def monte_carlo_integrate(f, lb, ub, n=10000): #XXX ok default for n?
"""
Returns the integral of an m-dimensional function f from lb to ub
using a Monte Carlo integration of n points
Inputs:
f -- a function that takes a list and returns a number.
lb -- a list of lower bounds
ub -- a list of upper bounds
n -- the number of points to sample [Default is n=10000]
References:
1. "A Primer on Scientific Programming with Python", by Hans Petter
Langtangen, page 443-445, 2014.
2. http://en.wikipedia.org/wiki/Monte_Carlo_integration
3. http://math.fullerton.edu/mathews/n2003/MonteCarloMod.html
"""
from mystic.math.stats import volume
from mystic.math.samples import random_samples
vol = volume(lb, ub)
x = [random_samples(lb, ub, npts=1) for k in range(1, n+1)]
r = map(f, x) #FIXME: , nnodes=nnodes, launcher=launcher)
s = sum(r)[0]
I = (vol/n)*s
return float(I)
def monte_carlo_integrate(f, lb, ub, n=10000): #XXX ok default for n?
"""
Returns the integral of an m-dimensional function f from lb to ub
using a Monte Carlo integration of n points
Inputs:
f -- a function that takes a list and returns a number.
lb -- a list of lower bounds
ub -- a list of upper bounds
n -- the number of points to sample [Default is n=10000]
References:
This code is adapted from A Primer on Scientific Programming with Python
by Hans Petter Langtangen, page 443-445.
Also, see http://en.wikipedia.org/wiki/Monte_Carlo_integration
and http://math.fullerton.edu/mathews/n2003/MonteCarloMod.html
"""
from mystic.math.stats import volume
from mystic.math.samples import random_samples
vol = volume(lb, ub)
x = [random_samples(lb, ub, npts=1) for k in range(1, n + 1)]
r = map(f, x) #FIXME: , nnodes=nnodes, launcher=launcher)
s = sum(r)[0]
I = (vol / k) * s
return float(I)
def samplepts(lb, ub, npts, dist=None):
"""
takes lower and upper bounds (e.g. lb = [0,3], ub = [2,4])
produces a list of sample points s = [[1,3],[1,4],[2,3],[2,4]]
Inputs:
lb -- a list of the lower bounds
ub -- a list of the upper bounds
npts -- number of sample points
dist -- a mystic.math.Distribution instance (or list of Distributions)
"""
from mystic.math.samples import random_samples
q = random_samples(lb, ub, npts, dist)
return q.T.tolist()
def samplepts(lb,ub,npts,dist=None):
"""
takes lower and upper bounds (e.g. lb = [0,3], ub = [2,4])
produces a list of sample points s = [[1,3],[1,4],[2,3],[2,4]]
Inputs:
lb -- a list of the lower bounds
ub -- a list of the upper bounds
npts -- number of sample points
dist -- a mystic.math.Distribution instance
"""
from mystic.math.samples import random_samples
q = random_samples(lb,ub,npts,dist)
return q.T.tolist()
def samplepts(lb,ub,npts):
"""
takes upper and lower bounds (e.g. ub = [2,4], lb = [0,3])
produces a list of sample points s = [[1,3],[1,4],[2,3],[2,4]]
Inputs:
lower bounds -- a list of the lower bounds
upper bounds -- a list of the upper bounds
npts -- number of sample points
"""
from mystic.math.samples import random_samples
q = random_samples(lb,ub,npts)
q = [list(i) for i in q]
q = zip(*q)
return [list(i) for i in q]
def samplepts(lb, ub, npts):
"""
takes upper and lower bounds (e.g. ub = [2,4], lb = [0,3])
produces a list of sample points s = [[1,3],[1,4],[2,3],[2,4]]
Inputs:
lower bounds -- a list of the lower bounds
upper bounds -- a list of the upper bounds
npts -- number of sample points
"""
from mystic.math.samples import random_samples
q = random_samples(lb, ub, npts)
q = [list(i) for i in q]
q = zip(*q)
return [list(i) for i in q]
def __call__(self, axis=None, **kwds):
"""apply the reducer to the sampled statistical quantity
Input:
axis: int, the index of y on which to find bound (all, by default)
Additional Input:
reducer: function to reduce a list to a single value (e.g. mean, max)
dist: a mystic.tools.Distribution instance (or list of Distributions)
npts: number of sample points [default = 10000]
clip: if True, clip at bounds, else resample [default = False]
Returns:
sampled statistical quantity, for the specified axis, reduced to a float
"""
#XXX: return what? "energy and solution?" reduced?
reducer = kwds.pop('reducer', None)
if kwds.get('npts', None) is None: kwds.pop('npts', None)
s = random_samples(self.lb, self.ub, **kwds).T
fobj = cached(archive=dict_archive())(self.objective) #XXX: bad idea?
self._pts = fobj.__cache__(
) #XXX: also bad idea? include from *_bounds?
objective = lambda rv: fobj(self.constraint(rv), axis=axis)
import multiprocess.dummy as mp #FIXME: process pickle/recursion Error
pool = mp.Pool() # len(s)
map = pool.map #TODO: don't hardwire map
s = map(objective, s) #NOTE: s = [(...),(...)] or [...]
pool.close()
pool.join()
if axis is None and self.axes is not None: # apply per axis
if reducer is None:
return tuple(sum(si) / len(si) for si in zip(*s))
#XXX: better tuple(reducer(s, axis=0).tolist()) if numpy ufunc?
return tuple(reducer(si) for si in zip(*s))
s = tuple(s)
return sum(s) / len(s) if reducer is None else reducer(s)
def test_calculate_methods(npts=2):
upper_bounds = [1.0]
lower_bounds = [0.0]
# -------------------------------------
# generate initial coordinates, weights
# -------------------------------------
# get a random distribution of points
if disp: print("generate random points and weights")
coordinates = samplepts(lower_bounds, upper_bounds, npts)
D0 = D = [i[0] for i in coordinates]
if disp: print("positions: %s" % D)
# calculate sample range
R0 = R = spread(D)
if disp: print("range: %s" % R)
# select weights randomly in [0,1], then normalize so sum(weights) = 1
wts = random_samples([0], [1], npts)[0]
weights = normalize(wts, 0.0, zsum=True)
if disp: print("weights (when normalized to 0.0): %s" % weights)
assert almostEqual(sum(weights), 0.0, tol=1e-15)
weights = normalize(wts, 1.0)
assert almostEqual(sum(weights), 1.0, tol=1e-15)
if disp: print("weights (when normalized to 1.0): %s" % weights)
w = norm(weights)
if disp: print("norm: %s" % w)
assert almostEqual(w, sum(weights) / npts)
# calculate sample mean
m0 = m = mean(D, weights)
if disp: print("mean: %s" % m)
if disp: print("")
# -------------------------------------
# modify coordinates, maintaining mean & range
# -------------------------------------
# get new random distribution
if disp: print("modify positions, maintaining mean and range")
coordinates = samplepts(lower_bounds, upper_bounds, npts)
D = [i[0] for i in coordinates]
# impose a range and mean on the points
D = impose_spread(R, D, weights)
D = impose_mean(m, D, weights)
# print results
if disp: print("positions: %s" % D)
R = spread(D)
if disp: print("range: %s" % R)
assert almostEqual(R, R0)
m = mean(D, weights)
if disp: print("mean: %s" % m)
assert almostEqual(m, m0)
if disp: print("")
# -------------------------------------
# modify weights, maintaining mean & norm
# -------------------------------------
# select weights randomly in [0,1]
if disp: print("modify weights, maintaining mean and range")
wts = random_samples([0], [1], npts)[0]
# print intermediate results
#print("weights: %s" % wts)
#sm = mean(D, wts)
#print("tmp mean: %s" % sm)
#print("")
# impose mean and weight norm on the points
D = impose_mean(m, D, wts)
DD, weights = impose_weight_norm(D, wts)
# print results
if disp: print("weights: %s" % weights)
w = norm(weights)
if disp: print("norm: %s" % w)
assert almostEqual(w, sum(weights) / npts)
if disp: print("positions: %s" % DD)
R = spread(DD)
if disp: print("range: %s" % R)
assert almostEqual(R, R0)
sm = mean(DD, weights)
if disp: print("mean: %s" % sm)
assert almostEqual(sm, m0)
sv = variance(DD, weights)
if disp: print("var: %s" % sv)
assert not almostEqual(sv, R)
assert almostEqual(sv, 0.0, tol=.3)
# -------------------------------------
# modify variance, maintaining mean
# -------------------------------------
if disp: print("\nmodify variance, maintaining mean")
DD = impose_variance(R, DD, weights)
sm = mean(DD, weights)
if disp: print("mean: %s" % sm)
assert almostEqual(sm, m0)
sv = variance(DD, weights)
if disp: print("var: %s" % sv)
assert almostEqual(sv, R)
def test_calculate_methods(npts=2):
upper_bounds = [1.0]
lower_bounds = [0.0]
# -------------------------------------
# generate initial coordinates, weights
# -------------------------------------
# get a random distribution of points
print "generate random points and weights"
coordinates = samplepts(lower_bounds, upper_bounds, npts)
D = [i[0] for i in coordinates]
print "positions: %s" % D
# calculate sample range
R = spread(D)
print "range: %s" % R
# select weights randomly in [0,1], then normalize so sum(weights) = 1
wts = random_samples([0], [1], npts)[0]
weights = normalize(wts, 0.0, zsum=True)
print "weights (when normalized to 0.0): %s" % weights
weights = normalize(wts)
print "weights (when normalized to 1.0): %s" % weights
w = norm(weights)
print "norm: %s" % w
# calculate sample mean
m = mean(D, weights)
print "mean: %s" % m
print ""
# -------------------------------------
# modify coordinates, maintaining mean & range
# -------------------------------------
# get new random distribution
print "modify positions, maintaining mean and range"
coordinates = samplepts(lower_bounds, upper_bounds, npts)
D = [i[0] for i in coordinates]
# impose a range and mean on the points
D = impose_spread(R, D, weights)
D = impose_mean(m, D, weights)
# print results
print "positions: %s" % D
R = spread(D)
print "range: %s" % R
m = mean(D, weights)
print "mean: %s" % m
print ""
# -------------------------------------
# modify weights, maintaining mean & norm
# -------------------------------------
# select weights randomly in [0,1]
print "modify weights, maintaining mean and range"
wts = random_samples([0], [1], npts)[0]
# print intermediate results
#print "weights: %s" % wts
#sm = mean(D, wts)
#print "tmp mean: %s" % sm
#print ""
# impose mean and weight norm on the points
D = impose_mean(m, D, wts)
DD, weights = impose_weight_norm(D, wts)
# print results
print "weights: %s" % weights
w = norm(weights)
print "norm: %s" % w
print "positions: %s" % DD
R = spread(DD)
print "range: %s" % R
sm = mean(DD, weights)
print "mean: %s" % sm
sv = variance(DD, weights)
print "var: %s" % sv
# -------------------------------------
# modify variance, maintaining mean
# -------------------------------------
print "\nmodify variance, maintaining mean"
DD = impose_variance(R, DD, weights)
sm = mean(DD, weights)
print "mean: %s" % sm
sv = variance(DD, weights)
print "var: %s" % sv
else:
param_string += ", "
print " parameters: %s" % param_string
# get diameter for entire cuboid
lb,ub = [lower_bounds],[upper_bounds]
cuboid_volume = volume(lb[0],ub[0])
params0, subdiams0, diam0, probmass0 = test_cuboids(lb,ub,RVstart,RVend,\
cuboid_volume)
SOLVED_PARAMETERS, SUB_DIAMETERS = params0, subdiams0
TOTAL_DIAMETERS, PROBABILITY_MASS = diam0, probmass0
if DEBUG: print "\nSTATUS = %s" % STATUS
if not DEBUG:
pts = random_samples(lb[0],ub[0])
pof = sampled_pof(model,pts)
print "Exact PoF: %s" % pof
# prepare new set of random samples (across entire domain) as 'data'
if not PER_AI:
pts = random_samples(lb[0],ub[0],num_sample_points)
for i in range(len(lb)):
print "\n"
print " lower bounds: %s" % lb[i]
print " upper bounds: %s" % ub[i]
for solved in params0[0]:
print "solved: %s" % solved
print "subdiameters (squared): %s" % subdiams0[0]
print "diameter (squared): %s" % diam0[0]
print " probability mass: %s" % probmass0[0]
def test_calculate_methods(npts=2): upper_bounds = [1.0] lower_bounds = [0.0] # ------------------------------------- # generate initial coordinates, weights # ------------------------------------- # get a random distribution of points if disp: print "generate random points and weights" coordinates = samplepts(lower_bounds, upper_bounds, npts) D0 = D = [i[0] for i in coordinates] if disp: print "positions: %s" % D # calculate sample range R0 = R = spread(D) if disp: print "range: %s" % R # select weights randomly in [0,1], then normalize so sum(weights) = 1 wts = random_samples([0],[1], npts)[0] weights = normalize(wts, 0.0, zsum=True) if disp: print "weights (when normalized to 0.0): %s" % weights assert almostEqual(sum(weights), 0.0, tol=1e-15) weights = normalize(wts) assert almostEqual(sum(weights), 1.0, tol=1e-15) if disp: print "weights (when normalized to 1.0): %s" % weights w = norm(weights) if disp: print "norm: %s" % w assert almostEqual(w, sum(weights)/npts) # calculate sample mean m0 = m = mean(D,weights) if disp: print "mean: %s" % m if disp: print "" # ------------------------------------- # modify coordinates, maintaining mean & range # ------------------------------------- # get new random distribution if disp: print "modify positions, maintaining mean and range" coordinates = samplepts(lower_bounds, upper_bounds, npts) D = [i[0] for i in coordinates] # impose a range and mean on the points D = impose_spread(R, D, weights) D = impose_mean(m, D, weights) # print results if disp: print "positions: %s" % D R = spread(D) if disp: print "range: %s" % R assert almostEqual(R, R0) m = mean(D, weights) if disp: print "mean: %s" % m assert almostEqual(m, m0) if disp: print "" # ------------------------------------- # modify weights, maintaining mean & norm # ------------------------------------- # select weights randomly in [0,1] if disp: print "modify weights, maintaining mean and range" wts = random_samples([0],[1], npts)[0] # print intermediate results #print "weights: %s" % wts #sm = mean(D, wts) #print "tmp mean: %s" % sm #print "" # impose mean and weight norm on the points D = impose_mean(m, D, wts) DD, weights = impose_weight_norm(D, wts) # print results if disp: print "weights: %s" % weights w = norm(weights) if disp: print "norm: %s" % w assert almostEqual(w, sum(weights)/npts) if disp: print "positions: %s" % DD R = spread(DD) if disp: print "range: %s" % R assert almostEqual(R, R0) sm = mean(DD, weights) if disp: print "mean: %s" % sm assert almostEqual(sm, m0) sv = variance(DD, weights) if disp: print "var: %s" % sv assert not almostEqual(sv, R) assert almostEqual(sv, 0.0, tol=.2) # ------------------------------------- # modify variance, maintaining mean # ------------------------------------- if disp: print "\nmodify variance, maintaining mean" DD = impose_variance(R, DD, weights) sm = mean(DD, weights) if disp: print "mean: %s" % sm assert almostEqual(sm, m0) sv = variance(DD, weights) if disp: print "var: %s" % sv assert almostEqual(sv, R)
def test_calculate_methods(npts=2): upper_bounds = [1.0] lower_bounds = [0.0] # ------------------------------------- # generate initial coordinates, weights # ------------------------------------- # get a random distribution of points print "generate random points and weights" coordinates = samplepts(lower_bounds, upper_bounds, npts) D = [i[0] for i in coordinates] print "positions: %s" % D # calculate sample range R = spread(D) print "range: %s" % R # select weights randomly in [0,1], then normalize so sum(weights) = 1 wts = random_samples([0],[1], npts)[0] weights = normalize(wts, 0.0, zsum=True) print "weights (when normalized to 0.0): %s" % weights weights = normalize(wts) print "weights (when normalized to 1.0): %s" % weights w = norm(weights) print "norm: %s" % w # calculate sample mean m = mean(D,weights) print "mean: %s" % m print "" # ------------------------------------- # modify coordinates, maintaining mean & range # ------------------------------------- # get new random distribution print "modify positions, maintaining mean and range" coordinates = samplepts(lower_bounds, upper_bounds, npts) D = [i[0] for i in coordinates] # impose a range and mean on the points D = impose_spread(R, D, weights) D = impose_mean(m, D, weights) # print results print "positions: %s" % D R = spread(D) print "range: %s" % R m = mean(D, weights) print "mean: %s" % m print "" # ------------------------------------- # modify weights, maintaining mean & norm # ------------------------------------- # select weights randomly in [0,1] print "modify weights, maintaining mean and range" wts = random_samples([0],[1], npts)[0] # print intermediate results #print "weights: %s" % wts #sm = mean(D, wts) #print "tmp mean: %s" % sm #print "" # impose mean and weight norm on the points D = impose_mean(m, D, wts) DD, weights = impose_weight_norm(D, wts) # print results print "weights: %s" % weights w = norm(weights) print "norm: %s" % w print "positions: %s" % DD R = spread(DD) print "range: %s" % R sm = mean(DD, weights) print "mean: %s" % sm sv = variance(DD, weights) print "var: %s" % sv # ------------------------------------- # modify variance, maintaining mean # ------------------------------------- print "\nmodify variance, maintaining mean" DD = impose_variance(R, DD, weights) sm = mean(DD, weights) print "mean: %s" % sm sv = variance(DD, weights) print "var: %s" % sv
