Creating a new project with Eclipse for a microcontroller these days is fairly easy, and I have the choice if I want to start the project with C or C++:
Choice of C and C++ for a new project
Articles
In this post, you will learn about how to train a decision tree classifier machine learning model using Python. The following points will be covered in this post:
- What is decision tree?
- Decision tree python code sample
What Is a Decision Tree?
Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is the information gain. The diagram below represents a sample decision tree.
At QuestDB we’ve had a UDP version of the InfluxDB Line Protocol (ILP) reader in QuestDB for quite some time, but we’ve had customers ask for a TCP version of it, so we delivered!
Using it, and configuring it, are relatively simple so don’t expect this to be a long post but I’ll walk you through the basics of how to set it up and use it.
The Builder Pattern is extremely popular in Java applications. Unfortunately, it’s often misunderstood and incorrectly applied, which results to runtime errors.
Let’s remember the purpose of Builder: set only necessary fields in some object and keep remaining fields set to default values. For example, if we’re preparing a configuration object, then it’s convenient to change only the necessary parameters and keep other parameters set to default values.
In this article, we will see the actual difference between gradient descent and the normal equation in a practical approach. Most of the newbie machine learning enthusiasts learn about gradient descent during the linear regression and move further without even knowing about the most underestimated Normal Equation that is far less complex and provides very good results for small to medium size datasets.
If you are new to machine learning, or not familiar with a normal equation or gradient descent, don’t worry I’ll try my best to explain these in layman’s terms. So, I will start by explaining a little about the regression problem.
Introduction
Software to be build is to compare data from independent satellite systems. As fighter jets traverse airspace, each system determines its identity, recording the name of the jet type or an equivalent letter or number abbreviation. For data collected over time, the simulation software must ascertain whether systems agree on identification and if so, whether contrasting the data by single or multiple processors would be more efficient.
This article begins with a proof of concept in Scala 2.12. A small program with function taking generic collection type parameters verifies the basic strategy. The actual software is written in 2.13, in which generic collection types – those inheriting the GenTraversable hierarchy – have been deprecated [1]. The parallel package is also gone but can be brought back [2].
In the previous article in this series introduced our thoughts on setting up a mission-critical open source Java platform using high availability in our Web layer. In this article we’ll continue by installing WildFly and configure our platforms topology.
Note: the terminology in this article, where possible, has been adjusted from the traditional master / slave descriptions to master / subordinate. Some of the images displayed have not yet been updated and show the older terminology.
Currently the Java platform is one of the most consolidated in the world, much of this is due to platform’s ability to support other languages such as Kotlin, Groovy and Scala, etc. Thousands of web and mobile applications are developed using the platform as a base. The Java platform has changed a lot in recent years and probably will keep evolving.
In parallel with these changes, we see the evolution application servers, that increasingly offer highly complex features such as load balancing components, smart asynchronous messaging, transaction control and many other technologies facilitating application development and standardization. They also provide a stable and scalable infrastructure for mission critical applications. One of the biggest challenges for application servers is to couple highly complex services, making them stable and flexible.
Intro
Organizations are increasingly looking to containers and distributed applications to provide the agility and scalability needed to satisfy their clients. While doing so, modern enterprises also need the ability to benchmark their application and be aware of certain metrics in relation to their infrastructure.
In this post, I am introducing you to a cloud-native bench-marking tool known as Kubestone. This tool is meant to assist your development teams with getting performance metrics from your Kubernetes clusters.
How Does Kubestone Work?
At it’s core, Kubestone is implemented as a Kubernetes Operator in Go language with the help of Kubebuilder. You can find more info on the Operator Framework via this blog post.
Kubestone leverages Open Source benchmarks to measure Core Kubernetes and Application performance. As benchmarks are executed in Kubernetes, they must be containerized to work on the cluster. A certified set of benchmark containers is provided via xridge’s DockerHub space. Here is a list of currently supported benchmarks:
In a post published on our blog earlier this year, we described some of the decision-making that went into the design and architecture of Snuba, the primary storage and query service for Sentry’s event data. This project started out of necessity; months earlier, we discovered that the time and effort required to continuously scale our existing PostgreSQL-based solution for indexing event data was becoming an unsustainable burden.
Sentry’s growth led to increased write and read load on our databases, and, even after countless rounds of query and index optimizations, we felt that our databases were always a hair’s breadth from the next performance tipping point or query planner meltdown. Increased write load also led to increased storage requirements (if you’re doing more writes, you’re going to need more places to put them), and we were running what felt like an inordinate number of servers with a lot of disks for the data they were responsible for storing. We knew that something had to change.
