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Lambda functions in Python are anonymous functions that can take any number of arguments but can only have a single expression. They are defined using the lambda keyword followed by arguments and an expression. Lambda functions can be used wherever function objects are required and are useful for passing functions as arguments. For example, a lambda function can double an input argument or add two arguments and return the result.
The document defines and explains different types of functions in Python. It discusses defining functions, calling functions, passing arguments by reference versus value, writing functions using different approaches like anonymous functions and recursive functions. Some key points covered include: defining a function uses the def keyword followed by the function name and parameters; functions can be called by their name with arguments; arguments are passed by reference for mutable objects and by value for immutable objects; anonymous functions are defined using the lambda keyword and return a single expression; recursive functions call themselves to break down problems into sub-problems until a base case is reached.
Functions are blocks of reusable code that perform specific tasks. There are three types of functions in Python: built-in functions, anonymous lambda functions, and user-defined functions. Functions help organize code by breaking programs into smaller, modular chunks. They reduce code duplication, decompose complex problems, improve clarity, and allow code reuse. Functions can take arguments and return values. Built-in functions are pre-defined to perform operations and return results when given the required arguments.
Functions allow programmers to organize code into reusable blocks. A function is defined using the def keyword and can accept parameters. The body of a function contains a set of statements that run when the function is called. Functions can return values and allow code to be reused, reducing errors and improving readability. Parameters allow information to be passed into functions, while return values allow functions to provide results.
The document provides information on Python functions including defining, calling, passing arguments to, and scoping of functions. Some key points covered:
- Functions allow for modular and reusable code. User-defined functions in Python are defined using the def keyword.
- Functions can take arguments, have docstrings, and use return statements. Arguments are passed by reference in Python.
- Functions can be called by name and arguments passed positionally or by keyword. Default and variable arguments are also supported.
- Anonymous lambda functions can take arguments and return an expression.
- Variables in a function have local scope while global variables defined outside a function can be accessed anywhere. The global keyword is used to modify global variables from within a function
The document provides information on Python functions including defining, calling, passing arguments to, and scoping of functions. Some key points covered:
- Functions allow for modular and reusable code. User-defined functions in Python are defined using the def keyword.
- Functions can take arguments, have docstrings, and use return statements. Arguments are passed by reference in Python.
- Functions can be called by name and arguments passed positionally or by keyword. Default and variable arguments are also supported.
- Anonymous lambda functions can take arguments and return an expression.
- Variables in a function have local scope while global variables defined outside a function can be accessed anywhere. The global keyword is used to modify global variables from within a function
04_python_functions.ppt You can define functions to provide the required func...anaveenkumar4
You can define functions to provide the required functionality. Here are simple
rules to define a function in Python.
❖ Function blocks begin with the keyword def followed by the function name
and parentheses ( ).
❖ Any input parameters or arguments should be placed within these
parentheses. You can also define parameters inside these parentheses.
❖ The first statement of a function can be an optional statement - the
documentation string of the function or docstring.
❖ The code block within every function starts with a colon : and is indented.
❖ The statement return [expression] exits a function, optionally passing back
an expression to the caller. A return statement with no arguments is the
same as return None.
A little bit of Lamba Calculus.
What is that and how it is applied in functional programming languages.
Currying, High Order Functions, Anonymous Functions
function in python programming languges .pptxrundalomary12
This document discusses functions in math and Python. It defines a function in math and shows an example function. It then defines a function in Python as a block of reusable code that takes parameters, performs operations, and returns a value. It discusses benefits like simplifying code and reusing code. It shows how to define a function in Python using the def keyword and how to call a function by its name. It defines parameters as variable names in a function definition and arguments as the actual values passed when calling a function. It provides an example of a function that takes name and age as parameters and returns a greeting string. It also discusses assigning default values to parameters. At the end it provides examples of functions to write like counting letters in
Python lambda functions with filter, map & reduce functionARVIND PANDE
Lambda functions allow the creation of small anonymous functions and can be passed as arguments to other functions. The map() function applies a lambda function to each element of a list and returns a new list. The filter() function filters a list based on the return value of a lambda function. The reduce() function iteratively applies a lambda function to consecutive pairs in a list and returns a single value. User-defined functions in Python can perform tasks like converting between temperature scales, finding max/min/average of lists, generating Fibonacci series, reversing strings, summing digits in numbers, and calculating powers using recursion.
This document discusses lambda functions and the import keyword in Python. It defines lambda functions as small anonymous functions that can take any number of arguments but only have one expression. Examples are provided to demonstrate lambda functions that add, multiply, and return values. The import keyword is used to import libraries into code. Syntax for importing a whole library, a single method, or all methods using wildcards is described. Examples import the os, matplotlib, scikit-learn, and cryptography libraries.
This document discusses Python functions. It covers built-in functions, user-defined functions, fruitful and void functions, parameters and arguments, recursion, and strings. Specifically, it provides examples and explanations of various math functions like abs(), ceil(), exp(), and more. It also covers defining new functions, importing functions from modules, boolean functions, and using for loops to traverse strings.
The document discusses Python objects and data types. It covers strings, numbers, lists, tuples, and other basic data types in Python. Strings can be indexed and sliced, and operations like concatenation and length calculation can be performed on strings. Lists and tuples are sequence data types that allow ordered collections of elements. Numbers support arithmetic operators and can include integers, floats, complexes, decimals, and rationals.
This document provides an overview of a lecture on functional programming in Scala. It covers the following topics:
1. A recap of functional programming principles like functions as first-class values and no side effects.
2. An introduction to the Haskell programming language including its syntax for defining functions.
3. How functions are defined in Scala and how they are objects at runtime.
4. Examples of defining the factorial function recursively in Haskell and Scala, and making it tail recursive.
5. Concepts of first-class functions, currying, partial application, and an example of implementing looping in Scala using these techniques.
Here is how to convert the method into a curried function:
Function<A, Function<B, Function<C, Function<D, String>>>> func =
a -> b -> c -> d -> String.format("%s, %s, %s, %s", a, b, c, d);
This defines func as a function that takes an argument of type A and returns a function that takes B and returns a function that takes C and returns a function that takes D and returns a String.
Java is Object Oriented Programming. Java 8 is the latest version of the Java which is used by many companies for the development in many areas. Mobile, Web, Standalone applications.
Functions allow programmers to organize code into reusable blocks. A function is defined using the def keyword and can accept parameters. The body of a function contains a set of statements that run when the function is called. Functions can return values and allow code to be reused, reducing errors and improving readability. Parameters allow information to be passed into functions, while return values allow functions to provide results.
The document provides information on Python functions including defining, calling, passing arguments to, and scoping of functions. Some key points covered:
- Functions allow for modular and reusable code. User-defined functions in Python are defined using the def keyword.
- Functions can take arguments, have docstrings, and use return statements. Arguments are passed by reference in Python.
- Functions can be called by name and arguments passed positionally or by keyword. Default and variable arguments are also supported.
- Anonymous lambda functions can take arguments and return an expression.
- Variables in a function have local scope while global variables defined outside a function can be accessed anywhere. The global keyword is used to modify global variables from within a function
The document provides information on Python functions including defining, calling, passing arguments to, and scoping of functions. Some key points covered:
- Functions allow for modular and reusable code. User-defined functions in Python are defined using the def keyword.
- Functions can take arguments, have docstrings, and use return statements. Arguments are passed by reference in Python.
- Functions can be called by name and arguments passed positionally or by keyword. Default and variable arguments are also supported.
- Anonymous lambda functions can take arguments and return an expression.
- Variables in a function have local scope while global variables defined outside a function can be accessed anywhere. The global keyword is used to modify global variables from within a function
04_python_functions.ppt You can define functions to provide the required func...anaveenkumar4
You can define functions to provide the required functionality. Here are simple
rules to define a function in Python.
❖ Function blocks begin with the keyword def followed by the function name
and parentheses ( ).
❖ Any input parameters or arguments should be placed within these
parentheses. You can also define parameters inside these parentheses.
❖ The first statement of a function can be an optional statement - the
documentation string of the function or docstring.
❖ The code block within every function starts with a colon : and is indented.
❖ The statement return [expression] exits a function, optionally passing back
an expression to the caller. A return statement with no arguments is the
same as return None.
A little bit of Lamba Calculus.
What is that and how it is applied in functional programming languages.
Currying, High Order Functions, Anonymous Functions
function in python programming languges .pptxrundalomary12
This document discusses functions in math and Python. It defines a function in math and shows an example function. It then defines a function in Python as a block of reusable code that takes parameters, performs operations, and returns a value. It discusses benefits like simplifying code and reusing code. It shows how to define a function in Python using the def keyword and how to call a function by its name. It defines parameters as variable names in a function definition and arguments as the actual values passed when calling a function. It provides an example of a function that takes name and age as parameters and returns a greeting string. It also discusses assigning default values to parameters. At the end it provides examples of functions to write like counting letters in
Python lambda functions with filter, map & reduce functionARVIND PANDE
Lambda functions allow the creation of small anonymous functions and can be passed as arguments to other functions. The map() function applies a lambda function to each element of a list and returns a new list. The filter() function filters a list based on the return value of a lambda function. The reduce() function iteratively applies a lambda function to consecutive pairs in a list and returns a single value. User-defined functions in Python can perform tasks like converting between temperature scales, finding max/min/average of lists, generating Fibonacci series, reversing strings, summing digits in numbers, and calculating powers using recursion.
This document discusses lambda functions and the import keyword in Python. It defines lambda functions as small anonymous functions that can take any number of arguments but only have one expression. Examples are provided to demonstrate lambda functions that add, multiply, and return values. The import keyword is used to import libraries into code. Syntax for importing a whole library, a single method, or all methods using wildcards is described. Examples import the os, matplotlib, scikit-learn, and cryptography libraries.
This document discusses Python functions. It covers built-in functions, user-defined functions, fruitful and void functions, parameters and arguments, recursion, and strings. Specifically, it provides examples and explanations of various math functions like abs(), ceil(), exp(), and more. It also covers defining new functions, importing functions from modules, boolean functions, and using for loops to traverse strings.
The document discusses Python objects and data types. It covers strings, numbers, lists, tuples, and other basic data types in Python. Strings can be indexed and sliced, and operations like concatenation and length calculation can be performed on strings. Lists and tuples are sequence data types that allow ordered collections of elements. Numbers support arithmetic operators and can include integers, floats, complexes, decimals, and rationals.
This document provides an overview of a lecture on functional programming in Scala. It covers the following topics:
1. A recap of functional programming principles like functions as first-class values and no side effects.
2. An introduction to the Haskell programming language including its syntax for defining functions.
3. How functions are defined in Scala and how they are objects at runtime.
4. Examples of defining the factorial function recursively in Haskell and Scala, and making it tail recursive.
5. Concepts of first-class functions, currying, partial application, and an example of implementing looping in Scala using these techniques.
Here is how to convert the method into a curried function:
Function<A, Function<B, Function<C, Function<D, String>>>> func =
a -> b -> c -> d -> String.format("%s, %s, %s, %s", a, b, c, d);
This defines func as a function that takes an argument of type A and returns a function that takes B and returns a function that takes C and returns a function that takes D and returns a String.
Java is Object Oriented Programming. Java 8 is the latest version of the Java which is used by many companies for the development in many areas. Mobile, Web, Standalone applications.
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in the further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further, this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi-angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array, and then optimization is done in data analysis software Minitab 17. The results of ANOVA shows that 15 degrees die semi-angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degrees die semi-angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally, the results of experimentation are validated with Finite Element Analysis technique using ANSYS.
Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek
This presentation provides detailed guidance and tools for conducting Current State and Future State Value Stream Mapping workshops for Intelligent Continuous Security.
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
Raish Khanji GTU 8th sem Internship Report.pdfRaishKhanji
This report details the practical experiences gained during an internship at Indo German Tool
Room, Ahmedabad. The internship provided hands-on training in various manufacturing technologies, encompassing both conventional and advanced techniques. Significant emphasis was placed on machining processes, including operation and fundamental
understanding of lathe and milling machines. Furthermore, the internship incorporated
modern welding technology, notably through the application of an Augmented Reality (AR)
simulator, offering a safe and effective environment for skill development. Exposure to
industrial automation was achieved through practical exercises in Programmable Logic Controllers (PLCs) using Siemens TIA software and direct operation of industrial robots
utilizing teach pendants. The principles and practical aspects of Computer Numerical Control
(CNC) technology were also explored. Complementing these manufacturing processes, the
internship included extensive application of SolidWorks software for design and modeling tasks. This comprehensive practical training has provided a foundational understanding of
key aspects of modern manufacturing and design, enhancing the technical proficiency and readiness for future engineering endeavors.
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
2. Python lambda (Anonymous Functions)
In Python, anonymous function means that a function is without a
name. As we already know that def keyword is used to define the
normal functions and the lambda keyword is used to create
anonymous functions. It has the following syntax:
lambda arguments: expression
• This function can have any number of arguments but only one
expression, which is evaluated and returned.
• One is free to use lambda functions wherever function objects
are required.
• You need to keep in your knowledge that lambda functions are
syntactically restricted to a single expression.
• It has various uses in particular fields of programming besides
other types of expressions in functions.
3. Python lambda (Anonymous Functions)
The following terms may be used interchangeably depending on the
programming language type and culture:
• Anonymous functions
• Lambda functions
• Lambda expressions
• Lambda abstractions
• Lambda form
• Function literals
4. Example of Lambda Function in python
Here is an example of lambda function that doubles the input
value.
double = lambda x: x * 2#
Output: 10
print(double(5))
In the above program, lambda x: x * 2 is the lambda function.
Here x is the argument and x * 2 is the expression that gets
evaluated and returned.
We can now call it as a normal function.
double = lambda x: x * 2
is nearly the same as
def double(x):
return x * 2
5. Use of Lambda Function in python
The power of lambda is better shown when you use them as an
anonymous function inside another function.
Say you have a function definition that takes one argument,
and that argument will be multiplied with an unknown
number:
def myfunc(n):
return lambda a : a * n
Use that function definition to make a function that always
doubles the number you send in:
Example
def myfunc(n):
return lambda a : a * n
mydoubler = myfunc(2)
6. Use of Lambda Function in python
Or, use the same function definition to make a function that
always triples the number you send in:
Example
def myfunc(n):
return lambda a : a * n
mytripler = myfunc(3)
print(mytripler(11))
7. Use of Lambda Function in python
Or, use the same function definition to make both functions, in
the same program:
Example
def myfunc(n):
return lambda a : a * n
mydoubler = myfunc(2)
mytripler = myfunc(3)
print(mydoubler(11))
print(mytripler(11))
Use lambda functions when an anonymous function is
required for a short period of time.
8. Example use with filter()
The filter() function in Python takes in a function and a list as
arguments.
The function is called with all the items in the list and a new list is
returned which contains items for which the function evaluats to
True.
Here is an example use of filter() function to filter out only even
numbers from a list.
# Program to filter out only the even items from a list
my_list = [1, 5, 4, 6, 8, 11, 3, 12]
new_list = list(filter(lambda x: (x%2 == 0) , my_list))
# Output: [4, 6, 8, 12]
print(new_list)
9. Example use with map()
The map() function in Python takes in a function and a list.
The function is called with all the items in the list and a new list is
returned which contains items returned by that function for each
item.
Here is an example use of map() function to double all the items in a
list.
# Program to double each item in a list using map()
my_list = [1, 5, 4, 6, 8, 11, 3, 12]
new_list = list(map(lambda x: x * 2 , my_list))
# Output: [2, 10, 8, 12, 16, 22, 6, 24]
print(new_list)