def step(context):
from dcprogs.likelihood import inv, svd
from numpy import abs, all, dot, identity
for matrix, idealg in zip(context.qmatrices, context.idealgs):
occupancies = idealg.final_occupancies
kernel = dot( dot(inv(matrix.ff), matrix.fa), dot(inv(matrix.aa), matrix.af) )
kernel = identity(kernel.shape[0]) - kernel
U, singvals, V = svd(kernel)
try:
assert sum(abs(singvals) < context.tolerance) == 1
assert all(dot(occupancies, kernel) < context.tolerance)
except:
print(matrix)
print("Equilibrium: {0}".format(occupancies))
print("Kernel Application: {0}".format(dot(occupancies, kernel)))
raise
def step(context):
from dcprogs.likelihood import inv, svd
from numpy import abs, all, dot, identity
for matrix, idealg in zip(context.qmatrices, context.idealgs):
occupancies = idealg.final_occupancies
kernel = dot(dot(inv(matrix.ff), matrix.fa),
dot(inv(matrix.aa), matrix.af))
kernel = identity(kernel.shape[0]) - kernel
U, singvals, V = svd(kernel)
try:
assert sum(abs(singvals) < context.tolerance) == 1
assert all(dot(occupancies, kernel) < context.tolerance)
except:
print(matrix)
print("Equilibrium: {0}".format(occupancies))
print("Kernel Application: {0}".format(dot(occupancies, kernel)))
raise
def step(context):
from numpy import abs, all, dot, identity
from dcprogs.likelihood import inv
for idealg, matrix in zip(context.idealgs, context.qmatrices):
for scale in context.scales:
value = dot(inv(scale * identity(matrix.ff.shape[0]) - matrix.ff), matrix.fa)
try: assert all(abs(idealg.laplace_fa(scale) - value) < context.tolerance)
except:
print(matrix)
raise
def step(context):
from numpy import abs, all, dot, identity
from dcprogs.likelihood import inv
for idealg, matrix in zip(context.idealgs, context.qmatrices):
for scale in context.scales:
value = dot(inv(scale * identity(matrix.ff.shape[0]) - matrix.ff),
matrix.fa)
try:
assert all(
abs(idealg.laplace_fa(scale) - value) < context.tolerance)
except:
print(matrix)
raise
