rfflearn.cpu.RFFRegression

Regression with random Fourier features.

def rfflearn.cpu.RFFRegression(self, dim_kernel=16, std_kernel=0.1, W=None, b=None, **args)

Parameters:

dim_kernel: int, default=16

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 arguments will be passed to the constructor of scikit-learn's LinearRegression model directly.

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.

Member functions

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

Trains the RFF regression 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, deafault={}

Extra arguments. This arguments will be passed to the sklearn's fit function.

Returnd:

rfflearn.cpu.RFFRegression

Fitted estimator.

def predict(self, X, **args)

Performs prediction on the given data.

Parameters:

X: np.ndarray

Input matrix with shape (n_samples, n_features_input).

args: dict, deafault={}

Extra arguments. This arguments will be passed to the sklearn's predict function directly.

Returnd:

np.ndarray

Predicted vector.

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, deafault={}

Extra arguments. This arguments will be passed to sklearn's score function.

Returnd:

float

R2 score of the prediction.

Minimal Example

>>> import numpy as np
>>> import rfflearn.cpu as rfflearn
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> reg = rfflearn.RFFRegression().fit(X, y)
>>> reg.score(X, y)
1.0
>>> reg.predict(np.array([[-0.8, -1]]))
array([1.00065865])