normalization

问题 I have just read @PerformanceDBA's arguments re: 6NF and E-A-V. I am intrigued. I had previously been skeptical of 6NF as it was presented as "merely" sticking some timestamp columns on tables. I have always worked with a data dictionary and do not need to be convinced to use one, or to generate SQL code. So I expect an answer that would require a dictionary (or catalog) that is used to generate code. So I would like to know how 6NF would deal with an extremely simple example. A table of

问题 I'm trying to filter properties of an object in jmespath based on the value of a subproperty and want to include only those properties where the subproperty is set to a specific value. Based on this example data: { "a": { "feature": { "enabled": true, } }, "b": { }, "c": { "feature": { "enabled": false } } } I'd like to get an object with all properties where the feature is enabled. { "a": { "feature": { "enabled": true, } } } I figured I could use this jmespath query to filter the objects

问题 database design question here. Say we had a webapp or something that uses hashtags for 20-40 word notes. What is the best way to store a user's hashtags. For instance, if a user entered. "I like to have #lunch at #sizzler" we would store the sentence as text and we could store the hashtags as JSON, a comma separated list or some other mechanism. Its also worth pointing out that the tags need to be searchable, such as how many people have been hash tagging lunch, etc. Advise on the matter

问题 I have consistently had problems with my colour maps when using imshow , some colours seem to just become black. I have finally realised that imshow seems to, by default, normalise the matrix of floating point values I give it. I would have expected an array such as [[0,0.25],[0.5,0.75]] to display the appropriate colours from the map, corresponding to those absolute values but the 0.75 will be interpreted as a 1. In the extreme case, an N x N array of 0.2 (for example), would just produce

问题 in order to do pattern matching properly convolutions require normalization https://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation unfortunately I can't find a way how to make input normalization for conv2d function. is it hidden in implementation? 回答1: If I'm not mis-reading that, it's in the image library in TF 1.x: tf.image.per_image_standardization https://www.tensorflow.org/api_guides/python/image By the way, that particular function is a little annoying in that it

问题 in order to do pattern matching properly convolutions require normalization https://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation unfortunately I can't find a way how to make input normalization for conv2d function. is it hidden in implementation? 回答1: If I'm not mis-reading that, it's in the image library in TF 1.x: tf.image.per_image_standardization https://www.tensorflow.org/api_guides/python/image By the way, that particular function is a little annoying in that it

问题 in order to do pattern matching properly convolutions require normalization https://en.wikipedia.org/wiki/Cross-correlation#Normalized_cross-correlation unfortunately I can't find a way how to make input normalization for conv2d function. is it hidden in implementation? 回答1: If I'm not mis-reading that, it's in the image library in TF 1.x: tf.image.per_image_standardization https://www.tensorflow.org/api_guides/python/image By the way, that particular function is a little annoying in that it

问题 There are several discussions about messaging systems but mostly related to email structure. How can be the most efficient way for members messaging in a normalized database? I am thinking of creating a messages table with five columns: ID (PRIMARY KEY) First_Person (FK user_id) Second_Person (FK user_id) Message date My worry is about reading this large table. finding all message for a person (e.g. user_id 876) SELECT * FROM messages WHERE First_Person='876' OR Second_Person='876' and

问题 Say we have six columns A B C D E F, while removing transitive dependency we encounter the dependency where F-> E,D,C,B,A (say F is the primary key) A->B (A is a transitive key towards B) C->D (C is a transitive key towards D) In this case what would we do, would we make a new table with four columns or will we make two tables with two columns each? 回答1: If you have a relation schema R(A B C D) with dependencies: A → B C → D then the following facts hold: The key of the relation is: A C The

问题 Can anyone advise on how to go about denormalising a fully normalised table? I've not been able to find much on it despite googling. Whenever a record is updated (though not inserted, but lets not worry about that now) in TableA a record is inserted into HistoryOfTableA with the values of the fields which have changed and an associated timestamp when it was carried out. eg. TableA fields: TableA_Id, FieldA, FieldB, FieldC, FieldD.... etc HistoryOfTableA records: HistID, TableA_Id,