Restore commented np.random.seed

Author: vahtras

advanced/mathematical_optimization/index.rst

@@ -881,20 +881,22 @@ Curve fitting
     :target: auto_examples/plot_curve_fit.html
     :align: right
 
+.. Comment to make doctest pass
+    >>> np.random.seed(0)
+
 Least square problems occur often when fitting a non-linear to data.
 While it is possible to construct our optimization problem ourselves,
 scipy provides a helper function for this purpose:
 :func:`scipy.optimize.curve_fit`::
 
-    >>> np.random.seed(0) #doctest: +SKIP
 
     >>> def f(t, omega, phi):
     ...     return np.cos(omega * t + phi)
     
     >>> x = np.linspace(0, 3, 50)
     >>> y = f(x, 1.5, 1) + .1*np.random.normal(size=50)
 
-    >>> optimize.curve_fit(f, x, y) #doctest: +SKIP
+    >>> optimize.curve_fit(f, x, y)
     (array([ 1.51854577,  0.92665541]), array([[ 0.00037994, -0.00056796],
            [-0.00056796,  0.00123978]]))
 

intro/scipy.rst

@@ -446,9 +446,12 @@ our initial guess: ::
 
 **Curve fitting**
 
+.. Comment to make doctest pass
+    >>> np.random.seed(42)
+
 Suppose we have data sampled from ``f`` with some noise: ::
 
-    >>> np.random.seed(42)
+
     >>> xdata = np.linspace(-10, 10, num=20)
     >>> ydata = f(xdata) + np.random.randn(xdata.size)