large-data

问题 In AD, I'm trying to identify user accounts where the same EmployeeID value is populated in 2 or more records. Below is my piece of code (Credit: I'm using a Show-Progress function defined here) and the Get-ADUser command alone has taken more than 2 hours to fetch all the records. The other steps (2 to 5) have been pretty quick. While I've completed the work, I'm trying to know if this could've been done more efficiently with PowerShell. Get-ADUser -LDAPFilter "(&(ObjectCategory=Person)

问题 In AD, I'm trying to identify user accounts where the same EmployeeID value is populated in 2 or more records. Below is my piece of code (Credit: I'm using a Show-Progress function defined here) and the Get-ADUser command alone has taken more than 2 hours to fetch all the records. The other steps (2 to 5) have been pretty quick. While I've completed the work, I'm trying to know if this could've been done more efficiently with PowerShell. Get-ADUser -LDAPFilter "(&(ObjectCategory=Person)

来源： https://stackoverflow.com/questions/42847396/fuzzy-wuzzy-string-matching-on-2-large-data-sets-based-on-a-condition-python

来源： https://stackoverflow.com/questions/42847396/fuzzy-wuzzy-string-matching-on-2-large-data-sets-based-on-a-condition-python

来源： https://stackoverflow.com/questions/61757726/convert-large-csv-to-sparse-matrix-for-use-in-sklearn

来源： https://stackoverflow.com/questions/61757726/convert-large-csv-to-sparse-matrix-for-use-in-sklearn

来源： https://stackoverflow.com/questions/61757726/convert-large-csv-to-sparse-matrix-for-use-in-sklearn

问题 Goal Implement a Shiny app to efficiently visualize and adjust uploaded data sets. Each set may contain 100000 to 200000 rows. After data adjustments are done, the adjusted data can be downloaded. In steps: Data upload Data selection and visualization Data (point) removal Download option Issue While the app works in principal, data visualization and removal take too much time. Code Sample data Some sample data is generated. The data can be uploaded onto the shiny app. The sample data

问题 i have a multidimensional array with 3070 Values $tbl= array( array( "KDNR" => 1, "GESCHL" => "test", "TITEL" => "test", "VORNAME" => "test", "FAMNAME" => "test", "PLZ" => "test", "ORT" => "test", "STRASSE" => "test", "EMAIL" => "test", "PRIVTEL" => "test" ), "KDNR" => 2, "GESCHL" => "test2", "TITEL" => "test2", "VORNAME" => "test2", "FAMNAME" => "test2", "PLZ" => "test2", "ORT" => "test2", "STRASSE" => "test2", "EMAIL" => "test2", "PRIVTEL" => "test2" ), etc... ); I want to write the array

问题 Is it logical to do as below in Keras in order not to run out of memory? for path in ['xaa', 'xab', 'xac', 'xad']: x_train, y_train = prepare_data(path) model.fit(x_train, y_train, batch_size=50, epochs=20, shuffle=True) model.save('model') 回答1: It is, but prefer model.train_on_batch if each iteration is generating a single batch. This eliminates some overhead that comes with fit . You can also try to create a generator and use model.fit_generator() : def dataGenerator(pathes, batch_size):