Fixed Point Method | Numerical analysis and methods

Numerical analysis and numerical methods | Math for AI - ML - Engineering, we talk about the fixed point method and fixed point theorem. This lecture is outlined as follows:

Chapters
00:00 Intro
03:49 Choice of g(x)
10:20 Fixed Point Theorem
19:24 MATLAB examples
26:40 Outro

Note: The MATLAB code could easily be used in Python. Minimal changes could be done. The code is open-source with detailed explanation found within the shared link. Fixed point iteration and fixed point iteration method are popular recursive techniques to estimate zeros or roots to almost any given equation.

Lecture notes with detailed MATLAB code are on website 👉 https://bazziahmad.com/2021/11/13/fixedpointmethod 

Instructor: Dr. Ahmad Bazzi

Subscribe: https://www.youtube.com/c/AhmadBazzi/featured 

#machine-learning 

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Fixed Point Method | Numerical analysis and methods
Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension â€” understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally â€śunderstand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

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import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

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TokenInfo(type=62 (ENCODING),  string='utf-8')

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TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

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TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

…

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Madaline  Mertz

Madaline Mertz

1621628640

Comprehensive Guide To Python Dunder Methods

Python has a set of magic methods that can be used to enrich data classes; they are special in the way they are invoked. These methods are also called “dunder methods” because they start and end with double underscores. Dunder methods allow developers to emulate built-in methods, and it’s also how operator overloading is implemented in Python. For example, when we add two integers together, 4 + 2, and when we add two strings together, â€śmachine” + “learning”, the behaviour is different. The strings get concatenated while the integers are actually added together.

The “Essential” Dunder Methods

If you have ever created a class of your own, you already know one of the dunder methods, __init__(). Although it’s often referred to as the constructor, it’s not the real constructor; the __new__() method is the constructor. The superclass’s  __new__() , super().__new__(cls[, ...]), method is invoked, which creates an instance of the class, which is then passed to the __init__() along with other arguments. Why go through the ordeal of creating the __new__() method? You don’t need to; the __new__() method was created mainly to facilitate the creation of subclasses of immutable types (such as int, str, list) and metaclasses.

#developers corner #uncategorized #dunder methods #magic methods #operator overriding #python dunder methods #python magic methods

Fixed Point Method | Numerical analysis and methods

Numerical analysis and numerical methods | Math for AI - ML - Engineering, we talk about the fixed point method and fixed point theorem. This lecture is outlined as follows:

Chapters
00:00 Intro
03:49 Choice of g(x)
10:20 Fixed Point Theorem
19:24 MATLAB examples
26:40 Outro

Note: The MATLAB code could easily be used in Python. Minimal changes could be done. The code is open-source with detailed explanation found within the shared link. Fixed point iteration and fixed point iteration method are popular recursive techniques to estimate zeros or roots to almost any given equation.

Lecture notes with detailed MATLAB code are on website 👉 https://bazziahmad.com/2021/11/13/fixedpointmethod 

Instructor: Dr. Ahmad Bazzi

Subscribe: https://www.youtube.com/c/AhmadBazzi/featured 

#machine-learning