Changeset 777

Timestamp:

01/03/15 09:21:20 (17 months ago)

Author:

mmckerns

Message:

add std, impose_std, impose_reweighted_std to measures; with_std to constraints

Location:

mystic

Files:

• _math/measures.py (modified) (3 diffs)
• mystic/constraints.py (modified) (1 diff)

mystic/_math/measures.py

@@ -174 +174 @@
   return mean(svar, weights)
 
+def std(samples, weights=None): #, _mean=None):
+  """calculate the (weighted) standard deviation for a list of points
+
+Inputs:
+    samples -- a list of sample points
+    weights -- a list of sample weights
+"""
+  from numpy import sqrt
+  return sqrt(variance(samples, weights)) # _mean)
+
 
 ##### coordinate shift methods #####
@@ -218 +228 @@
 #       ...so likely, must scale the weights... or scale each point differently
 
+def impose_std(s, samples, weights=None):
+  """impose a standard deviation on a list of (weighted) points
+  (this function is 'mean-preserving')
+
+Inputs:
+    s -- the target standard deviation
+    samples -- a list of sample points
+    weights -- a list of sample weights
+"""
+  return impose_variance(s**2, samples, weights)
 
 def impose_spread(r, samples, weights=None): #FIXME: fails if len(samples) = 1
@@ -482 +502 @@
     return samples, wts  # "mean-preserving"
 
+def impose_reweighted_std(s, samples, weights=None, solver=None):
+    """impose a standard deviation on a list of points, using reweighting"""
+    return impose_reweighted_variance(s**2, samples, weights, solver)
 
 ##### misc methods #####

mystic/mystic/constraints.py

@@ -146 +146 @@
         return factory
     return decorate
+
+
+def with_std(target):
+    """bind a standard deviation constraint to a given constraints function.
+
+Inputs:
+    target -- the target standard deviation
+
+A constraints function takes an iterable x as input, returning a modified x.
+This function is an "outer" coupling of "impose_std" onto another
+constraints function c(x), such that:  x' = impose_std(target, c(x)).
+
+    For example:
+    >>> @with_std(1.0)
+    ... def constraint(x):
+    ...   x[-1] = x[0]
+    ...   return x
+    ... 
+    >>> x = constraint([1,2,3])
+    >>> print x
+    [0.6262265521467858, 2.747546895706428, 0.6262265521467858]
+    >>> std(x)
+    0.99999999999999956
+    """
+    return with_variance(target**2)