Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.
Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
The estimator to chain onto the end of this pipeline
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
Chains a pipeline onto the end of this one, producing a new pipeline.
Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.
the pipeline to chain
The application of this Transformer to a single input item.
The application of this Transformer to a single input item. This method MUST be overridden by ML developers.
The output value
The application of this Transformer to an RDD of input items.
The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.
The bulk RDD input to pass into this transformer
The bulk RDD output for the given input
Stride for keypoints on the grid
Starting offset for the keypoints
Size of neighborhood for each keypoint is -2*subPatchSize to subPatchSize
A method that converts this object into a Pipeline.
A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.
Computes the local color statistic of (LCS) on a regular spaced grid [1]: "...each patch is also subdivided into 16 square sub-regions and each sub-region is described with the means and standard deviations of the 3 RGB channels, which leads to a 96 dimensional feature vector."
[1] Clinchant S, Csurka G, Perronnin F, Renders JM XRCE's participation to ImageEval. In: ImageEval Workshop at CVIR. 2007
Based on code by Ben Recht <brecht@cs.berkeley.edu>