The answer is to use Welford’s algorithm, which is very clearly defined after the “naive methods” in: Wikipedia: Algorithms for calculating variance It’s more numerically stable than either the two-pass or online simple sum of squares collectors suggested in other responses. The stability only really matters when you have lots of values that are close … Read more
sklearn.linear_model.LinearRegression will do it: from sklearn import linear_model clf = linear_model.LinearRegression() clf.fit([[getattr(t, ‘x%d’ % i) for i in range(1, 8)] for t in texts], [t.y for t in texts]) Then clf.coef_ will have the regression coefficients. sklearn.linear_model also has similar interfaces to do various kinds of regularizations on the regression.
I would use a recursive function. Here’s a (working) example with comments. Hope this works for you! function sampling($chars, $size, $combinations = array()) { # if it’s the first iteration, the first set # of combinations is the same as the set of characters if (empty($combinations)) { $combinations = $chars; } # we’re done if … Read more
Give the basic info, with a clear route for how to go further – update your browser! I think sacrificing functionality for 99% of users to accommodate 1% is sheer bloody mindedness. Sure, it is possible to allow for non-javascript enabled content for every aspect of a website, plus provide the optimal experience – but … Read more
If the sampling is with replacement, you can use this algorithm (implemented here in Python): import random items = [(10, “low”), (100, “mid”), (890, “large”)] def weighted_sample(items, n): total = float(sum(w for w, v in items)) i = 0 w, v = items[0] while n: x = total * (1 – random.random() ** (1.0 / … Read more
I generally break my projects into 4 pieces: load.R clean.R func.R do.R load.R: Takes care of loading in all the data required. Typically this is a short file, reading in data from files, URLs and/or ODBC. Depending on the project at this point I’ll either write out the workspace using save() or just keep things … Read more
In MariaDB / MySQL: SELECT AVG(dd.val) as median_val FROM ( SELECT d.val, @rownum:=@rownum+1 as `row_number`, @total_rows:=@rownum FROM data d, (SELECT @rownum:=0) r WHERE d.val is NOT NULL — put some where clause here ORDER BY d.val ) as dd WHERE dd.row_number IN ( FLOOR((@total_rows+1)/2), FLOOR((@total_rows+2)/2) ); Steve Cohen points out, that after the first pass, … Read more
Distribution Fitting with Sum of Square Error (SSE) This is an update and modification to Saullo’s answer, that uses the full list of the current scipy.stats distributions and returns the distribution with the least SSE between the distribution’s histogram and the data’s histogram. Example Fitting Using the El Niño dataset from statsmodels, the distributions are … Read more
How to find the statistical mode?
You look like a new user to stackoverflow. Welcome. Your question is getting down voted for at least three reasons: 1) It’s not really clear what you want from your description of the problem and the data you’re providing 2) You haven’t shown any attempts at what you’ve tried 3) Providing your data as a … Read more