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`

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