nearest | k-NN algorithm - | Learning library

Given a rational number, I need to get the nearest integer, with exact halves rounded up (that is, towards positive infinity). The results must be exact, which rules out float and Decimal, both of which have finite precision.

The following code works perfectly in all cases except for negative numbers:

İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, the accuracy of the model is as much lower than the expectation. İn addition train and valid accuracy do not increase. Besides that İ ma trying to decrease the batch size which is still 383

My code:

Let's say I have an arbitrary integer 874,623,123 how do I round it down to 800,000,000 and up to 900,000,000? Another example is 759 round up to 800 or down to 700. The size of the integer is unknown.

Is there a built-in method to do it? If not, what's the best way to work it out? Sorry not a math expert here.

EDIT: So far I have looked at

Rounding down integers to nearest multiple

Python round up integer to next hundred

Both use a predefined divider

I'm solving the example probation problem in Python and had only partial success so far (passed 30 test cases, wrong answer in the 31st). The test cases themselves are not disclosed.

Description.

A network of n nodes connected by n-1 links is given. Find two nodes so that the distance from the fatherst node to the nearest of those two would be minimal. If several answers are possible, any of them will be accepted.

The input is given as a list of pairs of numbers. Each number represents node, pair is the connection between two nodes. The result should be the list of two nodes.

Example 1 in = [[1, 2], [2, 3]] result = [3, 1]

Example 2 in = [[1, 2], [3, 2], [2, 4], [4, 5], [4, 6]] result = [2, 4]

My solution.

The net will always be a tree, not a graph. For the above examples the corresponding trees will be:

example 1

I want to come up with a sed command where once every 10 character will look for the nearest space and substitute it with "|"

I tried sed -E -e 's/ /|/\( *?[0-9a-zA-Z]*\)\{10,\}' new.file, but it shows errors.

Example input:

İ am working on an image dataset that is categorical 12 classes. İ am using transfer learning with VGG16. However, İ have faced an error: Shapes (None, None) and (None, 28, 28, 12) are incompatible. My code:

I have n points in a 3D space. I want to stochastically sample a subset of points with all nearest-neighbor distances larger than r. The size of the subset m is unknown, but I want the sampled points to be as dense as possible, i.e. maximize m.

There are similar questions, but they are all about generating points, rather than sampling from given points.
Generate random points in 3D space with minimum nearest-neighbor distance

Generate 3-d random points with minimum distance between each of them?

Say I have 300 random 3D points,

I have tens of thousands of unstructured points in a multidimensional space. I would like to interpolate these to tens of thousands of other unstructured points. The interpolation can be smoothing, but piecewise linear is preferred, and I would prefer for it to be smoother than a nearest neighbour interpolation. Extrapolation may be required, so LinearNDInterpolator is out. The input and output points will consist of clusters, so it is inefficient to use methods such as scipy's RBF interpolator as I believe this will use all of the input points to calculate each output point, even though many of the input points will be similar to each other.

This answer to a similar question indicated that it is possible to make the RBF interpolation more efficient when the number of inputs is large, but did not include the details. How can it be done?

As I can tolerate smoothing, and many of the inputs will be similar to each other, support vector regression might be appropriate. Are there other methods that are suited to this situation (preferably ones with a Python interface)?

I am working on an Android App for handheld Scan Devices and want to download around 4.500 items from an MySQL database via Retrofit2 into a SQlite Database on the device; hence, when I tried to download all items at once, it slowed down the UI and froze the app up to 5 minutes; I googled a lot on how to solve the problem and couldn´t come up with a fitting solution for now; hence I tried to download the Database with 7 columns for each item - hence, around 31.500 entries in the database - in "Chunks" by iterating in a For-each loop and using .stream() and .limit() in a Background threat, like this: