in 3D space. Equivalent to rect() in the default rendering mode. | |
| plane(width, height, detailX, detailY) // Creates a plane in 3D space with the number of triangle subdivisions specified. | |
| box(width) // Creates a cube in 3D space. | |
| box(width, height, depth) // Creates a cuboid in 3D space. | |
| box(width, height, depth, detailX, detailY) // Creates a cuboid in 3D space with triangle subdivisions. | |
| sphere(radius) // Creates a sphere in 3D space. |
