def zero_eig(result): """ Qff and Qaa cannot be singular. """ from dcprogs.likelihood import svd from numpy import all, any, abs try: singular = svd(result.aa)[1] except: return False if any(abs(singular) < 1e-8): return False try: singular = svd(result.ff)[1] except: return False return all(abs(singular) > 1e-8)
def zero_eig(result):
""" Qff and Qaa cannot be singular. """
from dcprogs.likelihood import svd
from numpy import all, any, abs
try:
singular = svd(result.aa)[1]
except:
return False
if any(abs(singular) < 1e-8): return False
try:
singular = svd(result.ff)[1]
except:
return False
return all(abs(singular) > 1e-8)
def zero_eig(result):
from dcprogs.likelihood import svd
from numpy import sum, abs
try:
U, sing, V = svd(result)
except:
return False
else:
return sum(abs(sing) < tolerance) == 1
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, name):
from dcprogs.likelihood import svd
from numpy import dot, identity, abs, all
for qmatrix, G, occ in zip(context.qmatrices, context.likelihoods, context.occupancies):
eqmatrix = dot(G.laplace_af(0), G.laplace_fa(0)) if name == "initial" \
else dot(G.laplace_fa(0), G.laplace_af(0))
eqmatrix -= identity(eqmatrix.shape[0])
left, sings, right = svd(eqmatrix)
sings = sorted(sings)
try:
assert abs(sings[0]) < context.tolerance
assert sings[0] < sings[1] + context.tolerance
assert all(abs(dot(occ, eqmatrix)) < context.tolerance)
except:
print(G)
print(" * occupancies: {0}".format(occ))
print(" * matrix:\n{0}".format(eqmatrix))
print(" * error: {0}".format(dot(occ, eqmatrix)))
print(" * singular values: {0}".format(sings))
raise
def step(context, name):
from dcprogs.likelihood import svd
from numpy import dot, identity, abs, all
for qmatrix, G, occ in zip(context.qmatrices, context.likelihoods,
context.occupancies):
eqmatrix = dot(G.laplace_af(0), G.laplace_fa(0)) if name == "initial" \
else dot(G.laplace_fa(0), G.laplace_af(0))
eqmatrix -= identity(eqmatrix.shape[0])
left, sings, right = svd(eqmatrix)
sings = sorted(sings)
try:
assert abs(sings[0]) < context.tolerance
assert sings[0] < sings[1] + context.tolerance
assert all(abs(dot(occ, eqmatrix)) < context.tolerance)
except:
print(G)
print(" * occupancies: {0}".format(occ))
print(" * matrix:\n{0}".format(eqmatrix))
print(" * error: {0}".format(dot(occ, eqmatrix)))
print(" * singular values: {0}".format(sings))
raise
def zero_eig(result): from dcprogs.likelihood import svd from numpy import sum, abs try: U, sing, V = svd(result) except: return False else: return sum(abs(sing) < tolerance) == 1