I’m calculating distances between cities globally (shipments, origins to destinations). The data is sourced in Spanish language (Puerto Rico), so countries, states, and cities are in Spanish. I’ve translated countries to English but have not found a worldwide translation table for cities and states. Is there a better to do this? My code looks like ..
I have a pandas dataframe with the shape of: df.shape (1664599, 3935) that basically looks like: rws = ["user1","user2","user3","user4","user5","user6","user7","user8"] cols = ["prod1","prod2","prod3","prod4","prod5"] np.random.seed(0) df = pd.DataFrame(np.random.binomial(1, 0.3, size=(len(rws), len(cols))), columns=cols, index=rws) prod1 prod2 prod3 prod4 prod5 user1 0 1 0 0 0 user2 0 0 1 1 0 user3 1 0 0 1 0 user4 ..
I have a dataframe that holds the Word Mover’s Distance between each document in my dataframe. I am running kmediods on this to generate clusters. 1 2 3 4 5 1 0.00 0.05 0.07 0.04 0.05 2 0.05 0.00 0.06 0.04 0.05 3. 0.07 0.06 0.00 0.06 0.06 4 0.04 0.04. 0.06 0.00 0.04 5 ..
I am learning about Levenshtein distance for the first time and have only been coding for a few months. I’m trying to modify the algorithm such that the different editing operations carry different weights as follows: insertion weighs 20, deletion weighs 20 and replacement weighs 5. I have been able to implement the basic code ..
In python, given one matrix of size 3*2 like A=[[x11,x12,x13],[x21,x22,x23]] and a column vector b=[mu1;mu2]. If I want to compute the Euclidean distance between each column of A and vector b. For example, for the first column, the distance ‘d1` is given by A=[[x11,x12,x13],[x21,x22,x23]] b=[[mu1],[mu2]] d1=(x11-mu1)^2+(x21-mu2)^2 #second column d2=(x12-mu1)^2+(x22-mu2)^2 # so on So the distance ..
I have a picture like this: My goal is to measure the distance in pixels along this object. The sampling rate is dynamic, i.e. it can be 5, 10 or 15, for example. It is something like this: I have a lot of problems with the shadow in the middle of it, but I could ..
I’m new and don’t understand things. I want to make a new list of distances between every coordinates I have, a list of the distances of point1-point2, point1-point3, point2-point3. so my code is: list_of_coords = [(5.55, 95.3175), (3.583333, 98.666667), (-0.95556, 100.36056)] list_of_distances = [geopy.distance.geodesic(combo).km for combo in combinations(list_of_coords,2)] anddd when I try to run it, ..
Im having trouble calculating the distance between two points on a Data Frame. I used this formula for regression and I got the expected values, however when I use it on this other Data Frame for classification I get values equal to 0 or one (as distance) which is unlikely to be true. I’m wondering ..
I am trying to run Density-Based Spatial Clustering (DBSCAN) on a Point Cloud dataset which is a series of points with x,y,z coordinates. One of the parameters in min distance. How do I find the minimal distance between a point and another in space in Python? Many thanks! Data Sample: Source: Python..
I’m studying the KNN algorithm to classify images using some material from a 2017 Stanford course. We’re given a dataset consisting of many images, later those sets are represented as 2D numpy arrays, and we’re supposed to write functions that calculate distances between those images. More specifically, given a 2D array of the test images ..