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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (20) Chapters Close

Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java Java Tools Index

Java example


In Figure 3-11, we simulated a time series using random integers for the event times. To properly simulate events occurring at random times, we should instead use a process that generates timestamps whose elapsed time between events is exponentially distributed.

The CDF for any probability distribution is an equation that relates the probability P = F(t) to the independent variable t. A simulation uses random numbers that represent probabilities. Therefore, to obtain the corresponding time t for a given random probability P, we must solve following the equation for t:

That is:

Here, y = ln(x) is the natural logarithm, which is the inverse of the exponential function x = ey.

To apply this to our preceding Help Desk example, where λ = 0.25, we have:

Note that, this time, t will be positive because the expression on the right is a double negative (ln (1–P) will be negative since 1 – P < 1).

The program in Listing 3-7 implements that formula at lines 14-17. At line 15, the time() method...

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