jFransham

Achieving warp speed with Rust

Contents:

• Number one optimization tip: don't
• Never optimize blindly
• Don't bother optimizing one-time costs
• Improve your algorithms
• CPU architecture primer
• Keep as much as possible in cache
• Keep as much as possible in registers

Mistobaan

# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
def tf_confusion_metrics(model, actual_classes, session, feed_dict):
  predictions = tf.argmax(model, 1)
  actuals = tf.argmax(actual_classes, 1)

  ones_like_actuals = tf.ones_like(actuals)
  zeros_like_actuals = tf.zeros_like(actuals)
  ones_like_predictions = tf.ones_like(predictions)
  zeros_like_predictions = tf.zeros_like(predictions)

shentonfreude

#!/usr/bin/env python

# An exponential backoff around Boto3 DynamoDB, whose own backoff eventually
# fails on long multipage scans. We'd like to use this as a wrapper somehow,
# see: https://gist.github.com/numberoverzero/cec21b8ca715401c5662

from time import sleep

import boto3
from boto3.dynamodb.conditions import Attr

veselosky

# vim: set fileencoding=utf-8 :
#
# How to store and retrieve gzip-compressed objects in AWS S3
###########################################################################
#
#   Copyright 2015 Vince Veselosky and contributors
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at

hadley

ks.default <- function(rows) seq(2, max(3, rows %/% 4))

many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) {
  ldply(seq_along(ks), function(i) {
    cl <- kmeans(x, centers = ks[i], ...)
    data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster)
  })
}

all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") {