Given a collection of Dense Matrices, this will generate a sample of
Transformer that extracts random cosine features from a feature vector
This transformer applies a Linear Rectifier, an activation function defined as: f(x) = max({@param maxVal}, x - {@param alpha})
This transformer pads input vectors to the nearest power of two, then returns the real values of the first half of the fourier transform on the padded vectors.
A node that takes in DenseVector[Double] and randomly flips the sign of some of the elements
Takes a sample of an input RDD of size size.
Standardizes features by removing the mean and scaling to unit std using column summary statistics on the samples in the training set.
Represents a StandardScaler model that can transform dense vectors.
Transformer that maps a Seq[Any] of objects to a Seq[(Any, Double)] of (unique object, weighting_scheme(tf)), where tf is the number of times the unique object appeared in the original Seq[Any], and the weighting_scheme is a lambda of Double => Double that defaults to the identity function.
Companion Object to generate random cosine features from various distributions
Divides each row by the max of its two-norm and 2.
Apply power normalization: z <- sign(z)|z|^{\rho} with \rho = \frac{1}{2} This a "signed square root"