lego | fast static site generator | Static Site Generator library

First thing, driver.find_elements_by_xpath is deprecated, use driver.find_element(By.XPATH, 'locator') instead.

Now, if you'd like to get all hrefs of the links on the page:

Colon is used for namespaces and per "Namespaces in XML" specification, it cannot be used in entity names.

The specification states:

[Definition: A document is namespace-well-formed if it conforms to this specification. ]

It follows that in a namespace-well-formed document:

• All element and attribute names contain either zero or one colon;
• No entity names, processing instruction targets, or notation names contain any colons.

You can use a trick that is to declare "SomeName" as a namespace as it is suggested in this question: xml schema validation error "prefix is not bound".

On the other hand, "Extensible Markup Language" Specification state that:

Note:

The Namespaces in XML Recommendation [XML Names] assigns a meaning to names containing colon characters. Therefore, authors should not use the colon in XML names except for namespace purposes, but XML processors must accept the colon as a name character.

If you change the parser you can get what you want:

I think this is a prime example on when to use a validator library. (personal favorite: https://pydantic-docs.helpmanual.io/)

Advantages:

1. you can validate the data that you are fetching over the API by strongly type the expected json and nested structures.
2. It has all the helpers methods to convert dict to class and to convert class to dict, as well as methods for reading up json strings if necessary.

Example:

The algorithm simply works to train (optimize) a shallow neural-network model that's good at predicting words, from other nearby words.

That's the only internal training goal – subject to the neural network's constraints on how the words are represented (N floating-point dimensions), or combined with the model's internal weights to render an interpretable prediction (forward propagation rules).

There's no other 'coaching' about what words 'should' do in relation to each other. All words are still just opaque tokens to word2vec. It doesn't even consider their letters: the whole-token is just a lookup key for a whole-vector. (Though, the word2vec variant FastText varies that somewhat by also training vectors for subwords – & thus can vaguely simulate the same intuitions that people have for word-roots/suffixes/etc.)

The interesting 'neighborhoods' of nearby words, and relative orientations that align human-interpretable aspects to vague directions in the high-dimensional coordinate space, fall out of the prediction task. And those relative orientations are what gives rise to the surprising "analogical arithmetic" you're asking about.

Internally, there's a tiny internal training cycle applied over and over: "nudge this word-vector to be slightly better at predicting these neighboring words". Then, repeat with another word, and other neighbors. And again & again, millions of times, each time only looking at a tiny subset of the data.

But the updates that contradict each other cancel out, and those that represent reliable patterns in the source training texts reinforce each other.

From one perspective, it's essentially trying to "compress" some giant vocabulary – tens of thousands, to millions, of unique words – into a smaller N-dimensional representation - usually 100-400 dimensions when you have enough training data. The dimensional-values that become as-good-as-possible (but never necessary great) at predicting neighbors turn out to exhibit the other desirable positionings, too.

I am sure there is a more elegant solution, but I believe this will accomplish what you want!

You can try to let condition C.ID = 1 to ON from where and using LEFT JOIN

Note that the draw function is continuously executed.

This is what's happening:

• mouseDragged draws a line
• draw function runs in the next frame and whatever's inside this function gets re-drawn on top

So if there's a background() call inside the draw function, it will re-draw the background on top of whatever was previously drawn. Hence the order of operation is so important in processing.

In your sketch, instead of drawing the line in mouseDragged, you can to draw it inside the draw loop after background() is called. For example:

You can do it in one line. The trick is to create a temporary column where quantity is negative for spare_pieces and positive for normal_pieces:

With a dict comprehension to grab all attributes of hub.Image which are upper-case only: