Fix doctest issues

advanced/mathematical_optimization/index.rst

@@ -613,16 +613,16 @@ are also supported by L-BFGS-B::
     >>> def jacobian(x):
     ...     return np.array((-2*.5*(1 - x[0]) - 4*x[0]*(x[1] - x[0]**2), 2*(x[1] - x[0]**2)))
     >>> sp.optimize.minimize(f, [2, 2], method="L-BFGS-B", jac=jacobian)
-     message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
-     success: True
-      status: 0
-         fun: 1.4417677473...e-15
-           x: [ 1.000e+00  1.000e+00]
-         nit: 16
-         jac: [ 1.023e-07 -2.593e-08]
-        nfev: 17
-        njev: 17
-    hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
+      message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
+      success: True
+       status: 0
+          fun: 1.4417677473...e-15
+            x: [ 1.000e+00  1.000e+00]
+          nit: 16
+          jac: [ 1.023e-07 -2.593e-08]
+         nfev: 17
+         njev: 17
+     hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>
 
 Gradient-less methods
 ----------------------

advanced/scipy_sparse/solvers.rst

@@ -150,7 +150,7 @@ LinearOperator Class
     ...
     >>> A = sp.sparse.linalg.LinearOperator((2, 2), matvec=mv)
     >>> A
-    <2x2 _CustomLinearOperator with dtype=float64>
+    <2x2 _CustomLinearOperator with dtype=int8>
     >>> A.matvec(np.ones(2))
     array([2.,  3.])
     >>> A * np.ones(2)

packages/statistics/index.rst

@@ -365,7 +365,7 @@ will affect the conclusions of the test, we can use a `Wilcoxon signed-rank test
 this assumption at the expense of test power::
 
     >>> sp.stats.wilcoxon(data['VIQ'])
-    WilcoxonResult(statistic=np.float64(0.0), pvalue=np.float64(1.8189894...e-12))
+    WilcoxonResult(statistic=np.float64(0.0), pvalue=np.float64(3.4881726...e-08))
 
 Two-sample t-test: testing for difference across populations
 ............................................................