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RandomProjections

Generate features from random linear projections of the input data.

This node will essentially generate a random matrix, and transform the data by that matrix. Typically, this is used either to reduce the dimensionality of the data, or to generate novel combinations of features when used with subsequent non-linear transformations. By default this node will auto-determine the number of components (and thus the number of output features) from the data. Like most other feature extraction nodes, this node can compute features between elements of an axis of your choice while treating elements of another axis as the observations, trials, or samples. It can also optionally compute multiple separate models on different slices of the data along some axis of choice. It is also possible to pool multiple axes for any of these roles. More Info... Version 1.0.0

Ports/Properties

domain_axes

Axes which form the input domain of the transformation. Features are computed between elements along these axes (or in other words, elements along these axes will be combined with each other to yield features). This is a comma-separated list of axis names (for example: "space, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. For time-series data, this is usually the space axis, and if features have already been extracted from the data through some other method, it would be the features axis. In rare cases it can also include other axes, such as frequency, lag, and time. This axis drops out of the data and is replaced by a feature axis that has a number of elements that corresponds to the output feature dimension of the model.

  • verbose name: Compute Features Between Axes
  • default value: (all others)
  • port type: ComboPort
  • value type: str (can be None)

aggregate_axes

Axes that have the statistical observations in them. The elements along these axes are treated as the "trials", "samples", or, equivalently, "observations". Adaptive feature extractors will aggregate statistics along the elements of these axes during training. When the transformer applies to data, this axis remains in the data (if multiple, they will be flattened into one axis, however). This is a comma-separated list of axis names (for example: "time, instance"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This is almost always the instance axis (especially if the data has already been segmented, i.e., if the Segmentation node was used), but in some cases it may also be the time axis, or occasionally other axes.

  • verbose name: Treat Elements As Trials/samples Along Axes
  • default value: instance
  • port type: ComboPort
  • value type: str (can be None)

separate_axes

Axes along which to learn separate models. It is possible to use multiple separate feature-extraction models, each of which operates on a different slice of the data. This node does not combine data between elements along these axes in any way (although features between these elements may of course be combined in later stages, for instance in a classifier node). This is a comma-separated list of axis names (for example: "time, frequency"), possibly empty, or the special string "(all others)", which stands for all axes that are not listed in the other two lists of axes. This axis, if any, will also persist in the data.

  • verbose name: Compute Separate Models Along Axes
  • default value:
  • port type: ComboPort
  • value type: str (can be None)

data

Data to process.

  • verbose name: Data
  • default value: None
  • port type: DataPort
  • value type: Packet (can be None)
  • data direction: INOUT

num_components

Number of components to generate. If left to the default, the number of components will be determined based on the input data. This can be fairly conservative, i.e., the number of output components may be larger than strictly necessary to represent the input well enough.

  • verbose name: Number Of Components
  • default value: None
  • port type: IntPort
  • value type: int (can be None)

epsilon

Embedding quality. Only used when the number of components is auto-deduced. Smaller values yield higher-quality embeddings and higher-dimensional results.

  • verbose name: Epsilon
  • default value: 0.1
  • port type: FloatPort
  • value type: float (can be None)

random_seed

Random seed. Different values yield different projections.

  • verbose name: Random Seed
  • default value: 12345
  • port type: IntPort
  • value type: int (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)