import numpy as np
[docs]class linear:
"""
Attributes
==========
basis:
basis for random feature map
nbasis: int
number of basis
bias:
params:
_init_params:
initial value of the parameter
"""
def __init__(self, basis, params=None, bias=None):
self.basis = basis
self.nbasis = basis.nbasis
self._init_params = params
self.bias = bias
self.params = params
if params is None:
self.params = np.zeros(self.nbasis)
self.nparams = self.nbasis
[docs] def get_mean(self, X, Psi=None, params=None, bias=None):
"""
calculate mean values
Parameters
==========
X: numpy.ndarray
input as an N-by-d matrix
Psi: numpy.ndarray
feature maps ``Psi(X)`` as an N-by-l matrix
(default: self.get_basis(X))
params: numpy.ndarray
weight as a vector with size l
(default: self.params)
bias: float
(default: self.bias)
Returns
=======
numpy.ndarray
Psi * params + bias
"""
if params is None:
params = np.copy(self.params)
if bias is None:
bias = np.copy(self.bias)
if Psi is None:
Psi = self.get_basis(X)
return Psi.dot(params) + bias
[docs] def set_params(self, params):
"""
set parameters
Parameters
==========
params: np.ndarray
"""
self.params = params
[docs] def set_bias(self, bias):
"""
set bias
Parameters
==========
bias: float
"""
self.bias = bias
[docs] def _init_params(self, params):
"""
initialize parameters
Parameters
==========
params: np.ndarray
(default: numpy.zeros(self.nbasis))
"""
if params is None:
self.params = np.zeros(self.nbasis)
self.params = params
def _init_bias(self, bias):
"""
initialize bias
Parameters
==========
bias: float
(default: 0)
"""
if bias is None:
self.bias = 0
self.bias = bias