implement fisk distribution

src/ensemble/distributions.py

@@ -65,12 +65,14 @@ class Fisk(Distribution):
     def _create_scipy_dist(self):
         optim_params = scipy.optimize.minimize(
             fun=self._shape_scale,
-            x0=[2, self.mean * 2 / np.pi * np.sin(np.pi / 2)],
+            # start beta at 1.1 and solve for alpha
+            x0=[self.mean * 1.1 * np.sin(np.pi / 1.1) / np.pi, 1.1],
             args=(self.mean, self.variance),
+            # options={"disp": True},
         )
         shape, scale = np.abs(optim_params.x)
-        print("parameters from optimizer: ", shape, scale)
-        self._scipy_dist = scipy.stats.fisk(c=shape, scale=scale)
+        # print("parameters from optimizer: ", shape, scale)
+        self._scipy_dist = scipy.stats.fisk(c=scale, scale=shape)
 
     def _shape_scale(self, x, samp_mean, samp_var) -> None:
         alpha = x[0]

tests/test_distributions.py

@@ -19,8 +19,8 @@
 # @pytest.mark.parametrize("a, b, expected", [(1, 2, 3), (2, 3, 5)])
 # def test_add(a, b, expected):
 #     assert add(a, b) == expected
-MEAN = 5
-VARIANCE = 6.1
+MEAN = 2
+VARIANCE = 8
 
 
 def test_exp():
@@ -49,10 +49,10 @@ def test_invgamma():
 def test_fisk():
     fisk = Fisk(MEAN, VARIANCE)
     res = fisk.stats(moments="mv")
-    print("est mean and var: ", res)
-    assert False
-    # assert np.isclose(res[0], MEAN)
-    # assert np.isclose(res[1], VARIANCE)
+    print("resulting mean and var: ", res)
+    # assert False
+    assert np.isclose(res[0], MEAN)
+    assert np.isclose(res[1], VARIANCE)
 
 
 def test_gumbel():