System directories
| Method | Result |
|---|---|
| Environment.getDataDirectory() | /data |
| Environment.getDownloadCacheDirectory() | /cache |
| Environment.getRootDirectory() | /system |
External storage directories
System directories
| Method | Result |
|---|---|
| Environment.getDataDirectory() | /data |
| Environment.getDownloadCacheDirectory() | /cache |
| Environment.getRootDirectory() | /system |
External storage directories
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
| from __future__ import print_function | |
| import argparse | |
| import os | |
| import random | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.parallel | |
| import torch.backends.cudnn as cudnn | |
| import torch.optim as optim | |
| import torch.utils.data |
| def reduce_to_k_dim(M, k=2): | |
| """ Reduce a co-occurence count matrix of dimensionality (num_corpus_words, num_corpus_words) | |
| to a matrix of dimensionality (num_corpus_words, k) using the following SVD function from Scikit-Learn: | |
| - http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html | |
| Params: | |
| M (numpy matrix of shape (number of corpus words, number of corpus words)): co-occurence matrix of word counts | |
| k (int): embedding size of each word after dimension reduction | |
| Return: | |
| M_reduced (numpy matrix of shape (number of corpus words, k)): matrix of k-dimensioal word embeddings. |
| public class NetworkManager : MonoBehaviour { | |
| private void Start() { | |
| StartCoroutine(MakeRequests()); | |
| } | |
| private IEnumerator MakeRequests() { | |
| // GET | |
| var getRequest = CreateRequest("https://jsonplaceholder.typicode.com/todos/1"); | |
| yield return getRequest.SendWebRequest(); |