rfflearn.cpu.RFFSVR

Support vector regression with random Fourier features.

def rfflearn.cpu.RFFSVR(self, dim_kernel=128, std_kernel=0.1, W=None, b=None, **args)

Parameters:

dim_kernel: int  (default=128)

Dimension of the random matrix.

std_kernel: float  (default=0.1)

Standard deviation of the random matrix.

W: np.ndarray  (default=None)

Random matrix for the input X. If None then generated automatically.

b: np.ndarray  (default=None)

Random bias for the input X. If None then generated automatically.

args: dict  (default={})

Extra arguments. This will be passed to scikit-learn's LinearSVR class constructor.

Attributes:

dim: int

Dimension of the random matrix.

s_k: float

Standard deviation of the random matrix.

mat: Callable

A function to generate the random matrix W.

W: np.ndarray

Random matrix for the input X.

b: np.ndarray

Random bias for the input X.

svr: sklearn.svm.SVR

Linear SVR model instance.

Member functions

def fit(self, X, y, **args)

Trains the SVR model according to the given data.

Parameters:

X: np.ndarray

Input matrix with shape (n_samples, n_features_input).

y: np.ndarray

Output vector with shape (n_samples,).

args: dict  (default={})

Extra arguments. This arguments will be passed to scikit-learn's fit function.

Returns:

rfflearn.cpu.SVR

Fitted estimator.

def predict(self, X, **args)

Performs regression on the given data.

Parameters:

X: np.ndarray

Input matrix with shape (n_samples, n_features_input).

args: dict  (default={})

Extra arguments. This arguments will be passed to scikit-learn's predict function.

Returns:

np.ndarray

The predicted values.

def score(self, X, y, **args)

Returns the R2 score (coefficient of determination) of the prediction.

Parameters:

X: np.ndarray

Input matrix with shape (n_samples, n_features_input).

y: np.ndarray

True Output vector with shape (n_samples,).

args: dict  (default={})

Extra arguments. This arguments will be passed to scikit-learn's score function.

Returns:

float

The R2 score of the prediction.

Minimal Example

>>> import numpy as np                                  # Import Numpy
>>> import rfflearn.cpu as rfflearn                     # Import module
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])  # Define input data
>>> y = np.array([0.5, 1, 1.5, 2])                      # Defile label data
>>> svr = rfflearn.RFFSVR().fit(X, y)                   # Training (on CPU)
>>> svr.score(X, y)                                     # Inference (on CPU)
0.6160459856695937
>>> svr.predict(np.array([[-0.8, -1]]))
array([1.09586662])