{"id":6329,"date":"2025-04-20T18:06:04","date_gmt":"2025-04-21T02:06:04","guid":{"rendered":"https:\/\/wonghoi.humgar.com\/blog\/?p=6329"},"modified":"2025-12-01T01:47:39","modified_gmt":"2025-12-01T09:47:39","slug":"concepts-in-c-that-does-not-apply-to-python-or-matlab","status":"publish","type":"post","link":"https:\/\/wonghoi.humgar.com\/blog\/2025\/04\/20\/concepts-in-c-that-does-not-apply-to-python-or-matlab\/","title":{"rendered":"Concepts in C++ that do not apply to Python or MATLAB"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Static Data Members<\/h2>\n\n\n\n<p>In Python\/MATLAB, data members are called properties. It&#8217;s called (data) member in C++. I&#8217;ll use these names interchangably when comparing these languages.<\/p>\n\n\n\n<p>Python and MATLAB have the concept static methods, but static properties (data members) doesn&#8217;t really exist in either language. <\/p>\n\n\n\n<p>Python&#8217;s properties have a split personality (class AND instance), so it&#8217;s not like C++ that you choose between class XOR instance. Therefore I call those class variables because in C++, static variables do not have split personality: either you are classwise (<code>static<\/code>) or not. In C++ (or MATLAB) you don&#8217;t have both cases sharing the same variable name so a class variable can be shadowed by an instance variable.<\/p>\n\n\n\n<p>As for MATLAB, there&#8217;s no <code>Static<\/code> property. The only classwise properties allowed is <code>Constant<\/code> property. TMW (creator of MATLAB) decided not to allow non-<code>Const<\/code> classwise\/static properties because of this web of rules:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>A.B = C<\/code> can either mean <br>1) [Variable] write a new struct <code>A<\/code> with field <code>B<\/code>, or<br>2) [Class] attempt to write to property <code>B<\/code> of <strong>class<\/strong> (<strong>not<\/strong> instance of) <code>A<\/code>.<\/li>\n\n\n\n<li>If a class (definition) <code>A<\/code> is loaded into the current workspace, allowing case #2 might throw users who intended to make a struct A with field B out of nowhere off.<\/li>\n\n\n\n<li>MATLAB gives variables higher priorities than function\/classes, so case #2 has to be struck down to make it unambigious.<\/li>\n\n\n\n<li>By making <code>A.B<\/code>, a classwise access to field B, either read only (<code>Constant<\/code>) or tied to instances <code>a=A(); a.B=C<\/code>, MATLAB avoided the situation of <code>A.B=C<\/code> while <code>A<\/code> is a class (case#2) so A.B=C is unambiguously writing to a struct field (case#1).<\/li>\n<\/ul>\n\n\n\n<p>I know. This is quite lame. Matrices are first class citizens in MATLAB while classes are an after-thought that isn&#8217;t really a thing until 2008. You win some and you lose some whether you choose MATLAB or Python.<\/p>\n\n\n\n<p>The official TMW workaround is to use a <code>Static<\/code> method getter\/setter (not dependent properties because it only works for instances or if what it depended are exclusively <code>Constant<\/code> properties) with <code>persistent<\/code> variable holding the internally data that&#8217;s meant to be static. This is very convoluted and it sucks. I&#8217;d call it a weakness of MATLAB.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Constant properties in MATLAB are <code>static const<\/code> (classwise-only)<\/h2>\n\n\n\n<p>In C++, const properties (data members) can be either instance bound or classwise (static), which means instances could be initialized with a different set of constants. <\/p>\n\n\n\n<p>Instance-wise constant data members is possible in C++ through (member) initializer lists, which happens to be the only route for private constants as public constants could <strong>also<\/strong> be list (including brace) initialized (in newer C++ such as C++11).<\/p>\n\n\n\n<p>Constructor is not a first-hand initializer in C++ (directly assigning memory with predefined values). Members are fully constructed in C++ (just not necessarily with the values you wanted) by the time you get inside the constructor function. Therefore, in C++, constants and references (things that cannot be changed) for <strong>instances<\/strong> must be done in (member) initializer lists right before the constructor.<\/p>\n\n\n\n<p>Only <code>static const<\/code> can be <strong>optionally<\/strong> defined directly at the class with <code>=<\/code> sign since C++11. Before then it only worked for integers as it was enum under the hood with a primative compiler design. Otherwise you forward <strong>declare<\/strong> the data member (without specifying the details) in the class <code>C<\/code> such as <code>static const T x<\/code>, then define it (assign the value) like <code>T C::x = 42<\/code> outside the class definition.<\/p>\n\n\n\n<p>In MATLAB, <code>Constant<\/code> properties are classwise-only (in fact, it&#8217;s the only kind of <code>Static<\/code> property allowed as discussed in the first section). You simply declare the value at the class definition with <code>=<\/code> sign just like the fast way to type <code>static const<\/code> in C++11 because there is no concept of (member) initializer list in MATLAB so you can modify how consts and refs are stamped out, not remodel them in the constructor after they are already made.<\/p>\n\n\n\n<p>Accessing constant properties with an object instance has to be a shortcut for classwise constant in MATLAB, while it depends on whether the <code>const<\/code> is <code>static<\/code> in C++.<\/p>\n\n\n\n<p>In Python, everybody is a consenting adult (there&#8217;s no native concept of <code>const<\/code>). Scream your constants in ALL CAPS and hope everybody act like a gentleman and not touch them. Lol<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Static Native Getters\/Setters<\/h2>\n\n\n\n<p>C++ does not have native getter or setter using a data member&#8217;s name so the said data member do not store states but instead act as a proxy (potentially interacting to other state-holding data members).<\/p>\n\n\n\n<p>Let&#8217;s say the variable with a native getter\/setter in question is <code>x<\/code>. <\/p>\n\n\n\n<p>In MATLAB, this feature is called <code>Dependent<\/code> properties, where you define members under <code>properties (Dependent)<\/code> and the getter function is named <code>function get.x(self, ...)<\/code>.<\/p>\n\n\n\n<p>In Python, <code>@property<\/code> decorator mangles your function with the same name as the dependent member, aka <code>def x(self, ...)<\/code> so the functor can call the corresponding getter\/setter without the function call round brackets <code>()<\/code>.<\/p>\n\n\n\n<p>However, in both MATLAB and Python, dependent data members are mainly aimed at instances (objects created), not class-wise!<\/p>\n\n\n\n<p>In Python, it&#8217;s simply impossible to stack @staticmethod or @classmethod with @property. I tried doing that and Python just declared the function name (dependent property name) a <code>property<\/code> object so when I call it (without round brackets for function calls of course), it merely shows &#8220;<code>&lt;property at 0x...&gt;<\/code>&#8216;.<\/p>\n\n\n\n<p>The case of MATLAB is a lot weirder. It&#8217;s a web of rules for that are based on seemingly unrelated reasons which created a catch-22 to mean dependent properties are instance-bound without means to access it classwise:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>MATLAB doesn&#8217;t have classwise properties that are not <code>Const<\/code> properties (aka no <code>Static<\/code> data members\/properties) to avoid breaking backward compatibility (see the first section of this post for the web of rules that caused it).<\/li>\n\n\n\n<li>In C++&#8217;s lingo, MATLAB has no (mutable) <code>static<\/code> data member. The only nearest thing is (immutable) <code>static const<\/code> data member (which is called <code>Constant<\/code> property in MATLAB).<\/li>\n\n\n\n<li>MATLAB does not have instance-wise constants (see above)<\/li>\n\n\n\n<li><code>Dependent<\/code> property in MATLAB is neither classwise or instance-wise, because the concept itself is a method (function member) pretending to be a property (data member). More precisely, it&#8217;s an extra level of indirection that dispatches getters or setters depending on whether it&#8217;s &#8216;read&#8217; or &#8216;written&#8217;<\/li>\n\n\n\n<li>MATLAB not treating properties (data member) and methods (function member) the same way (when it comes to whether it&#8217;s class-bound or instance-bound) created an dilemma (identity crisis) for <code>Dependent<\/code> property. <\/li>\n\n\n\n<li>Despite <code>Dependent<\/code> property is really a method in disguise, its getters\/setters not allowed to take on a <code>Static<\/code> role like other methods because it claimed to be a property (data member) so it&#8217;s stuck following the more restrictive rules that applies to properties (aka no <code>static<\/code>)<\/li>\n<\/ol>\n\n\n\n<p>Without knowing the underlying rules above, the implications are less than intuitive (makes perfect sense if you know what constraints MATLAB is working under that made it nearly impossible for a sensible use case where the user simply wants to create a shortcut for computing with classwise\/<code>Const<\/code> properties):<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>MATLAB&#8217;s rules requires any property that are <strong>not<\/strong> <code>Constant<\/code> to be accessed exclusively through instances. <\/li>\n\n\n\n<li>A <code>Dependent<\/code> property is not a <code>Const<\/code> property, so it&#8217;s considered an instance-based member. <\/li>\n\n\n\n<li>This implies <code>Dependent<\/code> data member can only be called from an instance (regardless of whether you&#8217;re trying to do it from within the same class or outside the class).<\/li>\n\n\n\n<li>Since <code>Dependent<\/code> property is defined to be instance-bound, the instance object (<code>self<\/code>) is passed to getters\/setters as the first argument and the definition of the getters\/setters has to expect it so it&#8217;d be like <code>function get.x(self, ...)<\/code>, just like all instance-bound methods.<\/li>\n\n\n\n<li>There&#8217;s nothing that says such getters\/setters must use the <code>self<\/code> passed to it. However, if you use constant properties, it doesn&#8217;t matter where you call from the <code>self<\/code> (object instance) or the class name since constants are classwise-only in MATLAB to both syntax refers to the same thing.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">RAII available in C++ and MATLAB but not Python<\/h2>\n\n\n\n<p>Garbage collector is a concept only when you are allowed to make multiple aliases for the same underlying data, and don&#8217;t want to meticulously keep track of who&#8217;s ultimately responsible for winding it down and when.<\/p>\n\n\n\n<p>In particular, garbage collector has the lattitude to not clean the object right away when the reference count goes down to zero. <code>shared_ptr<\/code> (an automatic memory management technique) promptly cleans the object when the reference count touches zero. Therefore garbage collector can procrastinate painful release (cleanup) process so the program doesn&#8217;t have to frequently stumble to do the cleanup.<\/p>\n\n\n\n<p>C++ chose not to use garbage collectors (as class destructors running at a non-deterministic time breaks RAII). Python choose to embrace it at the expense of breaking RAII. MATLAB is kind of in between yet the way MATLAB does it does not break RAII, but it&#8217;s not obvious.<\/p>\n\n\n\n<p>MATLAB language has a very unique design choice (mental model) that users <a href=\"http:\/\/www.mathworks.com\/access\/helpdesk\/help\/techdoc\/matlab_prog\/f2-43934.html\"><strong>see\/reason<\/strong><\/a> variables as deep copies of <strong>stack<\/strong> objects, so there is no concept of aliases (let alone reassignable aliases) in the first place to need garbage collection in user-servicable mental model for memory management.<\/p>\n\n\n\n<p>There are people talking about JVM&#8217;s garbage collection in MATLAB, but that garbage collector only handle Java objects (which I almost never use unless it&#8217;s through Yair Altman&#8217;s code). Anything else is handled by MATLAB&#8217;s engine.<\/p>\n\n\n\n<p>Since MATLAB&#8217;s engine manages how the underlying data are shared, along with allocating and freeing, some people argue that it&#8217;s garbage collection.<\/p>\n\n\n\n<p>The popular mental picture of garbage collection is reference counting WITHOUT tracking the real owner (often the first creator): when the last guy drops the link (reference) to the underlying data, the data becomes orphaned and is ready to be garbage collected. <\/p>\n\n\n\n<p>For conventional garbage collectors, timing of object destruction (when the destructor is called) is not deterministic because the object does not die with its original creator. The &#8216;ownership&#8217; is effectively transferred to the last user of the underlying data.<\/p>\n\n\n\n<p>TMW specifically said they are not garbage collecting (at least for classes since the article is for classes), so whatever they are doing under the hood is not exactly garbage collection in the conventional sense (<a href=\"https:\/\/www.mathworks.com\/company\/technical-articles\/inside-matlab-objects-in-r2008a.html\">https:\/\/www.mathworks.com\/company\/technical-articles\/inside-matlab-objects-in-r2008a.html<\/a>)<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.mathworks.com\/company\/technical-articles\/inside-matlab-objects-in-r2008a.html\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"283\" src=\"https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1-1024x283.png\" alt=\"\" class=\"wp-image-6346\" srcset=\"https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1-1024x283.png 1024w, https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1-300x83.png 300w, https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1-768x212.png 768w, https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1-500x138.png 500w, https:\/\/wonghoi.humgar.com\/blog\/wp-content\/uploads\/2025\/04\/image-1.png 1410w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<p>Given MATLAB is using copy-on-write, and Loren dropped a clue that the copy-on-write won&#8217;t be triggered for the entire struct when only one field is changed (only the changed field is copied):<\/p>\n\n\n\n<p><a href=\"https:\/\/blogs.mathworks.com\/loren\/2006\/05\/10\/memory-management-for-functions-and-variables\/\"><img decoding=\"async\" src=\"data:image\/png;base64,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\" alt=\"\"><\/a><\/p>\n\n\n\n<p>This would imply that if MATLAB does anything close to garbage collecting or managing allocation and deallocation, it can only be done on whatever pieces (my guess would be PODs, simple native data types) that doesn&#8217;t involve a user-defined destructor.<\/p>\n\n\n\n<p>Decoupling automatic memory management from classes has the advantage of keeping RAII because user-defined destructors are called deterministically. It&#8217;s only after the destructors are unwounded down to the simple data types (some classes contain other complex objects, so their destructors are chain-called deterministically) with no more user-defined destructors attached to it (down to the leaves of a tree), the automatic memory management mechanism can decide how long it&#8217;d keep these simple chunks (if somebody else is still using it, an extra copy doesn&#8217;t need to be made).<\/p>\n\n\n\n<p>In Python, everything is perceived as (class) objects, even the PODs. MATLAB and C++ distinguishes between PODs (or native types) and user-defined classes. This means if Python choose to do garbage collection, it has to do it to classes with user-defined destructor as well, thus breaking RAII.<\/p>\n<div class=\"pvc_clear\"><\/div><p id=\"pvc_stats_6329\" class=\"pvc_stats all  \" data-element-id=\"6329\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/wonghoi.humgar.com\/blog\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p><div class=\"pvc_clear\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Static Data Members In Python\/MATLAB, data members are called properties. It&#8217;s called (data) member in C++. I&#8217;ll use these names interchangably when comparing these languages. Python and MATLAB have the concept static methods, but static properties (data members) doesn&#8217;t really &hellip; <a href=\"https:\/\/wonghoi.humgar.com\/blog\/2025\/04\/20\/concepts-in-c-that-does-not-apply-to-python-or-matlab\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_6329\" class=\"pvc_stats all  \" data-element-id=\"6329\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/wonghoi.humgar.com\/blog\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=0 \/><\/p>\n<div class=\"pvc_clear\"><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[25,10,34],"tags":[],"class_list":["post-6329","post","type-post","status-publish","format-standard","hentry","category-cpp","category-matlab","category-python"],"_links":{"self":[{"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/posts\/6329","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/comments?post=6329"}],"version-history":[{"count":53,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/posts\/6329\/revisions"}],"predecessor-version":[{"id":6697,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/posts\/6329\/revisions\/6697"}],"wp:attachment":[{"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/media?parent=6329"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/categories?post=6329"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wonghoi.humgar.com\/blog\/wp-json\/wp\/v2\/tags?post=6329"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}