distance

问题 Looking at the comments for the code behind scipy.stats.wasserstein_distance which invokes a function called _cdf_distance(p, u_values, v_values, u_weights=None, v_weights=None) , it says this function implements the following formula: l_p(u, v) = \left( \int_{-\infty}^{+\infty} |U-V|^p \right)^{1/p} However, this is not the Wasserstein distance as I know it since, although I see the distance matrix |U-V| in the above formula comment, the transport matrix is noticeably absent. The transport

问题 I have a question similar to the question asked here: simple way of fusing a few close points. I want to replace points that are located close to each other with the average of their coordinates. The closeness in cells is specified by the user (I am talking about euclidean distance). In my case I have a lot of points (about 1-million). This method is working, but is time consuming as it uses a double for loop. Is there a faster way to detect and fuse close points in a numpy 2d array? To be

问题 I have a question similar to the question asked here: simple way of fusing a few close points. I want to replace points that are located close to each other with the average of their coordinates. The closeness in cells is specified by the user (I am talking about euclidean distance). In my case I have a lot of points (about 1-million). This method is working, but is time consuming as it uses a double for loop. Is there a faster way to detect and fuse close points in a numpy 2d array? To be

问题 I have a question similar to the question asked here: simple way of fusing a few close points. I want to replace points that are located close to each other with the average of their coordinates. The closeness in cells is specified by the user (I am talking about euclidean distance). In my case I have a lot of points (about 1-million). This method is working, but is time consuming as it uses a double for loop. Is there a faster way to detect and fuse close points in a numpy 2d array? To be

问题 I have a question similar to the question asked here: simple way of fusing a few close points. I want to replace points that are located close to each other with the average of their coordinates. The closeness in cells is specified by the user (I am talking about euclidean distance). In my case I have a lot of points (about 1-million). This method is working, but is time consuming as it uses a double for loop. Is there a faster way to detect and fuse close points in a numpy 2d array? To be

问题 I'm trying to find the location between two points using google maps. Here is the code that I'm working with: function initialize() { var myOptions = { center: new google.maps.LatLng(36.8813329,-103.6975488), zoom: 4, mapTypeId: google.maps.MapTypeId.ROADMAP }; var map = new google.maps.Map(document.getElementById("map-canvas"), myOptions); var impactCoordinates = [ new google.maps.LatLng(37.772323, -122.214897), new google.maps.LatLng(34.1633766,-81.6487862), ]; var ImpactPath = new google

问题 I'm trying to find the location between two points using google maps. Here is the code that I'm working with: function initialize() { var myOptions = { center: new google.maps.LatLng(36.8813329,-103.6975488), zoom: 4, mapTypeId: google.maps.MapTypeId.ROADMAP }; var map = new google.maps.Map(document.getElementById("map-canvas"), myOptions); var impactCoordinates = [ new google.maps.LatLng(37.772323, -122.214897), new google.maps.LatLng(34.1633766,-81.6487862), ]; var ImpactPath = new google

来源： https://stackoverflow.com/questions/54628576/calculate-distance-traveled-not-distance-between

来源： https://stackoverflow.com/questions/54628576/calculate-distance-traveled-not-distance-between

来源： https://stackoverflow.com/questions/54628576/calculate-distance-traveled-not-distance-between