A quick few snippets of code today -- solving how to compactly and elegantly generate n-grams from your favorite iterable. For starters, let's talk about generating all bigrams from a python list (or anything we can iterate over). We'll write it generally so it can work over lists, strings, or whatever else you care to make iterable. Finally, I'll show the more general extension at the end. The ob
written on August 16, 2014 It’s no secret that I’m not a fan of Python 3 or where the language is currently going. This has led to a bunch of emails flying my way over the last few months about questions about what exactly I would prefer Python would do. So I figured I might share some of my thoughts publicly to maybe leave some food for thought for future language designers :) Python is definitel
Toga isn’t the world’s first widget toolkit - there are dozens of other options. So why build a new one? Native widgets - not themes Toga uses native system widgets, not themes. When you see a Toga app running, it doesn’t just look like a native app - it is a native app. Applying an operating system-inspired theme over the top of a generic widget set is an easy way for a developer to achieve a cro
What?¶ Arrow is a Python library that offers a sensible, human-friendly approach to creating, manipulating, formatting and converting dates, times, and timestamps. It implements and updates the datetime type, plugging gaps in functionality, and provides an intelligent module API that supports many common creation scenarios. Simply put, it helps you work with dates and times with fewer imports and
Sections Sections Introduction Principal Component Analysis vs. Linear Discriminant Analysis What is a “good” feature subspace? Summarizing the LDA approach in 5 steps Preparing the sample data set About the Iris dataset Reading in the dataset Histograms and feature selection Normality assumptions LDA in 5 steps Step 1: Computing the d-dimensional mean vectors Step 2: Computing the Scatter Matrice
こんにちは、得居です。先週末からインターンシップの3名を迎え、これからの二ヶ月間が楽しみです。 さて、昨年末に公開した実験用環境のmaf (Github)ですが、先週こっそりと v0.2 をリリースいたしました。今日は何が変わったのかをお伝えしたいと思います。 その前に、まずmafについて紹介します。mafは主に機械学習を用いた実験を書くための環境で、アルバイトの能地さん @nozyh と私の2人で開発しています。ビルドツールのwafを拡張する形で書かれていて、データセットから実験結果をビルドする過程を記述することができます。基本的な紹介は昨年末のブログ記事をご参照ください。特徴としては、学習や評価などの処理に付随するハイパーパラメータを管理する仕組みがあることです。詳細はmafのドキュメントをご参照ください。 それでは、v0.2で入った主な変更を紹介していきます。 お約束の簡素化、bui
So you just recorded yourself saying a word and try to match it against another instance. The signals look similar, but have varying lengths and different activations for different features. So, how do you decide the similarity. Dynamic time warping (DTW) is probably something which can come to your rescue. Quoting wikipedia: "In time series analysis, dynamic time warping (DTW) is an algorithm for
This is the first post in a multi-part series wherein I will explain the details surrounding the language prediction model I presented in my Pycon 2014 talk. If you make it all the way through, you will learn how to create and deploy a language prediction model of your own. Realtime predictive analytics using scikit-learn & RabbitMQ OSEMN I’m not sure if Hilary Mason originally coined the term OSE
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