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rbf.py
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rbf.py
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# -*- coding: utf8
from __future__ import division, print_function
'''
Implements the RBF model.
'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.base import BaseEstimator
from sklearn.base import RegressorMixin
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
class RidgeRBFModel(BaseEstimator, RegressorMixin):
'''
Implements rbf model by Pinto et al. 2013.
Parameters
----------
num_dists : integer
number of distances to consider
sigma : float
smoothing in the rbf
base_learner : None
the base learner to use (any scikit learn regressor)
'''
def __init__(self, num_dists=2, sigma=0.1, base_learner=None, **kwargs):
self.num_dists = num_dists
self.sigma = sigma
if base_learner is None:
base_learner = RidgeCV(fit_intercept=False, \
alphas=[0.001, 0.01, 0.1, 100, 1000], cv=None,
store_cv_values=True)
if 'fit_intercept' not in kwargs:
kwargs['fit_intercept'] = False
self.base_learner = base_learner.set_params(**kwargs)
self.R = None
self.model = None
def fit(self, X, y):
X = np.asanyarray(X, dtype='d')
y = np.asanyarray(y, dtype='d')
n = X.shape[0]
num_dists = self.num_dists
if self.num_dists > n:
raise ParameterException('Number of distances is greater than ' + \
'num rows in X')
if self.num_dists <= 0:
self.R = None
else:
rand_idx = np.random.choice(X.shape[0], int(num_dists), replace=False)
self.R = X[rand_idx]
D = np.exp(-1.0 * ((cdist(X, self.R) ** 2) / (2 * (self.sigma ** 2))))
X = np.hstack((X, D))
#Un-comment for mrse code
#X, y = mrse_transform(X, y)
self.model = self.base_learner.fit(X, y)
return self
def predict(self, X):
X = np.asanyarray(X, dtype='d')
if self.R is not None:
D = np.exp(-1.0 * ((cdist(X, self.R) ** 2) / (2 * (self.sigma ** 2))))
X = np.hstack((X, D))
return self.model.predict(X)
def get_params(self, deep=True):
rv = super(RidgeRBFModel, self).get_params(deep)
bpm = self.base_learner.get_params()
for name in self.base_learner.get_params():
rv[name] = bpm[name]
return rv