tomquisel

class VotingClassifier(object):
    """Stripped-down version of VotingClassifier that uses prefit estimators"""
    def __init__(self, estimators, voting='hard', weights=None):
        self.estimators = [e[1] for e in estimators]
        self.named_estimators = dict(estimators)
        self.voting = voting
        self.weights = weights

    def fit(self, X, y, sample_weight=None):
        raise NotImplementedError

jdegoes

Applied Functional Programming with Scala - Notes

Copyright © 2016-2018 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

whophil

# After Ubuntu 16.04, Systemd becomes the default. 
# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

[Unit]
Description=Jupyter Notebook

[Service]
Type=simple
PIDFile=/run/jupyter.pid
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py

kevindavenport

vancluever

Disabling GNOME Tracker and Other Info

GNOME's tracker is a CPU and privacy hog. There's a pretty good case as to why it's neither useful nor necessary here: http://lduros.net/posts/tracker-sucks-thanks-tracker/

After discovering it chowing 2 cores, I decided to go about disabling it.

Directories

kevindavenport

paulochf

from IPython.display import display, HTML

display(HTML(data="""
<style>
    div#notebook-container    { width: 95%; }
    div#menubar-container     { width: 65%; }
    div#maintoolbar-container { width: 99%; }
</style>
"""))

hadley

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

• Statistical knowledge
• Programming/hacking skills
• Domain expertise

Statistical knowledge

RAbraham

package org.apache.spark.graphx
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark._

object repl {
  
 val sc = new SparkContext("local", "test") //> sc  : org.apache.spark.SparkContext = org.apache.spark.SparkContext@3724af13

jiffyclub