def factor_max_marginalize(factor, var):
"""Marginalize over a list of variables by taking the max.
Args:
factor (Factor): Input factor
var (List): Variable to marginalize out.
Returns:
out: Factor with variables in 'var' marginalized out. The factor's
.val_argmax field should be a list of dictionary that keep track
of the maximizing values of the marginalized variables.
e.g. when out.val_argmax[i][j] = k, this means that
when assignments of out is index_to_assignment[i],
variable j has a maximizing value of k.
See test_lab1.py::test_factor_max_marginalize() for an example.
"""
out = Factor()
""" YOUR CODE HERE
Marginalize out the variables given in var.
You should make use of val_argmax to keep track of the location with the
maximum probability.
"""
out.var = np.setxor1d(factor.var, np.array(var))
for out_var in out.var:
index = np.where(factor.var == out_var)
out.card = np.append(out.card, factor.card[index])
assignment = factor.get_all_assignments()
index = []
out.val_argmax = []
for single_var in var:
index.append(np.where(factor.var == single_var))
delete_assignment = np.delete(assignment, index, axis=1)
out.val = np.zeros(np.prod(out.card))
for i in np.unique(delete_assignment, axis=0):
index_set = np.array(np.where(np.all(i == delete_assignment, axis=1)))
index_set_max_index = np.argmax(factor.val[index_set])
max_index_assignment = index_set[:, index_set_max_index][0]
single_assignment = assignment_to_index(i, out.card)
out.val[single_assignment] = factor.val[max_index_assignment]
temp_dict = {}
for single_var in var:
index = np.argwhere(factor.var == single_var)[0][0]
temp_dict[single_var] = assignment[max_index_assignment][index]
out.val_argmax.append(temp_dict)
return out
