granoeste

System directories

Method	Result
Environment.getDataDirectory()	/data
Environment.getDownloadCacheDirectory()	/cache
Environment.getRootDirectory()	/system

External storage directories

ptrblck

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

ashunigion

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.

Matthew-J-Spencer

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();