Convert data from standard neural network cell array form

`fromnndata(x,toMatrix,columnSample,cellTime)`

`fromnndata(x,toMatrix,columnSample,cellTime)`

takes
these arguments,

`net` | Neural network |

`toMatrix` | True if result is to be in matrix form |

`columnSample` | True if samples are to be represented as columns, false if rows |

`cellTime` | True if time series are to be represented as a cell array, false if represented with a matrix |

and returns the original data reformatted accordingly.

Here time-series data is converted from a matrix representation to standard cell array representation, and back. The original data consists of a 5-by-6 matrix representing one time-series sample consisting of a 5-element vector over 6 timesteps arranged in a matrix with the samples as columns.

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)

Here data is defined in standard neural network data cell form. Converting this data does not change it. The data consists of three time series samples of 2-element signals over 3 timesteps.

x = {rands(2,3);rands(2,3);rands(2,3)} columnSamples = true; cellTime = true; [y,wasMatrix] = tonndata(x) x2 = fromnndata(y,wasMatrix,columnSamples)

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