def sample(self, index={}):
""" in order to sample from this distributions, all parents must be known """
# mean = self.mean.copy()
# sigma = self.sigma.copy()
## if index:
## # discrete parents
## for v,i in enumerate(reversed(self.discrete_parents)):
## # reverse: avoid missing axes when taking in random
## # we start from the end, that way all other dimensions keep the same index
## if index.has_key(v.name):
## # take the corresponding mean; +1 because first axis is the mean
## mean = na.take(mean, index[v], axis=(i+1) )
## # take the corresponding covariance; +2 because first 2 axes are the cov
## sigma = na.take(sigma, index[v], axis=(i+2) )
##
## # continuous parents
## for v in reversed(self.continuous_parents):
## if index.has_key(v):
d_index, c_index = self._numIndexFromDict(index)
mean = na.array(self.mean[tuple([slice(None, None, None)] + d_index)])
sigma = self.sigma[tuple([slice(None, None, None)] * 2 +d_index)]
wi = na.sum(self.weights * na.array(c_index)[na.NewAxis,...], axis=1)
# if self.continuous_parents:
# wi = na.array(self.weights[tuple([slice(None,None,None)]+c_index)])
# else: wi = 0.0
# return a random number from a normal multivariate distribution
return float(ra.multivariate_normal(mean + wi, sigma))
# than zero given the forecasts.
if _use_numarray:
from numarray.random_array import multivariate_normal
import numarray.mlab as mlab
else:
from numpy.oldnumeric.random_array import multivariate_normal
import numpy.oldnumeric.mlab as mlab
# number of realizations.
nsamps = 100000
# correlations
r12 = 0.5 # average correlation between the first predictor and the obs.
r13 = 0.25 # avg correlation between the second predictor and the obs.
r23 = 0.125 # avg correlation between predictors.
# random draws from trivariate normal distribution
x = multivariate_normal(
NA.array([0, 0, 0]),
NA.array([[1, r12, r13], [r12, 1, r23], [r13, r23, 1]]), nsamps)
x2 = multivariate_normal(
NA.array([0, 0, 0]),
NA.array([[1, r12, r13], [r12, 1, r23], [r13, r23, 1]]), nsamps)
print 'correlations (r12,r13,r23) = ', r12, r13, r23
print 'number of realizations = ', nsamps
# training data.
obs = x[:, 0]
climprob = NA.sum((obs > 0).astype('f')) / nsamps
fcst = NA.transpose(x[:, 1:]) # 2 predictors.
obs_binary = obs > 0.
# independent data for verification.
obs2 = x2[:, 0]
fcst2 = NA.transpose(x2[:, 1:])
# compute logistic regression.
# the conditional probability that the observation will be greater
# than zero given the forecasts.
if _use_numarray:
from numarray.random_array import multivariate_normal
import numarray.mlab as mlab
else:
from numpy.oldnumeric.random_array import multivariate_normal
import numpy.oldnumeric.mlab as mlab
# number of realizations.
nsamps = 100000
# correlations
r12 = 0.5 # average correlation between the first predictor and the obs.
r13 = 0.25 # avg correlation between the second predictor and the obs.
r23 = 0.125 # avg correlation between predictors.
# random draws from trivariate normal distribution
x = multivariate_normal(NA.array([0,0,0]),NA.array([[1,r12,r13],[r12,1,r23],[r13,r23,1]]), nsamps)
x2 = multivariate_normal(NA.array([0,0,0]),NA.array([[1,r12,r13],[r12,1,r23],[r13,r23,1]]), nsamps)
print 'correlations (r12,r13,r23) = ',r12,r13,r23
print 'number of realizations = ',nsamps
# training data.
obs = x[:,0]
climprob = NA.sum((obs > 0).astype('f'))/nsamps
fcst = NA.transpose(x[:,1:]) # 2 predictors.
obs_binary = obs > 0.
# independent data for verification.
obs2 = x2[:,0]
fcst2 = NA.transpose(x2[:,1:])
# compute logistic regression.
beta,Jbar,llik = logistic_regression(fcst,obs_binary,verbose=True)
covmat = LA.inverse(Jbar)
stderr = NA.sqrt(mlab.diag(covmat))
def observe_pos(self):
"""
"""
self.pos = random_array.multivariate_normal(self.initial_pos,self.true_err)
def observe_pos(self):
"""
"""
self.pos = random_array.multivariate_normal(self.initial_pos,
self.true_err)
