def test_flatten_unflatten():
# build three sets (x,y,z)
sx = set([point(1.0, 1.0), point(2.0, 1.0), point(3.0, 2.0)])
sy = set([point(0.0, 1.0), point(3.0, 2.0)])
sz = set([point(1.0, 1.0), point(2.0, 3.0)])
# build a collection
c = collection([sx, sy, sz])
# flatten and unflatten
from mystic.math.discrete import flatten, unflatten
d = unflatten(flatten(c), c.pts)
# check if the same
assert c.npts == d.npts
assert c.weights == d.weights
assert c.positions == d.positions
# flatten() and load(...)
e = collection().load(c.flatten(), c.pts)
# check if the same
assert c.npts == e.npts
assert c.weights == e.weights
assert c.positions == e.positions
# decompose and compose
from mystic.math.discrete import decompose, compose
b = compose(*decompose(c))
# check if the same
assert c.npts == b.npts
assert c.weights == b.weights
assert c.positions == b.positions
return
def test_flatten_unflatten(): # build three sets (x,y,z) sx = set([point(1.0,1.0), point(2.0,1.0), point(3.0,2.0)]) sy = set([point(0.0,1.0), point(3.0,2.0)]) sz = set([point(1.0,1.0), point(2.0,3.0)]) # build a collection c = collection([sx,sy,sz]) # flatten and unflatten from mystic.math.discrete import flatten, unflatten d = unflatten(flatten(c), c.pts) # check if the same assert c.npts == d.npts assert c.weights == d.weights assert c.positions == d.positions # flatten() and load(...) e = collection().load(c.flatten(), c.pts) # check if the same assert c.npts == e.npts assert c.weights == e.weights assert c.positions == e.positions # decompose and compose from mystic.math.discrete import decompose, compose b = compose(*decompose(c)) # check if the same assert c.npts == b.npts assert c.weights == b.weights assert c.positions == b.positions return
def test_set_behavior(): from mystic.math import almostEqual from numpy import inf # check basic behavior for set of two points s = set([point(1.0,1.0), point(3.0,2.0)]) #XXX: s + [pt, pt] => broken assert almostEqual(s.mean, 2.33333333) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 3.0) # basic behavior for an admissable set s.normalize() s.mean = 1.0 s.range = 1.0 assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 1.0) assert almostEqual(s.mass, 1.0) # add and remove points # test special cases: SUM(weights)=0, RANGE(samples)=0, SUM(samples)=0 s.append(point(1.0,-1.0)) assert s.mean == -inf assert almostEqual(s.range, 1.0) assert almostEqual(s.mass, 0.0) ''' _ave = s.mean s.mean = 1.0 assert str(s.mean) == 'nan' assert str(s.range) == 'nan' assert almostEqual(s.mass, 0.0) s.normalize() assert str(s.mass) == 'nan' s.mean = _ave ''' s.pop() s[0] = point(1.0,1.0) s[1] = point(-1.0,1.0) assert almostEqual(s.mean, 0.0) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 2.0) s.normalize() s.mean = 1.0 assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 1.0) s[0] = point(1.0,1.0) s[1] = point(1.0,1.0) assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 0.0) assert almostEqual(s.mass, 2.0) s.range = 1.0 assert str(s.mean) == 'nan' assert str(s.range) == 'nan' assert almostEqual(s.mass, 2.0) return
def test_set_behavior(): from mystic.math import almostEqual from numpy import inf # check basic behavior for set of two points s = set([point(1.0,1.0), point(3.0,2.0)]) #XXX: s + [pt, pt] => broken assert almostEqual(s.mean, 2.33333333) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 3.0) # basic behavior for an admissable set s.normalize() s.mean = 1.0 s.range = 1.0 assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 1.0) assert almostEqual(s.mass, 1.0) # add and remove points # test special cases: SUM(weights)=0, RANGE(samples)=0, SUM(samples)=0 s.append(point(1.0,-1.0)) assert s.mean == -inf assert almostEqual(s.range, 1.0) assert almostEqual(s.mass, 0.0) ''' _ave = s.mean s.mean = 1.0 assert str(s.mean) == 'nan' assert str(s.range) == 'nan' assert almostEqual(s.mass, 0.0) s.normalize() assert str(s.mass) == 'nan' s.mean = _ave ''' s.pop() s[0] = point(1.0,1.0) s[1] = point(-1.0,1.0) assert almostEqual(s.mean, 0.0) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 2.0) s.normalize() s.mean = 1.0 assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 2.0) assert almostEqual(s.mass, 1.0) s[0] = point(1.0,1.0) s[1] = point(1.0,1.0) assert almostEqual(s.mean, 1.0) assert almostEqual(s.range, 0.0) assert almostEqual(s.mass, 2.0) s.range = 1.0 assert str(s.mean) == 'nan' assert str(s.range) == 'nan' assert almostEqual(s.mass, 2.0) return
def test_collection_behavior():
from mystic.math import almostEqual
from numpy import inf
def f(x):
return sum(x) # a test function for expectation value
# build three sets (x,y,z)
sx = set([point(1.0, 1.0), point(2.0, 1.0), point(3.0, 2.0)])
sy = set([point(0.0, 1.0), point(3.0, 2.0)])
sz = set([point(1.0, 1.0), point(2.0, 3.0)])
#NOTE: for marc_surr(x,y,z), we must have x > 0.0
sx.normalize()
sy.normalize()
sz.normalize()
assert sx.mass == sy.mass == sz.mass == 1.0
# build a collection
c = collection([sx, sy, sz])
xpos = c[0].positions
ypos = c[1].positions
zpos = c[2].positions
xwts = c[0].weights
ywts = c[1].weights
zwts = c[2].weights
if disp:
print("x_positions: %s" % xpos)
print("y_positions: %s" % ypos)
print("z_positions: %s" % zpos)
print("x_weights: %s" % xwts)
print("y_weights: %s" % ywts)
print("z_weights: %s" % zwts)
assert xpos == sx.positions
assert ypos == sy.positions
assert zpos == sz.positions
assert xwts == sx.weights
assert ywts == sy.weights
assert zwts == sz.weights
tol = .2
supp = c.support(tol)
positions = c.positions
weights = c.weights
assert supp == [p for (p, w) in zip(positions, weights) if w > tol]
if disp:
print("support points:\n %s" % supp)
print("npts: %s (i.e. %s)" % (c.npts, c.pts))
print("weights: %s" % weights)
print("positions: %s" % positions)
assert c.npts == sx.npts * sy.npts * sz.npts
assert len(weights) == len(positions) == c.npts
assert sx.positions in c.pos
assert sy.positions in c.pos
assert sz.positions in c.pos
assert sx.weights in c.wts
assert sy.weights in c.wts
assert sz.weights in c.wts
c_exp = c.expect(f)
if disp:
print("mass: %s" % c.mass)
print("expect: %s" % c_exp)
assert c.mass == [sx.mass, sy.mass, sz.mass]
assert c_exp == expectation(f, c.positions, c.weights)
#print("center: %s" % c.center)
#print("delta: %s" % c.delta)
# change the positions in the collection
points = [list(i) for i in positions[::3]]
for i in range(len(points)):
points[i][0] = 0.5
positions[::3] = points
c.positions = positions
_xpos = c[0].positions
_ypos = c[1].positions
_zpos = c[2].positions
_cexp = c.expect(f)
if disp:
print("x_positions: %s" % _xpos)
print("y_positions: %s" % _ypos)
print("z_positions: %s" % _zpos)
print("expect: %s" % _cexp)
assert _xpos == [0.5] + xpos[1:]
assert _ypos == ypos
assert _zpos == zpos
assert _cexp < c_exp # due to _xpos[0] is 0.5 and less than 1.0
_mean = 85.0
_range = 0.25
c.set_expect(_mean, f, npop=40, maxiter=200, tol=_range)
_exp = c.expect(f)
if disp:
print("mean: %s" % _mean)
print("range: %s" % _range)
print("expect: %s" % _exp)
assert almostEqual(_mean, _exp, tol=_mean * 0.01)
# a test function for probability of failure
def g(x):
if f(x) <= 0.0: return False
return True
pof = c.pof(g)
spof = c.sampled_pof(g, npts=10000)
if disp:
print("pof: %s" % pof)
print("sampled_pof: %s" % spof)
assert almostEqual(pof, spof, tol=0.02)
return
def test_collection_behavior():
from mystic.math import almostEqual
from numpy import inf
def f(x):
return sum(x) # a test function for expectation value
# build three sets (x,y,z)
sx = set([point(1.0, 1.0), point(2.0, 1.0), point(3.0, 2.0)])
sy = set([point(0.0, 1.0), point(3.0, 2.0)])
sz = set([point(1.0, 1.0), point(2.0, 3.0)])
#NOTE: for marc_surr(x,y,z), we must have x > 0.0
sx.normalize()
sy.normalize()
sz.normalize()
# build a collection
c = collection([sx, sy, sz])
print "x_positions: %s" % c[0].positions
print "y_positions: %s" % c[1].positions
print "z_positions: %s" % c[2].positions
print "x_weights: %s" % c[0].weights
print "y_weights: %s" % c[1].weights
print "z_weights: %s" % c[2].weights
print "randomly selected support:\n %s" % c.support(10)
print "npts: %s (i.e. %s)" % (c.npts, c.pts)
print "weights: %s" % c.weights
positions = c.positions
print "positions: %s" % positions
print "mass: %s" % c.mass
print "expect: %s" % c.expect(f)
#print "center: %s" % c.center
#print "delta: %s" % c.delta
# change the positions in the collection
positions[::3]
points = [list(i) for i in positions[::3]]
for i in range(len(points)):
points[i][0] = 0.5
positions[::3] = points
c.positions = positions
print "x_positions: %s" % c[0].positions
print "y_positions: %s" % c[1].positions
print "z_positions: %s" % c[2].positions
print "expect: %s" % c.expect(f)
_mean = 85.0
_range = 0.25
c.set_expect((_mean, _range), f)
print "mean: %s" % _mean
print "range: %s" % _range
print "expect: %s" % c.expect(f)
# a test function for probability of failure
def g(x):
if f(x) <= 0.0: return False
return True
print "pof: %s" % c.pof(g)
print "sampled_pof: %s" % c.sampled_pof(g, npts=10000)
return
def test_collection_behavior():
from mystic.math import almostEqual
from numpy import inf
def f(x): return sum(x) # a test function for expectation value
# build three sets (x,y,z)
sx = set([point(1.0,1.0), point(2.0,1.0), point(3.0,2.0)])
sy = set([point(0.0,1.0), point(3.0,2.0)])
sz = set([point(1.0,1.0), point(2.0,3.0)])
#NOTE: for marc_surr(x,y,z), we must have x > 0.0
sx.normalize()
sy.normalize()
sz.normalize()
assert sx.mass == sy.mass == sz.mass == 1.0
# build a collection
c = collection([sx,sy,sz])
xpos = c[0].positions
ypos = c[1].positions
zpos = c[2].positions
xwts = c[0].weights
ywts = c[1].weights
zwts = c[2].weights
if disp:
print "x_positions: %s" % xpos
print "y_positions: %s" % ypos
print "z_positions: %s" % zpos
print "x_weights: %s" % xwts
print "y_weights: %s" % ywts
print "z_weights: %s" % zwts
assert xpos == sx.positions
assert ypos == sy.positions
assert zpos == sz.positions
assert xwts == sx.weights
assert ywts == sy.weights
assert zwts == sz.weights
tol = .2
supp = c.support(tol)
positions = c.positions
weights = c.weights
assert supp == [p for (p,w) in zip(positions,weights) if w > tol]
if disp:
print "support points:\n %s" % supp
print "npts: %s (i.e. %s)" % (c.npts, c.pts)
print "weights: %s" % weights
print "positions: %s" % positions
assert c.npts == sx.npts * sy.npts * sz.npts
assert len(weights) == len(positions) == c.npts
assert sx.positions in c.pos
assert sy.positions in c.pos
assert sz.positions in c.pos
assert sx.weights in c.wts
assert sy.weights in c.wts
assert sz.weights in c.wts
c_exp = c.expect(f)
if disp:
print "mass: %s" % c.mass
print "expect: %s" % c_exp
assert c.mass == [sx.mass, sy.mass, sz.mass]
assert c_exp == expectation(f, c.positions, c.weights)
#print "center: %s" % c.center
#print "delta: %s" % c.delta
# change the positions in the collection
points = [ list(i) for i in positions[::3] ]
for i in range(len(points)):
points[i][0] = 0.5
positions[::3] = points
c.positions = positions
_xpos = c[0].positions
_ypos = c[1].positions
_zpos = c[2].positions
_cexp = c.expect(f)
if disp:
print "x_positions: %s" % _xpos
print "y_positions: %s" % _ypos
print "z_positions: %s" % _zpos
print "expect: %s" % _cexp
assert _xpos == [0.5] + xpos[1:]
assert _ypos == ypos
assert _zpos == zpos
assert _cexp < c_exp # due to _xpos[0] is 0.5 and less than 1.0
_mean = 85.0
_range = 0.25
c.set_expect((_mean,_range), f)
_exp = c.expect(f)
if disp:
print "mean: %s" % _mean
print "range: %s" % _range
print "expect: %s" % _exp
assert almostEqual(_mean, _exp, tol=_mean*0.01)
# a test function for probability of failure
def g(x):
if f(x) <= 0.0: return False
return True
pof = c.pof(g)
spof = c.sampled_pof(g, npts=10000)
if disp:
print "pof: %s" % pof
print "sampled_pof: %s" % spof
assert almostEqual(pof, spof, tol=0.02)
return
def test_collection_behavior():
from mystic.math import almostEqual
from numpy import inf
def f(x): return sum(x) # a test function for expectation value
# build three sets (x,y,z)
sx = set([point(1.0,1.0), point(2.0,1.0), point(3.0,2.0)])
sy = set([point(0.0,1.0), point(3.0,2.0)])
sz = set([point(1.0,1.0), point(2.0,3.0)])
#NOTE: for marc_surr(x,y,z), we must have x > 0.0
sx.normalize()
sy.normalize()
sz.normalize()
# build a collection
c = collection([sx,sy,sz])
print "x_positions: %s" % c[0].positions
print "y_positions: %s" % c[1].positions
print "z_positions: %s" % c[2].positions
print "x_weights: %s" % c[0].weights
print "y_weights: %s" % c[1].weights
print "z_weights: %s" % c[2].weights
print "randomly selected support:\n %s" % c.support(10)
print "npts: %s (i.e. %s)" % (c.npts, c.pts)
print "weights: %s" % c.weights
positions = c.positions
print "positions: %s" % positions
print "mass: %s" % c.mass
print "expect: %s" % c.expect(f)
#print "center: %s" % c.center
#print "delta: %s" % c.delta
# change the positions in the collection
positions[::3]
points = [ list(i) for i in positions[::3] ]
for i in range(len(points)):
points[i][0] = 0.5
positions[::3] = points
c.positions = positions
print "x_positions: %s" % c[0].positions
print "y_positions: %s" % c[1].positions
print "z_positions: %s" % c[2].positions
print "expect: %s" % c.expect(f)
_mean = 85.0
_range = 0.25
c.set_expect((_mean,_range), f)
print "mean: %s" % _mean
print "range: %s" % _range
print "expect: %s" % c.expect(f)
# a test function for probability of failure
def g(x):
if f(x) <= 0.0: return False
return True
print "pof: %s" % c.pof(g)
print "sampled_pof: %s" % c.sampled_pof(g, npts=10000)
return
