[PDF] Tackling Big Data Using MATLAB





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SoftwareX MATLAB tool for probability density assessment and

A MATLAB function is presented for nonparametric probability density estimation Comparative examples between ksdensity (row 1)



ksdensity

[fxi] = ksdensity(x) returns a probability density estimate



Introduction to Matlab programming

22 janv. 2008 1.1 Interacting with the Matlab Command Window . . . . . . . . . . 3 ... [f1]=ksdensity(cc2sort(cc2)); [f2]=ksdensity(yy2



Appendix A: MATLAB

[yx] = ksdensity(randn(100



Tackling Big Data Using MATLAB

Using the same intuitive MATLAB syntax you are used to Use tall arrays to work with the data like any MATLAB array ... histogram histogram2 ksdensity ...



Appendix A: Quick Review of Distributions Relevant in Finance with

Matlab. ®. Examples. ?. Laura Ballotta and Gianluca Fusai. In this Appendix we quickly review the properties of distributions relevant in finance



Coherent Intrinsic Images from Photo Collections upplemental

Lastly we provide the Matlab sampling code and 100 samples drawn



Application of Monte Carlo Method Based on Matlab: Calculation of

Matlab: Calculation of Definite Integrals and Matlab provides us with a very efficient function named ksdensity through which we can derive a.



Most Probable Phase Portraits of Stochastic Differential Equations

simulation stochastic differential equations



A Maximum-Entropy Method to Estimate Discrete Distributions from

13 août 2018 KDS: We used the Matlab Kernel density function ksdensity as implemented in Matlab R2017b with a normal kernel function support limited to ...



MATLAB ksdensity - MathWorks

This MATLAB function returns a probability density estimate f for the sample data in the vector or two-column matrix x



how to estimate cdf from ksdensity pdf - MATLAB Answers

I have a quick question about ksdensity For a given variable I derive distribution by binning into a specified number of bins 



ksdensity function for pdf estimation - MATLAB Answers - MathWorks

ksdensity function for pdf estimation Learn more about ksdensity i feed some data to ksdensity but i got a gaussian pdf with peak greater than 1 how 



ksdensity doesnt return a pdf which sums to 1 and has problems at

I'm using ksdensity (with optimal bw) to estimate a pdf but when I sum up the single entries I get 0 49 Shouldn't the sum be 1? Also it returns zeros at 



X-Axis in pdf are misinterpreted (ksdensity) - MATLAB Answers

is used to translate each y-axis value to probabilities However the x-value in the plot are greater than 1 - how can this be ?



how to estimate cdf from ksdensity pdf - MATLAB Answers - MATLAB

I was wondering if I can used ksdensity to do this as the more robust soluton So essentially finding CDF from PDF that was estimated using Kernel Desnity?



Probability Density Function using ksdensity is not normalized

Probability Density Function using ksdensity is I want to find the PDF Actually the output from ksdensity is normalized but you will have to use 



Fit Kernel Distribution Using ksdensity - MATLAB & Simulink

Use ksdensity to generate a kernel probability density estimate for the miles per The plot shows the pdf of the kernel distribution fit to the MPG data 



Convolution of CDF and a PDF using Kernel density estimator

12 sept 2019 · I have fitted the CDF of my data using gevcdf function and PDF of the data using ksdensity with normal kernel The CDF is based on 30 



How to use mhsample or slicesample with ksdensity? - MathWorks

I want to use ksdensity to estimate a pdf then draw samples from that pdf /distribution The function handle " pdf " takes only one argument but ksdensity 

  • What does Ksdensity do in Matlab?

    ksdensity computes the estimated inverse cdf of the values in x , and evaluates it at the probability values specified in pi . This value is valid only for univariate data.

  • How do you calculate density in Matlab?

    - Calculate for each object the density using the equation: Density = mass/volume. Store the results in 1D array.

  • How to calculate PDF using MATLAB?

    y = pdf( pd , x ) returns the pdf of the probability distribution object pd , evaluated at the values in x .
  • The kernel smoothing function defines the shape of the curve used to generate the pdf. Similar to a histogram, the kernel distribution builds a function to represent the probability distribution using the sample data.
1

© 2015 The MathWorks, Inc.

Tackling Big Data Using MATLAB

Alka Nair

Application Engineer

2

Building Machine Learning Models with Big Data

Access

Model Development

Scale up & Integrate with

Production Systems

Preprocess,

Exploration &

3

Case study: Predict Air Quality

Temperature

Pressure

Relative Humidity

Dew Point

Wind speed

Wind direction

Ozone CO NO2 SO2

Factors Affecting Air Quality

My Weather Page

www.myweather.com/stats.html 4 5

Building Machine Learning Models with Big Data

Access

Preprocess, Exploration

& Model Development

Scale up & Integrate with

Production Systems

6 Challenges in Modeling and Deploying Big Data Applications

Access

Preprocess,

Exploration & Model

Development

Distributed Data Storage

Different Data Sources &

Types

Preprocessing and Visualizing Big Data

Parallelizing Jobs and Scaling up

Computations to Cluster

Enterprise level

deployment

Managing Different APIs for Data

Sources and Data Formats

Rewriting Algorithms to Use Big

Data Platforms

Parallelizing Code to Scale up to

Use Cluster and Cloud Compute

Overhead in Moving the

Algorithm to Production

Scale up & Integrate

with Production Systems 7

Easily access data however it is stored

Prototype algorithms quickly using small data sets Scale up to big data sets running on large clusters Using the same intuitive MATLAB syntax you are used to 8

Building machine learning models with big data

Access

Model Development

Scale up & Integrate with

Production Systems

Preprocess,

Exploration &

9 Different Data TypesDifferent Data SourcesDifferent Applications Text

Images

Spreadsheet

Custom File Formats

Hadoop Distributed File

System (HDFS)

Amazon S3

Windows Azure Blob

Storage

Relational Database

HDFS on Hortonworks or

Cloudera

MapReduce

Image Segmentation

Image Classification

Denoising Images

Predictive Maintenance

Access and Manage Big Data

Datastores

10

Datastore

Cluster of

Machines

Memory

Single

Machine

Memory

One or more files

Cluster of

Machines

Memory

Single

Machine

Memory

Process

11

Air Quality Data on Local Folder

12

Accessing and Processing different types of data

TabularTextDatastoreText files containing column-oriented data, including

CSV files

ImageDatastoreImage files, including formats that are supported byimreadsuch as JPEG and PNG SpreadsheetDatastoreSpreadsheet files with a supported Excel®format such as.xlsx

MDFDatastoreDatastore for collection of MDF files

Custom DatastoreDatastore for custom or proprietary format

Image Collection

MDF Files 13 access this data? 14 Historical files are on HDFS and real time data are available through an API

Temperature

Pressure

Relative Humidity

Dew Point

Wind Speed

Wind Direction

Ozone CO NO2 SO2 15

Access air quality data using datastore

16 Preview the data and adjust properties to best represent the data of interest 17

Access data from anywhere with minimal changes

Local disk

18

Datastores enable big data workflows

Deep Learning

19

Datastores enable big data workflowsPredictive

Maintenance

20

Datastores enable big data workflows

Fleet

Analytics

21
Different Data TypesDifferent Data SourcesDifferent Applications Text

Images

Spreadsheet

Custom File Formats

Hadoop Distributed File

System (HDFS)

Amazon S3

Windows Azure Blob

Storage

Relational Database

HDFS on Hortonworks or

Cloudera

MapReduce

Image Segmentation

Image Classification

Denoising Images

Predictive Maintenance

Datastores: Access Big Data with Minimal Changes

22

Building machine learning models with big data

Access

Model Development

Scale up & Integrate with

Production Systems

Preprocess,

Exploration &

23
visualize and process the data? 24
Use tallarrays to work with the data like any MATLAB array 25

Introduction to Tall Arrays

Tall Arrays for Big Data Visualization and Preprocessing

Machine Learning for Big Data Using Tall Arrays

26

Cluster of

Machines

Memory

Single

Machine

Memory

Tall arrays

Data is in one or more files

Files stacked vertically

Typically tabular data

Challenge

(even cluster memory)

Takes a lot of time for even simple

operations on data 27
tall array

Cluster of

Machines

Memory

Single

Machine

Memory

Tall arrays (new R2016b)

Create tall table from datastore

Operate on whole tall table

just like ordinary table

Datastore

ds = datastore('*.csv') tt= tall(ds) summary(tt) max(tt.EndTimett.StartTime)

Single

Machine

Memory

Process

28
tall array

Cluster of

Machines

Memory

Single

Machine

Memory

tallarrays

With Parallel Computing Toolbox,

Can scale up to clusters with

MATLAB Distributed Computing Server

Single

Machine

Memory

Process

Single

Machine

Memory

Process

Single

Machine

Memory

Process

Single

Machine

Memory

Process

Single

Machine

Memory

Process

Single

Machine

Memory

Process

29

Use a Spark-enabled Hadoop cluster and MATLAB

Support for many other platforms through reference architectures 30

Spark Connection

Cluster Config for Spark

Hadoop Access

31

MATLAB Documentation for

32

Summary for tallarrays

Process out-of-memory data on

your Desktop to explore, analyze, gain insights and to develop analytics

MATLAB Distributed Computing Server,

Spark+Hadoop

Local disk,

Shared folders,

Databases

or Spark+ Hadoop (HDFS), for large scale analysis

Use Parallel Computing

Toolbox for increased

performance

Run on Compute Clusters

Develop your code locally using Tall Arrays

or

MapReduce only

once Use the same code to scale up to cluster 33

Create a tallarray for each datastore

ozone 34
Execution model makes operations more efficient on big data

Deferred evaluation

Commands are not executed right

away

Operations are added to a queue

Execution triggers include:

gatherfunction summaryfunction

Machine learning models

Plotting

tt: tall array 35
Execution model makes operations more efficient on big data

Unnecessary results are not

computed 36

Introduction to Tall Arrays

Tall Arrays for Big Data Visualization and Preprocessing

Machine Learning for Big Data Using Tall Arrays

37

Explore Big Data with Tall Visualizations

plot scatter binscatter histogram histogram2 ksdensity 38

Explore Big Data with Tall Visualizations

39

Get a summary of the data

tttall table 40

Use data types to best represent the data

41

Managing Big and Messy Time-stamped Data

42
Use the results of explorations to help make decisions -Synchronize to daily data -By location 43

Synchronize all data to daily times

44
Clean messy data using common preprocessing functions 45

Use familiar MATLAB functions on tallarrays

Functions Supported with Tall Arrays

46
47

Save preprocessed data

48

Introduction to Tall Arrays

Tall Arrays for Big Data Visualization and Preprocessing

Machine Learning for Big Data Using Tall Arrays

49

Predict air quality

Air Quality IndexAir Quality Label

RegressionClassification

50

How do you know which model to use?

Try them all

51
Use apps for model exploration on a subset of data

Air Quality Index

Regression Learner

Air Quality Label

Classification Learner

52

Validate and Compare Machine Learning Models

53

Validate and Compare Machine Learning Models

54

Validate and Compare Machine Learning Models

55

Validate and Compare Machine Learning Models

56

Scale up with tallmachine learning models

Linear Regression (fitlm)

Logistic & Generalized Linear Regression(fitglm)

Discriminant Analysis Classification (fitcdiscr)

K-means Clustering (kmeans)

Principal Component Analysis (pca)

Partition for Cross Validation (cvpartition)

Linear Support Vector Machine (SVM)Classification(fitclinear)

Naïve BayesClassification(fitcnb)

Random ForestEnsemble Classification(TreeBagger)

Lasso Linear Regression (lasso)

Linear Support Vector Machine (SVM) Regression (fitrlinear)

Single Classification Decision Tree (fitctree)

LinearSVMClassification with Random Kernel Expansion (fitckernel)

Gaussian Kernel Regression (fitrkernel)

57
Training Machine Learning Model against Spark for Air Quality

Classification

58
Train and validate with talldata for Air Quality Index Prediction 59

Select the most important features

61

Introduction to Tall Arrays

Tall Arrays for Big Data Visualization and Preprocessing

Machine Learning for Big Data Using Tall Arrays

62

Building machine learning models with big data

Access

Model Development

Scale up & Integrate with

Production Systems

Preprocess,

Exploration &

63
64

Predict air quality for given location

My Weather Page

www.myweather.com/stats.html

Your Weather Conditions

Get weather conditions for your area.

Location:01760

Temperature:32F

Humidity:76%

Wind:SSW 13 mph

My Weather Page

www.myweather.com/stats.html

Current Weather

MATLAB

Runtime

MATLAB

Runtime

Use MATLAB model running on Spark in Python web

framework 65

Integrate analytics with systems

MATLAB

Runtime

C/C++++ExcelAdd-inJavaHadoop/

Spark.NETMATLABProductionServer

StandaloneApplication

Enterprise Systems

Python

C, C++HDLPLC

Embedded Hardware

GPU 66

Package and test MATLAB code

67
68

Package and test MATLAB code

69

Call MATLAB in production environment

AirQual.ctf

70

MATLAB Production Server

Server software

Manages packaged MATLAB programs and worker pool

MATLAB Runtime libraries

Single server can use runtimes

from different releases

RESTful JSON interface

Lightweight client libraries

C/C++, .NET, Python, and Java

MATLAB Production Server

MATLAB

Runtime

Request Broker

Program

Manager

Applications/

Database

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