scikit-learn
diff --git a/‎doc/modules/linear_model.rst
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+14-10Lines changed: 14 additions & 10 deletions b/‎doc/modules/linear_model.rst
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+14-10Lines changed: 14 additions & 10 deletions
diff --git a/‎sklearn/linear_model/tests/test_glm.py
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+4-3Lines changed: 4 additions & 3 deletions b/‎sklearn/linear_model/tests/test_glm.py
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+4-3Lines changed: 4 additions & 3 deletions
@@ -816,14 +816,14 @@ Generalized linear regression
 =============================
 
 :class:`GeneralizedLinearRegressor` generalizes the :ref:`elastic_net` in two
-ways [1]_. First, the predicted values :math:`\hat{y}` are linked to a linear
+ways [8]_. First, the predicted values :math:`\hat{y}` are linked to a linear
 combination of the input variables :math:`X` via an inverse link function
 :math:`h` as
 
 .. math::    \hat{y}(w, x) = h(xw) = h(w_0 + w_1 x_1 + ... + w_p x_p).
 
 Secondly, the squared loss function is replaced by the deviance :math:`D` of an
-exponential dispersion model (EDM) [2]_. The objective function beeing minimized
+exponential dispersion model (EDM) [9]_. The objective function beeing minimized
 becomes
 
 .. math::    \frac{1}{2s}D(y, \hat{y}) + \alpha \rho ||P_1w||_1
@@ -850,12 +850,16 @@ it is convenient to apply a link function different from the identity link
 :math:`h(Xw)=\exp(Xw)`.
 
 Note that the feature matrix `X` should be standardized before fitting. This
-ensures that the penalty treats features equally.
+ensures that the penalty treats features equally. The estimator can be used as
+follows::
 
-    >>> from sklearn import linear_model
-    >>> reg = linear_model.GeneralizedLinearRegressor(alpha=0.5, l1_ratio=0)
-    >>> reg = linear_model.GeneralizedLinearRegressor(alpha=0.5, family='poisson', link='log')
+    >>> from sklearn.linear_model import GeneralizedLinearRegressor
+    >>> reg = GeneralizedLinearRegressor(alpha=0.5, family='poisson', link='log')
     >>> reg.fit([[0, 0], [0, 1], [2, 2]], [0, 1, 2])
+    GeneralizedLinearRegressor(alpha=0.5, copy_X=True, family='poisson',
+              fit_dispersion='chisqr', fit_intercept=True, l1_ratio=0,
+              link='log', max_iter=100, solver='irls', start_params=None,
+              tol=0.0001, verbose=0, warm_start=False)
     >>> reg.coef_
     array([ 0.24630255,  0.43373521])
     >>> reg.intercept_
@@ -905,7 +909,7 @@ Two remarks:
 
 * The deviances for at least Normal, Poisson and Gamma distributions are
   strictly consistent scoring functions for the mean :math:`\mu`, see Eq.
-  (19)-(20) in [3]_.
+  (19)-(20) in [10]_.
 
 * If you want to model a frequency, i.e. counts per exposure (time, volume, ...)
   you can do so by a Poisson distribution and passing
@@ -915,12 +919,12 @@ Two remarks:
 
 .. topic:: References:
 
-    .. [1] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.
+    .. [8] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.
 
-    .. [2] Jørgensen, B. (1992). The theory of exponential dispersion models and analysis of deviance. Monografias de matemática, no. 51.
+    .. [9] Jørgensen, B. (1992). The theory of exponential dispersion models and analysis of deviance. Monografias de matemática, no. 51.
            See also `Exponential dispersion model. <https://en.wikipedia.org/wiki/Exponential_dispersion_model>`_
 
-    .. [3] Gneiting, T. (2010). `Making and Evaluating Point Forecasts. <https://arxiv.org/pdf/0912.0902.pdf>`_
+    .. [10] Gneiting, T. (2010). `Making and Evaluating Point Forecasts. <https://arxiv.org/pdf/0912.0902.pdf>`_
 
 Stochastic Gradient Descent - SGD
 =================================
 
@@ -2,7 +2,7 @@
 
 from sklearn.linear_model.glm import (
     Link,
-    IdentityLink,
+    # IdentityLink,
     LogLink,
     TweedieDistribution,
     NormalDistribution, PoissonDistribution,
@@ -21,8 +21,9 @@ def test_link_properties():
     """
     rng = np.random.RandomState(0)
     x = rng.rand(100)*100
-    from sklearn.linear_model.glm import Link
-    for link in vars()['Link'].__subclasses__():
+    # from sklearn.linear_model.glm import Link
+    # for link in vars()['Link'].__subclasses__():
+    for link in Link.__subclasses__():
         link = link()
         assert_almost_equal(link.link(link.inverse(x)), x, decimal=10)
         assert_almost_equal(link.inverse_derivative(link.link(x)),