[CPP] Multiple Inheritance

Multiple Inheritance

  • In Java you can implement multiple interfaces but only extend one class
  • In C++ you can inherit from(extend) multiple classes
  • You will inherit all of the behaviour(i.e. function implementations), not just the interface.
  • But be careful of multiple inheritance, because
    • there are dangers, and confusing elements
    • there may be easier ways(e.g. composition)

There are multiple ways to support reuse of classes by new classes

Composition/Aggregation
  • Models the “has a” or “is a part of” relationship
  • Composition is a stronger form
    enter image description here
    • You need to expose the methods manually because they are on component objects
Inheritance
  • “is a” or “is a type of”
  • ImplementationL Make the “type of” a sub-class
    enter image description here
enter image description here

Don’t use C-style casting to keep the code short!

  • Use static_cast or dynamic_cast
  • Dynamic cast will check(at runtime) that the pointer really is to an object of that type
  • If you cast pointers or references when multiple inheritance is being used, then addresses may change
  • Normally, casting a pointer just changes the type, but leaves the address unchanged
  • If you go to or from a second base class, the address will change
  • If you go back to sub-class, the pointer value changes back again(use dynamic cast if necessary, to check the type)
Uses/Association
  • Implementation: Maintain a pointer or reference between them, to get to the other object

Multiple inheritance with common base classes

enter image description here

Multiple inheritance with virtual base classes

enter image description here

Dangers

  • Be aware of:
    • Inheriting the same names from multiple base classes
    • Inheriting the same base class twicem through two different intermediate classes
  • To resolve the problem:
    • Use scoping operator :: to dis-ambiguate
    • Or use virtual base classes, to keep one copy
    • Or ensure that only one base class has any data, or any non-abstract methods

Wait…What is abstract method in C++?(Pure-virtual functions)

enter image description here

评论

发表评论

此博客中的热门博文

[MLE]Decision Trees

图片
Overview Decision Trees Linear vs non-linear classifiers Entropy C4.5 Random Forests Some fundamental concepts The process of selecting a specific model, given a new input x, can be described by a sequential decision making process corresponding to the traversal of a binary tree (one that splits into two branches at each node). Here we focus on a particular tree-based framework called classification and regression trees Basic Decision Trees Decision trees apply a sequence of linear decisions, that often depend on only a single variable at a time. Such trees partition the input space into cuboid regions, gradually refining the level of detail of a decision until a leaf node has been reached, which provides the final predicted label As the above figure indicates, we should follow the rules for tree classification: Start at root node Evaluate relevant attribute of that node Follow branch according to attribute evaluation to descendent node Descendent node can then be co...
阅读全文

[SEC] Cryptography I

图片
Cryptography I Content What is encryption? Primitive types Historic ciphers The One Time Pad Stream Ciphers Cryptanalysis(密码分析学，密码翻译法) Encryption Encryption: We encode a message such that only authorised users may read it Cipher: takes a string of plaintext, and converts it into a string of ciphertext Encryption can provide: Confidentiality Integrity Authenticity Notation A cipher converts a plaintext message M into a ciphertext C under the control of a key K C is not a secret, but without knowledge of the key, it should be impossible to reconstruct M Comes in two forms: Symmetric- same key for encryption/decryption Asymmetric- separate keys The Caesar Cipher An early substitution cipher , we replace each letter of plaintext with a shifted letter further down the alphabet Vulnerable to frequency analysis Frequency Analysis The frequency of occurrences of each character are very consistent across the same language The longer the ciphertext, the e...
阅读全文

[MLE] W2 Computing Parameters Analytically

Computing Parameters Analytically Normal Equation We have introduced a way of computing our parameter \(\theta\) – Gradient Descent But in Gradient Descent, we need to compute it by iteration, and it is obviously very complicated. In some cases, we still need feature scaling and mean normalization. Now there is a analytically way of computing parameter – Normal Equation . In the “Normal Equation” method, we will minimize J by explicitly taking its derivatives with respect to the θj ’s, and setting them to zero. This allows us to find the optimum theta without iteration. The normal equation formula is given below: \(θ=(X^TX)^{−1}X^Ty\) There is no need to do feature scaling with the normal equation. In Octave , use pinv(x'*x)*x'*y Gradient Descent Normal Equation Need to choose alpha No need to choose alpha Need lots of iterations No need to iterate \(O(kn^2)\) \(O(n^3)\) Inverse needs \(O(n^3)\) and \(X^TX\) needs \(O(n^2)\) Works well n is lar...
阅读全文