Keymacro Editor is a powerful macro editor which lets you create a macro for any button on your keyboard. You can use macros for any software which supports macros.
Keymacro Editor has two major modes: full-screen mode and non-full-screen mode. The full-screen mode is similar to the Edit Menu (System) – Edit – Macros – Macros Mode. You can use this mode to create, edit and save macros for any key on your keyboard. There are two major differences from the Edit Menu:
1) You can select a specific button instead of a whole keyboard to be selected.
2) You can assign more than one macro to a key.
Keymacro Editor is a non-full-screen mode. You can use this mode to create, edit and save macros for any key.
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With this feature you can define, for each line of code, a macro that will be executed whenever the cursor hovers over the code.Q:
Python scikit-learn outlier removal preprocessing function
I’m trying to use a custom preprocessing function in scikit-learn to remove outliers from my data.
I’m trying to use the meanshift preprocessing as an example, but I’m unsure what should be the input arguments and the output returned.
The documentation lists 4 arguments for the meanshift preprocessing function, but I only see the following:
X = preprocessing.MeanShift(n_features)
so the documentation is unclear.
If I set the n_neighbors to 0, I end up removing my features and I get the error:
ValueError: Got an invalid input shape (0, 0, 1)
Also, the documentation does not tell what to return.
So what do I need to do to use the meanshift function?
A:
I’m trying to use the meanshift preprocessing function
meanshift is a local outlier detector. By this you mean it returns, depending on the input dataset, a distance matrix of the outliers.
It is not a preprocessing function.
Instead, what you’re looking for is either a preprocessing function or an unsupervised outlier detector.
From your error message:
Got an invalid input shape (0, 0, 1)
This suggests a problem with your data. Perhaps a tutorial you followed did not properly normalize the data before calculating meanshift or something of the sort.
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