diagram | CLI app to convert ASCII arts into hand drawn diagrams | Command Line Interface library

Use inheritance very carefully. Prefer association over inheritance whenever your inheritance is not a permanent truth from the birth to the death of an object. ANd keep in mind that dependencies between classes do not make any promises about objects of those classes.

Some clarifications on the UML syntax:

• I understand that your plain thick lines ended with a small hollow triangle represent a specialization/generalization relationship (aka inheritance). If this is correct, the triangle should be larger to avoid visual confusion. Moreover multiplicity makes no sense with inheritance and should be removed.
• I understand that with the dotted line you intend to represent an association with multiplicity. If this is correct:
  • the association should be plain lines, because dotted lines are for dependencies, and multiplicity would make no sense.
  • the meaning you give to the arrow head would be unclear. Do you use them to document navigability? In case of doubt, remove them. If you want to document ownership of the association end, use the dot notation instead. (we cannot say if it’s right or wrong, since it’s a decision on how your model sees the associations)

In view of your comments and the third model linked therein, there are some key UML semantics you need to keep in mind:

• Inheritance is an ultra-strong relationship which means "is (always) a". In your example, you may say that an Actor is always a Person, and hence, everything you say about a Person is also true for an Actor. By the way, you should avoid repeating inherited properties or methods, since it could create some ambiguity.
• Inheritance is not a substitute for aggregation, composition or association. You can for example not say that a CrewMember (one person) is always a Crew (a group of persons). A CrewMember belongs to a Crew but there are things that the crew can do together that the individual member can't.
• Inheritance is not suitable if it's not always true. A Character for example may often be a Person. But not always;: you can have ghost characters, comic characters, robot characters, or animals.
• Association means that there is some structural relation between some instances of the associated classes. For example, that one ore several persons live in one city, seems a very relevant assocaition.
• Dependency means that a class is dependent on another, i.e. without the other class it would not work or something would be missing. It's a statement about the classes and not the objects. I.e. If you meant to say that Person is dependent on City you just say that the class Person would not work without knowing about City (for example, because the operation sendPostCardFrom(city: City) needs an argument of that type) but you do not say anything about persons X, Y and Z. If you want that X, Y and Z are related to city a, b, and c, you need an association.

About the model content, I'll not decide for you and there is no single truth. I therefore prefer to draw your attention at potential issues. I raise them in form of a question, up-to you to adjust the model based on your own answer:

• Common issues between the both variants:

  • Are Actor and Director really Persons ? In this case, what with the Persons that are Actors and Directors at the same time (g.g. Clint Eastwood) ? Or are Actor and Director just roles that a Persons takes for a given Movie ?
  • Is a Character really related only to 1 Movie? What to do with Indiana Jones, where the same character appears in several movies?
  • In a similar way, I wonder if there's really a single Person who lives in a City or if this should not be a many-to-many association?

• Issues regarding the differences: `

  • Is a Person in version 1 a Character (inheritance) ? Or is a Character a representation of a Person (association: represents) ?
  • Is a Character in version 2 really an Actor and by transitivity also a Person (inheritance)? Or is the Character only associated with an Actor (association: plays) ?

It's up to you to decide, but wherever I asked a question about association or inheritance, you should better think twice before using inheritance (i.e. prefer association/composition over inheritance)

The problem is that your document class already selects a bibliography style, which you can't change afterwards. Two workarounds:

• use the style your document class sets by removing \bibliographystyle{IEEEannot} from your code

• if you actually do need the other style, save olplainarticle.cls under a new name and change l.8 \ProvidesClass{olplainarticle}[06/12/2015, v1.0] to the new name, remove line 43/44 \RequirePackage{natbib} \bibliographystyle{apalike} from the new .cls file and then change \documentclass{olplainarticle} to the new name

Your current code is drawing a bunch of white rectangles with varying outline colors. The top left corner of your diagram is what you're looking for. If you want it to fill up the whole screen, here is what I would do:

The syntax you want comes from:

https://www.powershellstation.com/2016/04/29/introducing-visiobot3000-part-2-superman/

So the syntax for the line of code to drop a shape on a page is:

You are using np.array wrongly: instead of np.array([79, 2, 45], [87, 12, 67]), you should use np.array([[79, 2, 45], [87, 12, 67]]). Note that the first argument should contain the data, so you should define an array of an array, and not giving np.array a series of vectors.

The function plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931 can use the numpy array (I was wrong in comments), as you see in the example in plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931 documentation.

Here is a functional equivalent of the main Model forward(x) method. It is much more verbose, but it is "unravelling" the flow of operations, making it more easily understandable.

I assumed that the length of the list-arguments are always 5 (i is in the [0, 4] range, inclusive) so I could unpack properly (and it follows the default set of parameters).

the calibration_curve code is correct. I am comparing the logistic regression calibration versus the xgboost calibration. the dataframes hold predict_proba[:,1] values or the probability of happening. see (https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Loan%20Risk%20.ipynb)

Set the colours of the lines and fills to match with shiny grey - #F5F5F5:

As suggested by @krfurlong ggvenn package can be used to plot Venn diagrams, and customize your plot according to requirements with the help of ggplot2.

you can install ggvenn by: