From 09bedfaa1fd69af17de1ae45b9793ca11fd30bce Mon Sep 17 00:00:00 2001 From: tayyabpw Date: Thu, 27 Oct 2016 01:46:46 -0400 Subject: [PATCH 1/2] Changed self.rng to private (self.rng_) in sklearn/gaussian_process/gpr.py --- sklearn/gaussian_process/gpr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/gaussian_process/gpr.py b/sklearn/gaussian_process/gpr.py index 4f4941fe1d706..78d8531fe2257 100644 --- a/sklearn/gaussian_process/gpr.py +++ b/sklearn/gaussian_process/gpr.py @@ -161,7 +161,7 @@ def fit(self, X, y): else: self.kernel_ = clone(self.kernel) - self.rng = check_random_state(self.random_state) + self.rng_ = check_random_state(self.random_state) X, y = check_X_y(X, y, multi_output=True, y_numeric=True) @@ -211,7 +211,7 @@ def obj_func(theta, eval_gradient=True): bounds = self.kernel_.bounds for iteration in range(self.n_restarts_optimizer): theta_initial = \ - self.rng.uniform(bounds[:, 0], bounds[:, 1]) + self.rng_.uniform(bounds[:, 0], bounds[:, 1]) optima.append( self._constrained_optimization(obj_func, theta_initial, bounds)) From bc494de77e4f8f616f0117b1fb3609f36422ec32 Mon Sep 17 00:00:00 2001 From: tayyabpw Date: Fri, 11 Nov 2016 23:27:18 -0500 Subject: [PATCH 2/2] Add rng_ to Attribute section of docstring --- sklearn/gaussian_process/gpr.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sklearn/gaussian_process/gpr.py b/sklearn/gaussian_process/gpr.py index 4f4941fe1d706..4a17b4a311ba1 100644 --- a/sklearn/gaussian_process/gpr.py +++ b/sklearn/gaussian_process/gpr.py @@ -128,6 +128,8 @@ def optimizer(obj_func, initial_theta, bounds): log_marginal_likelihood_value_ : float The log-marginal-likelihood of ``self.kernel_.theta`` + rng_ : numpy.RandomState + """ def __init__(self, kernel=None, alpha=1e-10, optimizer="fmin_l_bfgs_b", n_restarts_optimizer=0, @@ -161,7 +163,7 @@ def fit(self, X, y): else: self.kernel_ = clone(self.kernel) - self.rng = check_random_state(self.random_state) + self.rng_ = check_random_state(self.random_state) X, y = check_X_y(X, y, multi_output=True, y_numeric=True) @@ -211,7 +213,7 @@ def obj_func(theta, eval_gradient=True): bounds = self.kernel_.bounds for iteration in range(self.n_restarts_optimizer): theta_initial = \ - self.rng.uniform(bounds[:, 0], bounds[:, 1]) + self.rng_.uniform(bounds[:, 0], bounds[:, 1]) optima.append( self._constrained_optimization(obj_func, theta_initial, bounds))