# tf.contrib.kernel_methods.RandomFourierFeatureMapper

## Class RandomFourierFeatureMapper

Class that implements Random Fourier Feature Mapping (RFFM) in TensorFlow.

The RFFM mapping is used to approximate the Gaussian (RBF) kernel:

<div> $$(exp(-||x-y||_2^2 / (2 * \sigma^2))$$ </div>


The implementation of RFFM is based on the following paper: "Random Features for Large-Scale Kernel Machines" by Ali Rahimi and Ben Recht. (link: https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf)

The mapping uses a matrix \$$Omega \in R^{d x D}\$$ and a bias vector \$$b \in R^D\$$ where d is the input dimension (number of dense input features) and D is the output dimension (i.e., dimension of the feature space the input is mapped to). Each entry of Omega is sampled i.i.d. from a (scaled) Gaussian distribution and each entry of b is sampled independently and uniformly from [0, $$2 * pi$$].

For a single input feature vector x in R^d, its RFFM is defined as:

<div>     $$sqrt(2/D) * cos(x * Omega + b)$$ </div>


where cos is the element-wise cosine function and x, b are represented as row vectors. The aforementioned paper shows that the linear kernel of RFFM-mapped vectors approximates the Gaussian kernel of the initial vectors.

## Properties

### name

Returns a name for the RandomFourierFeatureMapper instance.

If the name provided in the constructor is None, then the object's unique id is returned.

#### Returns:

A name for the RandomFourierFeatureMapper instance.

## Methods

### __init__

__init__(
input_dim,
output_dim,
stddev=1.0,
seed=1,
name=None
)


Constructs a RandomFourierFeatureMapper instance.

#### Args:

• input_dim: The dimension (number of features) of the tensors to be mapped.
• output_dim: The output dimension of the mapping.
• stddev: The standard deviation of the Gaussian kernel to be approximated. The error of the classifier trained using this approximation is very sensitive to this parameter.
• seed: An integer used to initialize the parameters (Omega and b) of the mapper. For repeatable sequences across different invocations of the mapper object (for instance, to ensure consistent mapping both at training and eval/inference if these happen in different invocations), set this to the same integer.
• name: name for the mapper object.

### map

map(input_tensor)


Maps each row of input_tensor using random Fourier features.

#### Args:

• input_tensor: a Tensor containing input features. It's shape is [batch_size, self._input_dim].

#### Returns:

A Tensor of shape [batch_size, self._output_dim] containing RFFM-mapped features.

#### Raises:

• InvalidShapeError: if the shape of the input_tensor is inconsistent with expected input dimension.