Fixed ESS scheduler and eta default value

cbx/scheduler.py

@@ -22,10 +22,12 @@ class param_update():
     maximum : float
         The maximum value of the parameter. The default is 1e5.
     """
-    def __init__(self, 
-                 name: str ='alpha', 
-                 maximum: float = 1e5,
-                 minimum: float = 1e-5):
+    def __init__(
+        self, 
+        name: str ='alpha', 
+        maximum: float = 1e5,
+        minimum: float = 1e-5
+    ):
         self.name = name
         self.maximum = maximum
         self.minimum = minimum
@@ -144,7 +146,7 @@ class effective_sample_size(param_update):
 
 
     """
-    def __init__(self, name = 'alpha', eta=1.0, maximum=1e5, solve_max_it = 15):
+    def __init__(self, name = 'alpha', eta=.5, maximum=1e5, solve_max_it = 15):
         super().__init__(name = name, maximum=maximum)
         if self.name != 'alpha':
             warnings.warn('effective_number scheduler only works for alpha parameter! You specified name = {}!'.format(self.name), stacklevel=2)
@@ -174,7 +176,7 @@ def __call__(self, alpha):
         denom = logsumexp(-2 * alpha[:, None] * self.energy, axis=-1)
         return np.exp(2 * nom - denom) - self.eta * self.N
 
-def bisection_solve(f, low, high, max_it = 15, thresh = 1e-2):
+def bisection_solve(f, low, high, max_it = 100, thresh = 1e-2, verbosity=0):
     r"""simple bisection optimization to solve for roots
     
     Parameters
@@ -202,10 +204,12 @@ def bisection_solve(f, low, high, max_it = 15, thresh = 1e-2):
         gtzero = np.where(fx[idx] > 0)[0]
         ltzero  = np.where(fx[idx] < 0)[0]
         # update low and high
-        high[idx[gtzero]] = x[idx[gtzero]]
-        low[idx[ltzero]]  = x[idx[ltzero]]
+        low[idx[gtzero]] = x[idx[gtzero]]
+        high[idx[ltzero]]  = x[idx[ltzero]]
         # update running idx and iteration
         idx = np.where(np.abs(fx) > thresh)[0]
         it += 1
         term = (it > max_it) | (len(idx) == 0)
+    if verbosity > 0:
+        print('Finishing after ' + str(it) + ' Iterations')
     return x