convert numpy float64 to standard float

UM-Bridge · Jun 17, 2024 · 7b0f72c · 7b0f72c
1 parent 88217d2
commit 7b0f72c
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Showing 4 changed files with 19 additions and 19 deletions.
diff --git a/benchmarks/analytic-banana/server.py b/benchmarks/analytic-banana/server.py
@@ -25,11 +25,11 @@ def __call__(self, parameters, config):
         y = [(parameters[0][0] / a),
              (parameters[0][1] * a + a * b * (parameters[0][0]**2 + a**2))]
 
-        return [[multivariate_normal.logpdf(y, [0, 4], [[1.0*scale, 0.5*scale], [0.5*scale, 1.0*scale]])]]
+        return [[float(multivariate_normal.logpdf(y, [0, 4], [[1.0*scale, 0.5*scale], [0.5*scale, 1.0*scale]]))]]
 
     def supports_evaluate(self):
         return True
 
 model = Banana()
 
-umbridge.serve_models([model], 4243)
+umbridge.serve_models([model], 4243)
diff --git a/benchmarks/analytic-donut/server.py b/benchmarks/analytic-donut/server.py
@@ -19,7 +19,7 @@ def get_output_sizes(self, config):
 
     def __call__(self, parameters, config):
         r = np.linalg.norm(parameters[0])
-        return [[ - (r - Donut.radius)**2 / Donut.sigma2 ]]
+        return [[float(- (r - Donut.radius)**2 / Donut.sigma2) ]]
 
     def supports_evaluate(self):
         return True
@@ -28,8 +28,8 @@ def gradient(self, out_wrt, in_wrt, parameters, sens, config):
         r = np.linalg.norm(parameters[0])
         if (r == 0):
             return [0,0]
-        return [sens[0] * parameters[0][0] * (Donut.radius / r - 1) * 2 / Donut.sigma2,
-                sens[0] * parameters[0][1] * (Donut.radius / r - 1) * 2 / Donut.sigma2]
+        return [float(sens[0] * parameters[0][0] * (Donut.radius / r - 1) * 2 / Donut.sigma2),
+                float(sens[0] * parameters[0][1] * (Donut.radius / r - 1) * 2 / Donut.sigma2)]
 
     def supports_gradient(self):
         return True
@@ -38,12 +38,12 @@ def apply_jacobian(self, out_wrt, in_wrt, parameters, vec, config):
         r = np.linalg.norm(parameters[0])
         if (r == 0):
             return [0]
-        return [vec[0] * parameters[0][0] * (Donut.radius / r - 1) * 2 / Donut.sigma2
-              + vec[1] * parameters[0][1] * (Donut.radius / r - 1) * 2 / Donut.sigma2]
+        return [float(vec[0] * parameters[0][0] * (Donut.radius / r - 1) * 2 / Donut.sigma2
+              + vec[1] * parameters[0][1] * (Donut.radius / r - 1) * 2 / Donut.sigma2)]
 
     def supports_apply_jacobian(self):
         return True
 
 model = Donut()
 
-umbridge.serve_models([model], 4243)
+umbridge.serve_models([model], 4243)
diff --git a/benchmarks/analytic-funnel/server.py b/benchmarks/analytic-funnel/server.py
@@ -22,14 +22,14 @@ def f(x, m, s):
         s0 = 3
         m1 = 0
         s1 = np.exp(parameters[0][0] / 2)
-        return [[ f(parameters[0][0], m0, s0) + f(parameters[0][1], m1, s1) ]]
+        return [[ float(f(parameters[0][0], m0, s0) + f(parameters[0][1], m1, s1)) ]]
 
     def supports_evaluate(self):
         return True
 
     def gradient(self, out_wrt, in_wrt, parameters, sens, config):
-        return [self.apply_jacobian(out_wrt, in_wrt, parameters, [sens[0], 0], config)[0],
-                self.apply_jacobian(out_wrt, in_wrt, parameters, [0, sens[0]], config)[0]]
+        return [float(self.apply_jacobian(out_wrt, in_wrt, parameters, [sens[0], 0], config)[0]),
+                float(self.apply_jacobian(out_wrt, in_wrt, parameters, [0, sens[0]], config)[0])]
 
     def supports_gradient(self):
         return True
@@ -46,12 +46,12 @@ def dfds(x, m, s):
         m1 = 0
         s1 = np.exp(parameters[0][0] / 2)
 
-        return [vec[1] * dfdx(parameters[0][1], m1, s1)
-              + vec[0] * (dfdx(parameters[0][0], m0, s0) + .5 * s1 * dfds(parameters[0][1], m1, s1))]
+        return [float(vec[1] * dfdx(parameters[0][1], m1, s1)
+              + vec[0] * (dfdx(parameters[0][0], m0, s0) + .5 * s1 * dfds(parameters[0][1], m1, s1)))]
 
     def supports_apply_jacobian(self):
         return True
 
 model = Funnel()
 
-umbridge.serve_models([model], 4243)
+umbridge.serve_models([model], 4243)
diff --git a/benchmarks/analytic-gaussian-mixture/server.py b/benchmarks/analytic-gaussian-mixture/server.py
@@ -23,14 +23,14 @@ def __call__(self, parameters, config):
 
         if dens1 + dens2 + dens3 == 0: # log(0) not defined, so return minimal float value
             return [[ sys.float_info.min ]]
-        return [[ np.log(dens1 + dens2 + dens3) ]]
+        return [[ float(np.log(dens1 + dens2 + dens3)) ]]
 
     def supports_evaluate(self):
         return True
 
     def gradient(self, out_wrt, in_wrt, parameters, sens, config):
-        return [self.apply_jacobian(out_wrt, in_wrt, parameters, [sens[0], 0], config)[0],
-                self.apply_jacobian(out_wrt, in_wrt, parameters, [0, sens[0]], config)[0]]
+        return [float(self.apply_jacobian(out_wrt, in_wrt, parameters, [sens[0], 0], config)[0]),
+                float(self.apply_jacobian(out_wrt, in_wrt, parameters, [0, sens[0]], config)[0])]
 
     def supports_gradient(self):
         return True
@@ -43,14 +43,14 @@ def apply_jacobian(self, out_wrt, in_wrt, parameters, vec, config):
         if dens1 + dens2 + dens3 == 0: # Return zero in log(0) case above
             return [0]
 
-        return [- vec[0] / (dens1 + dens2 + dens3)
+        return [float(- vec[0] / (dens1 + dens2 + dens3)
                          * (dens1 * (parameters[0][0] - -1.5) / 0.8
                          + dens2 * (parameters[0][0] - 1.5) / 0.8
                          + dens3 * (parameters[0][0] - -2) / 0.5)
                 - vec[1] / (dens1 + dens2 + dens3)
                          * (dens1 * (parameters[0][1] - -1.5) / 0.8
                          + dens2 * (parameters[0][1] - 1.5) / 0.8
-                         + dens3 * (parameters[0][1] - 2) / 0.5)
+                         + dens3 * (parameters[0][1] - 2) / 0.5))
               ]
 
     def supports_apply_jacobian(self):