Hva gjør du når Asp.Net web-applikasjonen fint klarer å håndterer 999 999 forespørsler uten problemer, men av en eller annen grunn går opp i flammer på nummer 1 million? Eller når .NET-applikasjonen din putrer og går fint når den starter, men etter 10 dager har spist opp alt minne på serveren? Frykt ikke; med noen små triks, gode verktøy og en porsjon tålmodighet kan man komme langt i å etterforske åstedet for selv de mest bestialske applikasjons-dødsfall. Og med litt flaks får du aldri bruk for det du lærer her.
(presentert for NNUG Trondheim 4.okt 2018; https://www.meetup.com/nnug-trondheim/events/254068518/)
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
Scientific Computing with Python Webinar March 19: 3D Visualization with MayaviEnthought, Inc.
In this webinar, Didrik Pinte provides an introduction to MayaVi, the 3D interactive visualization library for the open source Enthought Tool Suite. These tools provide scientists and engineers a sophisticated Python development framework for analysis and visualization.
This document provides an introduction and overview of NumPy, a Python library used for numerical computing. It discusses NumPy's origins and capabilities, how to install NumPy on Linux, key NumPy concepts like the ndarray object, and how NumPy can be used with Matplotlib for plotting. Examples are given of common NumPy operations and functions for arrays, as well as plotting simple graphs with Matplotlib.
This document contains a presentation by Abhijeet Anand on NumPy. It introduces NumPy as a Python library for working with arrays, which aims to provide array objects that are faster than traditional Python lists. NumPy arrays benefit from being stored continuously in memory, unlike lists. The presentation covers 1D, 2D and 3D arrays in NumPy and basic array properties and operations like shape, size, dtype, copying, sorting, addition, subtraction and more.
This presentation contains a quick tour in Python world. First by By comparing Java code, and the equivalent Python side by side, Second by listing some cool features in Python, finally by listing downs and ups of Python in usage; when to use python and when not.
SciPy and NumPy are Python packages that provide scientific computing capabilities. NumPy provides multidimensional array objects and fast linear algebra functions. SciPy builds on NumPy and adds modules for optimization, integration, signal and image processing, and more. Together, NumPy and SciPy give Python powerful data analysis and visualization capabilities. The community contributes to both projects to expand their functionality. Memory mapped arrays in NumPy allow working with large datasets that exceed system memory.
This document discusses plotting data with Python and Pylab. It begins by describing a sample data table and the problem of reading and plotting the data. It then reviews options for plotting in Python like Pylab, Enthought, RPy, and Sage. The remainder of the document demonstrates how to use Pylab to read CSV data, and create bar charts, pie charts, line plots, and histograms of the sample data.
Hva gjør du når Asp.Net web-applikasjonen fint klarer å håndterer 999 999 forespørsler uten problemer, men av en eller annen grunn går opp i flammer på nummer 1 million? Eller når .NET-applikasjonen din putrer og går fint når den starter, men etter 10 dager har spist opp alt minne på serveren? Frykt ikke; med noen små triks, gode verktøy og en porsjon tålmodighet kan man komme langt i å etterforske åstedet for selv de mest bestialske applikasjons-dødsfall. Og med litt flaks får du aldri bruk for det du lærer her.
(presentert for NNUG Trondheim 4.okt 2018; https://www.meetup.com/nnug-trondheim/events/254068518/)
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
Scientific Computing with Python Webinar March 19: 3D Visualization with MayaviEnthought, Inc.
In this webinar, Didrik Pinte provides an introduction to MayaVi, the 3D interactive visualization library for the open source Enthought Tool Suite. These tools provide scientists and engineers a sophisticated Python development framework for analysis and visualization.
This document provides an introduction and overview of NumPy, a Python library used for numerical computing. It discusses NumPy's origins and capabilities, how to install NumPy on Linux, key NumPy concepts like the ndarray object, and how NumPy can be used with Matplotlib for plotting. Examples are given of common NumPy operations and functions for arrays, as well as plotting simple graphs with Matplotlib.
This document contains a presentation by Abhijeet Anand on NumPy. It introduces NumPy as a Python library for working with arrays, which aims to provide array objects that are faster than traditional Python lists. NumPy arrays benefit from being stored continuously in memory, unlike lists. The presentation covers 1D, 2D and 3D arrays in NumPy and basic array properties and operations like shape, size, dtype, copying, sorting, addition, subtraction and more.
This presentation contains a quick tour in Python world. First by By comparing Java code, and the equivalent Python side by side, Second by listing some cool features in Python, finally by listing downs and ups of Python in usage; when to use python and when not.
SciPy and NumPy are Python packages that provide scientific computing capabilities. NumPy provides multidimensional array objects and fast linear algebra functions. SciPy builds on NumPy and adds modules for optimization, integration, signal and image processing, and more. Together, NumPy and SciPy give Python powerful data analysis and visualization capabilities. The community contributes to both projects to expand their functionality. Memory mapped arrays in NumPy allow working with large datasets that exceed system memory.
This document discusses plotting data with Python and Pylab. It begins by describing a sample data table and the problem of reading and plotting the data. It then reviews options for plotting in Python like Pylab, Enthought, RPy, and Sage. The remainder of the document demonstrates how to use Pylab to read CSV data, and create bar charts, pie charts, line plots, and histograms of the sample data.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
This document provides an introduction to NumPy, the fundamental package for scientific computing with Python. It discusses what NumPy is, why it is useful compared to regular Python lists, how to define arrays of different dimensions, and how to initialize, manipulate, and perform operations on NumPy arrays. Some key capabilities of NumPy include N-dimensional arrays, broadcasting functions, integration with C/C++ and Fortran code, and tools for linear algebra and Fourier transforms.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexing, and using sparse matrix representations. The document provides examples of how to efficiently perform tasks like applying functions element-wise to sparse matrices and calculating norms. It also presents a case study for efficiently computing a formula that appears in a machine learning paper using different sparse matrix representations in SciPy.
The document discusses several Java and Android internals topics:
1. How ArrayList and StringBuilder work internally using arrays and memory copying as the size increases. This can lead to inefficient memory usage.
2. How inner classes are implemented by compilers by generating additional accessor methods, increasing method count and affecting optimizations.
3. How the Android zygote process improves startup and memory usage by loading the framework once and sharing it across apps.
4. How the CPU cache works and how optimizing code to improve cache locality can significantly increase performance despite doing less work.
5. Issues like memory fragmentation that can occur if the Android garbage collector and compactor are unable to run due to the app being
Biopython is a set of freely available Python tools for bioinformatics and molecular biology. It provides features like parsing bioinformatics files into Python structures, a sequence class to store sequences and features, and interfaces to popular bioinformatics programs. Biopython can be used to address common bioinformatics problems like sequence manipulation, searching for primers, and running BLAST searches. The current version is 1.53 from December 2009 and future plans include updating the multiple sequence alignment object and adding a Bio.Phylo module.
This document summarizes recent updates to Python, including updates in versions 3.7 and 3.8. It discusses new features like the pathlib module for handling file paths, type hints for function annotations, data classes for defining classes with default implementations, and the retirement of Guido van Rossum as Benevolent Dictator For Life of Python. It also previews the addition of assignment expressions using := in Python 3.8 as a more Pythonic way to write conditional assignments.
NumPy is a fundamental package for scientific computing in Python that provides multidimensional array objects and tools to work with arrays. Arrays store values of the same data type and are faster than lists. Two-dimensional arrays are commonly used for exploratory data analysis. Array operations are very fast.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
The document discusses various data structures including stacks, queues, binary heaps, and binary indexed trees. It provides descriptions of each data structure, their common operations like push(), pop(), and top(), as well as discussing their time complexities and providing C++ code examples for implementation. It also gives examples of applications for each data structure.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
Scientific Computing with Python - NumPy | WeiYuanWei-Yuan Chang
This document provides an overview of NumPy, the fundamental package for scientific computing in Python. It discusses NumPy's powerful N-dimensional array object and sophisticated broadcasting functions. The document outlines topics including Ndarray, creating and manipulating arrays, array properties, basic operations, matrices, and advanced usages like boolean indexing and masking. NumPy allows efficient storage and manipulation of multi-dimensional data, and integration with languages like C/C++ and Fortran.
The document provides coding conventions and best practices for writing readable code. It recommends using meaningful names for variables, constants, and methods to improve readability. It also suggests using library utilities when possible to reduce code duplication. Additional tips include returning copies instead of references for immutable objects, catching specific exceptions instead of generic ones, using final keywords, removing unused code, and following object-oriented principles like access modifiers.
1. The document discusses the evolution of the C# programming language from versions 1.0 through 4.0, highlighting new features introduced in each version such as generics, LINQ, dynamic programming, and covariance/contravariance.
2. It provides code examples to illustrate key concepts like generics, LINQ queries, dynamic binding, and how covariance/contravariance enables safer usage of generics.
3. The last section summarizes some of the main features introduced in each C# version from managed code to generics to dynamic programming.
Cvpr2010 open source vision software, intro and training part-iii introduct...zukun
The document introduces the Generic Image Library (GIL), Boost libraries, and generic programming. It provides an overview of GIL for image processing and Boost for peer-reviewed C++ libraries. It describes how generic programming separates algorithms from data structures and allows algorithms to work with different types through templates and polymorphism. Accumulating a data set is used as an example to compare object-oriented and generic programming approaches.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
This document provides an introduction to NumPy, the fundamental package for scientific computing with Python. It discusses what NumPy is, why it is useful compared to regular Python lists, how to define arrays of different dimensions, and how to initialize, manipulate, and perform operations on NumPy arrays. Some key capabilities of NumPy include N-dimensional arrays, broadcasting functions, integration with C/C++ and Fortran code, and tools for linear algebra and Fourier transforms.
Effective Numerical Computation in NumPy and SciPyKimikazu Kato
This document provides an overview of effective numerical computation in NumPy and SciPy. It discusses how Python can be used for numerical computation tasks like differential equations, simulations, and machine learning. While Python is initially slower than languages like C, libraries like NumPy and SciPy allow Python code to achieve sufficient speed through techniques like broadcasting, indexing, and using sparse matrix representations. The document provides examples of how to efficiently perform tasks like applying functions element-wise to sparse matrices and calculating norms. It also presents a case study for efficiently computing a formula that appears in a machine learning paper using different sparse matrix representations in SciPy.
The document discusses several Java and Android internals topics:
1. How ArrayList and StringBuilder work internally using arrays and memory copying as the size increases. This can lead to inefficient memory usage.
2. How inner classes are implemented by compilers by generating additional accessor methods, increasing method count and affecting optimizations.
3. How the Android zygote process improves startup and memory usage by loading the framework once and sharing it across apps.
4. How the CPU cache works and how optimizing code to improve cache locality can significantly increase performance despite doing less work.
5. Issues like memory fragmentation that can occur if the Android garbage collector and compactor are unable to run due to the app being
Biopython is a set of freely available Python tools for bioinformatics and molecular biology. It provides features like parsing bioinformatics files into Python structures, a sequence class to store sequences and features, and interfaces to popular bioinformatics programs. Biopython can be used to address common bioinformatics problems like sequence manipulation, searching for primers, and running BLAST searches. The current version is 1.53 from December 2009 and future plans include updating the multiple sequence alignment object and adding a Bio.Phylo module.
This document summarizes recent updates to Python, including updates in versions 3.7 and 3.8. It discusses new features like the pathlib module for handling file paths, type hints for function annotations, data classes for defining classes with default implementations, and the retirement of Guido van Rossum as Benevolent Dictator For Life of Python. It also previews the addition of assignment expressions using := in Python 3.8 as a more Pythonic way to write conditional assignments.
NumPy is a fundamental package for scientific computing in Python that provides multidimensional array objects and tools to work with arrays. Arrays store values of the same data type and are faster than lists. Two-dimensional arrays are commonly used for exploratory data analysis. Array operations are very fast.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
The document discusses various data structures including stacks, queues, binary heaps, and binary indexed trees. It provides descriptions of each data structure, their common operations like push(), pop(), and top(), as well as discussing their time complexities and providing C++ code examples for implementation. It also gives examples of applications for each data structure.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
Scientific Computing with Python - NumPy | WeiYuanWei-Yuan Chang
This document provides an overview of NumPy, the fundamental package for scientific computing in Python. It discusses NumPy's powerful N-dimensional array object and sophisticated broadcasting functions. The document outlines topics including Ndarray, creating and manipulating arrays, array properties, basic operations, matrices, and advanced usages like boolean indexing and masking. NumPy allows efficient storage and manipulation of multi-dimensional data, and integration with languages like C/C++ and Fortran.
The document provides coding conventions and best practices for writing readable code. It recommends using meaningful names for variables, constants, and methods to improve readability. It also suggests using library utilities when possible to reduce code duplication. Additional tips include returning copies instead of references for immutable objects, catching specific exceptions instead of generic ones, using final keywords, removing unused code, and following object-oriented principles like access modifiers.
1. The document discusses the evolution of the C# programming language from versions 1.0 through 4.0, highlighting new features introduced in each version such as generics, LINQ, dynamic programming, and covariance/contravariance.
2. It provides code examples to illustrate key concepts like generics, LINQ queries, dynamic binding, and how covariance/contravariance enables safer usage of generics.
3. The last section summarizes some of the main features introduced in each C# version from managed code to generics to dynamic programming.
Cvpr2010 open source vision software, intro and training part-iii introduct...zukun
The document introduces the Generic Image Library (GIL), Boost libraries, and generic programming. It provides an overview of GIL for image processing and Boost for peer-reviewed C++ libraries. It describes how generic programming separates algorithms from data structures and allows algorithms to work with different types through templates and polymorphism. Accumulating a data set is used as an example to compare object-oriented and generic programming approaches.
The document provides an overview of the Kinect SDK and how to work with Kinect data in 12 slides or less. It discusses setting up the Kinect runtime, handling event-driven data streams for depth, color, and skeleton frames. Depth and skeleton frames contain raw byte data that needs to be processed to extract useful information like joint positions. The Coding4Fun library provides helpful methods for mapping skeleton joint coordinates to pixel coordinates on the screen.
The document provides an overview of the Kinect SDK and how to work with Kinect data in 12 slides or less. It discusses setting up the Kinect runtime, handling event-driven data streams for depth, color, and skeleton frames. Depth and skeleton frames contain raw byte data that needs to be processed to extract useful information like joint positions. The Coding4Fun library provides helpful methods for mapping skeleton joint coordinates to pixel coordinates on the screen.
This is a java lab assignment. I have added the first part java re.pdffeetshoemart
This is a java lab assignment. I have added the first part \"java retail class\" at the bottom. I am
unsure how to read the txt file into an array from the retail class. The stock txt file has been
added also.
A beginning framework and inventory list has been provided to you to use your Retail Item Class
as an array. Develop a program to accomplish the following menu items that can be selected by
the end user. Discussion of what each menu item should do is provided below the menu item.
Modify your object class file as desired. Please append initials to files created. Use Select-Case
for menu choices and methods to perform those choices. Please make a selection: 1. Open
Inventory File (This should ask the user for the file name, open the file and load the data into the
object array). 2. Display All (This should show a table of all items and data) 3. Display Reorder
Only (This should show only the stock number, Description and current quantity for items
requiring reorder) 4. Find Stock number (Allows the user to enter part of the description and
displays all matching stock numbers and descriptions matching that - case insensitive, such as
“cubs” or “cubs”) 5. Display Stock number (Allows user to enter stock number and displays
record of item) 6. Add Quantity (Asks for stock number and quantity for units, adds it to current,
displays record results) 7. Subtract Quantity (Asks for stock number and quantity, subtracts,
displays results including if reorder is needed.) 8. Change Price (Asks for stick number, changes
price to provided value, display results). 9. New Item (Allows adding a new item, updates count.
Display record results). 10. Save Inventory File (asks user for file name, save all inventory to
that file in a format that can be read back.)
public class RetailItem{
private String description;
private int unitsOnHand;
private double price;
private int restock;
public void setDescription(String userDescription){
description=userDescription;
}
public void setUnitsOnHand(int userUnitsOnHand){
unitsOnHand=userUnitsOnHand;
}
public void setPrice(double userPrice){
price=userPrice;
}
public String getDescription(){
return description;
}
public int getUnitsOnHand(){
return unitsOnHand;
}
public double getPrice(){
return price;
}
public double getTotal(){
int total = unitsOnHand=(int) price;
return total;
}
public boolean getRestock(){
return false;
}
public RetailItem(String descriptionGiven, int unitsOnHandGiven, double priceGiven, int
restockGiven){
description=descriptionGiven;
unitsOnHand=unitsOnHandGiven;
price=priceGiven;
restock=restockGiven;
}
}
Solution
public class RetailItem{
private int stocknumber;
private String description;
private int unitsOnHand;
private double price;
private int restock;
public void setDescription(String userDescription){
description=userDescription;
}
public int getStocknumber() {
return stocknumber;
}
public void setStocknumber(int stocknumber) {
this.stocknumber = stocknumber;
}
public void setUnitsOnHand(in.
This document discusses object initialization in Java. It explains that in Java, constructors allow objects to be initialized when they are created. Constructors ensure that class member variables and objects have initial values before an object is used. The document provides examples of how to write constructors, how to overload constructors, and how to use the this keyword in constructors. It also discusses static initialization and the use of toString() methods.
This document discusses object initialization in Java. It explains that in Java, constructors allow objects to be initialized when they are created. Constructors ensure that class member variables and objects are properly initialized before use. The document provides examples of how to write classes with constructors and use constructor overloading. It also discusses static initialization, the this keyword, and recursion.
C# 4.0 introduced several new features to the C# language including dynamic programming, optional and named parameters, and improved support for covariance and contravariance. It also added the ability to invoke members of dynamic objects at runtime instead of compile-time. The .NET Framework 4.0 implemented these language changes and added support for variance in interfaces like IEnumerable and delegates.
The document discusses C++ concepts including classes, pointers, references, and memory management. It explains that C++ is an object-oriented superset of C with new features like classes, templates, and operator overloading. It also covers defining classes with headers and source files, using pointers to dynamically allocate memory with new and delete, the role of destructors, and differences between passing by value versus passing by reference.
An Introduction to Programming in Java: ArraysMartin Chapman
An Introduction to Programming in Java: Arrays. Last delivered in 2012. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
OrderTest.javapublic class OrderTest { Get an arra.pdfakkhan101
OrderTest.java
public class OrderTest {
/**
* Get an array of specified size and pass it to Order.order().
* Report the results.
*/
public static void main(String[] args) {
if (args.length != 1) {//1
System.out.println(\"Usage: java OrderTest sizeOfArray\ \"
+ \"\\tor\ \\tjava OrderTest arrayFile\");
System.exit(1);
}
// create or read the int[]
int size = 0;
int[] array = new int[0];//5
try {
size = Integer.parseInt(args[0]);
array = ArrayOfInts.randomizedArray(size);
} catch (NumberFormatException nfe) {//8
try {
array = ArrayOfInts.arrayFromFile(args[0]);
size = array.length;
} catch (Exception e) {
System.err.println(\"unable to read array from \" + args[0]);
System.exit(1);//14
}
}
System.out.println(\"before:\");//15
for (int i = 0; i < array.length; i++) {//2 n
System.out.printf(((i+1) % 10 > 0) ? \" %d\" : \" %d\ \", array[i]);//1
}
int myNum = Order.order(array); //this is the call we want to measure
System.out.println(\"\ after:\");//18
for (int i = 0; i < array.length; i++) {//2 n
System.out.printf(((i+1) % 10 > 0) ? \" %d\" : \" %d\ \", array[i]);
}
System.out.println(myNum);
}
}
ArrayOfInts.java
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.Collections;
import java.util.InputMismatchException;
import java.util.Scanner;
public class ArrayOfInts {
/**
* Returns an array of consecutive ints from 1 to size.
*/
public static int[] orderedArray(int size) {
int[] a = new int[size];
for (int i = 0; i < size; i++) {
a[i] = i+1;
}
return a;
}
/**
* Returns a randomized array containing ints from 1 to size.
*/
public static int[] randomizedArray(int size) {
ArrayList aL = new ArrayList();
for (int i = 0; i < size; i++) {
aL.add(i+1);
}
Collections.shuffle(aL);
int[] a = new int[size];
for (int i = 0; i < size; i++) {
a[i] = aL.get(i);
}
return a;
}
/**
* Writes an int[] to a plain-text file with ints separated by spaces.
* Useful for creating input files for repeatable tests.
*/
public static void arrayToFile(int[] array, String outfile) {
try {
FileWriter fw = new FileWriter(outfile);
BufferedWriter bw = new BufferedWriter(fw);
PrintWriter outFile = new PrintWriter(bw);
for (int i : array) {
outFile.print(i + \" \");
}
outFile.close();
} catch (IOException e) {
System.err.println(\"Could not write to \" + outfile + \"\ \" + e);
}
}
/**
* Read ints from a file and return them in an int[]
*/
public static int[] arrayFromFile(String infile) throws FileNotFoundException,
InputMismatchException {
Scanner scan = new Scanner(new File(infile));
ArrayList aL = new ArrayList();
while (scan.hasNext()) {
aL.add(scan.nextInt());
}
scan.close();
int[] a = new int[aL.size()];
for (int i = 0; i < a.length; i++) {
a[i] = aL.get(i);
}
return a;
}
}
Order.java
public class Order {
/**
* Take an int[] and reorganize it so they are in ascending order.
*/
public static int order(int[] array) .
This document provides lessons learned about developing Internet of Things (IoT) applications. It discusses using unit tests to debug IoT applications, implementing a data transformation layer to convert between data formats, and unit testing models. It also recommends simulating Bluetooth Low Energy (BLE) functionality on iOS simulators using wrappers and protocols. The document emphasizes simulating functionality as much as possible and applying server-side development principles to IoT apps.
This document summarizes concepts related to computer graphics and programming in Processing, including:
1) Transformations like translate, rotate, and scale are discussed for drawing objects like a rocket in different positions. Methods and classes are introduced.
2) Arrays and how they can be used to structure data are explained. Examples of integer arrays are provided.
3) The Rocket is turned into a class with fields like position and rotation, and a draw method is added to instantiate Rocket objects that can be drawn.
The .NET garbage collector can be your best friend or your worst enemy; and it’s not friendly with a lot of people. The GC left more than a few production systems burning in smoke after developers failed to anticipate the effects of real production loads on the memory subsystem. In this talk, we will methodically measure and improve the .NET garbage collector’s performance. We will begin with a quick refresher on dynamic performance tools that can identify GC issues: CLR performance counters, ETW GC events, and ETW object allocation events; as well as static analysis tools, such as the Roslyn-based heap allocations analyzer. Then, we will inspect multiple issues at the source code level: excessive boxing, unintended effects of lambdas closing over local variables, await-generated state machines, intermediate objects in LINQ queries, and many others. We will also discuss higher-level memory problems: how to get rid of large object allocations, how to avoid finalization, and how to convert heap-based designs to local objects. Some of these ideas are now being applied at the language and framework level in C# 7 and .NET Core. At the end of the talk, you will be equipped to reduce memory traffic and GC overhead in your own applications, often by a factor of 10 or more!
JAVA - Design a data structure IntSet that can hold a set of integers-.docxolsenlinnea427
JAVA - Design a data structure IntSet that can hold a set of integers. Hide the private implementation: a Binary Search Tree of Integer objects. Provide the following public methods only.
• Constructor to make an empty set,
• Constructor to create a set by initializing the root,
• void add(int x) to add x if it is not present,
• void remove(int x) to remove x if it is present,
• void print() to print all the elements currently in the set,
• boolean contains(int x) to test whether x is present,
• IntSet copy() to create a new BTS as a copy of the current one.
Note that the root of the BST is a private data member and there is no need to define getRoot(), and setRoot() functions. All the above functions should be defined public and should call recursive private functions to do the task. e.g.
// remove
private Node recRemove(Node loc, int x) { // To do }
public void remove(int x) { root= recRemove(root, x) ; }
// contains
private boolean recContains(Node loc, int x) { // to do }
public boolean contains(int x) { return recContains(root, x) ; }
Complete the class IntSet by supplying the intersection and union functions. Use the same format as above i.e. each public function calls a private recursive function to do the task:
public IntSet union(IntSet other)
public IntSet intersection(IntSet other)
Solution
import java.util.Scanner; class BinarySearchExample { public static void main(String args[]) { int counter, num, item, array[], first, last, middle; //To capture user input Scanner input = new Scanner(System.in); System.out.println(\"Enter number of elements:\"); num = input.nextInt(); //Creating array to store the all the numbers array = new int[num]; System.out.println(\"Enter \" + num + \" integers\"); //Loop to store each numbers in array for (counter = 0; counter < num; counter++) array[counter] = input.nextInt(); System.out.println(\"Enter the search value:\"); item = input.nextInt(); first = 0; last = num - 1; middle = (first + last)/2; while( first <= last ) { if ( array[middle] < item ) first = middle + 1; else if ( array[middle] == item ) { System.out.println(item + \" found at location \" + (middle + 1) + \".\"); break; } else { last = middle - 1; } middle = (first + last)/2; } if ( first > last ) System.out.println(item + \" is not found.\ \"); } }
.
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This document provides an overview of the C++ Data Structures lab manual. It covers topics like C++ review, implementation of various data structures like stack, queue, linked list, binary tree, graph. It also discusses sorting and searching techniques, file input/output, functions, classes, templates and exercises for students to practice implementing different data structures and algorithms. The instructor's contact details are provided at the beginning.
Android Performance Optimization presentation which is told on @ DevFest Ankara 2013, @ DevFest Eskişehir 2013, @ DevFest Konya 2014, @DevFest İstanbul 2014
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do it in eclips and make sure it compile Goals1)Be able to.docxjameywaughj
do it in eclips and make sure it compile
Goals
1)
Be able to work with individual bits in java
.
2)
Understand the serializable interface.
3)
Understand the comparable interface.
4)
Answer questions about a general-purpose class to be developed.
5)
Understand the use of a driver program for ‘glass box’ debugging.
6)
Develop a program that can grade true/false te
Description
1)
The first step is to develop a general-purpose class that will be able to perform operations on strings of bits. The class API follows:
public class
BitMap
implements Comparable,
Serializable
{ public static final int BITSIZE = 64;
private
long bitString;
public BitMap
() // Three constructors.
public
BitMap
(String s)
throws
IndexOutOfBoundsException,ArithmeticException
public
BitMap
(
boolean
[] bits)
throws
IndexOutOfBoundsException
private
long
bitMask
(
int
b) // Other class methods.
public void
setBit
(
int
b)
public void
clearBit
(
int
b)
public
boolean
checkBit
(
int
b)
public
int
countTrue
()
public void
clearAll()
public void
setAll()
public
int
compareTo
(Object
bm
) //For Comparable.
public
boolean
equals(
BitMap
bm
)
public String
toString()
}
Notes:
a.
The only instance variable that is needed is
bitString
.
b.
Use BITSIZE for the maximum index value of your loops.
The above looks like a lot of methods, but the whole class requires about a page of code when the methods are filled in. Some methods can use others instead of duplicating code. For example, the first constructor can just call the method,
clearAll
(
)
. The method
b
itMask
(
)
can be used by four or five other methods whenever a bit operation is called for. We'll discuss that in class.
The operations to be performed by each method are briefly described below:
a)
Constructors
(i) The first constructor just needs to set all bits to
false
(or
zero
). The method
clearAll()
does the same thing.
(ii) The second constructor takes a character string as input. Each character of the string is either a
t
(for
true
) or
f
(for
false
) value. The bits with a
t
character are to be set on in the bit map; bits with an
f
character should be set off in the bit map. Throw an
ArithmeticException
if the input string has characters other
than ‘t’
, ‘T’, ‘f’, or ‘F’ appear. Throw
IndexOutOfBoundsException
if the string is too long.
(iii) The third constructor works just like the second except the input is a
boolean
array. The bits corresponding to the array elements that have a
true
value should be set to a value of one;
the elements having a
false
value shoule
be
set to a value of zero.
b)
Primary Methods
The methods,
setBit(int), clearBit(int), and checkBit(int)
respectively set a given bit on, off or check a bits current value. The method,
countTrue()
returns the total number of bits that are set. It can be used by the
compareTo()
method. The method
setAll(
)
.
Doze mode is just around the corner. Introduced in Marshmallow, as the shy guy that was allowed to step in only when your device went to sleep. ZzzZzzzz With the new N release, Doze mode becomes the rockstar. He rushes into the room with a big shabang by shooting all background services and network requests that forgot to get away. He should no longer be shy. He just shoots and dances on bones of your scheduled alarms, while you wonder why this particular task is no longer running. With the new Doze mode, you require to adapt your app architecture to the new reality. On this talk, we will learn how to survive Doze mode using Job Scheduler and stay in one piece.
The document discusses best practices for managing memory in Android applications to avoid memory leaks and performance issues. It provides 3 key points:
1. Be aware of how memory is managed in Android and the garbage collection process. Allocations during critical parts like onDraw can cause stutters.
2. Learn techniques to detect memory issues like using LeakCanary, tracking allocations, and dumping the heap. Understanding memory profiles of an app is important.
3. Apply best practices like avoiding long-lived references to activities, using WeakReferences for inner classes, and cleaning up resources on stop to prevent leaks and improve performance. Memory management is important for smooth experiences.
This document discusses using Inter-Process Communication (IPC) mechanisms like Intent, Messenger, and Android Interface Definition Language (AIDL) to allow processes to communicate with each other in an Android app. It provides code examples of implementing IPC using each mechanism to allow a Service to communicate long-running tasks back to an Activity. AIDL is presented as the preferred solution as it allows for synchronous calls, treats the communication in an object-oriented way, and is transparent to the business logic.
This document discusses using Inter-Process Communication (IPC) mechanisms like Intent, Messenger, and Android Interface Definition Language (AIDL) to allow processes to communicate with each other in an Android app. It provides code examples of implementing IPC using each mechanism to allow a Service running in a separate process to communicate tasks and results back to an Activity. AIDL is presented as the most robust solution, allowing synchronous and asynchronous remote calls between processes through generated Java interface stubs.
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The document summarizes the top 10 new features in Android M:
1. Android M preview timeline and expected Q3 2015 release.
2. Changes to app permissions including runtime permissions and reduced install/update friction.
3. How to properly request and handle permissions.
4. New fingerprint API and authentication without sharing credentials.
5. Doze mode for better battery life when idle and postponing non-important tasks.
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Lecture #1 intro,setup, new project, sunshineYonatan Levin
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Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Home security is of paramount importance in today's world, where we rely more on technology, home
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AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
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and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
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Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
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Fluke Solar Application Specialist Will White is presenting on this engaging topic:
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3. OutOfMemory - Bitmap memory usage
Image 1080X1920pixels, 124KB .png compressed
Bitmap size = Width * Height * depth(4 bytes) = 8 MB!!!!
Reduce the size before loading it to the memory.
BitmapFactory.Options options = new BitmapFactory.Options();
options.inJustDecodeBounds = true;
BitmapFactory.decodeResource(getResources(), R.id.myimage, options);
int imageHeight = options.outHeight;
int imageWidth = options.outWidth;
String imageType = options.outMimeType;
4. OutOfMemory - Garbage collector
A large number of small allocations can also cause heap
fragmentation
List<Object> mTempObjects = new ArrayList<Object>();
for(int i=0; i<10000; i++){
mTempObjects.add(new Object());
}
5. OutOfMemory - Garbage collector
for(int i=0; i<mTempObjects.size(); i++){
Object c = mTempObjects.get(i);
Log.d(TAG, "Object data:" + c.getValue());
}
10,000 references waiting to be collected by GC
6. OutOfMemory - Garbage collector
“To improve is to change; to be perfect is to change often.” – Winston Churchill
Object c;
for(int i=0; i<mTempObjects.size(); i++){
c = mTempObjects.get(i);
Log.d(TAG, "Object data:" + c.getValue());
}
7. OutOfMemory - Garbage collector
Reuse of the same object
private StringBuilder buildSomething(StringBuilder sb){
sb.append(readString(fileStream));
sb.append(readOrderDetails(AnotheFileStream));
return sb;
}
No short term temporary object for String
8. Memory overhead
Object with just one int take 16 bytes at minimum
Class Integer {
private int value;
}
Object overhead (8 bytes ) + Overhead of dlmalloc(8 bytes) + data (n
bytes) = >16 bytes
10. Primitive Type vs Object
Integer (16 bytes) vs int(4 bytes)
Boolean(16 bytes) vs boolean(4 bytes)
or
bit-field(1 bit) //even better!
11. Enums vs Ints
Very simple - don’t use Enums
public final static enum Things {
THING_1,
THING_2,
}; == + 1,112 bytes
12. Enums vs Ints
Use final static variable:
public static int THING_1 = 1;
public static int THING_2 = 2;
------------------------
128 bytes
13. Discover by yourself
import java.lang.instrument.Instrumentation;
public class ObjectSizeFetcher {
private static Instrumentation instrumentation;
public static void premain(String args, Instrumentation inst) { instrumentation = inst; }
public static long getObjectSize(Object o) { return instrumentation.getObjectSize(o); } }
Use getObjectSize:
public class C { private int x; private int y;
public static void main(String [] args) {
System.out.println(ObjectSizeFetcher.getObjectSize(new C())); } }
14. Anonymous and Inner Classes
~500 bytes of code
button.setOnClickListener(new Runnable() {
public void run(){
//do some stuff
}
});
unregister as soon as possible
15. Service
- Very usefull, but overused.
- Bind to Lifecycle
- Stop it when no need.
- Use IntentService instead (will stop when job is done)
16. - Release memory when your
activity no longer visible
- Don’t hold direct references.
Use WeakReference
- Stop all your handlers,
threads in onPause/onStop
- Configuration changes
Memory Leak