SVM RBF Kernel Parameters With Code Examples

In this post, you will learn about **SVM RBF (Radial Basis Function) kernel hyperparameters with the python code example. **The following are the two hyperparameters which you need to know while training a machine learning model with SVM and RBF kernel:

• Gamma
• C (also called regularization parameter)

Knowing the concepts on SVM parameters such as Gamma and C used with RBF kernel will enable you to select the appropriate values of Gamma and C and train the most optimal model using the SVM algorithm. Let’s understand why we should use kernel functions such as RBF.

Why Use RBF Kernel?

When the data set is linearly inseparable or in other words, the data set is non-linear, it is recommended to use kernel functions such as RBF. For a linearly separable dataset (linear dataset) one could use linear kernel function (kernel=“linear”). Getting a good understanding of when to use kernel functions will help train the most optimal model using the SVM algorithm. We will use Sklearn Breast Cancer data set to understand SVM RBF kernel concepts in this post. The scatter plot given below represents the fact that the dataset is linearly inseparable and it may be a good idea to apply the kernel method for training the model.

#tutorial #machine learning #ai #rbf kernel

What is GEEK

Buddha Community

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 — 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 “understand” and execute a piece of code, it goes through a series of complicated transformations:

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., 7, Bob, 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

2

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

1

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

2

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='')

9

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

SVM RBF Kernel Parameters With Code Examples

In this post, you will learn about **SVM RBF (Radial Basis Function) kernel hyperparameters with the python code example. **The following are the two hyperparameters which you need to know while training a machine learning model with SVM and RBF kernel:

• Gamma
• C (also called regularization parameter)

Knowing the concepts on SVM parameters such as Gamma and C used with RBF kernel will enable you to select the appropriate values of Gamma and C and train the most optimal model using the SVM algorithm. Let’s understand why we should use kernel functions such as RBF.

Why Use RBF Kernel?

When the data set is linearly inseparable or in other words, the data set is non-linear, it is recommended to use kernel functions such as RBF. For a linearly separable dataset (linear dataset) one could use linear kernel function (kernel=“linear”). Getting a good understanding of when to use kernel functions will help train the most optimal model using the SVM algorithm. We will use Sklearn Breast Cancer data set to understand SVM RBF kernel concepts in this post. The scatter plot given below represents the fact that the dataset is linearly inseparable and it may be a good idea to apply the kernel method for training the model.

#tutorial #machine learning #ai #rbf kernel

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

…

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

• Readability
• Maintainability
• Code Quality
• Premature Optimization

Solutions

1. Refactor the code
2. Use better names

Examples

• Optimized loops

Exceptions

• Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

50+ Basic Python Code Examples

List, strings, score calculation and more…

1. How to print “Hello World” on Python?

2. How to print “Hello + Username” with the user’s name on Python?

3. How to add 2 numbers entered on Python?

4. How to find the Average of 2 Entered Numbers on Python?

5. How to calculate the Entered Visa and Final Grade Average on Python?

6. How to find the Average of 3 Written Grades entered on Python?

7. How to show the Class Pass Status (PASSED — FAILED) of the Student whose Written Average Has Been Entered on Python?

8. How to find out if the entered number is odd or even on Python?

9. How to find out if the entered number is Positive, Negative, or 0 on Python?

…

#programming #python #coding #50+ basic python code examples #python programming examples #python code