normalize

问题 I am working on a document search problem where given a set of documents and a search query I want to find the document closest to the query. The model that I am using is based on TfidfVectorizer in scikit. I created 4 different tf_idf vectors for all the documents by using 4 different types of tokenizers. Each tokenizer splits the string into n-grams where n is in the range 1 ... 4 . For example: doc_1 = "Singularity is still a confusing phenomenon in physics" doc_2 = "Quantum theory still

问题 I read some similar article, but they are not what I want. Get the matrix after imagesc? imagesc plot to matrix in matlab My Problem I have a matrix A with all elements are double. I do imagesc(A) and then I have an image. Now, I want to get the matrix that make the image. How can I do that? From those articles, if I do I = imagesc(A) B = get(I, 'CData') Then B == A that is not what I want. 回答1: To scale the image in the same way as imagesc do the following Amin = min(A(:)); Amax = max(A(:));

问题 I have the following example code: String n = "Péña"; n = Normalizer.normalize(n, Normalizer.Form.NFC); How do I normalize the string n excepting the ñ ? And not only that string, I'm making a form and I want to keep just the ñ's , and everything else without diacritics. 回答1: Replace all occurrences of "ñ" with a non-printable character "\001", so "Péña" becomes "Pé\001a". Then call Normalizer.normalize() to decompose the "é" into "e" and a separate diacritical mark. Finally remove the

问题 I have a list of decimal numbers as follows: [-23.5, -12.7, -20.6, -11.3, -9.2, -4.5, 2, 8, 11, 15, 17, 21] I need to normalize this list to fit into the range [-5,5] . How can I do it in python? 回答1: To get the range of input is very easy: old_min = min(input) old_range = max(input) - old_min Here's the tricky part. You can multiply by the new range and divide by the old range, but that almost guarantees that the top bucket will only get one value in it. You need to expand your output range

问题 Thanks for your replies/time. We need to build a Normalized text file from DeNormalized text file . We explored couple of options such as unix shell , and Loading into data base etc. I am looking pick up better ideas for resolutions from this community. The input text file is various length with comma delimited records. The content may look like this: **XXXXXXXXXX , YYYYYYYYYY, TTTTTTTTTTT, UUUUUUUUUU, RRRRRRRRR,JJJJJJJJJ 111111111111, 22222222222, 333333333333, 44444444, 5555555, 666666

问题 I find the ICU docs somewhat challenging. My question is: How do I normalize a string using ICU4C? I'm looking at unorm2_normalize, but what if the buffer isn't large enough? How would I know this before? Naturally, I want to normalize the entire string. Thanks! :> P.S. Here is the API doc on that function: http://icu-project.org/apiref/icu4c/unorm2_8h.html#a0a596802db767da410b4b04cb75cbc53 回答1: You get a error code back from all these function call in the pErrorCode parameter. This is how

问题 (Originally from previous question but re-framed for the more general question) This is a sample json file I'm working with with 2 records: [{"Time":"2016-01-10", "ID" :13567, "Content":{ "Event":"UPDATE", "Id":{"EventID":"ABCDEFG"}, "Story":[{ "@ContentCat":"News", "Body":"Related Meeting Memo: Engagement with target firm for potential M&A. Please be on call this weekend for news updates.", "BodyTextType":"PLAIN_TEXT", "DerivedId":{"Entity":[{"Id":"Amy","Score":70}, {"Id":"Jon","Score":70}]}

问题 I have the following df: Date Event_Counts Category_A Category_B 20170401 982457 0 1 20170402 982754 1 0 20170402 875786 0 1 I am preparing the data for a regression analysis and want to standardize the column Event_Counts, so that it's on a similar scale like the categories. I use the following code: from sklearn import preprocessing df['scaled_event_counts'] = preprocessing.scale(df['Event_Counts']) While I do get this warning: DataConversionWarning: Data with input dtype int64 was

问题 I am trying to normalize a Y scale by converting all values to percentages. Therefore, I need to divide every number in a column by the first number in that column. In Excel, this would be equivalent to locking a cell A1/$A1, B1/$A1, C1/$A1 then D1/$D1, E1/$D1... The data needs to first meet four criteria (Time, Treatment, Concentration and Type) and the reference value changes at every new treatment. Each treatment has 4 concentrations (0, 0.1, 2 and 50). I would like for the values

问题 I'm having trouble with dual quaternions, and I believe it's because they're not properly normalized. A, B and A' are dual quaternions where the latter is conjugated. When doing this: Q = A * B * A' I should theoretically always end up with Q = B if A and B are properly normalized. But in some cases, I don't, and it's completely messing up my whole skeletal hierarchy. Many pages show that the norm of a dual quaternion is ||Q|| = sqrt(QQ'), but that means taking the square root of a dual