Add references to docstrings

pysindy/optimizers/stlsq.py

@@ -14,7 +14,15 @@ class STLSQ(BaseOptimizer):
     Attempts to minimize the objective function
     :math:`\\|y - Xw\\|^2_2 + \\alpha \\|w\\|^2_2`
     by iteratively performing least squares and masking out
-    elements of the weight that are below a given threshold.
+    elements of the weight array w that are below a given threshold.
+
+    See the following reference for more details:
+
+        Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz.
+        "Discovering governing equations from data by sparse
+        identification of nonlinear dynamical systems."
+        Proceedings of the national academy of sciences
+        113.15 (2016): 3932-3937.
 
     Parameters
     ----------

pysindy/pysindy.py

@@ -387,6 +387,7 @@ def print(self, lhs=None, precision=3):
         ----------
         lhs: list of strings, optional (default None)
             List of variables to print on the left-hand sides of the learned equations.
+            By defualt :code:`self.input_features` are used.
 
         precision: int, optional (default 3)
             Precision to be used when printing out model coefficients.
@@ -411,7 +412,7 @@ def score(
         **metric_kws
     ):
         """
-        Returns a score for the time derivative prediction.
+        Returns a score for the time derivative prediction produced by the model.
 
         Parameters
         ----------
@@ -442,9 +443,12 @@ def score(
             If True, x contains multiple trajectories and must be a list of
             data from each trajectory. If False, x is a single trajectory.
 
-        metric: metric function, optional
+        metric: callable, optional
             Metric function with which to score the prediction. Default is the
-            coefficient of determination R^2.
+            R^2 coefficient of determination.
+            See `Scikit-learn \
+            <https://scikit-learn.org/stable/modules/model_evaluation.html>`_
+            for more options.
 
         metric_kws: dict, optional
             Optional keyword arguments to pass to the metric function.