Documentation |
Convert data to standard neural network cell array form
[y,wasMatrix] = tonndata(x,columnSamples,cellTime)
[y,wasMatrix] = tonndata(x,columnSamples,cellTime) takes these arguments,
x | Matrix or cell array of matrices |
columnSamples | True if original samples are oriented as columns, false if rows |
cellTime | True if original samples are columns of a cell array, false if they are stored in a matrix |
and returns
y | Original data transformed into standard neural network cell array form |
wasMatrix | True if original data was a matrix (as apposed to cell array) |
If columnSamples is false, then matrix x or matrices in cell array x will be transposed, so row samples will now be stored as column vectors.
If cellTime is false, then matrix samples will be separated into columns of a cell array so time originally represented as vectors in a matrix will now be represented as columns of a cell array.
The returned value wasMatrix can be used by fromnndata to reverse the transformation.
Here data consisting of six timesteps of 5-element vectors, originally represented as a matrix with six columns, is converted to standard neural network representation and back.
x = rands(5,6) columnSamples = true; % samples are by columns. cellTime = false; % time-steps in matrix, not cell array. [y,wasMatrix] = tonndata(x,columnSamples,cellTime) x2 = fromnndata(y,wasMatrix,columnSamples,cellTime)
fromnndata | nndata | nndata2sim | sim2nndata