def test_1():
r("require('coda')")
random = np.random.RandomState(1)
for i in range(10):
x = generate_AR1(phi=0.95, sigma=1, n_steps=1000, c=0, y0=0, random_state=random)
r.assign('x', x)
tau = r('nrow(x)/effectiveSize(x)')[0]
np.testing.assert_approx_equal(tau, integrated_autocorr2(x))
def test_1():
r("require('coda')")
random = np.random.RandomState(1)
for i in range(10):
x = generate_AR1(phi=0.95,
sigma=1,
n_steps=1000,
c=0,
y0=0,
random_state=random)
r.assign('x', x)
tau = r('nrow(x)/effectiveSize(x)')[0]
np.testing.assert_approx_equal(tau, integrated_autocorr2(x))
def summarize(self):
counts_nz = np.count_nonzero(self.countsmat_)
cnz = self.countsmat_[np.nonzero(self.countsmat_)]
correlation_times = integrated_autocorr2(self.all_timescales_[:, :5])
fmt = lambda x: ', '.join(['{:.2f}'.format(xx) for xx in x])
fmt_d = lambda x: ', '.join(
['{:d}'.format(xx) for xx in x.astype(int)])
return """Bayesian Markov State Model
---------------------------
Lag time : {lag_time}
Alpha : {alpha}
Beta : {beta}
n_samples : {n_samples}
n_steps / sample : {n_steps}
epsilon : {epsilon}
Number of states : {n_states}
Rejection Rate : {rej}
Walltime / sample : {walltime:.3} s
Timescales:
mean : [{mean_ts}] units
stdev: [{std_ts}] units
Timescale correlation times:
[{correlation_times}] HMC steps
Approximate number of independent samples:
[{independent_samples}]
""".format(lag_time=self.lag_time,
alpha=self.prior_alpha,
beta=self.prior_beta,
n_samples=self.n_samples,
n_steps=self.n_steps,
epsilon=self.epsilon,
n_states=self.n_states_,
rej=self.diag_['rej'],
mean_ts=fmt(self.all_timescales_.mean(0)[:5]),
std_ts=fmt(self.all_timescales_.std(0)[:5]),
correlation_times=fmt(correlation_times),
walltime=self.walltime_ / self.n_samples,
independent_samples=fmt_d(self.n_samples / correlation_times))
def summarize(self):
counts_nz = np.count_nonzero(self.countsmat_)
cnz = self.countsmat_[np.nonzero(self.countsmat_)]
correlation_times = integrated_autocorr2(self.all_timescales_[:, :5])
fmt = lambda x: ', '.join(['{:.2f}'.format(xx) for xx in x])
fmt_d = lambda x: ', '.join(['{:d}'.format(xx) for xx in x.astype(int)])
return """Bayesian Markov State Model
---------------------------
Lag time : {lag_time}
Alpha : {alpha}
Beta : {beta}
n_samples : {n_samples}
n_steps / sample : {n_steps}
epsilon : {epsilon}
Number of states : {n_states}
Rejection Rate : {rej}
Walltime / sample : {walltime:.3} s
Timescales:
mean : [{mean_ts}] units
stdev: [{std_ts}] units
Timescale correlation times:
[{correlation_times}] HMC steps
Approximate number of independent samples:
[{independent_samples}]
""".format(
lag_time=self.lag_time, alpha=self.prior_alpha, beta=self.prior_beta,
n_samples=self.n_samples, n_steps=self.n_steps, epsilon=self.epsilon,
n_states=self.n_states_, rej=self.diag_['rej'],
mean_ts=fmt(self.all_timescales_.mean(0)[:5]),
std_ts=fmt(self.all_timescales_.std(0)[:5]),
correlation_times=fmt(correlation_times),
walltime = self.walltime_ / self.n_samples,
independent_samples=fmt_d(self.n_samples/correlation_times)
)
def test_2():
x = np.random.randn(100, 2)
integrated_autocorr2(x)
tau3 = []
tau4 = []
tau5 = []
tau6 = []
n_trials = 10
for i in range(n_trials):
y = generate_AR1(phi=PHI,
sigma=1,
n_steps=n_steps,
c=0,
y0=0,
random_state=None)
tau1.append([integrated_autocorr1(y[:n]) for n in grid])
tau2.append([integrated_autocorr2(y[:n]) for n in grid])
tau3.append([integrated_autocorr3(y[:n]) for n in grid])
tau4.append([integrated_autocorr4(y[:n]) for n in grid])
tau5.append([integrated_autocorr5(y[:n]) for n in grid])
tau6.append([integrated_autocorr6(y[:n]) for n in grid])
pp.errorbar(grid,
y=np.mean(tau1, axis=0),
yerr=np.std(tau1, axis=0),
c='b',
label='tau 1')
pp.errorbar(grid - 1,
y=np.mean(tau2, axis=0),
yerr=np.std(tau2, axis=0),
c='r',
label='tau 2')
n_steps = 1000000
grid = np.logspace(2, np.log10(n_steps), 10)
tau1 = []
tau2 = []
tau3 = []
tau4 = []
tau5 = []
tau6 = []
n_trials = 10
for i in range(n_trials):
y = generate_AR1(phi=PHI, sigma=1, n_steps=n_steps, c=0, y0=0, random_state=None)
tau1.append([integrated_autocorr1(y[:n]) for n in grid])
tau2.append([integrated_autocorr2(y[:n]) for n in grid])
tau3.append([integrated_autocorr3(y[:n]) for n in grid])
tau4.append([integrated_autocorr4(y[:n]) for n in grid])
tau5.append([integrated_autocorr5(y[:n]) for n in grid])
tau6.append([integrated_autocorr6(y[:n]) for n in grid])
pp.errorbar(grid, y=np.mean(tau1, axis=0), yerr=np.std(tau1, axis=0), c='b', label='tau 1')
pp.errorbar(grid-1, y=np.mean(tau2, axis=0), yerr=np.std(tau2, axis=0), c='r', label='tau 2')
pp.errorbar(grid-5, y=np.mean(tau3, axis=0), yerr=np.std(tau3, axis=0), c='g', label='tau 3')
pp.errorbar(grid-10, y=np.mean(tau4, axis=0), yerr=np.std(tau4, axis=0), c='gold', label='tau 4')
pp.errorbar(grid-20, y=np.mean(tau5, axis=0), yerr=np.std(tau5, axis=0), c='m', label='tau 5')
pp.errorbar(grid-30, y=np.mean(tau6, axis=0), yerr=np.std(tau6, axis=0), c='cyan', label='tau 6')
pp.plot(grid, [TRUE]*len(grid), 'k-')
pp.xscale('log')
def test_2():
x = np.random.randn(100, 2)
integrated_autocorr2(x)
