Deterministic Clustering (#46)

* fix for deterministic clusterMAF sampling with repeated loads

* bumping version number

* moving random seed set to load function

* setting seed for cluster choice then reset after

* setting random seed for cluster choice based on hypercube sample

README.rst

@@ -7,7 +7,7 @@ Introduction
 
 :margarine: Marginal Bayesian Statistics
 :Authors: Harry T.J. Bevins
-:Version: 1.1.3
+:Version: 1.1.4
 :Homepage:  https://github.com/htjb/margarine
 :Documentation: https://margarine.readthedocs.io/
 

margarine/clustered.py

@@ -361,8 +361,8 @@ def log_like(self, params, logevidence, prior_de=None):
 
         return loglike
 
-    @tf.function(jit_compile=True)
-    def __call__(self, u):
+    #@tf.function(jit_compile=True)
+    def __call__(self, u, seed=1420):
 
         r"""
 
@@ -373,6 +373,9 @@ def __call__(self, u):
 
             u: **numpy array**
                 | Samples on the uniform hypercube.
+            
+            seed: **int / default=1420**
+                | Set the seed for the cluster choice.
 
         """
 
@@ -381,8 +384,11 @@ def __call__(self, u):
         flow_weights = np.array(flow_weights)
         probabilities = flow_weights / np.sum(flow_weights)
         options = np.arange(0, self.cluster_number)
+
+        np.random.seed(int(round(u[0][0]*1000)))
         choice = np.random.choice(options,
                                   p=probabilities, size=len(u))
+        np.random.seed(None)
 
         totals = [len(choice[choice == options[i]])
                   for i in range(len(options))]

setup.py

@@ -9,7 +9,7 @@ def readme(short=False):
 
 setup(
     name='margarine',
-    version='1.1.3',
+    version='1.1.4',
     description='margarine: Posterior Sampling and Marginal Bayesian Statistics',
     long_description=readme(),
     author='Harry T. J. Bevins',