mcapodici

Ralph Instructions

1. You will pick a next task, you decide either:

• Pick a bug to fix from BUGS.md
• Pick a undone task from PLAN.md
• Do a bit of testing to find new bugs and add them to BUGS.md

Decide based on your best intition.

mcapodici

import sys
import modal
import os

# Script to run your local code
# 
# * Requires python package modal-client install, and you have obtained a token, e.g.:
# ```
# pip install modal-client
# modal token new

mcapodici

wpietri 6 days ago | next [–]

Great! There's a picket line I won't be crossing. Not just for those subs, of course, but for all of Reddit.
The reason Reddit is valuable is not the few execs making these (IMHO terrible) decisions. It's the thousands of mods and the millions of people creating and organizing the content that I go there to read. Until those people are happy with things, I'm not going back.

reply

	
jcims 6 days ago | parent | next [–]

mcapodici

Certainly! I can help you understand how to use the get() function in Firestore security rules to make permissions dependent on data within another collection. Although I don't have the ability to share photos directly, I can guide you through the process step by step.

Let's assume you have a Firestore database structure as follows:

- parentCollection (collection)
  - documentA (document)
    - subCollectionX (collection)
      - subDocumentX (document)
 - documentB (document)

mcapodici

digraph G {
    
  node [shape="none"]
  rankdir="LR"
    
  R [label=<𝓡>, fontcolor=blue]    
  S [label=<𝓢>, fontcolor=blue]
  E [label=<𝓔>, fontcolor=darkgreen]

  h [label=<<b>h</b>>, fontcolor=blue]

mcapodici

import torch

W1 = torch.tensor([[1,-1,0,0],[0,1,-1,0],[0,0,1,-1]])
W1

mcapodici

Using autograd to confirm answers to CSC321 Winter 2018, Hw2, Q3

import torch
from torch.autograd import Variable

torch.manual_seed(0)

mcapodici

# Based on https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/homeworks/hw2.pdf, 1. b.

import numpy
import pylab
import random

# Training Data

x = numpy.array([
    [1, -2],

mcapodici

import numpy as np
from numpy import genfromtxt

iris = genfromtxt("IRIS.csv", delimiter=",", skip_header=True)

training_examples = iris[:, list(range(3))]
targets = iris[:, -2]
bias_column = np.ones((training_examples.shape[0], 1))

inputs = np.hstack((bias_column, training_examples))

mcapodici

interface Bet {
  who: string;
  outcome: number; // 1 for A, 2 for B
  shares: number; // Number of shares allocated
  money: number; // Money placed for given shares
}

interface Pool {
  initialMoney: number; // Initial pool
  initialProb: number; // Initial probability