Documentation fix and proper handling of tolerance

1 files changed, 15 insertions, 10 deletions

diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index a6698e1ee..982efe741 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -4356,9 +4356,9 @@ cdef class RandomState:
 
     # Multivariate distributions:
     def multivariate_normal(self, mean, cov, size=None, check_valid='warn',
-                            tol=1e-8):
+                            rtol=1e-05, atol=1e-8):
         """
-        multivariate_normal(mean, cov[, size])
+        multivariate_normal(mean, cov[, size, check_valid, rtol, atol])
 
         Draw random samples from a multivariate normal distribution.
 
@@ -4381,10 +4381,14 @@ cdef class RandomState:
             generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because
             each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
             If no shape is specified, a single (`N`-D) sample is returned.
-        check_valid : 'warn', 'raise', 'ignore'
-            Behavior when the covariance matrix is not Positive Semi-definite.
-        tol : float
-            Tolerance of the singular values in covariance matrix.
+        check_valid : { 'warn', 'raise', 'ignore' }, optional
+            Behavior when the covariance matrix is not positive semidefinite.
+        rtol : float, optional
+            Relative tolerance to use when checking the singular values in
+	    covariance matrix.
+	atol : float, optional
+	    Absolute tolerance to use when checking the singular values in
+	    covariance matrix
 
         Returns
         -------
@@ -4500,15 +4504,16 @@ cdef class RandomState:
         (u, s, v) = svd(cov)
 
         if check_valid != 'ignore':
-            psd = np.allclose(np.dot(v.T * s, v), cov)
+            if check_valid != 'warn' and check_valid != 'raise':
+                raise ValueError("check_valid must equal 'warn', 'raise', or 'ignore'")
+
+            psd = np.allclose(np.dot(v.T * s, v), cov, rtol=rtol, atol=atol)
             if not psd:
                 if check_valid == 'warn':
                     warnings.warn("covariance is not positive-semidefinite.",
                                   RuntimeWarning)
-                elif check_valid == 'raise':
-                    raise ValueError("covariance is not positive-semidefinite.")
                 else:
-                    raise ValueError("check_valid must equal 'warn', 'raise', or 'ignore'")
+                    raise ValueError("covariance is not positive-semidefinite.")
 
         x = np.dot(x, np.sqrt(s)[:, None] * v)
         x += mean