impredicative

"""
Usage example:

    from logger import get_logger
    log = get_logger()
    log.info('my_event', my_key1='val 1', my_key2=5, my_key3=[1, 2, 3], my_key4={'a': 1, 'b': 2})

List of metadata keys in each log message:
    event
    _func

mnutt

Instrument Anything in Rails 3

With Rails 3.0 released a few weeks ago I've migrated a few apps and I'm constantly finding useful new improvements. One such improvement is the ability to log anything in the same way that Rails internally logs ActiveRecord and ActionView. By default Rails 3 logs look slightly spiffier than those produced by Rails 2.3: (notice the second line has been cleaned up)

Started GET "/" for 127.0.0.1 at Mon Sep 06 01:07:11 -0400 2010
  Processing by HomeController#index as HTML
  User Load (0.2ms)  SELECT `users`.* FROM `users` WHERE (`users`.`id` = 3) LIMIT 1
  CACHE (0.0ms)  SELECT `users`.* FROM `users` WHERE (`users`.`id` = 3) LIMIT 1

Rendered layouts/_nav.html.erb (363.4ms)

stuarthalloway

;; Datomic example code
(use '[datomic.api :only (db q) :as d])

;; ?answer binds a scalar
(q '[:find ?answer :in ?answer]
   42)

;; of course you can bind more than one of anything
(q '[:find ?last ?first :in ?last ?first]
   "Doe" "John")

kachayev

// Channels-driven concurrency with Go
// Code examples from Rob Pike's talk on Google I/O 2012:
// http://www.youtube.com/watch?v=f6kdp27TYZs&feature=youtu.be
//
// Concurrency is the key to designing high performance network services. 
// Go's concurrency primitives (goroutines and channels) provide a simple and efficient means 
// of expressing concurrent execution. In this talk we see how tricky concurrency 
// problems can be solved gracefully with simple Go code.

// (1) Generator: function that returns the channel

tomrunia

import numpy as np
from tensorflow.python.summary.event_accumulator import EventAccumulator

import matplotlib as mpl
import matplotlib.pyplot as plt

def plot_tensorflow_log(path):

    # Loading too much data is slow...
    tf_size_guidance = {

aabadie

"""Example of usage of Joblib with Amazon S3."""

import s3io
import joblib
import numpy as np

big_obj = [np.ones((500, 500)), np.random.random((1000, 1000))]

# Customize the following values with yours
bucket = "my-bucket"

fabianp

"""
Low rank approximation for the lena image
"""
import numpy as np
import scipy as sp
from scipy import linalg
import pylab as pl

X = sp.lena().astype(np.float)
pl.gray()

bitemyapp

The Learning Haskell guide has been moved to a git repo to enable outside contribution

Find it here: https://github.com/bitemyapp/learnhaskell

mmmikael

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

import random
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import *
from keras.optimizers import SGD, RMSprop

mflatt

#lang racket
(require rackunit)

;; A singly linked list is either
;; - NULL
;; - pointer to data and a pointer a sll
(struct node (data-ptr next-ptr))

(define n4 (node 4 null))