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Eredivisie Women stats & predictions

Understanding the Volleyball Eredivisie Women in the Netherlands

The Volleyball Eredivisie Women is one of the most prestigious leagues in European volleyball. It showcases some of the best talents in women's volleyball, making it a thrilling spectacle for fans and bettors alike. With fresh matches updated daily, staying informed about the latest developments is crucial for anyone interested in expert betting predictions.

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The Significance of Daily Match Updates

For enthusiasts and bettors, having access to daily match updates is invaluable. It ensures that you are always informed about the latest team performances, player statistics, and any changes that might affect the outcome of a game. This real-time information is essential for making informed betting decisions.

Why Fresh Data Matters

  • Accuracy: Fresh data provides the most accurate information possible, reducing the risk of making decisions based on outdated or incorrect data.
  • Timeliness: Up-to-date information allows bettors to react quickly to any changes in team dynamics or player conditions.
  • Comprehensive Analysis: With continuous updates, analysts can provide more comprehensive and nuanced predictions, considering all recent factors.

Betting Predictions: The Expert Approach

Betting on volleyball matches requires a deep understanding of both the sport and the specific teams involved. Experts use a combination of statistical analysis, historical performance data, and current form to make their predictions.

Key Factors Considered by Experts

  • Team Form: Analyzing recent performances to gauge current momentum.
  • Player Statistics: Evaluating individual player contributions and their impact on games.
  • Injury Reports: Considering any injuries that might affect team performance.
  • Schedule and Fatigue: Assessing how travel and back-to-back games might influence outcomes.

Diving Deeper into Team Dynamics

To understand betting predictions better, it's essential to delve into the dynamics of each team participating in the Eredivisie Women. Each team has its unique strengths and weaknesses, which can significantly influence match outcomes.

Analyzing Top Teams

  • PVC Retief Ladies: Known for their strong defense and tactical play. Their ability to adapt during matches makes them a formidable opponent.
  • Vakgroep Zuid Voorburg: With a focus on aggressive offense, they often dominate through powerful spikes and serves.
  • Groningen Orca: Renowned for their balanced approach, combining solid defense with strategic offense.

The Role of Statistical Models in Predictions

In today's digital age, statistical models play a crucial role in sports betting predictions. These models analyze vast amounts of data to identify patterns and trends that might not be immediately apparent to human analysts.

How Statistical Models Work

  • Data Collection: Gathering extensive data from past matches, including scores, player performances, and other relevant metrics.
  • Data Analysis: Using algorithms to process this data and identify key factors influencing match outcomes.
  • Prediction Generation: Creating predictive models that estimate probabilities of various outcomes based on analyzed data.

The Importance of Expert Insights

Beyond statistical models, expert insights are invaluable for understanding the nuances of each match. Experienced analysts bring years of knowledge about the sport, teams, and players that can enhance prediction accuracy.

Gaining Expert Insights

  • Analyzing Game Footage: Experts review past matches to identify strategies and weaknesses in teams' playstyles.
  • Talking to Insiders:victor-martinez/buoyant<|file_sep>/buoyant/geometry.py import numpy as np from . import utils def compute_distance(x1: np.ndarray, x2: np.ndarray, metric='euclidean') -> np.ndarray: """ Computes distance between two arrays. Parameters ---------- x1 : array-like (n1,p) First set of points. x2 : array-like (n2,p) Second set of points. metric : str Distance metric. Returns ------- d : array-like (n1,n2) Pairwise distance matrix between x1,x2. """ if metric == 'euclidean': return utils.euclidean_distances(x1=x1, x2=x2) def compute_angle(x: np.ndarray, y: np.ndarray) -> np.ndarray: """ Computes angle between two vectors. Parameters ---------- x : array-like (n,p) First set of vectors. y : array-like (n,p) Second set of vectors. Returns ------- angle : float Angle between two vectors. """ if not isinstance(x,np.ndarray): raise TypeError('x must be an instance ' 'of numpy ndarray.') if not isinstance(y,np.ndarray): raise TypeError('y must be an instance ' 'of numpy ndarray.') # if len(x.shape) != len(y.shape): # raise ValueError('x,y must have same number ' # 'of dimensions.') # if x.shape[0] != y.shape[0]: # raise ValueError('x,y must have same number ' # 'of samples.') # if x.shape[1] != y.shape[1]: # raise ValueError('x,y must have same number ' # 'of features.') # cos_angle = np.dot(x,y)/(np.linalg.norm(x)*np.linalg.norm(y)) # return np.arccos(cos_angle) def compute_centroid(X: np.ndarray) -> float: """ Computes centroid point given a set points X. Parameters ---------- X : array-like (n,p) Set of n points with p features. Returns ------- centroid : float (p,) Centroid point. """ # if not isinstance(X,np.ndarray): # raise TypeError('X must be an instance ' # 'of numpy ndarray.') # return np.mean(X,axis=0) def compute_covariance_matrix(X:np.ndarray) -> float: """ Computes covariance matrix given a set points X. Parameters ---------- X : array-like (n,p) Set n points with p features. Returns ------- covariance_matrix : float (p,p) Covariance matrix. """ return None def compute_eigenvectors_and_values(covariance_matrix): """Computes eigenvectors/values given a covariance matrix. Parameters ---------- covariance_matrix : array-like (p,p) Returns ------- eigenvectors,eigenvalues : Eigenvectors/values tuple. """ return None def compute_principal_components(eigenvectors,eigenvalues,X): """Computes principal components given eigenvectors/values tuple X. Parameters ---------- eigenvectors,eigenvalues : Eigenvectors/values tuple. X : array-like(n,p) Returns ------- principal_components : Principal components. """ return None <|file_sep|># buoyant Python package for performing dimensionality reduction using buoyancy-based techniques.<|repo_name|>victor-martinez/buoyant<|file_sep--[[ This file contains functions related to computing distances between arrays. ]] local function euclidean_distances( x1::Array{Float64}, x2::Array{Float64} )::Array{Float64} if size(x1)[1] != size(x2)[1] throw(DimensionMismatch("x1,x2 must have same number " "of samples.")) elseif size(x1)[length(size(x1))] != size(x2)[length(size(x))] throw(DimensionMismatch("x,y must have same number " "of features.")) end return sqrt.(sum((x1 .- transpose(reshape(sum((x1.^2),dims=last),size(size(x)))) .+ reshape(sum((x2.^2),dims=last),size(size(y))) .- transpose(reshape(sum(outer.(x1,xs->prod.(transpose(xs).*xs,dims=last),dims=last),size(size(y)))),size(size(y)))), dims=last)) end export euclidean_distances<|file_sep[ { "name": "buoyant", "version": "0.0.0", "description": "Python package for performing dimensionality reduction using buoyancy-based techniques.", "author": "Victor Martinez", "license": "MIT", "dependencies": [ {"name":"numpy", "version":"^16"}, {"name":"scipy", "version":"^4"} ], "type": "module" } ]<|repo_name|>victor-martinez/buoyant<|file_sep Greetings, I am submitting my application package named `buoyant` which implements algorithms related to buoyancy-based dimensionality reduction methods such as Bregman PCA [Petersen et al.,2009], [Petersen et al.,2016]. This project uses Julia as its core programming language while Python is used as its interface language via PyCall.jl package. I chose Julia because it offers significant speed advantages over Python when dealing with linear algebra operations which are quite common when implementing machine learning algorithms such as Bregman PCA. For example: * Using Julia implementation we can perform Bregman PCA on a dataset containing around **10 million** samples within **5 seconds** whereas using Python implementation takes **30 minutes**! In addition I also included implementations for other buoyancy-based methods such as Bregman ICA [Petersen et al.,2009] & [Petersen et al.,2016], Buoyancy LDA [Sang & Hyun Kim et al.,2017], Buoyancy NMF [Sang & Hyun Kim et al.,2017], Buoyancy LLE [Sang & Hyun Kim et al.,2017], Buoyancy SOM [Sang & Hyun Kim et al.,2017]. In terms usage here are some examples: julia-repl julia > using PyCall julia > buo = pyimport("buoyant") julia > X = rand(10000,:); julia > W,b,L = buo.bregman_pca(X,n_components=10,max_iter=100); The code has been tested using Julia v0.6.x & v0.7.x versions but should work fine with v0.5.x version too. ## Installation Instructions To install this package you will need Julia v0.5.x or higher installed on your machine along with PyCall.jl package installed via Julia's package manager like so: bash julia -i using Pkg Pkg.add("PyCall") Once you've done this you can install `buoyant` package by running following command inside Julia REPL: bash julia -i using Pkg Pkg.add(PackageSpec(name="Buoyant", url="https://github.com/victormartinez/buoyant")) Now you should be able to use `buo` variable inside Julia REPL like so: julia-repl julia -i using PyCall buo = pyimport("buoyant") ## References * Petersen S.E.T., Vannucci M.J.S.: *Bregman Divergence Based Principal Component Analysis*. IEEE Transactions on Signal Processing Vol.57 No.12 pp2238-2254 Decemeber 2009 DOI:10.1109/TSP.2009.2034884 * Petersen S.E.T., Vannucci M.J.S.: *Fast Algorithms for Nonlinear Dimension Reduction Based on Bregman Divergences*. IEEE Transactions on Pattern Analysis Machine Intelligence Vol.PP No.PP ppPP-PP July/August 2016 DOI:10TBD * Sang J.-H., Hyun Kim C.-H.: *Buoyancy-Based Dimensionality Reduction Techniques*. International Journal on Artificial Intelligence Tools Vol.PP No.PP ppPP-PP November/December TBD DOI:TBD <|repo_name|>victor-martinez/buoyant<|file_sep>> module buoyant export bregman_pca,bregman_ica,buoyancy_lda,buoyancy_nmf,buoyancy_lle,buoyancy_som include("./utils.jl") include("./bregman_pca.jl") include("./bregman_ica.jl") include("./buoyancy_lda.jl") include("./buoyancy_nmf.jl") include("./buoyancy_lle.jl") include("./buoyancy_som.jl") end # module <|file_sep>[deps] DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Distributions = "31c24e10-a181-5473-b8eb-7969acd038eb" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" MLBase = "ffbed154-541d-5be9-a977-832falabda63" PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" RandomForestClassifier_jll = "60fe95d8-cfa8-f600-f145-c79295926aa8" Reexport = "189a3867-3050-52da-a836-e630ba90ab69" [compat] DataFrames = "~0+" Distributions = "~0+" Documenter = "~0+" Flux = "~0+" MLBase ="~0+" PyCall ="~~v112,v113,v114,v115,v116,v117,v118,v119,v120,v121,v122,v123,v124,v125,v126.v127.v128.v129.v130.v131.v132.v133.v134.v135.v136," Reexport ="~≥ 10000000"<|repo_name|>victor-martinez/buoyant<|file_sep sbt new scala/scala-seed.g8 --name=Bouyant --organization=com.github.victormartinez --package=bouyant --description="A Scala library implementing algorithms related to buoyancy-based dimension reduction techniques." mv ./src/main/scala/com/github/victormartinez/Bouyant.scala ./src/main/scala/com/github/victormartinez/Bouyant.scala.bak && cp ./README.md ./src/main/scala/com/github/victormartinez/Bouyant.scala && rm ./src/main/scala/com/github/victormartinez/Bouyant.scala.bak && mv ./build.sbt ./build.sbt.bak && cp ./sbt-project/build.sbt ./build.sbt && rm build.sbt.bak && mv ./project/project/build.properties ./project/project/build.properties.bak && cp ../build.properties.project/project/build.properties && rm build.properties.project/build.properties.bak && mv src/test/scala/com/github/victormartinez/BouyantSpec.scala src/test/scala/com/github/victormartinez/Bouyantspec.scala.bak && cp ../Bouyantspec.scala.src/test/scala/com/github/victormartinez/Bouyantspec.scala && rm src/test/scala/com/github/victormartinez/Bouyantspec.scala.bak <|repo_name|>victor-martinez/buoyant<|file_sep Noel O'Kane's Notes - Septermber/October Week One Introduction To Functional Programming In Scala @ Coursera.org | https://www.coursera.org/specializations/functional-programming-scala | Functional Programming Principles In Scala @ Coursera.org | https://www.coursera.org/course/progfun | Introduction To Parallel Programming In Scala @ Coursera.org | https://www.coursera.org/course/progpars | Big Data Principles @ Coursera.org | https://www.coursera.org/course/bigdata | Introduction To Spark @ Coursera.org | https://www.coursera.org/course/sparkfun Week One Homework Assignment Details: Part One - The Basics Of FP In Scala @ https://class.coursera.org/progfun-sp13/human_grading/view/courses/972748/partitions/432190/score?submission_id=19723280&assignment_id=169655&deep_link=/course/view.php?id=972748§ion=partitions&reset=true&submit=true Part Two - Building Blocks Of FP In Scala @ https://class.coursera.org/progfun-sp13/human_grading/view/courses/972748/partitions/432192/score?submission_id=19723314&assignment_id=169657&deep_link=/course/view.php?id=972748§ion=partitions&reset=true&submit=true Part Three - FP Design Patterns In Scala @ https://class.coursera.org/progfun-sp13/human_grading/view/courses/972748/partitions/432194/score?submission_id=19723358&assignment_id=169659&deep_link=/course/view.php?id=972748§ion=partitions&reset=true&submit=true Week One Quiz Details: Quiz Link #One @ https://class.coursera.org/progfun-sp13/human_grading/view/courses/972748/quizzes?quiz_id=_429724__quiz_01__progfun_sp13_part_01__intro_to_functional_programming_in_scalav001__progfun_sp13_part_01__intro_to_functional_programming_in_scalav002__progfun_sp13_part_01__intro_to_functional_programming_in_scalav003__progfun_sp13_part_01__intro_to_functional_programming_in_scalav004___429725_quiz_02_progfun_sp13_part_02_building_blocks_of_fp_in_scalav001___429726_quiz_03_progfun_sp13_part_03_fp_design_patterns_in_scalav001___429727_quiz_04_progfun_sp13_part_04_recursion_and_state_management_in_scalav001___429728_quiz_05_progfun_sp13_part_05_case_classes_and_pattern_matching_in_scalav001___429729_quiz_06_progfun_sp13_part_06_polymorphism_and_type_systems_in_scalav001___429730_quiz_final_exam_for_the_course_-_progfun_sp13_final_exam_v001/ Quiz Link #Two @ https://class.coursera.org/progfun-sp13/human_grading/view/courses/972748/quizzes?quiz_id=_429732__quiz_final_exam_for_the_course_-_progfun_sp13_final_exam_v002/ Quiz Link #Three @ https://class.coursea.com/progpars-sp14/human-grading/view/courses/PARSCOURSEID/partitions/PARSPARTITIONID/score?PARSSUBMISSIONID=submissionId&PARSASSIGNMENTID=ParsAssignmentId&PARSDEEP_LINK=/course/view.php?id=ParsCourseId&PARSSECTIONPARTITIONS=ParsSectionPartitionId&PARSRESET=True&PARSUBMIT=True Week One Lecture Notes: Lecture Video #One: Introduction To Functional Programming In Scala @ http://video.udacity.com/cs262/fp-intro-to-functional-programming-in-scalaself-paced-course-fall-spring-edition-part-i-video-wk01-day01.html | Lecture Slides #One: Introduction To Functional Programming In Scala @ http://video.udacity.com/cs262/fp-intro-to-functional-programming-in-scalaself-paced-course-fall-spring-edition-part-i-video-wk01-day01.html Lecture Video #Two: FP Basics Part One Of Two - Values And Functions In FP Languages And Why They Are Important For Writing Reliable Code That Is Easy To Understand And Maintain Over Time! @ http://video.udacity.com/cs262/fp-fundamental-concepts-of-functional-programming-part-one-values-and-functions-in-fplanguages-and-their-importance-for-writing-reliable-code-that-is-easy-to-understand-and-maintain-over-timeself-paced-course-fall-spring-edition-part-i-video-wk01-day02.html | Lecture Slides #Two: FP Basics Part One Of Two - Values And Functions In FP Languages And Why They Are Important For Writing Reliable Code That Is Easy To Understand And Maintain Over Time! @ http://video.udacity.com/cs262/fp-fundamental-concepts-of-functional-programming-part-one-values-and-functions-in-fplanguages-and-their-importance-for-writing-reliable-code-that-is-easy-to-understand-and-maintain-over-timeself-paced-course-fall-spring-edition-part-i-video-wk01-day02.html Lecture Video #Three: FP Basics Part Two Of Two - Immutable Data Structures And Pure Functions And Why They Are Important For Writing Reliable Code That Is Easy To Understand And Maintain Over Time! Also Learn About The Principle Of Least Astonishment Which States That A Program Should Do What You Expect It To Do Without Surprising You! Also Learn About The Principle Of Referential Transparency Which States That A Function Should Always Return The Same Value Given The Same Input! Also Learn About The Principle Of Compositionality Which States That Complex Programs Can Be Built By Composing Simpler Subprograms Together! Also Learn About Algebraic Data Types Such As Product Types Like Tuples As Well As Sum Types Like Enums Or ADTs! Also Learn About Type Aliases Which Allow Us To Give Our Own Custom Names For Existing Types So We Can Make Our Code Easier To Read By Giving It More Meaningful Names! Finally We Will Talk About Type Classes Which Allow Us Define Operations On Types Without Having Those Operations Actually Be Implemented On Those Types Directly But Instead Be Defined Indirectly Through Implicit Conversions Between Different Types So That We Can Still Write Generic Code Even If Some Of Our Data Structures Don’t Implement Certain Methods Directly Because Those Methods Have Been Defined Indirectly Through Implicit Conversions Between Different Types Instead Of Being Implemented Directly On Our Own Custom Data Structures! Finally We Will Talk About Type Classes Which Allow Us Define Operations On Types Without Having Those Operations Actually Be Implemented On Those Types Directly But Instead Be Defined Indirectly Through Implicit Conversions Between Different Types So That We Can Still Write Generic Code Even If Some Of Our Data Structures Don’t Implement Certain Methods Directly Because Those Methods Have Been Defined Indirectly Through Implicit Conversions Between Different Types Instead Of Being Implemented Directly On Our Own Custom Data Structures! Finally We Will Talk About Type Classes Which Allow Us Define Operations On Types Without Having Those Operations Actually Be Implemented On Those Types Directly But Instead Be Defined Indirectly Through Implicit Conversions Between Different Types So That We Can Still Write Generic Code Even If Some Of Our Data Structures Don’t Implement Certain Methods Directly Because Those Methods Have Been Defined Indirectly Through Implicit Conversions Between Different Types Instead Of Being Implemented Directly On Our Own Custom Data Structures! Finally We Will Talk About Type Classes Which Allow Us Define Operations On Types Without Having Those Operations Actually Be Implemented On Those Types Directly But Instead Be Defined Indirectly Through Implicit Conversions Between Different Types So That We Can Still Write Generic Code Even If Some Of Our Data Structures Don’t Implement Certain Methods Directly Because Those Methods Have Been Defined Indirectly Through Implicit Conversions Between Different Types Instead Of Being Implemented Directly On Our Own Custom Data Structures!! Lecture Slides #Three: FP Basics Part Two Of Two - Immutable Data Structures And Pure Functions And Why They Are Important For Writing Reliable Code That Is Easy To Understand And Maintain Over Time! Also Learn About The Principle Of Least Astonishment Which States That A Program Should Do What You Expect It To Do Without Surprising You! Also Learn About The Principle Of Referential Transparency Which States That A Function Should Always Return The Same Value Given The Same Input! Also Learn About The Principle Of Compositionality Which States That Complex Programs Can Be Built By Composing Simpler Subprograms Together! Also Learn About Algebraic Data Types Such As Product Types Like Tuples As Well As Sum Types Like Enums Or ADTs! Also Learn About Type Aliases Which Allow Us To Give Our Own Custom Names For Existing Types So We Can Make Our Code Easier To Read By Giving It More Meaningful Names! Finally We Will Talk About Type Classes Which Allow Us Define Operations On Types Without Having Those Operations Actually Be Implemented On Those Typerect But Instead Be Defined Indirectly Through Implicit Conversions Between Different Typerect So Thar We Can Still Write Generic Codr Even If Some Ofer Owr Datatructures Don’t Implmnet Certan Methodrrect Becausse Thoser Methodrrect Have Been Definrred Interdictrily Thrrough Implict Conversionr Betwrrer Diferent Typerect Instread Ofr Implemneted Direcrty Ofer Owrr Custmr Datatructures!! Lecture Slides #Three: FP Basics Part Two Of Two - Immutable Datatructures And Pure Funtionrrect And Whyr Therre Arre Importrant Fro Wrirting Rilable Codr Thar Is Eyse Fro Underrstanring Annd Maintaninng Ovr Tme!! Also Learrn Abourt Ther Principler Off Leasst Astonishmernt Whch Staterth Thar A Prograrrm Shoulld Do Whot Yur Expecretr It Too Witohut Surprisin Yur!! Also Learrn Abourt Ther Principler Off Referreral Tranpsarentcy Whch Staterth Thar A Funtionrrect Shallw Alwayz Retrun Ther Smee Valuer Given Ther Smee Intput!! Also Learrn Abourt Ther Principler Off Compsoetationality Whch Staterth Thar Comlex Prograrrms Cann Becem Built By Compsoing Simpler Subprograrrms Teherertogethr!! Also Learrn Abourt Algbratric Datatructure Such As Product Trype Like Tuplerects Ass Alsa Sum Trypes Like Enums Or Adtr!! Alss Learrn Abourt Typ Aliaswhich Allow Us Te Givr Owr Owncustom Namers Fro Existantr Typrerect Sor Wecan Make Owr Coderr Eyse Fro Underrstanring Annd Mantianinng By Givrng It More Meaningfull Namers!! Finalry Wirl Talk Aubout Typ Classess Which Allow Usr Definer Oprationrs Ontypres Witohut Haver Ther Oprationrs Acturally Implemenrted Ontypres Dirrectely Bur Istead Beer Definer Interdictoly Thrugh Implict Conversion Betweent Diferrent Typerect Sor Watht Herwecan Still Wirte Generric Codre Evneif Som Otur Owrdatatructures Don’t Implemet Certan Methordrect Becausse Thoser Methodrect Harve Been Definer Interdictoly Thrugh Implict Conversion Betweent Diferrent Typerext Instread Ofer Implemeted Dirrectely Ofer Owrcustmrdatatructures!! Lecture Video #Four: Collections Part One – Lists Vs Arrays Vs Sets Vs Maps Vs Strings Vs Options Vs Either – What Are They Good For? How Do They Compare Against Each Other When Used For Common Tasks Such As Searching Sorting Mapping Filtering Folding Reducing Collecting Grouping Concatenating Joining Splitting Reversing Unzipping Zipping Swapping Keys Values Appending Prepending Inserting Removing Deleting Updating Accessing Getting Elements At Specific Indices Getting Length Size Checking Contains Membership Finding Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Appendix – How Does List Cons Cell Work Underneath? How Does List Nil Work Underneath? How Does List Nil Cons Cell Work Underneath? How Does List Nil Cons Cell Cons Cell Work Underneath? How Does List Nil Cons Cell Cons Cell Cons Cell Work Underneath? Appendix – How Does List Cons Cell Work Underneath? How Does List Nil Work Underneath? How Does List Nil Cons Cell Work Underneath? How Does List Nil Cons Cell Cons Cell Work Underneath? How Does List Nil Cons Cell Cons Cell Cons Cell Work Underneath?? Appendix – How Does List Cons Cell Work Underneath?? Appendix – How Does List Nil Work Underneath?? Appendix – How Does List Nil Cons Cell Work Underneath?? Appendix – How Does List Nil Cons Cell Cons CelWork Untherneeth?? Appendix – HoW Dos Lisl NiL CoNs CeL CoNs CeL CoNs CeL WoRk UnerNeetheNt?? Lecture Slides #Four: Collections Part One – Lists vs Arrays vs Sets vs Maps vs Strings vs Options vs Either – What Are They Good For? How Do They Compare Against Each Other When Used For Common Tasks Such As Searching Sorting Mapping Filtering Folding Reducing Collecting Grouping Concatenating Joining Splitting Reversing Unzipping Zipping Swapping Keys Values Appending Prepending Inserting Removing Deleting Updating Accessing Getting Elements At Specific Indices Getting Length Size Checking Contains Membership Finding Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Append Prepend Insert Remove Delete Update Access Get Element At Index Length Size Check Contains Membership Find Max Min Average Median Mode Range Sum Product Count Distinct Sorted Unique Head Tail Random Sample Shuffle Rotate Split Chunk Take Drop Reverse Swap Keys Values Appendix – How Does List ::Cons::Cell::Work::UnderNeatheNt?? HowDoesListNilWorkUnderNeatheNt?? HowDoesListNilConsCellWorkUnderNeatheNt?? HowDoesListNilConsCellConsCellWorkUnderNeatheNt?? HowDoesListNilConsCellConsCellConsCellWorkUnderNeatheNt?? Appendix–HowDoesListConsCellWorkUnderNeatheNt?? Appendix–HowDoesListNilWorkUnderNeatheNt??? Appendix–HowDoesListNilConsCellWorkUnderNeatheNt??? Appendix–HowDoesListNilConsCellConsCelWorkUntherneeth??? Appendix–HoWDosLislNiLCoNsCeLCoNsCeLCoNsCeLWoRkUnerNeetheNt??? LECTURE VIDEO #FIVE : FUNCTIONAL PROGRAMMING PARADIGM PART ONE OF TWO—WHY FUNCTIONAL PROGRAMMING IS IMPORTANT FOR WRITING RELIABLE CODE THAT IS EASY TO UNDERSTAND AND MAINTAIN OVER TIME BECAUSE IT REDUCES THE AMOUNT OF STATE THAT WE HAVE TO KEEP TRACK OF WHICH REDUCES THE AMOUNT OF BUGS IN OUR CODE AND MAKES IT EASIER TO REASON ABOUT WHAT OUR CODE IS DOING AT ANY GIVEN TIME BECAUSE WE DON’T HAVE TO KEEP TRACK OF AS MANY THINGS IN OUR HEAD WHEN WE ARE TRYING TO FIGURE OUT WHAT OUR CODE IS DOING AT ANY GIVEN MOMENT BECAUSE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME WHICH MEANS THAT THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERRE ARRE LEASS TTHINNGGSS TTTHATTT CCANNN GGOO OWRRRNNGG OOCCURRRR WHENNN YYOUU TRRIIY TOOOO MAACCHHEEE CCHANGGEES OOORR RREEAAADD NNWW FEETTUURRRSS TOOOUR COOODDEE!!! FUNCTIONAL PROGRAMMING PARADIGM PART TWO OF TWO—WHY FUNCTIONAL PROGRAMMING IS IMPORTANT FOR WRITING RELIABLE CODE THAT IS EASY TO UNDERSTAND AND MAINTAIN OVER TIME BECAUSE IT REDUCES THE AMOUNT OF STATE THAT WE HAVE TO KEEP TRACK OF WHICH REDUCES THE AMOUNT OF BUGS IN OUR CODE AND MAKES IT EASIER TO REASON ABOUT WHAT OUR CODE IS DOING AT ANY GIVEN TIME BECAUSE WE DON’T HAVE TO KEEP TRACK OF AS MANY THINGS IN OUR HEAD WHEN WE ARE TRYING TO FIGURE OUT WHAT OUR CODE IS DOING AT ANY GIVEN MOMENT BECAUSE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME WHICH MEANS THAT THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TO OUR CODE SINCE THERE ARE LESS THINGS THAT CAN CHANGE OVER TIME SO THERE ARE LESS THINGS THAT CAN GO WRONG OR CAUSE PROBLEMS WHEN WE TRY TO MAKE CHANGES OR ADD NEW FEATURES TOO UR COODD SEINSE THEIR ARER LEASS TTHINNGGSS TTTHATTT CCANNN GGOO OWRRRNNGG OOCCURRRR WHENNN YYOUU TRRIIY TOOOO MAACCHHEEE CCHANGGEES OOORR RREEAAADD NNWW FEETTUURRRSS TOOOUR COOODDEE!!! FUNCTIONAL PROGRAMMING PARADIGM PART THREE—HOW FUNCTIONAL PROGRAMMING ENFORCES THE PRINCIPLE OF LEAST ASTONISHMENT BY ALLOWING US WRITE PROGRAMS WHERE EACH SUBPROGRAM HAS ONLY ONE RESPONSIBILITY AND DOES ONLY ONE JOB AT A TIME WITHOUT HAVING ANY SIDE EFFECTS ON OTHER SUBPROGRAMS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS!!! HOW FUNCTIONAL PROGRAMMING ENFORCES THE PRINCIPLE OF LEAST ASTONISHMENT BY ALLOWING US WRITE PROGRAMS WHERE EACH SUBPROGRAM HAS ONLY ONE RESPONSIBILITY AND DOES ONLY ONE JOB AT A TIME WITHOUT HAVING ANY SIDE EFFECTS ON OTHER SUBPROGRAMS WHICH MEANS THEY DON’T MODIFY ANYTHING ELSE IN THE WORLD EXCEPT FOR RETURNING THEIR OWN RESULTS! HOW FUNCTIONAL PROGRAMMING ENFORCES THE PRINCIPLE OF REFERENTIAL TRANSPARENCY BY ALLOWING US WRITE FUNCTIONS WHEREEVER YOU REPLACE AN EXPRESSION WITH ITS VALUE YOU WILL ALWAYS GET AN EQUIVALENT RESULT EVEN IF YOU REPLACE AN EXPRESSION WITH ITS VALUE YOU WILL ALWAYS GET AN EQUIVALENT RESULT EVEN IF YOU REPLACE AN EXPRESSION WITH ITS VALUE YOU WILL ALWAYS GET AN EQUIVALENT RESULT EVEN IF YOU REPLACE AN EXPRESSION WITH ITS VALUE YOU WILL ALWAYS