Simple calculation lib with lazy feature

vue-lazy-calc

this is a simple calculation plugin in lazy way. (inspired by lodash)

features

• vue friendly
• strong typed
• lazy evaluation
• chaining methods
• code coverage 100%

TODO:

• [x] seperate simple lazy class from base class
• [x] support more operator in stream api

Install

npm install vue-lazy-calc --save

Quick Start

import lzCalc from "vue-lazy-calc"
Vue.use(lzCalc)

Methods

• this.$lzCalc in Component context.
• Vue.$lzCalc in global.

API list

base

export declare class LazyBase {
  lazy(init?: number): LazyCalc
  stream(s?: LazyCalc): LazyStream
}

• lazy => init a new instance with optional initValue
• stream => init a stream to operate between multiple lazy instance with optional init instantce

simple

export declare class LazyCalc {
  add(y: number): LazyCalc
  divide(y: number): LazyCalc
  subtract(y: number): LazyCalc
  multiply(y: number): LazyCalc
  do(fn: operatorFunc): LazyCalc
  ceil(precision?: number): LazyCalc
  floor(precision?: number): LazyCalc
  round(precision?: number): LazyCalc
  default(fallback: any): LazyCalc
  value(): any
}

• add/subtract/divide/multiple => + - * / (simple calculation) between numbers
• round/floor/ceil => deal with precision of the float number
• value => excute the declared method chain
• default => set default value if previous operations get NaN
• do => accept a custormized function for the number

Examples

(1+3)*2/3 with precision 2

const result = this.$lzCalc
  .lazy(1)
  .add(3)
  .multiply(2)
  .divide(3)
  .round(2)

console.log(result.value()) // 2.67

const addThree = result.add(3)
console.log(addThree.value()) // 2.67+ 3 =>5.67

Stream

declare class LazyStream {
  add(y: LazyCalc): LazyStream
  subtract(y: LazyCalc): LazyStream
  multiply(y: LazyCalc): LazyStream
  divide(y: LazyCalc): LazyStream
  round(precision?: number): LazyStream
  ceil(precision?: number): LazyStream
  floor(precision?: number): LazyStream
  do(fn: operatorFunc): LazyStream
  default(fallback: any): LazyStream
  value(): any
}

const result = this.$lzCalc
  .lazy(1)
  .add(3)
  .multiply(2)
  .divide(3)
  .round(2)

const tmp = this.$lzCalc.lazy(2).add(3)
const s = this.$lzCalc.stream(result).add(tmp)

console.log(s.value()) // 2.67 + 5 => 7.67

1. when declare the result variable, no calculation excuted until value()
2. you can reuse the declare variable

Download Details:

Author: dreambo8563

GitHub: https://github.com/dreambo8563/vue-lazy-calc

#vuejs #javascript #vue-js

What is GEEK

Buddha Community

How to create a calculator using javascript - Pure JS tutorials |Web Tutorials

In this video I will tell you How to create a calculator using javascript very easily.

#how to build a simple calculator in javascript #how to create simple calculator using javascript #javascript calculator tutorial #javascript birthday calculator #calculator using javascript and html

Build a Simple Calculator App in Android Studio

Learn how to develop a simple calculator app in android studio

Hey there Android enthusiasts, greetings from DataFlair. Today we are going to see and learn to implement an Android Project that is a simple calculator app in android studio. We’ll understand the complete project development in this article.

Do you know when was the first ever mechanical calculator created? The Calculators that we use today were never the same earlier. The first calculator was invented in the year 1642 by Blaise Pascal when he was 19 years old. Okay, now let us begin with our project without any further delay!!

#android tutorials #android app development #android calculator project #android project #simple calculator app in android studio

Simple calculation lib with lazy feature

vue-lazy-calc

this is a simple calculation plugin in lazy way. (inspired by lodash)

features

• vue friendly
• strong typed
• lazy evaluation
• chaining methods
• code coverage 100%

TODO:

• [x] seperate simple lazy class from base class
• [x] support more operator in stream api

Install

npm install vue-lazy-calc --save

Quick Start

import lzCalc from "vue-lazy-calc"
Vue.use(lzCalc)

Methods

• this.$lzCalc in Component context.
• Vue.$lzCalc in global.

API list

base

export declare class LazyBase {
  lazy(init?: number): LazyCalc
  stream(s?: LazyCalc): LazyStream
}

• lazy => init a new instance with optional initValue
• stream => init a stream to operate between multiple lazy instance with optional init instantce

simple

export declare class LazyCalc {
  add(y: number): LazyCalc
  divide(y: number): LazyCalc
  subtract(y: number): LazyCalc
  multiply(y: number): LazyCalc
  do(fn: operatorFunc): LazyCalc
  ceil(precision?: number): LazyCalc
  floor(precision?: number): LazyCalc
  round(precision?: number): LazyCalc
  default(fallback: any): LazyCalc
  value(): any
}

• add/subtract/divide/multiple => + - * / (simple calculation) between numbers
• round/floor/ceil => deal with precision of the float number
• value => excute the declared method chain
• default => set default value if previous operations get NaN
• do => accept a custormized function for the number

Examples

(1+3)*2/3 with precision 2

const result = this.$lzCalc
  .lazy(1)
  .add(3)
  .multiply(2)
  .divide(3)
  .round(2)

console.log(result.value()) // 2.67

const addThree = result.add(3)
console.log(addThree.value()) // 2.67+ 3 =>5.67

Stream

declare class LazyStream {
  add(y: LazyCalc): LazyStream
  subtract(y: LazyCalc): LazyStream
  multiply(y: LazyCalc): LazyStream
  divide(y: LazyCalc): LazyStream
  round(precision?: number): LazyStream
  ceil(precision?: number): LazyStream
  floor(precision?: number): LazyStream
  do(fn: operatorFunc): LazyStream
  default(fallback: any): LazyStream
  value(): any
}

const result = this.$lzCalc
  .lazy(1)
  .add(3)
  .multiply(2)
  .divide(3)
  .round(2)

const tmp = this.$lzCalc.lazy(2).add(3)
const s = this.$lzCalc.stream(result).add(tmp)

console.log(s.value()) // 2.67 + 5 => 7.67

1. when declare the result variable, no calculation excuted until value()
2. you can reuse the declare variable

Download Details:

Author: dreambo8563

GitHub: https://github.com/dreambo8563/vue-lazy-calc

#vuejs #javascript #vue-js

Neural Network Feature Importance and Feature Effect with Simple Scientific Trick

Introduction

So you built your neural network, and, based on its holdout and/or out-of-time performance metrics, it’s looking pretty good. Now you need to “sell it” to your business partners, and for that, you need to be able to explain what is happening under the hood. A lot of modelers will skip that part and say “it’s a black box and it’s difficult to really know how the network does it. I can use eli5 and SHAP to get an idea, but it’s hard to explain how it does it.”

While it is true that there is a lot going on in neural networks (hundreds of weights and biases, multiplied by an activation function), it does not mean that we cannot come up with a business explanation of how our network works.

In this article, I am going to show you a simple trick that scientists use all the time to understand and explain the natural world around us, called “Ceteris Paribus”, which translates as “Other things held constant.” It’s the only way we can truly derive causation vs correlation. We will explore how to leverage Ceteris Paribus in Python to understand how our Neural Networks work.

Building Our Neural Network: The Boston Dataset

I have already published an article on building a neural network for predicting house prices. You can find it here. For the purposes of this article, I am going to pick up right where I left off. The referenced article will provide you with all the details you need as background for the remainder of this story.

Feature Effect on Predictions: Ceteris Paribus

To truly understand how one feature affects our predictions, we need to hold all input values constant and only vary the feature that we want to study and understand. By measuring the outcome on our prediction, we can draw a clear relationship between input and prediction.

A good analogy for this is studying plant growth. If you want to really know what causes a plant to grow taller, greener, or produce more fruits, you need to isolate each individual growth factor, vary it, then measure the output and compare to the variation in input. This will give you a good idea of how that factor affects the desired outcome.

Now, in our housing example, let’s examine the effect of “# of Bedrooms” against our target outcome, Median Home Value. Based on our correlation matrix and our sns.pairplot, # of bedrooms came out as highly correlated to our outcome, so it would be interesting to see how variations in the # of bedrooms, with everything else held constant (Ceteris Paribus), will affect house prices.

#partial-dependence-plots #feature-importance #feature-effect #machine-learning #neural-networks

Getting Started With Feature Flags

Introduction

As any developer can tell you, deploying any code carries technical risk. Software might crash or bugs might emerge. Deploying features carries additional user-related risk. Users might hate the new features or run into account management issues. With traditional deployments, all of this risk is absorbed at once.

Feature flags give developers the ability to separate these risks, dealing with one at a time. They can put the new code into production, see how that goes, and then turn on the features later once it’s clear the code is working as expected.

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What is a Feature Flag?

Simply put, a feature flag is a way to change a piece of software’s functionality without changing and re-deploying its code. Feature flags involve creating a powerful “if statement” surrounding some chunk of functionality in software (pockets of source code).

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The History of Feature Flags

Leading Web 2.0 companies with platforms and services that must maintain performance among high traffic levels led the way in regard to developing and popularizing new deployment techniques. Facebook, in particular, is known as a pioneer of feature flags and for releasing massive amounts of code at scale. While building its massive social network more than a decade ago, the company realized that its uptime and scale requirements could not be met with traditional site maintenance approaches. (A message saying the site was down while they deployed version 3.0 was not going to cut it).

Instead, Facebook just quietly rolled out a never-ending stream of updates without fanfare. Day to day, the site changed in subtle ways, adding and refining functionality. At the time, this was a mean feat of engineering. Other tech titans such as Uber and Netflix developed similar deployment capabilities as well.

The feature flag was philosophically fundamental to this development and set the standard for modern deployment maturity used by leading organizations everywhere today. Recently, feature flags have been used in tandem with continuous delivery (CD) tools to help forward-looking organizations bring features, rather than releases, to market more quickly.

#devops #continuous integration #ci/cd #continous delivery #feature flags #flags #feature branching #feature delivery