scikits

I'm generating feature vector for random forest classifier of scikit-learn . The feature vector represents the name of 9 protein amino acid residues. There are 20 possible residue names. So, I use 20 dummy variables to represent one residue name, for 9 residue, I have 180 dummy variables. For example, if the 9 residues in the sliding window are: ARNDCQEGH (every one letter represent a name of a protein residue),my feature vector will be: "True\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\tFalse\t False\tTrue

Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that. Missing values are simply not supported in scikit-learn. There has been discussion on the mailing list about this before, but no attempt to actually write code to handle them. Whatever you do, don't use NaN to encode missing values, since many of the algorithms refuse to handle samples containing NaNs. The above answer is outdated; the latest release of scikit-learn has a class Imputer that does simple, per-feature missing value imputation. You can feed it

I have a 2D numpy array. Is there a way to create a view onto it that would include the first k rows and all columns? The point is to avoid copying the underlying data (the array is so large that making partial copies is not feasible.) Joe Kington Sure, just index it as you normally would. E.g. y = x[:k, :] This will return a view into the original array. No data will be copied, and any updates made to y will be reflected in x and vice versa. Edit: I commonly work with >10GB 3D arrays of uint8's, so I worry about this a lot... Numpy can be very efficient at memory management if you keep a few