normalization

问题 I have a system which is using phone numbers as unique identifiers. For this reason, I want to format all phone numbers as they come in using a normalized format. Because I have no control over my source data, I need to parse out these numbers myself and format them before adding them to my DB. I'm about to write a parser that can read phone numbers in and output a normalized phone format, but before I do I was wondering if anyone knew of any pre-existing libraries I could use to format phone

问题 I have a system which is using phone numbers as unique identifiers. For this reason, I want to format all phone numbers as they come in using a normalized format. Because I have no control over my source data, I need to parse out these numbers myself and format them before adding them to my DB. I'm about to write a parser that can read phone numbers in and output a normalized phone format, but before I do I was wondering if anyone knew of any pre-existing libraries I could use to format phone

问题 I am trying to stick to the practice of keeping the database normalized, but that leads to the need to run multiple join queries. Is there a performance degradation if many queries use joins vs having a call to a single table that might contain redundant data? 回答1: Keep the Database normalised UNTIL you have discovered a bottleneck. Then only after careful profiling should you denormalise. In most instances, having a good covering set of indexes and up to date statistics will solve most

问题 I am new to Keras/TF/Deep Learning and I am trying to build a model to predict house prices. I have some features X (no. of bathrooms , etc.) and target Y (ranging around $300,000 to $800,000) I have used sklearn's Standard Scaler to standardize Y before fitting it to the model. Here is my Keras model: def build_model(): model = Sequential() model.add(Dense(36, input_dim=36, activation='relu')) model.add(Dense(18, input_dim=36, activation='relu')) model.add(Dense(1, activation='sigmoid'))

问题 I am currently developing an mvc application to read from an existing sql server database. The database is denormalized - and I was looking at modifying some tables to normalize it to a degree. This led to a discussion with a fellow developer as the most preformant way to read the data, or if the structure should change or not. The data will be read via ado.net with a stored procedure. Question I have is, is it more performant to have numerous fields in a table (denormalized) OR have several

问题 I've been playing with some SVM implementations and I am wondering - what is the best way to normalize feature values to fit into one range? (from 0 to 1) Let's suppose I have 3 features with values in ranges of: 3 - 5. 0.02 - 0.05 10-15. How do I convert all of those values into range of [0,1]? What If, during training, the highest value of feature number 1 that I will encounter is 5 and after I begin to use my model on much bigger datasets, I will stumble upon values as high as 7? Then in

问题 I've been playing with some SVM implementations and I am wondering - what is the best way to normalize feature values to fit into one range? (from 0 to 1) Let's suppose I have 3 features with values in ranges of: 3 - 5. 0.02 - 0.05 10-15. How do I convert all of those values into range of [0,1]? What If, during training, the highest value of feature number 1 that I will encounter is 5 and after I begin to use my model on much bigger datasets, I will stumble upon values as high as 7? Then in

问题 Consider this situation: a Car is bought from a Salesperson . A Salesperson works at a Showroom (and at only one Showroom). A Showroom is affiliated to a Manufacturer , and only sells cars made by that Manufacturer. At the same time, a Car is of a particular Model , and a Model is made by a Manufacturer. Restriction R: A Car's Model's Manufacturer must be the same Manufacturer as the Car's Salesperson's Showroom's affiliated Manufacturer. The diagram shows the obvious foreign key

问题 Consider this situation: a Car is bought from a Salesperson . A Salesperson works at a Showroom (and at only one Showroom). A Showroom is affiliated to a Manufacturer , and only sells cars made by that Manufacturer. At the same time, a Car is of a particular Model , and a Model is made by a Manufacturer. Restriction R: A Car's Model's Manufacturer must be the same Manufacturer as the Car's Salesperson's Showroom's affiliated Manufacturer. The diagram shows the obvious foreign key

问题 When doing regression or classification, what is the correct (or better) way to preprocess the data? Normalize the data -> PCA -> training PCA -> normalize PCA output -> training Normalize the data -> PCA -> normalize PCA output -> training Which of the above is more correct, or is the "standardized" way to preprocess the data? By "normalize" I mean either standardization, linear scaling or some other techniques. 回答1: You should normalize the data before doing PCA. For example, consider the