mkolod

malcolmgreaves

case class Scored[T](item: T, value: Double)

trait Val[-A] {
  def valueOf(a: A): Double
}

object Val {
  def apply[T: Val]: Val[T] =
    implicitly[Val[T]]
  

outofcoffee

#!/usr/bin/env bash
# Example:
#    ./find-ecr-image.sh foo/bar mytag

if [[ $# -lt 2 ]]; then
    echo "Usage: $( basename $0 ) <repository-name> <image-tag>"
    exit 1
fi

IMAGE_META="$( aws ecr describe-images --repository-name=$1 --image-ids=imageTag=$2 2> /dev/null )"

malcolmgreaves

cbaziotis

class SelfAttention(nn.Module):
    def __init__(self, attention_size, batch_first=False, non_linearity="tanh"):
        super(SelfAttention, self).__init__()

        self.batch_first = batch_first
        self.attention_weights = Parameter(torch.FloatTensor(attention_size))
        self.softmax = nn.Softmax(dim=-1)

        if non_linearity == "relu":
            self.non_linearity = nn.ReLU()

malcolmgreaves

#!/bin/bash

# Bash function to download a file with wget, showing a progress bar and enables 
# re-downloading if interrupted. Also can automatically determine filename from
# supplied URL or override from command line.
# First argument is URL.
# Second optional argument is filename.
download () {
    local URL="$1"
    local FI="$2"

HarshTrivedi

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

stevenhao

// ==UserScript==
// @name         PrestoDB Linter v0.1.3
// @namespace    http://tampermonkey.net/
// @version      0.1
// @description  try to take over the world!
// @author       Steven Hao
// @match        https://modeanalytics.com/editor/*
// @grant        none
// ==/UserScript==

mollymerp

How to mount a GCP compute instance filesystem locally using sshfs

This guide assumes that:

• you already have an instance set up on GCP that you want to mount locally
• the GCP CLI (gcloud) is installed on your local machine
• you have authenticated locally to your google account gcloud auth login

1. make sure your gcloud config is correct for the instance you're trying to access:

khuangaf

import torch
from torch_geometric.data import InMemoryDataset


class MyOwnDataset(InMemoryDataset):
    def __init__(self, root, transform=None, pre_transform=None):
        super(MyOwnDataset, self).__init__(root, transform, pre_transform)
        self.data, self.slices = torch.load(self.processed_paths[0])

    @property