Merge branch 'main' into docu

Author: frazane

scoringrules/core/crps/_closed.py

@@ -316,8 +316,8 @@ def gtclogistic(
 
     c = (1 - lmass - umass) / (F_u - F_l)
 
-    s1_u = B.where(u_inf and umass == 0.0, 0.0, u * umass**2)
-    s1_l = B.where(l_inf and lmass == 0.0, 0.0, l * lmass**2)
+    s1_u = B.where(u_inf & (umass == 0.0), 0.0, u * umass**2)
+    s1_l = B.where(l_inf & (lmass == 0.0), 0.0, l * lmass**2)
 
     s1 = B.abs(ω - z) + s1_u - s1_l
     s2 = c * z * ((1 - 2 * lmass) * F_u + (1 - 2 * umass) * F_l) / (1 - lmass - umass)
@@ -359,8 +359,8 @@ def gtcnormal(
 
     u = B.where(u_inf, B.nan, u)
     l = B.where(l_inf, B.nan, l)
-    s1_u = B.where(u_inf and umass == 0.0, 0.0, u * umass**2)
-    s1_l = B.where(l_inf and lmass == 0.0, 0.0, l * lmass**2)
+    s1_u = B.where(u_inf & (umass == 0.0), 0.0, u * umass**2)
+    s1_l = B.where(l_inf & (lmass == 0.0), 0.0, l * lmass**2)
 
     c = (1 - lmass - umass) / (F_u - F_l)
 
@@ -410,8 +410,8 @@ def gtct(
     u = B.where(u_inf, B.nan, u)
     l = B.where(l_inf, B.nan, l)
 
-    s1_u = B.where(u_inf and umass == 0.0, 0.0, u * umass**2)
-    s1_l = B.where(l_inf and lmass == 0.0, 0.0, l * lmass**2)
+    s1_u = B.where(u_inf & (umass == 0.0), 0.0, u * umass**2)
+    s1_l = B.where(l_inf & (lmass == 0.0), 0.0, l * lmass**2)
 
     G_u = B.where(u_inf, 0.0, -f_u * (df + u**2) / (df - 1))
     G_l = B.where(l_inf, 0.0, -f_l * (df + l**2) / (df - 1))

scoringrules/core/crps/_gufuncs.py

@@ -118,10 +118,10 @@ def _crps_ensemble_nrg_gufunc(obs: np.ndarray, fct: np.ndarray, out: np.ndarray)
     e_1 = 0
     e_2 = 0
 
-    for x_i in fct:
-        e_1 += abs(x_i - obs)
-        for x_j in fct:
-            e_2 += abs(x_i - x_j)
+    for i in range(M):
+        e_1 += abs(fct[i] - obs)
+        for j in range(i + 1, M):
+            e_2 += 2 * abs(fct[j] - fct[i])
 
     out[0] = e_1 / M - 0.5 * e_2 / (M**2)
 
@@ -145,10 +145,10 @@ def _crps_ensemble_fair_gufunc(obs: np.ndarray, fct: np.ndarray, out: np.ndarray
     e_1 = 0
     e_2 = 0
 
-    for x_i in fct:
-        e_1 += abs(x_i - obs)
-        for x_j in fct:
-            e_2 += abs(x_i - x_j)
+    for i in range(M):
+        e_1 += abs(fct[i] - obs)
+        for j in range(i + 1, M):
+            e_2 += 2 * abs(fct[j] - fct[i])
 
     out[0] = e_1 / M - 0.5 * e_2 / (M * (M - 1))
 
@@ -250,10 +250,10 @@ def _owcrps_ensemble_nrg_gufunc(
     e_1 = 0.0
     e_2 = 0.0
 
-    for i, x_i in enumerate(fct):
-        e_1 += abs(x_i - obs) * fw[i] * ow
-        for j, x_j in enumerate(fct):
-            e_2 += abs(x_i - x_j) * fw[i] * fw[j] * ow
+    for i in range(M):
+        e_1 += abs(fct[i] - obs) * fw[i] * ow
+        for j in range(i + 1, M):
+            e_2 += 2 * abs(fct[i] - fct[j]) * fw[i] * fw[j] * ow
 
     wbar = np.mean(fw)
 
@@ -286,10 +286,10 @@ def _vrcrps_ensemble_nrg_gufunc(
     e_1 = 0.0
     e_2 = 0.0
 
-    for i, x_i in enumerate(fct):
-        e_1 += abs(x_i - obs) * fw[i] * ow
-        for j, x_j in enumerate(fct):
-            e_2 += abs(x_i - x_j) * fw[i] * fw[j]
+    for i in range(M):
+        e_1 += abs(fct[i] - obs) * fw[i] * ow
+        for j in range(i + 1, M):
+            e_2 += 2 * abs(fct[i] - fct[j]) * fw[i] * fw[j]
 
     wbar = np.mean(fw)
     wabs_x = np.mean(np.abs(fct) * fw)

scoringrules/core/energy/_gufuncs.py

@@ -21,8 +21,8 @@ def _energy_score_gufunc(
     e_2 = 0.0
     for i in range(M):
         e_1 += float(np.linalg.norm(fct[i] - obs))
-        for j in range(M):
-            e_2 += float(np.linalg.norm(fct[i] - fct[j]))
+        for j in range(i + 1, M):
+            e_2 += 2 * float(np.linalg.norm(fct[i] - fct[j]))
 
     out[0] = e_1 / M - 0.5 / (M**2) * e_2
 
@@ -49,8 +49,8 @@ def _owenergy_score_gufunc(
     e_2 = 0.0
     for i in range(M):
         e_1 += float(np.linalg.norm(fct[i] - obs) * fw[i] * ow)
-        for j in range(M):
-            e_2 += float(np.linalg.norm(fct[i] - fct[j]) * fw[i] * fw[j] * ow)
+        for j in range(i + 1, M):
+            e_2 += 2 * float(np.linalg.norm(fct[i] - fct[j]) * fw[i] * fw[j] * ow)
 
     wbar = np.mean(fw)
 
@@ -81,8 +81,8 @@ def _vrenergy_score_gufunc(
     for i in range(M):
         e_1 += float(np.linalg.norm(fct[i] - obs) * fw[i] * ow)
         wabs_x += np.linalg.norm(fct[i]) * fw[i]
-        for j in range(M):
-            e_2 += float(np.linalg.norm(fct[i] - fct[j]) * fw[i] * fw[j])
+        for j in range(i + 1, M):
+            e_2 += 2 * float(np.linalg.norm(fct[i] - fct[j]) * fw[i] * fw[j])
 
     wabs_x = wabs_x / M
     wbar = np.mean(fw)

scoringrules/core/variogram/_gufuncs.py

@@ -42,12 +42,12 @@ def _owvariogram_score_gufunc(obs, fct, p, ow, fw, out):
                 rho1 = abs(fct[k, i] - fct[k, j]) ** p
                 rho2 = abs(obs[i] - obs[j]) ** p
                 e_1 += (rho1 - rho2) ** 2 * fw[k] * ow
-        for m in range(M):
+        for m in range(k + 1, M):
             for i in range(D):
                 for j in range(D):
                     rho1 = abs(fct[k, i] - fct[k, j]) ** p
                     rho2 = abs(fct[m, i] - fct[m, j]) ** p
-                    e_2 += (rho1 - rho2) ** 2 * fw[k] * fw[m] * ow
+                    e_2 += 2 * ((rho1 - rho2) ** 2) * fw[k] * fw[m] * ow
 
     wbar = np.mean(fw)
 
@@ -75,12 +75,12 @@ def _vrvariogram_score_gufunc(obs, fct, p, ow, fw, out):
                 rho2 = abs(obs[i] - obs[j]) ** p
                 e_1 += (rho1 - rho2) ** 2 * fw[k] * ow
                 e_3_x += (rho1) ** 2 * fw[k]
-        for m in range(M):
+        for m in range(k + 1, M):
             for i in range(D):
                 for j in range(D):
                     rho1 = abs(fct[k, i] - fct[k, j]) ** p
                     rho2 = abs(fct[m, i] - fct[m, j]) ** p
-                    e_2 += (rho1 - rho2) ** 2 * fw[k] * fw[m]
+                    e_2 += 2 * ((rho1 - rho2) ** 2) * fw[k] * fw[m]
 
     e_3_x *= 1 / M
     wbar = np.mean(fw)