def step(context, name, start, end):
from numpy import abs, any, dot
from dcprogs.likelihood import expm
times = context.times
times = times[times < end]
times = times[times > start]
for exact, missed_events_G, qmatrix in zip(context.exact_survivors,
context.likelihoods,
context.qmatrices):
if missed_events_G is None: continue
tau = missed_events_G.tau
if name == "af": factor = dot(qmatrix.af, expm(tau * qmatrix.ff))
else: factor = dot(qmatrix.fa, expm(tau * qmatrix.aa))
for t in times:
try:
value = getattr(missed_events_G, name)(t)
check = dot(getattr(exact, name)(t-tau), factor)
assert any(abs(value - check) < context.tolerance)
except:
print(missed_events_G)
print(" * tmax: {0}".format(missed_events_G.tmax))
print("{name}({t}*tau): \n{value}".format(name=name, t=t/tau, value=value))
print("exact.af({t}*tau) * factor: \n{value}".format(name=name, t=(t-tau)/tau, value=check))
print("factor:\n{factor}".format(factor=factor))
raise AssertionError()
def step(context, name, start):
from numpy import abs, any, dot
from dcprogs.likelihood import expm
times = context.times
times = times[times > start]
for approx, missed_events_G, qmatrix in zip(context.approx_survivors,
context.likelihoods,
context.qmatrices):
if missed_events_G is None: continue
tau = missed_events_G.tau
if name == "af": factor = dot(qmatrix.af, expm(tau * qmatrix.ff))
else: factor = dot(qmatrix.fa, expm(tau * qmatrix.aa))
for t in times:
try:
value = getattr(missed_events_G, name)(t)
check = dot(getattr(approx, name)(t - tau), factor)
assert any(abs(value - check) < context.tolerance)
except:
print(missed_events_G)
print(" * tmax: {0}".format(missed_events_G.tmax))
print("{name}({t}*tau): \n{value}".format(name=name,
t=t / tau,
value=value))
print("approx.{name}({t}*tau) * factor: \n{value}"\
.format(name=name, t=(t-tau)/tau, value=check))
print("factor:\n{factor}".format(factor=factor))
raise
def step(context):
from numpy import abs, all, dot
from dcprogs.likelihood import expm
for idealg, matrix in zip(context.idealgs, context.qmatrices):
for t in context.times:
value = dot(expm(t * matrix.ff), matrix.fa)
try: assert all(abs(idealg.fa(t) - value) < context.tolerance)
except:
print(matrix)
print(t)
raise
def step(context):
from numpy import abs, all, dot
from dcprogs.likelihood import expm
for idealg, matrix in zip(context.idealgs, context.qmatrices):
for t in context.times:
value = dot(expm(t * matrix.ff), matrix.fa)
try:
assert all(abs(idealg.fa(t) - value) < context.tolerance)
except:
print(matrix)
print(t)
raise
def compute_Hfa(qmatrix, tau, tcrit):
from dcprogs.likelihood import ApproxSurvivor
from dcprogs.likelihood import expm
from numpy import exp, dot
approx = ApproxSurvivor(qmatrix, tau)
result = None
# This sums ^{F}R_i tau_i e^(-(tcrit - tau) / tau_i )
for matrix, root in approx.fa_components:
if result is None: result = -matrix / root * exp( root * (tcrit - tau) )
else: result += -matrix / root * exp( root * (tcrit - tau) )
# multiply by Q_{FA} e^{Q_AA}
return dot(result, dot(qmatrix.fa, expm(tau * qmatrix.aa)))
def compute_Hfa(qmatrix, tau, tcrit):
from dcprogs.likelihood import ApproxSurvivor
from dcprogs.likelihood import expm
from numpy import exp, dot
approx = ApproxSurvivor(qmatrix, tau)
result = None
# This sums ^{F}R_i tau_i e^(-(tcrit - tau) / tau_i )
for matrix, root in approx.fa_components:
if result is None: result = -matrix / root * exp(root * (tcrit - tau))
else: result += -matrix / root * exp(root * (tcrit - tau))
# multiply by Q_{FA} e^{Q_AA}
return dot(result, dot(qmatrix.fa, expm(tau * qmatrix.aa)))
from numpy import dot, identity, abs, all
from dcprogs.likelihood import QMatrix, IdealG, expm
qmatrix = QMatrix([ [-3050, 50, 3000, 0, 0],
[2./3., -1502./3., 0, 500, 0],
[ 15, 0, -2065, 50, 2000],
[ 0, 15000, 4000, -19000, 0],
[ 0, 0, 10, 0, -10] ], 2)
idealG = IdealG(qmatrix)
print(idealG)
idealG_fa = dot(expm(2e-4 * qmatrix.ff), qmatrix.fa)
assert all( abs(idealG_fa - idealG.fa(2e-4)) < 1e-8 )
inversion = -0.5 * identity(2) - qmatrix.aa
assert all( abs( dot(inversion, idealG.laplace_af(-0.5)) - qmatrix.af ) < 1e-8 )
from numpy import dot, identity, abs, all
from dcprogs.likelihood import QMatrix, IdealG, expm
qmatrix = QMatrix([[-3050, 50, 3000, 0, 0], [2. / 3., -1502. / 3., 0, 500, 0],
[15, 0, -2065, 50, 2000], [0, 15000, 4000, -19000, 0],
[0, 0, 10, 0, -10]], 2)
idealG = IdealG(qmatrix)
print(idealG)
idealG_fa = dot(expm(2e-4 * qmatrix.ff), qmatrix.fa)
assert all(abs(idealG_fa - idealG.fa(2e-4)) < 1e-8)
inversion = -0.5 * identity(2) - qmatrix.aa
assert all(abs(dot(inversion, idealG.laplace_af(-0.5)) - qmatrix.af) < 1e-8)