Dataset

Dataset for holding training data

Constructors

Constructor	Description
`new(s)` Signature: (s:seq<DV * DV>) -> Dataset	Construct a dataset from the given sequence of input-output vector pairs
`new(xi, y)` Signature: (xi:seq<int> * y:DM) -> Dataset	Construct a dataset with one-hot input elements and given output matrix `y`. `xi` are the input indices, input dimensions is max(xi) + 1.
`new(x, yi)` Signature: (x:DM * yi:seq<int>) -> Dataset	Construct a dataset with given input matrix `x` and one-hot output elements. `yi` are the output indices, output dimensions is max(yi) + 1.
`new(xi, onehotdimsx, y)` Signature: (xi:seq<int> * onehotdimsx:int * y:DM) -> Dataset	Construct a dataset with one-hot input elements and given output matrix `y`. `xi` are the input indices, `onehotdimsx` is the input dimensions.
`new(x, yi, onehotdimsy)` Signature: (x:DM * yi:seq<int> * onehotdimsy:int) -> Dataset	Construct a dataset with given input matrix `x` and one-hot output elements. `yi` are the output indices, `onehotdimsy` is the output dimensions.
`new(xi, yi)` Signature: (xi:seq<int> * yi:seq<int>) -> Dataset	Construct a dataset of one-hot input and output elements. `xi` are the input indices, input dimensions is max(xi) + 1, `yi` are the output indices, output dimensions is max(yi) + 1.
`new(xi, onehotdimsx, yi, onehotdimsy)` Signature: (xi:seq<int> * onehotdimsx:int * yi:seq<int> * onehotdimsy:int) -> Dataset	Construct a dataset of one-hot input and output elements. `xi` are the input indices, `onehotdimsx` is the input dimensions, `yi` are the output indices, `onehotdimsy` is the output dimensions.
`new(x, y)` Signature: (x:DM * y:DM) -> Dataset	Construct a dataset with given input matrix `x` and output matrix `y`. Columns of `x` and `y` are the individual inputs and corresponding outputs.

Instance members

Instance member	Description
`AppendBiasRowX()` Signature: unit -> Dataset	Append a row of all ones to the input matrix X
`AppendRowX(v)` Signature: v:DV -> Dataset	Append a new row `v` to the input matrix X
`AppendRowY(v)` Signature: v:DV -> Dataset	Append a new row `v` to the output matrix Y
`Filter(predicate)` Signature: (predicate:(DV * DV -> bool)) -> Dataset	Get a new dataset of the entries for which the `predicate` is true
`GetSlice(lower, upper)` Signature: (lower:int option * upper:int option) -> Dataset	Get a slice of the dataset between `lower` and `upper` indices
`[i]` Signature: i:int -> DV * DV	Get element `i`
`Length` Signature: int	The length of the dataset, i.e., the number of columns in input matrix X and output matrix Y
`Normalize()` Signature: unit -> Dataset	Normalize the values in the input matrix X and output matrix Y to be in the range [0,1]
`NormalizeX()` Signature: unit -> Dataset	Normalize the values in the input matrix X to be in the range [0,1]
`NormalizeY()` Signature: unit -> Dataset	Normalize the values in the output matrix Y to be in the range [0,1]
`RandomSubset(n)` Signature: n:int -> Dataset	Sample a random subset of length `n` from this dataset
`Shuffle()` Signature: unit -> Dataset	Shuffle the order of elements in the dataset
`Standardize()` Signature: unit -> Dataset	Standardize the values in the input matrix X and output matrix Y to have zero mean and unit variance
`StandardizeX()` Signature: unit -> Dataset	Standardize the values in the input matrix X to have zero mean and unit variance
`StandardizeY()` Signature: unit -> Dataset	Standardize the values in the output matrix Y to have zero mean and unit variance
`ToSeq()` Signature: unit -> seq<DV * DV>	Get the input-output pairs as a sequence
`ToStringFull()` Signature: unit -> string	Get a string representation of this dataset showing all values
`Visualize()` Signature: unit -> string	Get a string visualization of this dataset
`VisualizeXColsAsImageGrid(imagerows)` Signature: imagerows:int -> string	Visualize the values of the input matrix X where each column will be reshaped to an image with `imagerows` rows
`VisualizeYColsAsImageGrid(imagerows)` Signature: imagerows:int -> string	Visualize the values of the output matrix Y where each column will be reshaped to an image with `imagerows` rows
`X` Signature: DM	The matrix X of input values, where columns are the individual inputs Xi
`Xi` Signature: int []	The index of the maximum elements of individual inputs Xi, used for one-hot representations
`Y` Signature: DM	The matrix Y of output values, where columns are the individual outputs Yi
`Yi` Signature: int []	The index of the maximum elements of individual outputs Yi, used for one-hot reprsentations

Static members

Static member	Description
`appendBiasRowX(d)` Signature: d:Dataset -> Dataset	Append a row of ones to the input matrix X of dataset `d`
`appendRowX(v d)` Signature: v:DV -> d:Dataset -> Dataset	Append a new row `v` to the input matrix X of dataset `d`
`appendRowY(v d)` Signature: v:DV -> d:Dataset -> Dataset	Append a new tow `v` to the output matrix Y of dataset `d`
`empty` Signature: Dataset	The empty dataset
`isEmpty(d)` Signature: d:Dataset -> bool	Check whether dataset `d` is empty
`item(i d)` Signature: i:int -> d:Dataset -> DV * DV	Get the input-output pair with index `i` from dataset `d`
`length(d)` Signature: d:Dataset -> int	The length of dataset `d`, i.e., the number of columns in input matrix X and output matrix Y
`normalize(d)` Signature: d:Dataset -> Dataset	Normalize the values in the input matrix X and output matrix Y of dataset `d` to be in the range [0,1]
`normalizeX(d)` Signature: d:Dataset -> Dataset	Normalize the values in the input matrix X of dataset `d` to be in the range [0,1]
`normalizeY(d)` Signature: d:Dataset -> Dataset	Normalize the values in the output matrix Y of dataset `d` to be in the range [0,1]
`randomSubset(n d)` Signature: n:int -> d:Dataset -> Dataset	Sample a random subset of length `n` from dataset `d`
`shuffle(d)` Signature: d:Dataset -> Dataset	Shuffle the order of elements in dataset `d`
`standardize(d)` Signature: d:Dataset -> Dataset	Standardize the values in the input matrix X and output matrix Y of dataset `d` to have zero mean and unit variance
`standardizeX(d)` Signature: d:Dataset -> Dataset	Standardize the values in the input matrix X of dataset `d` to have zero mean and unit variance
`standardizeY(d)` Signature: d:Dataset -> Dataset	Standardize the values in the output matrix Y of dataset `d` to have zero mean and unit variance
`toSeq(d)` Signature: d:Dataset -> seq<DV * DV>	Get the input-output pairs of dataset `d` as a sequence
`toString(d)` Signature: d:Dataset -> string	Get a summary string of dataset `d`
`toStringFull(d)` Signature: d:Dataset -> string	Get a string representation of dataset `d` showing all values

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