def fill_joint_bins(self):
"""Fill the joint bins with the probability weights. Does the most
important work!"""
binheights = np.zeros((np.max(self.bins), np.max(self.bins),
len(list(iter_combi(range(self.numParams),2)))))
all_binwidths = Marginals.getbinwidths(self)
all_pairs = list(iter_combi(range(self.numParams),2))
in_bin = np.zeros([np.max(self.bins), len(self.logweights)], dtype=bool)
#in_bin = np.zeros([np.max(self.bins), np.max(self.bins)], dtype=bool)
bin_sum = np.zeros([np.max(self.bins), np.max(self.bins)])
#print(in_bin)
for pair_index, pair in enumerate(all_pairs):
binwidthX = all_binwidths[pair[0]]
binwidthY = all_binwidths[pair[1]]
for indX in range(self.bins[pair[0]]):
for indY in range(self.bins[pair[1]]):
#print pair, indX, indY
in_bin[indX][:] = ((((self.lower_bounds[pair[0]] + indX*binwidthX) <= self.parameters[:,pair[0]]) & \
(self.parameters[:,pair[0]] < (self.lower_bounds[pair[0]] + (indX + 1)*binwidthX))) &\
(((self.lower_bounds[pair[1]] + indY*binwidthY) <= self.parameters[:,pair[1]]) & \
(self.parameters[:,pair[1]] < (self.lower_bounds[pair[1]] + (indY + 1)*binwidthY))))
#print in_bin[0][:]
#if indY==9: sys.exit()
bin_sum[indX][indY] = np.sum(self.logweights[in_bin[indX][:]])
#print bin_sum
binheights[indX][indY][pair_index] = bin_sum[indX][indY]
return binheights
def print_joints(self):
"""Currently does ??????
It's possible the binned probabilities need to be divided by their
binwidths either in fillbins() or in a separate function. Do this to
normalise properly (i.e. so area = 1)."""
bin_heights = Marginals.fill_joint_bins(self)
bin_widths = Marginals.getbinwidths(self)
all_pairs = list(iter_combi(range(self.numParams),2))
for pair_index, pair in enumerate(all_pairs):
parameterX = pair[0]
parameterY = pair[1]
bin_widthX = bin_widths[parameterX]
bin_widthY = bin_widths[parameterY]
for index, probability in enumerate(bin_heights):
for another_index in range(len(bin_heights)):
#print index, another_index, probability[another_index][pair_index]
print("{:g}\t{:g}\t{:g}\tParam{:d}\tParam{:d}\t{:g}\t{:g}".format(self.lower_bounds[parameterX] + bin_widthX*(index + 0.5), self.lower_bounds[parameterY] + bin_widthY*(another_index + 0.5), probability[another_index][pair_index]/bin_widthX/bin_widthY, parameterX, parameterY, bin_widthX, bin_widthY))
return