Merge branch 'CUQI-DTU:main' into RegularizedGaussian-Additional-Constraints

Author: jeverink

.github/workflows/tests.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest] # Add windows-latest, macos-latest to test on multiple OS
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}

cuqi/distribution/_beta.py

@@ -48,7 +48,7 @@ def logpdf(self, x):
 
         # Check bounds
         if np.any(x<=0) or np.any(x>=1) or np.any(self.alpha<=0) or np.any(self.beta<=0):
-            return -np.Inf
+            return -np.inf
 
         # Compute logpdf
         return np.sum(sps.beta.logpdf(x, a=self.alpha, b=self.beta))

cuqi/distribution/_cauchy.py

@@ -75,14 +75,14 @@ def _is_out_of_bounds(self, x):
     def logpdf(self, x):
 
         if self._is_out_of_bounds(x):
-            return -np.Inf
+            return -np.inf
 
         return np.sum(-np.log(np.pi*self.scale*(1+((x-self.location)/self.scale)**2)))
 
     def cdf(self, x):
 
         if self._is_out_of_bounds(x):
-            return -np.Inf
+            return -np.inf
 
         return np.sum(sps.cauchy.cdf(x, loc=self.location, scale=self.scale))
 

cuqi/distribution/_truncated_normal.py

@@ -37,7 +37,7 @@ class TruncatedNormal(Distribution):
         p = cuqi.distribution.TruncatedNormal(mean=0, std=1, low=-2, high=2)
         samples = p.sample(5000)
     """
-    def __init__(self, mean=None, std=None, low=-np.Inf, high=np.Inf, is_symmetric=False, **kwargs):
+    def __init__(self, mean=None, std=None, low=-np.inf, high=np.inf, is_symmetric=False, **kwargs):
         # Init from abstract distribution class
         super().__init__(is_symmetric=is_symmetric, **kwargs)  
 
@@ -97,7 +97,7 @@ def logpdf(self, x):
         # the unnormalized logpdf
         # check if x falls in the range between np.array a and b
         if np.any(x < self.low) or np.any(x > self.high):
-            return -np.Inf
+            return -np.inf
         else:
              return self._normal.logpdf(x)
 

cuqi/geometry/_geometry.py

@@ -228,9 +228,15 @@ def __repr__(self) -> str:
         if self.par_shape == self.fun_shape:
             return "{}[{}]".format(self.__class__.__name__,
                                    self.par_shape if len(self.par_shape) != 1 else self.par_shape[0])
-        return "{}[{}: {}]".format(self.__class__.__name__,
-                                   self.par_shape if len(self.par_shape) != 1 else self.par_shape[0],
-                                   self.fun_shape if len(self.fun_shape) != 1 else self.fun_shape[0])
+        return "{}[{}: {}]".format(
+            self.__class__.__name__,
+            self.par_shape if len(self.par_shape) != 1 else self.par_shape[0],
+            (
+                self.fun_shape
+                if (self.fun_shape is None or len(self.fun_shape) != 1)
+                else self.fun_shape[0]
+            ),
+        )
 
     def _all_values_equal(self, obj):
         """Returns true of all values of the object and self are equal"""

demos/demo38_truncated_normal.py

@@ -12,8 +12,8 @@
 p = cuqi.distribution.TruncatedNormal(
     mean=np.array([0, 0]),
     std=np.array([1, 1]),
-    low=np.array([0, -np.Inf]),
-    high=np.array([np.Inf, 0]))
+    low=np.array([0, -np.inf]),
+    high=np.array([np.inf, 0]))
 
 plt.figure()
 plot_2D_density(p, -5, 5, -5, 5)
@@ -40,8 +40,8 @@
 x = cuqi.distribution.TruncatedNormal(
     mean=np.array([0, 0]),
     std=np.array([1, 1]),
-    low=np.array([0, -np.Inf]),
-    high=np.array([np.Inf, 0]))
+    low=np.array([0, -np.inf]),
+    high=np.array([np.inf, 0]))
 # the data distribution
 b = cuqi.distribution.Gaussian(A@x, 0.1)
 

pyproject.toml

@@ -12,7 +12,7 @@ maintainers = [
     {name = "Chao Zhang", email = "[email protected]"}
 ]
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license = {file = "LICENSE"}
 dynamic = ["dependencies", "version"]