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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Implementing basic matrix operations

There are several different types of matrix operations, including simple addition, subtraction, scalar multiplication, and various forms of multiplication. To illustrate the matrix operations, we will focus on what is known as matrix product. This is a common approach that involves the multiplication of two matrixes to produce a third matrix.

Consider two matrices, A and B, where matrix A has n rows and m columns. Matrix B will have m rows and p columns. The product of A and B, written as AB, is an n row and p column matrix. The m entries of the rows of A are multiplied by the m entries of the columns of matrix B. This is more explicitly shown here, where:

Implementing basic matrix operations

Where the product is defined as follows:

Implementing basic matrix operations

We start with the declaration and initialization of the matrices. The variables n, m, p represent the dimensions of the matrices. The A matrix is n by m, the B matrix is m by p, and the C matrix representing the product is n by p:

int n = 4; 
int m = 2; 
int p...
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