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I have a list of binary strings and two center strings which are not in the list.
I would like to classify that list around the center strings in order to create two clusters. A string of the list will be assigned to the cluster whose center is nearest to that string (hamming distance as metric).
I've seen that there are alghoritms like Neighbours Classifier, k-medoids, Affinity propagation, but all these procedure calculate centroids on their own; I have to use my center strings instead.
Any suggestion?
Perform 1-nearest neighbor classification using your centers only.
No need for anything special here. Just assign to the nearest center.
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My goal is to display a non-decreasing stepwise curve F. For each x-axis point, computing the corresponding F(x) is fairly computational, hence discretizing the x-axis and computing every corresponding F(x) point may take a lot of time.
My idea is to compute the curve by dichotomy.
Starting with 0 and the end-point of the x-axis (say 100).
If F(0)=F(100), then F is constant.
Else F is not constant and I compute F(50).
If F(50) = F(0), then F is constant on [0,50] and I compute F(75) and so on.
Else if F(50) = F(100), then F is constant on [50,100] and I compute F(25) and so on.
Else I compute F(25) and F(75) and so on.
Is there any python librairy which would be useful to implement such an algorithm?
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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I'm looking to locate the position of this purple circle and similiar situations for different datasets in which there is a (mainly) horizontal lag before the sensor starts gathering data.
Any ideas?
numpy.diff gives you the difference between each consecutive item in your array. It can be very useful but you may need to add some smoothing to prevent noise from giving false positives. Here is an easy smoothing function to use:
def moving_average(x, w):
return np.convolve(x, np.ones(w), 'valid') / w
(talken from here)
I suggest you plot np.diff(data) and see what that looks like. It will be much easier to find the purple point on that graph and it might even be easier yet to find it if you do np.diff twice.
For this single case, I would do:
purple_index = np.argmax(np.diff(data) < -1)
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I have a lot of latitude and longitude information that belong to different countries. How do I map each of these lat-long to the country it belongs to in an efficient manner?
I came across googleapis, but that would be very inefficient and time consuming if I had to query this api for every lat-long pair that I have. I thought of grouping all the lat-long pairs that are closer and then figure out a way to find the country that each of these groups belongs to.
Could you please point me to the right algorithms that I could use for this purpose? Or are there better ways to do this?
import reverse_geocode
coordinates = (-37.81, 144.96), (31.76, 35.21)
reverse_geocode.search(coordinates)
Can find the locations offline like this using the python reverse_geocode library
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Does anyone know of any tools to do a natural neighbor interpolation in python? unless i'm being stupid, i can't seem to find it in the scipy.interpolate module
Here is a 3D discrete natural neighbor implementation:
https://github.com/innolitics/natural-neighbor-interpolation
Note that the "discrete" is important here; it is an approximation of the true geometric natural neighbor.