python kmedoids – calculating new medoid centers more efficiently

I’m following an excellent medium article: https://towardsdatascience.com/k-medoids-clustering-on-iris-data-set-1931bf781e05 to implement kmedoids from scratch. There is a place in the code where each pixel’s distance to the medoid centers is calculated and it is VERY slow. It has numpy.linalg.norm inside a loop. Is there a way to optimize this with numpy.linalg.norm or with numpy broadcasting or scipy.spatial.distance.cdist…

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WebDriverException: Failed to connect to binary FirefoxBinary(C:\Program Files\Mozilla Firefox\firefox.exe) with GeckoDriver Firefox and Selenium Java

Using Selenium 3.1.0, firefox latest version 72.0, default firefox driver 2.53.1 here is my code System.setProperty(“webdriver.gecko.driver” ,”C:\\Users\\sindhusha.tummala\\Downloads\\geckodriver.exe”); driver = new FirefoxDriver(); Still i am getting the error org.openqa.selenium.WebDriverException: Failed to connect to binary FirefoxBinary(C:\Program Files\Mozilla Firefox\firefox.exe) on port 7055; Could any one help with this

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