from numpy import all, abs, arange
from dcprogs.likelihood import QMatrix, DeterminantEq, MissedEventsG
# Define parameters.
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)
tau = 1e-4
# Create eG from prior knowledge of roots
determinant_eq = DeterminantEq(qmatrix, tau)
af_roots = [( -3045.285776037674, 1), (-162.92946543451328, 1)]
fa_roots = [(-17090.192769236815, 1), (-2058.0812921673496, 1), (-0.24356535498785126, 1)]
eG_from_roots = MissedEventsG(determinant_eq, af_roots, determinant_eq.transpose(), fa_roots)
# Create eG automaticallye
eG_automatic = MissedEventsG(qmatrix, tau)
# Checks the three initialization are equivalent at tau
assert all(abs(eG_from_roots.af(tau) - eG_automatic.af(tau)) < 1e-8)
assert all(abs(eG_from_roots.fa(tau) - eG_automatic.fa(tau)) < 1e-8)
# Checks the three initialization are equivalent at different times
# The functions can be applied to arrays.
times = arange(tau, 10*tau, 0.1*tau)
assert eG_from_roots.af(times).shape == (len(times), 2, 3)
assert eG_from_roots.fa(times).shape == (len(times), 3, 2)
assert all(abs(eG_from_roots.af(times) - eG_automatic.af(times)) < 1e-8)
assert all(abs(eG_from_roots.fa(times) - eG_automatic.fa(times)) < 1e-8)
from dcprogs.likelihood import DeterminantEq, QMatrix
# Define parameters.
matrix = [[-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]]
qmatrix = QMatrix(matrix, 2)
tau = 1e-4
det0 = DeterminantEq(qmatrix, 1e-4)
det1 = DeterminantEq(matrix, 2, 1e-4)
print("{0!s}\n\n{1!s}".format(det0, det1))
x = [0, -1, -1e2]
assert all(abs(det0(x) - det1(x)) < 1e-8)
roots = array([-3045.285776037674, -162.92946543451328])
assert all(abs(det0(roots)) < 1e-6 * abs(roots))
transpose = det0.transpose()
roots = array([-17090.192769236815, -2058.0812921673496, -0.24356535498785126],
dtype=internal_dtype)
assert all(abs(transpose(roots)) < 1e-6 * abs(roots))
print(" * H({0}):\n{1}\n".format(0, det0.H(0)))
print(" * H({0}):\n{1}\n".format(-1e2, det0.H(-1e2)))
print(" * d[sI-H(s)]/ds for s={0}:\n{1}\n".format(0, det0.s_derivative(0)))
print(" * d[sI-H(s)]/ds for s={0}:\n{1}\n".format(-1e2,
det0.s_derivative(-1e2)))
# Define parameters.
matrix = [ [-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] ]
qmatrix = QMatrix(matrix, 2)
tau = 1e-4
det0 = DeterminantEq(qmatrix, 1e-4);
det1 = DeterminantEq(matrix, 2, 1e-4);
print("{0!s}\n\n{1!s}".format(det0, det1))
x = [0, -1, -1e2]
assert all(abs(det0(x) - det1(x)) < 1e-8)
roots = array([-3045.285776037674, -162.92946543451328])
assert all(abs(det0(roots)) < 1e-6 * abs(roots))
transpose = det0.transpose()
roots = array( [-17090.192769236815, -2058.0812921673496, -0.24356535498785126],
dtype=internal_dtype )
assert all(abs(transpose(roots)) < 1e-6 * abs(roots))
print(" * H({0}):\n{1}\n".format(0, det0.H(0)))
print(" * H({0}):\n{1}\n".format(-1e2, det0.H(-1e2)))
print(" * d[sI-H(s)]/ds for s={0}:\n{1}\n".format(0, det0.s_derivative(0)))
print(" * d[sI-H(s)]/ds for s={0}:\n{1}\n".format(-1e2, det0.s_derivative(-1e2)))
from numpy import all, abs, arange
from dcprogs.likelihood import QMatrix, DeterminantEq, MissedEventsG
# Define parameters.
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)
tau = 1e-4
# Create eG from prior knowledge of roots
determinant_eq = DeterminantEq(qmatrix, tau)
af_roots = [(-3045.285776037674, 1), (-162.92946543451328, 1)]
fa_roots = [(-17090.192769236815, 1), (-2058.0812921673496, 1),
(-0.24356535498785126, 1)]
eG_from_roots = MissedEventsG(determinant_eq, af_roots,
determinant_eq.transpose(), fa_roots)
# Create eG automaticallye
eG_automatic = MissedEventsG(qmatrix, tau)
# Checks the three initialization are equivalent at tau
assert all(abs(eG_from_roots.af(tau) - eG_automatic.af(tau)) < 1e-8)
assert all(abs(eG_from_roots.fa(tau) - eG_automatic.fa(tau)) < 1e-8)
# Checks the three initialization are equivalent at different times
# The functions can be applied to arrays.
times = arange(tau, 10 * tau, 0.1 * tau)
assert eG_from_roots.af(times).shape == (len(times), 2, 3)
assert eG_from_roots.fa(times).shape == (len(times), 3, 2)
assert all(abs(eG_from_roots.af(times) - eG_automatic.af(times)) < 1e-8)
assert all(abs(eG_from_roots.fa(times) - eG_automatic.fa(times)) < 1e-8)
