![]() method, 'random ')Īssert( numel(pparam. X( nan_ix( start_ix : end_ix)) = X( nan_ix( end_ix)+ 1) īack_to_original_dims = Įlseif strcmp( pparam. Nan_ix = nan_ix( mod( nan_ix, sz( dim)) ~= 0) % remove index corresponding to end of each col since X( nan_ix( start_ix : end_ix)) = X( nan_ix( start_ix)- 1) Įlseif strcmp( pparam. % tell us where the contiguous blocks endĮnd_indices = % all contiguous blocks of Nans have d=1 so the breaks I want to randomly shuffle the numbers in this vector Thanks in advance for any sugestions regarding that. We found that this example causes the Matlab process to grow to at least 90MB. Nan_ix = nan_ix(~ isfinite( X( nan_ix))) Learn more about vector, random, permutation. used in the experiment comes from a random permutation of the labels. % other and we need to loop through these blocks of nans % if there are nans left it means that multiple nans follow each Nan_ix = nan_ix( mod( nan_ix, sz( dim)) ~= 1) % remove index corresponding to start of each col since Share Improve this answer Follow answered at 20:03 Jonas 74.5k 10 137 177 Add a comment 2 Jonas already explained what permute does. % and hence accessible via linear indexing It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. permute (A, 3,2,1) would produce a 1-by-2-by-2 array (because size (A,3)1 ), where the array is 'flipped up horizontally'. % permute such that the current impute dimension is in the columns impute_dimension( :) ' % make sure it's a row vector % error('mv_preprocess_replacenan currently only supports a singleton sample_dimension') % will lead to an artificially inflated performance. This will make life easier for the classifier and % contain identical samples (a sample could be in the training data and its ![]() % training and test sets are not independent any more because they might These numbers are not strictly random and independent in the mathematical sense, but they pass various statistical tests of randomness and independence, and their calculation can be repeated for testing or diagnostic purposes. % replacement is done globally (before starting cross-validation), the MATLAB ® uses algorithms to generate pseudorandom and pseudoindependent numbers. % needs to be done *within* each training fold. % Note: If you use impute_nan with cross-validation, the replacement If use_clabel=1 only slices from the same class are selected for % are filled in with values from a randomly chosen slice that is free of % Just supports a single impute dimension. % first impute dimension, any remaining nans along the second impute In this case, Nans are first filled along the % along the impute dimensions to fill missing values. % Simple imputation approach that uses the preceding or following values % apply imputation only to samples of the same class % is the sample dimension, then clabels can be used to use_clabel - (used only in 'random' impute) if the impute dimension % leftover Nans can be filled with the fill value (default fill - it is possible that after the imputation the array still % 'random': replace missing values by randomly drawing from non-NaN data method - can be 'forward': earlier elements in the array are used e.g. Matlab supports automated bootstrap analyses using the statistics. ![]() % across time, that is, other time points (within a given trial and electrode) the data, specifically random resampling with replacement. If impute_dimension = 3 imputation is performed impute_dimension - dimension(s) along with imputation is performed. % = mv_preprocess_impute_nan(pparam, X, clabel) % Imputes nan and inf values in the data, so that downstream train functions I am getting an error and I know that the use of '' will be variable number 1 then in the next iteration 'i' will be 2 and so on.Function = mv_preprocess_impute_nan( pparam, X, clabel) ![]() Yet, i want to apply to to the whole matrix. ![]() For the row by transpose multiplication I am following rayryeng answer. I am trying to do this using a for loop since I have 15 rows and I might increase it to have a large number of rows, so, it is non-sense to do it manually. I have a 15*15 binary matrix I multiply each row by its transpose to get another matrix from the outer product and then OR these matrices together to get a final matrix. ![]()
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