Python impose_spread Examples

Example #1

def bounded_mean(mean_x, samples, xmin, xmax, wts=None):
  from mystic.math.measures import impose_mean, impose_spread
  from mystic.math.measures import spread, mean
  from numpy import asarray
  a = impose_mean(mean_x, samples, wts)
  if min(a) < xmin:   # maintain the bound
    #print "violate lo(a)"
    s = spread(a) - 2*(xmin - min(a)) #XXX: needs compensation (as below) ?
    a = impose_mean(mean_x, impose_spread(s, samples, wts), wts)
  if max(a) > xmax:   # maintain the bound
    #print "violate hi(a)"
    s = spread(a) + 2*(xmax - max(a)) #XXX: needs compensation (as below) ?
    a = impose_mean(mean_x, impose_spread(s, samples, wts), wts)
  return asarray(a)

Example #2

File: discrete.py Project: agamdua/mystic

def bounded_mean(mean_x, samples, xmin, xmax, wts=None):
  from mystic.math.measures import impose_mean, impose_spread
  from mystic.math.measures import spread, mean
  from numpy import asarray
  a = impose_mean(mean_x, samples, wts)
  if min(a) < xmin:   # maintain the bound
    #print "violate lo(a)"
    s = spread(a) - 2*(xmin - min(a)) #XXX: needs compensation (as below) ?
    a = impose_mean(mean_x, impose_spread(s, samples, wts), wts)
  if max(a) > xmax:   # maintain the bound
    #print "violate hi(a)"
    s = spread(a) + 2*(xmax - max(a)) #XXX: needs compensation (as below) ?
    a = impose_mean(mean_x, impose_spread(s, samples, wts), wts)
  return asarray(a)

Example #3

File: test_constraints.py Project: vishalbelsare/mystic

def test_with_mean_spread():

    from mystic.math.measures import mean, spread, impose_mean, impose_spread

    @with_spread(50.0)
    @with_mean(5.0)
    def constrained_squared(x):
        return [i**2 for i in x]

    from numpy import array
    x = array([1, 2, 3, 4, 5])
    y = impose_spread(50.0, impose_mean(5.0, [i**2 for i in x]))
    assert almostEqual(mean(y), 5.0, tol=1e-15)
    assert almostEqual(spread(y), 50.0, tol=1e-15)
    assert constrained_squared(x) == y

Example #4

File: test_constraints.py Project: uqfoundation/mystic

def test_with_mean_spread():

  from mystic.math.measures import mean, spread, impose_mean, impose_spread

  @with_spread(50.0)
  @with_mean(5.0)
  def constrained_squared(x):
    return [i**2 for i in x]

  from numpy import array
  x = array([1,2,3,4,5])
  y = impose_spread(50.0, impose_mean(5.0,[i**2 for i in x]))
  assert almostEqual(mean(y), 5.0, tol=1e-15)
  assert almostEqual(spread(y), 50.0, tol=1e-15)
  assert constrained_squared(x) == y

Example #5

File: test_coupler.py Project: mmckerns/mmckerns.github.io

def test_inner_constraints():

    from mystic.math.measures import impose_mean, impose_spread

    def impose_constraints(x, mean=0.0, spread=1.0):
        x = impose_mean(mean, x)
        x = impose_spread(spread, x)
        return x

    @inner(inner=impose_constraints, kwds={'mean': 5.0, 'spread': 50.0})
    def constrained_squared(x):
        return [i**2 for i in x]

    from numpy import array
    x = array([1, 2, 3, 4, 5])
    y = impose_spread(50.0, impose_mean(5.0, x))
    assert constrained_squared(x) == [i**2 for i in y]

Example #6

File: test_coupler.py Project: uqfoundation/mystic

def test_inner_constraints():

  from mystic.math.measures import impose_mean, impose_spread

  def impose_constraints(x, mean=0.0, spread=1.0):
    x = impose_mean(mean, x)
    x = impose_spread(spread, x)
    return x

  @inner(inner=impose_constraints, kwds={'mean':5.0, 'spread':50.0})
  def constrained_squared(x):
    return [i**2 for i in x]

  from numpy import array
  x = array([1,2,3,4,5])
  y = impose_spread(50.0, impose_mean(5.0,x))
  assert constrained_squared(x) == [i**2 for i in y]

Example #7

File: test_coupler.py Project: uqfoundation/mystic

def test_proxified_constraints():

  from mystic.math.measures import impose_mean, impose_spread

  def impose_constraints(x, mean=0.0, spread=1.0):
    x = impose_mean(mean, x)
    x = impose_spread(spread, x)
    return x

  @inner_proxy(inner=impose_constraints)
  def constrained_squared(x): #XXX: proxy doesn't preserve function signature
    return [i**2 for i in x]

  from numpy import array
  x = array([1,2,3,4,5])
  y = impose_spread(50.0, impose_mean(5.0,x))
  assert constrained_squared(x, 5.0, 50.0) == [i**2 for i in y]

Example #8

def test_proxified_constraints():

  from mystic.math.measures import impose_mean, impose_spread

  def impose_constraints(x, mean=0.0, spread=1.0):
    x = impose_mean(mean, x)
    x = impose_spread(spread, x)
    return x

  @inner_proxy(inner=impose_constraints)
  def constrained_squared(x): #XXX: proxy doesn't preserve function signature
    return [i**2 for i in x]

  from numpy import array
  x = array([1,2,3,4,5])
  y = impose_spread(50.0, impose_mean(5.0,x))
  assert constrained_squared(x, 5.0, 50.0) == [i**2 for i in y]

Example #9

File: test_coupler.py Project: mmckerns/mmckerns.github.io

 def impose_constraints(x, mean=0.0, spread=1.0):
     x = impose_mean(mean, x)
     x = impose_spread(spread, x)
     return x

Example #10

File: test_coupler.py Project: mmckerns/mmckerns.github.io

 def range_constraint(x, spread=1.0):
     return impose_spread(spread, x)

Example #11

File: discrete.py Project: agamdua/mystic

 def __set_range(self, r):
   self.positions = impose_spread(r, self.positions, self.weights)
   return

Example #12

File: test_coupler.py Project: uqfoundation/mystic

 def impose_constraints(x, mean=0.0, spread=1.0):
   x = impose_mean(mean, x)
   x = impose_spread(spread, x)
   return x

Example #13

File: test_coupler.py Project: uqfoundation/mystic

 def range_constraint(x, spread=1.0):
   return impose_spread(spread, x)

Example #14

 def __set_range(self, r):
   self.positions = impose_spread(r, self.positions, self.weights)
   return