ENH: Remove unnecessary restriction in noncen-f

Set dfnum restriction to be > 0 as required by noncentral chisquare

closes #6638

2 files changed, 13 insertions, 7 deletions

diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index c4b4f7748..5da141b43 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -2110,9 +2110,9 @@ cdef class RandomState:
         Parameters
         ----------
         dfnum : int or array_like of ints
-            Parameter, should be > 1.
+            Parameter, should be > 0.
         dfden : int or array_like of ints
-            Parameter, should be > 1.
+            Parameter, should be > 0.
         nonc : float or array_like of floats
             Parameter, should be >= 0.
         size : int or tuple of ints, optional
@@ -2175,8 +2175,8 @@ cdef class RandomState:
             fdfden = PyFloat_AsDouble(dfden)
             fnonc = PyFloat_AsDouble(nonc)
 
-            if fdfnum <= 1:
-                raise ValueError("dfnum <= 1")
+            if fdfnum <= 0:
+                raise ValueError("dfnum <= 0")
             if fdfden <= 0:
                 raise ValueError("dfden <= 0")
             if fnonc < 0:
@@ -2184,8 +2184,8 @@ cdef class RandomState:
             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
                                   fdfnum, fdfden, fnonc, self.lock)
 
-        if np.any(np.less_equal(odfnum, 1.0)):
-            raise ValueError("dfnum <= 1")
+        if np.any(np.less_equal(odfnum, 0.0)):
+            raise ValueError("dfnum <= 0")
         if np.any(np.less_equal(odfden, 0.0)):
             raise ValueError("dfden <= 0")
         if np.any(np.less(ononc, 0.0)):
diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py
index 39c45889f..a530b9e13 100644
--- a/numpy/random/tests/test_random.py
+++ b/numpy/random/tests/test_random.py
@@ -1106,7 +1106,13 @@ class TestBroadcast(object):
         assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
         assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
         assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
-
+    
+    def test_noncentral_f_small_df(self):
+        self.setSeed()
+        desired = np.array([6.869638627492048, 0.785880199263955])
+        actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)
+        assert_array_almost_equal(actual, desired, decimal=14)
+        
     def test_chisquare(self):
         df = [1]
         bad_df = [-1]