def ciBatchMeans(M, N, k):
sim = simulateLindleyEfficient(lam, mu, M * N + k)
# throw away the first k observations, and divide the rest into
# subruns of length N each
run = sim[k:(M * N + k)]
p = reshape(run, (M, N))
sample = mean(p, axis=0) # take row means
meanW = mean(sample)
varW = var(sample)
ci = meanW - 1.96 * sqrt(varW / M), meanW + 1.96 * sqrt(varW / M)
return ci
def ciMultipleRuns(M, N, k):
sumW = 0
sumW2 = 0
for _ in range(M):
sim = simulateLindleyEfficient(lam, mu, N)
meanWrun = mean(sim[k:N])
sumW += meanWrun
sumW2 += meanWrun**2
meanW = sumW / M
varW = (sumW2 - sumW**2 / M) / (M - 1)
ci = meanW - 1.96 * sqrt(varW / M), meanW + 1.96 * sqrt(varW / M)
return ci
def find_mode_newton(self, return_full=False):
"""
Newton search for mode of p(y|f)p(f)
from GP book, algorithm 3.1, added step size
"""
K = self.gp.K
if self.newton_start is None:
f = zeros(len(K))
else:
f = self.newton_start
if return_full:
steps = [f]
iteration = 0
norm_difference = inf
objective_value = -inf
while iteration < self.newton_max_iterations and norm_difference > self.newton_epsilon:
# from GP book, algorithm 3.1, added step size
# scale log_lik_grad_vector and K^-1 f = a
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f)
w_sqrt = sqrt(w)
# diag(w_sqrt).dot(K.dot(diag(w_sqrt))) == (K.T*w_sqrt).T*w_sqrt
L = cholesky(eye(len(K)) + (K.T * w_sqrt).T * w_sqrt)
b = f * w + self.newton_step * self.gp.likelihood.log_lik_grad_vector(self.gp.y, f)
# a=b-diag(w_sqrt).dot(inv(eye(len(K)) + (K.T*w_sqrt).T*w_sqrt).dot(diag(w_sqrt).dot(K.dot(b))))
a = w_sqrt * (K.dot(b))
a = solve_triangular(L, a, lower=True)
a = solve_triangular(L.T, a, lower=False)
a = w_sqrt * a
a = b - a
f_new = K.dot(self.newton_step * a)
# convergence stuff and next iteration
objective_value_new = -0.5 * a.T.dot(f) + sum(self.gp.likelihood.log_lik_vector(self.gp.y, f))
norm_difference = norm(f - f_new)
if objective_value_new > objective_value:
f = f_new
if return_full:
steps.append(f)
else:
self.newton_step /= 2
iteration += 1
objective_value = objective_value_new
self.computed = True
if return_full:
return f, L, asarray(steps)
else:
return f
def write_beta_diff_files(path, twiss_cor, twiss_no):
file_bbx = open(os.path.join(path, "bbx.out"), "w")
file_bby = open(os.path.join(path, "bby.out"), "w")
print >> file_bbx, "* NAME S MEA ERROR MODEL"
print >> file_bbx, "$ %s %le %le %le %le"
print >> file_bby, "* NAME S MEA ERROR MODEL"
print >> file_bby, "$ %s %le %le %le %le"
if os.path.exists(os.path.join(path, 'getbetax_free.out')):
twiss_getbetax = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getbetax_free.out'))
else:
twiss_getbetax = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getbetax.out'))
for i in range(len(twiss_getbetax.NAME)):
bpm_name = twiss_getbetax.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
t_x = twiss_getbetax # Variable for abbreviation
error_beta = sqrt(t_x.STDBETX[i] ** 2 + t_x.ERRBETX[i] ** 2) / t_x.BETXMDL[i]
print >> file_bbx, bpm_name, t_x.S[i], (t_x.BETX[i] - t_x.BETXMDL[i]) / t_x.BETXMDL[i], error_beta, (twiss_cor.BETX[j] - twiss_no.BETX[j]) / twiss_no.BETX[j]
if os.path.exists(os.path.join(path, 'getbetay_free.out')):
twiss_getbetay = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getbetay_free.out'))
else:
twiss_getbetay = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getbetay.out'))
for i in range(len(twiss_getbetay.NAME)):
bpm_name = twiss_getbetay.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
t_y = twiss_getbetay # Variable for abbreviation
error_beta = sqrt(t_y.STDBETY[i] ** 2 + t_y.ERRBETY[i] ** 2) / t_y.BETYMDL[i]
print >> file_bby, bpm_name, t_y.S[i], (t_y.BETY[i] - t_y.BETYMDL[i]) / t_y.BETYMDL[i], error_beta, (twiss_cor.BETY[j] - twiss_no.BETY[j]) / twiss_no.BETY[j]
file_bbx.close()
file_bby.close()
def adapt(self, mcmc_chain, step_output):
# this is an extension of the base adapt call
KameleonWindow.adapt(self, mcmc_chain, step_output)
iter_no = mcmc_chain.iteration
if iter_no > self.sample_discard and iter_no < self.stop_adapt:
learn_scale = 1.0 / sqrt(iter_no - self.sample_discard + 1.0)
self.nu2 = exp(log(self.nu2) + learn_scale * (exp(step_output.log_ratio) - self.accstar))
def ciMultipleRunsParallel(M, N, k):
sumW = 0
sumW2 = 0
numCores = multiprocessing.cpu_count()
resultsAllWs = Parallel(n_jobs=numCores)(
delayed(simulateLindleyEfficient)(lam, mu, N) for _ in range(M))
# compute the mean and variance
for i in range(M):
sim = resultsAllWs[i]
meanWrun = mean(sim[k:N])
sumW += meanWrun
sumW2 += meanWrun**2
meanW = sumW / M
varW = (sumW2 - sumW**2 / M) / (M - 1)
ci = meanW - 1.96 * sqrt(varW / M), meanW + 1.96 * sqrt(varW / M)
return ci
def distance(centroid, observation):
dists = []
for i in range(0, len(centroid)):
# if i == 1:
# dists.append(pow(absolute(int(centroid[i]) - int(observation[i])),2))
# else:
if i != 1:
dists.append(pow(Levenshtein.distance(centroid[i], observation[i]), 2))
return sqrt(sum(dists))
def _distancePointToLineSegment(self, a, b, p):
'''
Returns tuple of minimum distance to the directed line segment AB from p, and the distance along AB of the point of intersection
'''
ab_mag2 = dot((b-a),(b-a))
pa_mag2 = dot((a-p),(a-p))
pb_mag2 = dot((b-p),(b-p))
if pa_mag2 + ab_mag2 <= pb_mag2:
return (sqrt(pa_mag2),0)
elif pb_mag2 + ab_mag2 <= pa_mag2:
return (sqrt(pb_mag2), sqrt(ab_mag2))
else:
c = cross((b-a),(p-a))
if ab_mag2 == 0:
raise ValueError, 'Division by zero magnitude line segment AB'
dist_to_line2 = dot(c,c)/ab_mag2
dist_to_line = sqrt(dist_to_line2)
dist_along_segment = sqrt(pa_mag2 - dist_to_line2)
return (dist_to_line, dist_along_segment)
def create_gaussian_kernel(gaussian_kernel_sigma = 1.5, gaussian_kernel_width = 11):
# 1D Gaussian kernel definition
gaussian_kernel = np.ndarray((gaussian_kernel_width))
mu = int(gaussian_kernel_width / 2)
#Fill Gaussian kernel
for i in range(gaussian_kernel_width):
gaussian_kernel[i] = (1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) * \
exp(-(((i - mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2)))
return gaussian_kernel
def construct_proposal(self, y):
assert(len(shape(y)) == 1)
m = MixtureDistribution(self.distribution.dimension, self.num_eigen)
m.mixing_proportion = Discrete((self.eigvalues + 1) / (sum(self.eigvalues) + self.num_eigen))
# print "current mixing proportion: ", m.mixing_proportion.omega
for ii in range(self.num_eigen):
L = sqrt(self.dwscale[ii] * self.eigvalues[ii]) * reshape(self.eigvectors[:, ii], (self.distribution.dimension, 1))
m.components[ii] = Gaussian(y, L, is_cholesky=True, ell=1)
# Z=m.sample(1000).samples
# Visualise.plot_data(Z)
return m
def adapt(self, mcmc_chain, step_output):
# this is an extension of the base adapt call
KameleonWindow.adapt(self, mcmc_chain, step_output)
iter_no = mcmc_chain.iteration
if iter_no > self.sample_discard and iter_no < self.stop_adapt:
learn_scale = 1.0 / sqrt(iter_no - self.sample_discard + 1.0)
self.nu2 = exp(
log(self.nu2) + learn_scale *
(exp(step_output.log_ratio) - self.accstar))
def adapt(self, mcmc_chain, step_output):
iter_no = mcmc_chain.iteration
if iter_no > self.sample_discard:
# 1./number_of_iterations (discard some samples in the beginning)
learn_scale=1.0 / sqrt(iter_no - self.sample_discard + 1.0)
#print "current learning rate: ", learn_scale
if self.adapt_scale:
self.scale_adapt(learn_scale,step_output)
if iter_no % self.sample_lag == 0:
self.mean_and_cov_adapt(learn_scale)
self.eigen_adapt()
def _grad(self, w):
self._compute_pls(w)
grad = numpy.zeros(len(self.mrf.formulas), numpy.float64)
for fidx, varval in self._stat.items():
for varidx, counts in varval.items():
evidx = self.mrf.variable(varidx).evidence_value_index()
g = counts[evidx]
for i, val in enumerate(counts):
g -= val * self._pls[varidx][i]
grad[fidx] += g
self.grad_opt_norm = sqrt(float(fsum([x * x for x in grad])))
return numpy.array(grad)
def get_gaussian_kernel(gaussian_kernel_width=11, gaussian_kernel_sigma=1.5):
"""Generate a gaussian kernel."""
# 1D Gaussian kernel definition
gaussian_kernel_1d = numpy.ndarray((gaussian_kernel_width))
norm_mu = int(gaussian_kernel_width / 2)
# Fill Gaussian kernel
for i in range(gaussian_kernel_width):
gaussian_kernel_1d[i] = (
(1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) *
exp(-(((i - norm_mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2))))
return gaussian_kernel_1d
def get_gaussian_kernel(gaussian_kernel_width=11, gaussian_kernel_sigma=1.5):
"""Generate a gaussian kernel."""
# 1D Gaussian kernel definition
gaussian_kernel_1d = numpy.ndarray((gaussian_kernel_width))
norm_mu = int(gaussian_kernel_width / 2)
# Fill Gaussian kernel
for i in range(gaussian_kernel_width):
gaussian_kernel_1d[i] = (1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) * exp(
-(((i - norm_mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2))
)
return gaussian_kernel_1d
def ciRegenerative(N):
sim = simulateLindleyEfficient(lam, mu, N)
# Now we're going to split the simulation results vector every time we encounter
# an ampty system (i.e. a waiting time of zero)
idx = where(sim == 0)[0] # the positions of the zeros
sa = split(sim, idx) # split the list into sub-lists
Yi = [sum(x) for x in sa] # the sum of the waiting times in each sub-list
Ni = [len(x) for x in sa] # the number of waiting times in each sub-list
M = len(Yi) # the number of sub-lists
Yavg = mean(Yi) # The average of the sums of the waiting times
Navg = mean(Ni) # the mean number of waiting times of the sub-lists
Wavg = Yavg / Navg # The overall mean waiting time
cv = cov(
Yi, Ni
)[0,
1] # sample covariance is element at (0, 1) or (1, 0) of the covariance matrix
sV2 = var(Yi) + Wavg**2 * var(Ni) - 2 * Wavg * cv
print(sV2)
ci = Wavg - 1.96 * sqrt(sV2 / M) / Navg, Wavg + 1.96 * sqrt(sV2 / M) / Navg
return (ci)
def create_gaussian_kernel(gaussian_kernel_sigma=1.5,
gaussian_kernel_width=11):
# 1D Gaussian kernel definition
gaussian_kernel = np.ndarray((gaussian_kernel_width))
mu = int(gaussian_kernel_width / 2)
#Fill Gaussian kernel
for i in range(gaussian_kernel_width):
gaussian_kernel[i] = (1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) * \
exp(-(((i - mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2)))
return gaussian_kernel
def _grad(self, w, **params):
if self.current_wts is None or not list(w) != self.current_wts:
self.current_wts = w
self.probs = self._compute_probs(w)
grad = numpy.zeros(len(w))
for fidx, partitions in self._stat.items():
for part, values in partitions.items():
v = values[self.evidx[part]]
for i, val in enumerate(values):
v -= self.probs[part][i] * val
grad[fidx] += v
self.grad_opt_norm = sqrt(float(fsum([x * x for x in grad])))
return numpy.array(grad)
def _grad(self, w, **params):
self._compute_pls(w)
grad = numpy.zeros(len(self.mrf.formulas), numpy.float64)
for fidx, varval in self._stat.iteritems():
for varidx, counts in varval.iteritems():
if self.mrf.variable(varidx).predicate.name in self.epreds: continue
evidx = self.mrf.variable(varidx).evidence_value_index()
g = counts[evidx]
for i, val in enumerate(counts):
g -= val * self._pls[varidx][i]
grad[fidx] += g
self.grad_opt_norm = float(sqrt(fsum(map(lambda x: x * x, grad))))
return numpy.array(grad)
def serie_std(serie):
serie_mean = mean(serie)
serie_std = []
std = 0.0
for value in serie:
std += pow(value - serie_mean, 2)
std = sqrt(std/(len(serie)-1))
for value in serie:
if std == 0.0:
serie_std.append(0.0)
else:
serie_std.append((value-serie_mean)/std)
return serie_std
def setMeasure(self, power, dt):
if power<0:
self.Power = 0
else:
self.Power = sqrt(power)
#Omega & Alpha
self.computeOmega(dt)
#thrust
self.computeThrust()
#tau_d
self.computeTauD()
#tau_z
self.computeTauZ()
def get_gaussian(self, f=None, L=None):
if f is None or L is None:
f, L, _ = self.find_mode_newton(return_full=True)
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f)
w_sqrt = sqrt(w)
K = self.gp.K
# gp book 3.27, matrix inversion lemma on
# (K^-1 +W)^-1 = K -KW^0.5 B^-1 W^0.5 K
C = (K.T * w_sqrt).T
C = solve_triangular(L, C, lower=True)
C = solve_triangular(L.T, C, lower=False)
C = (C.T * w_sqrt).T
C = K.dot(C)
C = K - C
return Gaussian(f, C, is_cholesky=False)
def get_gaussian(self, f=None, L=None):
if f is None or L is None:
f, L, _ = self.find_mode_newton(return_full=True)
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f)
w_sqrt = sqrt(w)
K = self.gp.K
# gp book 3.27, matrix inversion lemma on
# (K^-1 +W)^-1 = K -KW^0.5 B^-1 W^0.5 K
C = (K.T * w_sqrt).T
C = solve_triangular(L, C, lower=True)
C = solve_triangular(L.T, C, lower=False)
C = (C.T * w_sqrt).T
C = K.dot(C)
C = K - C
return Gaussian(f, C, is_cholesky=False)
def intersection (self, obj):
k = 0.0
if obj.radius > 0.0: #sphere
o2e = vector3.sub_vec(self.point, obj.point)
half_b = vector3.dot_vec(o2e, self.direction)
c = vector3.dot_vec(o2e,o2e) - obj.radius*obj.radius
if half_b*half_b - c >= 0:
k = -half_b - sqrt(half_b*half_b - c)
else :
k = 1.0e8
else : #face
o2e = vector3.sub_vec(self.point, obj.point)
b = vector3.dot_vec(self.direction,obj.normal)
if abs(b) > 1.0e-3 :
k = -(vector3.dot_vec(o2e,obj.normal)) / b
else :
k = 1.0e8
if k<0 : k = 1.0e8
return vector3.mul_vec(self.direction, k)
def fieldpoint(i, t, h, d, w):
'''Calculates a point in the field line.
Note that there is no time dependence.
'''
# Determine the next position.
o = map(lambda i, d: [i[0] + d[0] * h, i[1] + d[1] * h], i, d)
# Determine the magnetic field at the new position.
bx, by, bz = bfield(o[0][0], o[1][0], o[2][0], w)
b = sqrt(bx**2 + by**2 + bz**2)
# Avoid divide by zero errors
if b == 0:
# It's probably [[0, 0], [0, 0], [0, 0]]
# But due to floating point error, it might not actually be.
return [[bx, 0], [by, 0], [bz, 0]]
# Divide by magnetic field strength to arrive at the gradient.
return [[bx / b, 0], [by / b, 0], [bz / b, 0]]
def _calculatePointResiduals(self, curve, tube_radius = None):
if tube_radius is None:
X = self._X
else:
within_tube_indices = self.calculateCoverageIndices(curve, tube_radius)
X = self._X.take(list(within_tube_indices), axis = 0)
if self._maxSegmentLength is None:
self._maxSegmentLength = self._calculateMaxSegmentLength(curve)
lpc_points = curve['save_xd']
num_lpc_points = len(lpc_points)
tree_lpc_points = KDTree(lpc_points)
residuals = empty(len(X))
residuals_lamb = empty(len(X))
path_length = curve['lamb']
for j, p in enumerate(X):
closest_lpc_point = tree_lpc_points.query(p)
candidate_radius = sqrt(closest_lpc_point[0]**2 + 0.25*self._maxSegmentLength**2)
candidate_segment_ends = tree_lpc_points.query_ball_point(p, candidate_radius)
candidate_segment_ends.sort()
current_min_segment_dist = (closest_lpc_point[0],0)
current_closest_index = closest_lpc_point[1]
last_index = None
for i, index in enumerate(candidate_segment_ends):
if index!=0 and last_index != index - 1:
prv_segment_dist = self._distancePointToLineSegment(lpc_points[index-1], lpc_points[index], p)
if prv_segment_dist[0] < current_min_segment_dist[0]:
current_min_segment_dist = prv_segment_dist
current_closest_index = index - 1
if index != num_lpc_points - 1:
prv_segment_dist = self._distancePointToLineSegment(lpc_points[index], lpc_points[index+1], p)
if prv_segment_dist[0] < current_min_segment_dist[0]:
current_min_segment_dist = prv_segment_dist
current_closest_index = index
last_index = index
residuals[j] = current_min_segment_dist[0]
residuals_lamb[j] = path_length[current_closest_index] + current_min_segment_dist[1]
lamb_order = argsort(residuals_lamb)
return (residuals_lamb[lamb_order], residuals[lamb_order])
def predict(self, X_test, f_mode=None):
"""
Predictions for GP with Laplace approximation.
from GP book, algorithm 3.2,
"""
if f_mode is None:
f_mode = self.find_mode_newton()
predictions = zeros(len(X_test))
K = self.gp.K
K_train_test = self.gp.covariance.compute(self.gp.X, X_test)
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f_mode)
w_sqrt = sqrt(w)
# diag(w_sqrt).dot(K.dot(diag(w_sqrt))) == (K.T*w_sqrt).T*w_sqrt
L = cholesky(eye(len(K)) + (K.T * w_sqrt).T * w_sqrt)
# iterator for all testing points
for i in range(len(X_test)):
k = K_train_test[:, i]
k_self = self.gp.covariance.compute([X_test[i]], [X_test[i]])[0]
f_mean = k.dot(
self.gp.likelihood.log_lik_grad_vector(self.gp.y, f_mode))
v = solve_triangular(L, w_sqrt * k, lower=True)
f_var = k_self - v.T.dot(v)
predictions[i] = integrate.quad(
lambda x: norm.pdf(x, f_mean, f_var), -inf, inf)[0]
# # integrate over Gaussian using some crude numerical integration
# samples=randn(1000)*sqrt(f_var) + f_mean
#
# log_liks=self.gp.likelihood.log_lik_vector(1.0, samples)
# predictions[i]=1.0/len(samples)*GPTools.log_sum_exp(log_liks)
return predictions
def predict(self, X_test, f_mode=None):
"""
Predictions for GP with Laplace approximation.
from GP book, algorithm 3.2,
"""
if f_mode is None:
f_mode = self.find_mode_newton()
predictions = zeros(len(X_test))
K = self.gp.K
K_train_test = self.gp.covariance.compute(self.gp.X, X_test)
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f_mode)
w_sqrt = sqrt(w)
# diag(w_sqrt).dot(K.dot(diag(w_sqrt))) == (K.T*w_sqrt).T*w_sqrt
L = cholesky(eye(len(K)) + (K.T * w_sqrt).T * w_sqrt)
# iterator for all testing points
for i in range(len(X_test)):
k = K_train_test[:, i]
k_self = self.gp.covariance.compute([X_test[i]], [X_test[i]])[0]
f_mean = k.dot(self.gp.likelihood.log_lik_grad_vector(self.gp.y, f_mode))
v = solve_triangular(L, w_sqrt * k, lower=True)
f_var = k_self - v.T.dot(v)
predictions[i] = integrate.quad(lambda x: norm.pdf(x, f_mean, f_var), -inf, inf)[0]
# # integrate over Gaussian using some crude numerical integration
# samples=randn(1000)*sqrt(f_var) + f_mean
#
# log_liks=self.gp.likelihood.log_lik_vector(1.0, samples)
# predictions[i]=1.0/len(samples)*GPTools.log_sum_exp(log_liks)
return predictions
def update(self, mcmc_chain, step_output):
if mcmc_chain.iteration > self.plot_from and mcmc_chain.iteration % self.lag == 0:
# plot "traces"
num_plots = mcmc_chain.mcmc_sampler.distribution.dimension
samples = mcmc_chain.samples[0:mcmc_chain.iteration]
likelihoods = mcmc_chain.log_liks[0:mcmc_chain.iteration]
num_y = round(sqrt(num_plots))
num_x = num_plots / num_y + 1
for i in range(num_plots):
subplot(num_y, num_x, i + 1)
plot(samples[:, i], 'b.')
ylim([-0.2, 1.2])
title("Trace $x_" + str(i) + "$. Mean: %f" % mean(samples[:, i]))
subplot(num_y, num_x, num_plots + 1)
plot(likelihoods)
title("Log-Likelihood")
suptitle(mcmc_chain.mcmc_sampler.__class__.__name__)
show()
draw()
clf()
def bfield_segment(x, y, z, lx0, ly0, lz0, lx1, ly1, lz1):
'''Calculates magnetic field using Biot-Savart'''
# Midpoint of the line segment
mx = (lx0 + lx1) / 2
my = (ly0 + ly1) / 2
mz = (lz0 + lz1) / 2
# Distance from the midpoint of the line segment to the point
r = sqrt((x - mx)**2 + (y - my)**2 + (z - mz)**2)
# Check for divide by zero!
if r == 0:
return 0, 0, 0
# Multiply by constants.
c = k * I * r**(-3)
# Algebraic cross product
dbx = c * ((ly1 - ly0) * (z - mz) - (y - my) * (lz1 - lz0))
dby = -c * ((lx1 - lx0) * (z - mz) - (x - mx) * (lz1 - lz0))
dbz = c * ((lx1 - lx0) * (y - my) - (x - mx) * (ly1 - ly0))
return dbx, dby, dbz
def update(self, mcmc_chain, step_output):
if mcmc_chain.iteration > self.plot_from and mcmc_chain.iteration % self.lag == 0:
# plot "traces"
num_plots = mcmc_chain.mcmc_sampler.distribution.dimension
samples = mcmc_chain.samples[0:mcmc_chain.iteration]
likelihoods = mcmc_chain.log_liks[0:mcmc_chain.iteration]
num_y = round(sqrt(num_plots))
num_x = num_plots / num_y + 1
for i in range(num_plots):
subplot(num_y, num_x, i + 1)
plot(samples[:, i], 'b.')
ylim([-0.2, 1.2])
title("Trace $x_" + str(i) +
"$. Mean: %f" % mean(samples[:, i]))
subplot(num_y, num_x, num_plots + 1)
plot(likelihoods)
title("Log-Likelihood")
suptitle(mcmc_chain.mcmc_sampler.__class__.__name__)
show()
draw()
clf()
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin is the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
quantiles = zeros((len(self.experiments), len(self.ref_quantiles)))
norm_of_means = zeros(len(self.experiments))
acceptance_rates = zeros(len(self.experiments))
# ess_0 = zeros(len(self.experiments))
# ess_1 = zeros(len(self.experiments))
# ess_minima = zeros(len(self.experiments))
# ess_medians = zeros(len(self.experiments))
# ess_maxima = zeros(len(self.experiments))
times = zeros(len(self.experiments))
for i in range(len(self.experiments)):
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# use precomputed quantiles if they match with the provided ones
if hasattr(self.experiments[i], "ref_quantiles") and \
hasattr(self.experiments[i], "quantiles") and \
allclose(self.ref_quantiles, self.experiments[i].ref_quantiles):
quantiles[i, :] = self.experiments[i].quantiles
else:
try:
quantiles[i, :] = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(\
burned_in, self.ref_quantiles)
except NotImplementedError:
print "skipping quantile computations, distribution does", \
"not support it."
# quantiles should be about average error rather than average quantile
quantiles[i,:]=abs(quantiles[i,:]-self.ref_quantiles)
dim = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.dimension
norm_of_means[i] = norm(mean(burned_in, 0))
acceptance_rates[i] = mean(self.experiments[i].mcmc_chain.accepteds[burnin:])
# dump burned in samples to disc
# sample_filename=self.experiments[0].experiment_dir + self.experiments[0].name + "_burned_in.txt"
# savetxt(sample_filename, burned_in)
# store minimum ess for every experiment
#ess_per_covariate = asarray([RCodaTools.ess_coda(burned_in[:, cov_idx]) for cov_idx in range(dim)])
# ess_per_covariate = asarray([0 for _ in range(dim)])
# ess_0=ess_per_covariate[0]
# ess_1=ess_per_covariate[1]
# ess_minima[i] = min(ess_per_covariate)
# ess_medians[i] = median(ess_per_covariate)
# ess_maxima[i] = max(ess_per_covariate)
# save chain time needed
ellapsed = self.experiments[i].mcmc_chain.mcmc_outputs[0].times
times[i] = int(round(sum(ellapsed)))
mean_quantiles = mean(quantiles, 0)
std_quantiles = std(quantiles, 0)
sqrt_num_trials=sqrt(len(self.experiments))
# print median kernel width sigma
#sigma=GaussianKernel.get_sigma_median_heuristic(burned_in.T)
#lines.append("median kernel sigma: "+str(sigma))
lines.append("quantiles:")
for i in range(len(self.ref_quantiles)):
lines.append(str(mean_quantiles[i]) + " +- " + str(std_quantiles[i]/sqrt_num_trials))
lines.append("norm of means:")
lines.append(str(mean(norm_of_means)) + " +- " + str(std(norm_of_means)/sqrt_num_trials))
lines.append("acceptance rate:")
lines.append(str(mean(acceptance_rates)) + " +- " + str(std(acceptance_rates)/sqrt_num_trials))
# lines.append("ess dimension 0:")
# lines.append(str(mean(ess_0)) + " +- " + str(std(ess_0)/sqrt_num_trials))
#
# lines.append("ess dimension 1:")
# lines.append(str(mean(ess_1)) + " +- " + str(std(ess_1)/sqrt_num_trials))
#
# lines.append("minimum ess:")
# lines.append(str(mean(ess_minima)) + " +- " + str(std(ess_minima)/sqrt_num_trials))
#
# lines.append("median ess:")
# lines.append(str(mean(ess_medians)) + " +- " + str(std(ess_medians)/sqrt_num_trials))
#
# lines.append("maximum ess:")
# lines.append(str(mean(ess_maxima)) + " +- " + str(std(ess_maxima)/sqrt_num_trials))
lines.append("times:")
lines.append(str(mean(times)) + " +- " + str(std(times)/sqrt_num_trials))
# mean as a function of iterations, normalised by time
step = round((self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin)/5)
iterations = arange(self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin, step=step)
running_means = zeros(len(iterations))
running_errors = zeros(len(iterations))
for i in arange(len(iterations)):
# norm of mean of chain up
norm_of_means_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
norm_of_means_yet[j] = norm(mean(samples_yet, 0))
running_means[i] = mean(norm_of_means_yet)
error_level = 1.96
running_errors[i] = error_level * std(norm_of_means_yet) / sqrt(len(norm_of_means_yet))
ioff()
figure()
plot(iterations, running_means*mean(times))
fill_between(iterations, (running_means - running_errors)*mean(times), \
(running_means + running_errors)*mean(times), hold=True, color="gray")
# make sure path to save exists
try:
os.makedirs(self.experiments[0].experiment_dir)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_Y.txt", \
running_means*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_errors.txt", \
running_errors*mean(times))
# dont produce quantile convergence plots here for now
"""# quantile convergence of a single one
desired_quantile=0.5
running_quantiles=zeros(len(iterations))
running_quantile_errors=zeros(len(iterations))
for i in arange(len(iterations)):
quantiles_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
# just compute one quantile for now
quantiles_yet[j]=self.experiments[j].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(samples_yet, \
array([desired_quantile]))
quantiles_yet[j]=abs(quantiles_yet[j]-desired_quantile)
running_quantiles[i] = mean(quantiles_yet)
error_level = 1.96
running_quantile_errors[i] = error_level * std(quantiles_yet) / sqrt(len(quantiles_yet))
ioff()
figure()
plot(iterations, running_quantiles*mean(times))
fill_between(iterations, (running_quantiles - running_quantile_errors)*mean(times), \
(running_quantiles + running_quantile_errors)*mean(times), hold=True, color="gray")
plot([iterations.min(),iterations.max()], [desired_quantile*mean(times) for _ in range(2)])
title(str(desired_quantile)+"-quantile convergence")
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_Y.txt", \
running_quantiles*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_errors.txt", \
running_quantile_errors*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_reference.txt", \
[desired_quantile*mean(times)])
"""
# add latex table line
# latex_lines = []
# latex_lines.append("Sampler & Acceptance & ESS2 & Norm(mean) & ")
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('%.1f' % self.ref_quantiles[i] + "-quantile")
# if i < len(self.ref_quantiles) - 1:
# latex_lines.append(" & ")
# latex_lines.append("\\\\")
# lines.append("".join(latex_lines))
#
# latex_lines = []
# latex_lines.append(self.experiments[0].mcmc_chain.mcmc_sampler.__class__.__name__)
# latex_lines.append('$%.3f' % mean(acceptance_rates) + " \pm " + '%.3f$' % (std(acceptance_rates)/sqrt_num_trials))
# latex_lines.append('$%.3f' % mean(norm_of_means) + " \pm " + '%.3f$' % (std(norm_of_means)/sqrt_num_trials))
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('$%.3f' % mean_quantiles[i] + " \pm " + '%.3f$' % (std_quantiles[i]/sqrt_num_trials))
#
#
# lines.append(" & ".join(latex_lines) + "\\\\")
return lines
def __process_results__(self):
lines = []
if len(self.experiments) == 0:
lines.append("no experiments to process")
return
# burnin is the same for all chains
burnin = self.experiments[0].mcmc_chain.mcmc_params.burnin
quantiles = zeros((len(self.experiments), len(self.ref_quantiles)))
norm_of_means = zeros(len(self.experiments))
acceptance_rates = zeros(len(self.experiments))
# ess_0 = zeros(len(self.experiments))
# ess_1 = zeros(len(self.experiments))
# ess_minima = zeros(len(self.experiments))
# ess_medians = zeros(len(self.experiments))
# ess_maxima = zeros(len(self.experiments))
times = zeros(len(self.experiments))
for i in range(len(self.experiments)):
burned_in = self.experiments[i].mcmc_chain.samples[burnin:, :]
# use precomputed quantiles if they match with the provided ones
if hasattr(self.experiments[i], "ref_quantiles") and \
hasattr(self.experiments[i], "quantiles") and \
allclose(self.ref_quantiles, self.experiments[i].ref_quantiles):
quantiles[i, :] = self.experiments[i].quantiles
else:
try:
quantiles[i, :] = self.experiments[i].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(\
burned_in, self.ref_quantiles)
except NotImplementedError:
print "skipping quantile computations, distribution does", \
"not support it."
# quantiles should be about average error rather than average quantile
quantiles[i, :] = abs(quantiles[i, :] - self.ref_quantiles)
dim = self.experiments[
i].mcmc_chain.mcmc_sampler.distribution.dimension
norm_of_means[i] = norm(mean(burned_in, 0))
acceptance_rates[i] = mean(
self.experiments[i].mcmc_chain.accepteds[burnin:])
# dump burned in samples to disc
# sample_filename=self.experiments[0].experiment_dir + self.experiments[0].name + "_burned_in.txt"
# savetxt(sample_filename, burned_in)
# store minimum ess for every experiment
#ess_per_covariate = asarray([RCodaTools.ess_coda(burned_in[:, cov_idx]) for cov_idx in range(dim)])
# ess_per_covariate = asarray([0 for _ in range(dim)])
# ess_0=ess_per_covariate[0]
# ess_1=ess_per_covariate[1]
# ess_minima[i] = min(ess_per_covariate)
# ess_medians[i] = median(ess_per_covariate)
# ess_maxima[i] = max(ess_per_covariate)
# save chain time needed
ellapsed = self.experiments[i].mcmc_chain.mcmc_outputs[0].times
times[i] = int(round(sum(ellapsed)))
mean_quantiles = mean(quantiles, 0)
std_quantiles = std(quantiles, 0)
sqrt_num_trials = sqrt(len(self.experiments))
# print median kernel width sigma
#sigma=GaussianKernel.get_sigma_median_heuristic(burned_in.T)
#lines.append("median kernel sigma: "+str(sigma))
lines.append("quantiles:")
for i in range(len(self.ref_quantiles)):
lines.append(
str(mean_quantiles[i]) + " +- " +
str(std_quantiles[i] / sqrt_num_trials))
lines.append("norm of means:")
lines.append(
str(mean(norm_of_means)) + " +- " +
str(std(norm_of_means) / sqrt_num_trials))
lines.append("acceptance rate:")
lines.append(
str(mean(acceptance_rates)) + " +- " +
str(std(acceptance_rates) / sqrt_num_trials))
# lines.append("ess dimension 0:")
# lines.append(str(mean(ess_0)) + " +- " + str(std(ess_0)/sqrt_num_trials))
#
# lines.append("ess dimension 1:")
# lines.append(str(mean(ess_1)) + " +- " + str(std(ess_1)/sqrt_num_trials))
#
# lines.append("minimum ess:")
# lines.append(str(mean(ess_minima)) + " +- " + str(std(ess_minima)/sqrt_num_trials))
#
# lines.append("median ess:")
# lines.append(str(mean(ess_medians)) + " +- " + str(std(ess_medians)/sqrt_num_trials))
#
# lines.append("maximum ess:")
# lines.append(str(mean(ess_maxima)) + " +- " + str(std(ess_maxima)/sqrt_num_trials))
lines.append("times:")
lines.append(
str(mean(times)) + " +- " + str(std(times) / sqrt_num_trials))
# mean as a function of iterations, normalised by time
step = round(
(self.experiments[0].mcmc_chain.mcmc_params.num_iterations -
burnin) / 5)
iterations = arange(
self.experiments[0].mcmc_chain.mcmc_params.num_iterations - burnin,
step=step)
running_means = zeros(len(iterations))
running_errors = zeros(len(iterations))
for i in arange(len(iterations)):
# norm of mean of chain up
norm_of_means_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(
burnin + iterations[i] + 1 + step), :]
norm_of_means_yet[j] = norm(mean(samples_yet, 0))
running_means[i] = mean(norm_of_means_yet)
error_level = 1.96
running_errors[i] = error_level * std(norm_of_means_yet) / sqrt(
len(norm_of_means_yet))
ioff()
figure()
plot(iterations, running_means * mean(times))
fill_between(iterations, (running_means - running_errors)*mean(times), \
(running_means + running_errors)*mean(times), hold=True, color="gray")
# make sure path to save exists
try:
os.makedirs(self.experiments[0].experiment_dir)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
savefig(self.experiments[0].experiment_dir + self.experiments[0].name +
"_running_mean.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_Y.txt", \
running_means*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_mean_errors.txt", \
running_errors*mean(times))
# dont produce quantile convergence plots here for now
"""# quantile convergence of a single one
desired_quantile=0.5
running_quantiles=zeros(len(iterations))
running_quantile_errors=zeros(len(iterations))
for i in arange(len(iterations)):
quantiles_yet = zeros(len(self.experiments))
for j in range(len(self.experiments)):
samples_yet = self.experiments[j].mcmc_chain.samples[burnin:(burnin + iterations[i] + 1 + step), :]
# just compute one quantile for now
quantiles_yet[j]=self.experiments[j].mcmc_chain.mcmc_sampler.distribution.emp_quantiles(samples_yet, \
array([desired_quantile]))
quantiles_yet[j]=abs(quantiles_yet[j]-desired_quantile)
running_quantiles[i] = mean(quantiles_yet)
error_level = 1.96
running_quantile_errors[i] = error_level * std(quantiles_yet) / sqrt(len(quantiles_yet))
ioff()
figure()
plot(iterations, running_quantiles*mean(times))
fill_between(iterations, (running_quantiles - running_quantile_errors)*mean(times), \
(running_quantiles + running_quantile_errors)*mean(times), hold=True, color="gray")
plot([iterations.min(),iterations.max()], [desired_quantile*mean(times) for _ in range(2)])
title(str(desired_quantile)+"-quantile convergence")
savefig(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile.png")
close()
# also store plot X and Y
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_X.txt", \
iterations)
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_Y.txt", \
running_quantiles*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_errors.txt", \
running_quantile_errors*mean(times))
savetxt(self.experiments[0].experiment_dir + self.experiments[0].name + "_running_quantile_reference.txt", \
[desired_quantile*mean(times)])
"""
# add latex table line
# latex_lines = []
# latex_lines.append("Sampler & Acceptance & ESS2 & Norm(mean) & ")
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('%.1f' % self.ref_quantiles[i] + "-quantile")
# if i < len(self.ref_quantiles) - 1:
# latex_lines.append(" & ")
# latex_lines.append("\\\\")
# lines.append("".join(latex_lines))
#
# latex_lines = []
# latex_lines.append(self.experiments[0].mcmc_chain.mcmc_sampler.__class__.__name__)
# latex_lines.append('$%.3f' % mean(acceptance_rates) + " \pm " + '%.3f$' % (std(acceptance_rates)/sqrt_num_trials))
# latex_lines.append('$%.3f' % mean(norm_of_means) + " \pm " + '%.3f$' % (std(norm_of_means)/sqrt_num_trials))
# for i in range(len(self.ref_quantiles)):
# latex_lines.append('$%.3f' % mean_quantiles[i] + " \pm " + '%.3f$' % (std_quantiles[i]/sqrt_num_trials))
#
#
# lines.append(" & ".join(latex_lines) + "\\\\")
return lines
def computeK(self):
self.K = pow(self.K_v*self.K_tau*sqrt(2*self.Rho*self.A_swept)/self.K_t, 2)
def compute_ssim(im1, im2, gaussian_kernel_sigma=1.5, gaussian_kernel_width=11):
"""
The function to compute SSIM
@param im1: PIL Image object
@param im2: PIL Image object
@return: SSIM float value
"""
# 1D Gaussian kernel definition
gaussian_kernel_1d = numpy.ndarray((gaussian_kernel_width))
mu = int(gaussian_kernel_width / 2)
#Fill Gaussian kernel
for i in xrange(gaussian_kernel_width):
gaussian_kernel_1d[i] = (1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) * \
exp(-(((i - mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2)))
# convert the images to grayscale
img_mat_1, img_alpha_1 = _to_grayscale(im1)
img_mat_2, img_alpha_2 = _to_grayscale(im2)
# don't count pixels where both images are both fully transparent
if img_alpha_1 is not None and img_alpha_2 is not None:
img_mat_1[img_alpha_1 == 255] = 0
img_mat_2[img_alpha_2 == 255] = 0
#Squares of input matrices
img_mat_1_sq = img_mat_1 ** 2
img_mat_2_sq = img_mat_2 ** 2
img_mat_12 = img_mat_1 * img_mat_2
#Means obtained by Gaussian filtering of inputs
img_mat_mu_1 = convolve_gaussian_2d(img_mat_1, gaussian_kernel_1d)
img_mat_mu_2 = convolve_gaussian_2d(img_mat_2, gaussian_kernel_1d)
#Squares of means
img_mat_mu_1_sq = img_mat_mu_1 ** 2
img_mat_mu_2_sq = img_mat_mu_2 ** 2
img_mat_mu_12 = img_mat_mu_1 * img_mat_mu_2
#Variances obtained by Gaussian filtering of inputs' squares
img_mat_sigma_1_sq = convolve_gaussian_2d(img_mat_1_sq, gaussian_kernel_1d)
img_mat_sigma_2_sq = convolve_gaussian_2d(img_mat_2_sq, gaussian_kernel_1d)
#Covariance
img_mat_sigma_12 = convolve_gaussian_2d(img_mat_12, gaussian_kernel_1d)
#Centered squares of variances
img_mat_sigma_1_sq -= img_mat_mu_1_sq
img_mat_sigma_2_sq -= img_mat_mu_2_sq
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
#set k1,k2 & c1,c2 to depend on L (width of color map)
l = 255
k_1 = 0.01
c_1 = (k_1 * l) ** 2
k_2 = 0.03
c_2 = (k_2 * l) ** 2
#Numerator of SSIM
num_ssim = (2 * img_mat_mu_12 + c_1) * (2 * img_mat_sigma_12 + c_2)
#Denominator of SSIM
den_ssim = (img_mat_mu_1_sq + img_mat_mu_2_sq + c_1) * \
(img_mat_sigma_1_sq + img_mat_sigma_2_sq + c_2)
#SSIM
ssim_map = num_ssim / den_ssim
index = numpy.average(ssim_map)
return index
def get_sigma_median_heuristic(X):
dists=squareform(pdist(X, 'euclidean'))
median_dist=median(dists[dists>0])
sigma=sqrt(0.5*median_dist)
return sigma
def compute_ssim(self, im1, im2):
"""
The function to compute SSIM
@param im1: PIL Image object, or grayscale ndarray
@param im2: PIL Image object, or grayscale ndarray
@return: SSIM float value
"""
# 1D Gaussian kernel definition
gaussian_kernel_2d = np.ndarray((self.gaussian_kernel_width))
mu = int(self.gaussian_kernel_width / 2)
#Fill Gaussian kernel
for i in xrange(self.gaussian_kernel_width):
gaussian_kernel_2d[i] = (1 / (sqrt(2 * pi) * (self.gaussian_kernel_sigma))) * \
exp(-(((i - mu) ** 2)) / (2 * (self.gaussian_kernel_sigma ** 2)))
gshape = (self.gaussian_kernel_width, self.gaussian_kernel_width)
gsigma = (self.gaussian_kernel_sigma, self.gaussian_kernel_sigma)
gaussian_kernel_2d = gaussian(shape=gshape, sigma=gsigma)
# pdb.set_trace()
# convert the images to grayscale
if im1.__class__.__name__ == 'Image':
img_mat_1, img_alpha_1 = _to_grayscale(im1)
# don't count pixels where both images are both fully transparent
#if img_alpha_1 is not None:
#img_mat_1[img_alpha_1 == 255] = 0
else:
img_mat_1 = im1
if im2.__class__.__name__ == 'Image':
img_mat_2, img_alpha_2 = _to_grayscale(im2)
#if img_alpha_2 is not None:
#img_mat_2[img_alpha_2 == 255] = 0
else:
img_mat_2 = im2
#Squares of input matrices
img_mat_1_sq = img_mat_1**2
img_mat_2_sq = img_mat_2**2
img_mat_12 = img_mat_1 * img_mat_2
#Means obtained by Gaussian filtering of inputs
img_mat_mu_1 = self.convolve_gaussian_2d(img_mat_1, gaussian_kernel_2d)
img_mat_mu_2 = self.convolve_gaussian_2d(img_mat_2, gaussian_kernel_2d)
#Squares of means
img_mat_mu_1_sq = img_mat_mu_1**2
img_mat_mu_2_sq = img_mat_mu_2**2
img_mat_mu_12 = img_mat_mu_1 * img_mat_mu_2
#Variances obtained by Gaussian filtering of inputs' squares
img_mat_sigma_1_sq = self.convolve_gaussian_2d(img_mat_1_sq,
gaussian_kernel_2d)
img_mat_sigma_2_sq = self.convolve_gaussian_2d(img_mat_2_sq,
gaussian_kernel_2d)
#Covariance
img_mat_sigma_12 = self.convolve_gaussian_2d(img_mat_12,
gaussian_kernel_2d)
#Centered squares of variances
img_mat_sigma_1_sq -= img_mat_mu_1_sq
img_mat_sigma_2_sq -= img_mat_mu_2_sq
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
#set k1,k2 & c1,c2 to depend on L (width of color map)
#l = 255
k_1 = self.K[0]
c_1 = (k_1 * self.L)**2
k_2 = self.K[1]
c_2 = (k_2 * self.L)**2
#Numerator of SSIM
num_ssim = (2 * img_mat_mu_12 + c_1) * (2 * img_mat_sigma_12 + c_2)
#Denominator of SSIM
den_ssim = (img_mat_mu_1_sq + img_mat_mu_2_sq + c_1) * \
(img_mat_sigma_1_sq + img_mat_sigma_2_sq + c_2)
#SSIM
ssim_map = num_ssim / den_ssim
index = np.average(ssim_map)
return index
def write_dispersion_diff_files(path, twiss_cor, twiss_no):
try:
twiss_getdx = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getDx.out'))
file_dx = open(os.path.join(path, "dx.out"), "w")
print >> file_dx, "* NAME S MEA ERROR MODEL"
print >> file_dx, "$ %s %le %le %le %le"
for i in range(len(twiss_getdx.NAME)):
bpm_name = twiss_getdx.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_dx, bpm_name, twiss_getdx.S[i], twiss_getdx.DX[i] - twiss_getdx.DXMDL[i], twiss_getdx.STDDX[i], twiss_cor.DX[j] - twiss_no.DX[j]
file_dx.close()
except IOError:
print "NO dispersion"
except AttributeError:
print "Empty table in getDx.out?! NO dispersion"
try:
twiss_getdy = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getDy.out'))
file_dy = open(os.path.join(path, "dy.out"), "w")
print >> file_dy, "* NAME S MEA ERROR MODEL"
print >> file_dy, "$ %s %le %le %le %le"
for i in range(len(twiss_getdy.NAME)):
bpm_name = twiss_getdy.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_dy, bpm_name, twiss_getdy.S[i], twiss_getdy.DY[i] - twiss_getdy.DYMDL[i], twiss_getdy.STDDY[i], twiss_cor.DY[j] - twiss_no.DY[j]
file_dy.close()
except IOError:
print "NO dispersion."
except AttributeError:
print "Empty table in getDy.out?! NO dispersion"
try:
twiss_getndx = Python_Classes4MAD.metaclass.twiss(os.path.join(path, 'getNDx.out'))
file_ndx = open(os.path.join(path, "ndx.out"), "w")
print >> file_ndx, "* NAME S MEA ERROR MODEL"
print >> file_ndx, "$ %s %le %le %le %le"
for i in range(len(twiss_getndx.NAME)):
bpm_name = twiss_getndx.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_ndx, bpm_name, twiss_getndx.S[i], twiss_getndx.NDX[i] - twiss_getndx.NDXMDL[i], twiss_getndx.STDNDX[i], (twiss_cor.DX[j] / sqrt(twiss_cor.BETX[j])) - (twiss_no.DX[j] / sqrt(twiss_no.BETX[j]))
file_ndx.close()
except IOError:
print "NO normalized dispersion"
except AttributeError:
print "Empty table in getNDx.out?! NO normalized dispersion"
def update(self, mcmc_chain, step_output):
if mcmc_chain.iteration > self.plot_from and mcmc_chain.iteration%self.lag==0:
if mcmc_chain.mcmc_sampler.distribution.dimension==2:
subplot(2, 3, 1)
if self.distribution is not None:
Visualise.plot_array(self.Xs, self.Ys, self.P)
# only plot a number of random samples otherwise this is too slow
if self.num_samples_plot>0:
num_plot=min(mcmc_chain.iteration-1,self.num_samples_plot)
indices=permutation(mcmc_chain.iteration)[:num_plot]
else:
num_plot=mcmc_chain.iteration-1
indices=arange(num_plot)
samples=mcmc_chain.samples[0:mcmc_chain.iteration]
samples_to_plot=mcmc_chain.samples[indices]
# still plot all likelihoods
likelihoods=mcmc_chain.log_liks[0:mcmc_chain.iteration]
likelihoods_to_plot=mcmc_chain.log_liks[indices]
proposal_1d=step_output.proposal_object.samples[0,:]
y = samples[len(samples) - 1]
# plot samples, coloured by likelihood, or just connect
if self.colour_by_likelihood:
likelihoods_to_plot=likelihoods_to_plot.copy()
likelihoods_to_plot=likelihoods_to_plot-likelihoods_to_plot.min()
likelihoods_to_plot=likelihoods_to_plot/likelihoods_to_plot.max()
cm=get_cmap("jet")
for i in range(len(samples_to_plot)):
color = cm(likelihoods_to_plot[i])
plot(samples_to_plot[i,0], samples_to_plot[i,1] ,"o",
color=color, zorder=1)
else:
plot(samples_to_plot[:,0], samples_to_plot[:,1], "m", zorder=1)
plot(y[0], y[1], 'r*', markersize=15.0)
plot(proposal_1d[0], proposal_1d[1], 'y*', markersize=15.0)
if self.distribution is not None:
Visualise.contour_plot_density(mcmc_chain.mcmc_sampler.Q, self.Xs, \
self.Ys, log_domain=False)
else:
Visualise.contour_plot_density(mcmc_chain.mcmc_sampler.Q)
# axis('equal')
xlabel("$x_1$")
ylabel("$x_2$")
if self.num_samples_plot>0:
title(str(self.num_samples_plot) + " random samples")
subplot(2, 3, 2)
plot(samples[:, 0], 'b')
title("Trace $x_1$")
subplot(2, 3, 3)
plot(samples[:, 1], 'b')
title("Trace $x_2$")
subplot(2, 3, 4)
plot(mcmc_chain.log_liks[0:mcmc_chain.iteration], 'b')
title("Log-likelihood")
if len(samples) > 2:
subplot(2, 3, 5)
hist(samples[:, 0])
title("Histogram $x_1$")
subplot(2, 3, 6)
hist(samples[:, 1])
title("Histogram $x_2$")
else:
# if target dimension is not two, plot traces
num_plots=mcmc_chain.mcmc_sampler.distribution.dimension
samples=mcmc_chain.samples[0:mcmc_chain.iteration]
likelihoods=mcmc_chain.log_liks[0:mcmc_chain.iteration]
num_y=round(sqrt(num_plots))
num_x=num_plots/num_y+1
for i in range(num_plots):
subplot(num_y, num_x, i+1)
plot(samples[:, i], 'b')
title("Trace $x_" +str(i) + "$")
subplot(num_y, num_x, num_plots+1)
plot(likelihoods)
title("Log-Likelihood")
suptitle(mcmc_chain.mcmc_sampler.__class__.__name__)
show()
draw()
clf()
def write_dispersion_diff_files(path, twiss_cor, twiss_no):
try:
twiss_getdx = Python_Classes4MAD.metaclass.twiss(
os.path.join(path, 'getDx.out'))
file_dx = open(os.path.join(path, "dx.out"), "w")
print >> file_dx, "* NAME S MEA ERROR MODEL"
print >> file_dx, "$ %s %le %le %le %le"
for i in range(len(twiss_getdx.NAME)):
bpm_name = twiss_getdx.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[
twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_dx, bpm_name, twiss_getdx.S[i], twiss_getdx.DX[
i] - twiss_getdx.DXMDL[i], twiss_getdx.STDDX[
i], twiss_cor.DX[j] - twiss_no.DX[j]
file_dx.close()
except IOError:
print "NO dispersion"
except AttributeError:
print "Empty table in getDx.out?! NO dispersion"
try:
twiss_getdy = Python_Classes4MAD.metaclass.twiss(
os.path.join(path, 'getDy.out'))
file_dy = open(os.path.join(path, "dy.out"), "w")
print >> file_dy, "* NAME S MEA ERROR MODEL"
print >> file_dy, "$ %s %le %le %le %le"
for i in range(len(twiss_getdy.NAME)):
bpm_name = twiss_getdy.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[
twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_dy, bpm_name, twiss_getdy.S[i], twiss_getdy.DY[
i] - twiss_getdy.DYMDL[i], twiss_getdy.STDDY[
i], twiss_cor.DY[j] - twiss_no.DY[j]
file_dy.close()
except IOError:
print "NO dispersion."
except AttributeError:
print "Empty table in getDy.out?! NO dispersion"
try:
twiss_getndx = Python_Classes4MAD.metaclass.twiss(
os.path.join(path, 'getNDx.out'))
file_ndx = open(os.path.join(path, "ndx.out"), "w")
print >> file_ndx, "* NAME S MEA ERROR MODEL"
print >> file_ndx, "$ %s %le %le %le %le"
for i in range(len(twiss_getndx.NAME)):
bpm_name = twiss_getndx.NAME[i]
bpm_included = True
try:
check = twiss_cor.NAME[
twiss_cor.indx[bpm_name]] # @UnusedVariable
except:
print "No ", bpm_name
bpm_included = False
if bpm_included:
j = twiss_cor.indx[bpm_name]
print >> file_ndx, bpm_name, twiss_getndx.S[
i], twiss_getndx.NDX[i] - twiss_getndx.NDXMDL[
i], twiss_getndx.STDNDX[i], (twiss_cor.DX[j] / sqrt(
twiss_cor.BETX[j])) - (twiss_no.DX[j] /
sqrt(twiss_no.BETX[j]))
file_ndx.close()
except IOError:
print "NO normalized dispersion"
except AttributeError:
print "Empty table in getNDx.out?! NO normalized dispersion"
def incomplete_cholesky(X, kernel, eta, power=1, blocksize=100):
"""
Computes the incomplete Cholesky factorisation of the kernel matrix defined
by samples X and a given kernel. The kernel is evaluated on-the-fly.
The optional power parameter is used to multiply the kernel output with
itself.
Original code from "Kernel Methods for Pattern Analysis" by Shawe-Taylor and
Cristianini.
Modified to compute kernel on the fly, to use kernels multiplied with
themselves (tensor product), and optimised speed via using vector
operations and not pre-allocate full kernel matrix memory, but rather
allocate memory of low-rank kernel block-wise
Changes by Heiko Strathmann
parameters:
X - list of input vectors to evaluate kernel on
kernel - a kernel object with a kernel method that takes 2d-arrays
and returns a psd kernel matrix
eta - precision cutoff parameter for the low-rank approximation.
Lies is (0,1) where smaller means more accurate.
power - every kernel evaluation is multiplied with itself this number
of times. Zero is supported
blocksize - tuning parameter for speed, determines how rows elements are
allocated in a block for the (growing) kernel matrix. Larger
means faster algorithm (to some extend if low rank dimension
is larger than blocksize)
output:
K_chol, ell, I, R, W, where
K - is the kernel using only the pivot index features
I - is a list containing the pivots used to compute K_chol
R - is a low-rank factor such that R.T.dot(R) approximates the
original K
W - is a matrix such that W.T.dot(K_chol.dot(W)) approximates the
original K
"""
assert(eta>0 and eta<1)
assert(power>=0)
assert(blocksize>=0)
assert(len(X)>=0)
m=len(X)
# growing low rank basis
R=zeros((blocksize,m))
# diagonal (assumed to be one)
d=ones(m)
# used indices
I=[]
nu=[]
# algorithm is executed as long as a is bigger than eta precision
a=d.max()
I.append(d.argmax())
# growing set of evaluated kernel values
K=zeros((blocksize,m))
j=0
while a>eta:
nu.append(sqrt(a))
if power>=1:
K[j,:]=kernel.kernel([X[I[j]]], X)**power
else:
K[j,:]=ones(m)
if j==0:
R_dot_j=0
elif j==1:
R_dot_j=R[:j,:]*R[:j,I[j]]
else:
R_dot_j=R[:j,:].T.dot(R[:j,I[j]])
R[j,:]=(K[j,:] - R_dot_j)/nu[j]
d=d-R[j,:]**2
a=d.max()
I.append(d.argmax())
j=j+1
# allocate more space for kernel
if j>=len(K):
K=vstack((K, zeros((blocksize,m))))
R=vstack((R, zeros((blocksize,m))))
# remove un-used rows which were located unnecessarily
K=K[:j,:]
R=R[:j,:]
# remove list pivot index since it is not used
I=I[:-1]
# from low rank to full rank
W=solve(R[:,I], R)
# low rank K
K_chol=K[:,I]
return K_chol, I, R, W
def bfield_mag(x, y, z, w):
'''Magntitude of b field at the given point from the wires.'''
bx, by, bz = bfield(x, y, z, w)
return sqrt(bx**2 + by**2 + bz**2)
def compute_ssim(im1, im2, gaussian_kernel_sigma=1.5, gaussian_kernel_width=11):
"""
The function to compute SSIM
@param im1: PIL Image object
@param im2: PIL Image object
@return: SSIM float value
"""
# 1D Gaussian kernel definition
gaussian_kernel_1d = numpy.ndarray((gaussian_kernel_width))
mu = int(gaussian_kernel_width / 2)
#Fill Gaussian kernel
for i in xrange(gaussian_kernel_width):
gaussian_kernel_1d[i] = (1 / (sqrt(2 * pi) * (gaussian_kernel_sigma))) * \
exp(-(((i - mu) ** 2)) / (2 * (gaussian_kernel_sigma ** 2)))
# convert the images to grayscale
img_mat_1, img_alpha_1 = _to_grayscale(im1)
img_mat_2, img_alpha_2 = _to_grayscale(im2)
# don't count pixels where both images are both fully transparent
if img_alpha_1 is not None and img_alpha_2 is not None:
img_mat_1[img_alpha_1 == 255] = 0
img_mat_2[img_alpha_2 == 255] = 0
#Squares of input matrices
img_mat_1_sq = img_mat_1 ** 2
img_mat_2_sq = img_mat_2 ** 2
img_mat_12 = img_mat_1 * img_mat_2
#Means obtained by Gaussian filtering of inputs
img_mat_mu_1 = convolve_gaussian_2d(img_mat_1, gaussian_kernel_1d)
img_mat_mu_2 = convolve_gaussian_2d(img_mat_2, gaussian_kernel_1d)
#Squares of means
img_mat_mu_1_sq = img_mat_mu_1 ** 2
img_mat_mu_2_sq = img_mat_mu_2 ** 2
img_mat_mu_12 = img_mat_mu_1 * img_mat_mu_2
#Variances obtained by Gaussian filtering of inputs' squares
img_mat_sigma_1_sq = convolve_gaussian_2d(img_mat_1_sq, gaussian_kernel_1d)
img_mat_sigma_2_sq = convolve_gaussian_2d(img_mat_2_sq, gaussian_kernel_1d)
#Covariance
img_mat_sigma_12 = convolve_gaussian_2d(img_mat_12, gaussian_kernel_1d)
#Centered squares of variances
img_mat_sigma_1_sq -= img_mat_mu_1_sq
img_mat_sigma_2_sq -= img_mat_mu_2_sq
img_mat_sigma_12 = img_mat_sigma_12 - img_mat_mu_12
#set k1,k2 & c1,c2 to depend on L (width of color map)
l = 255
k_1 = 0.01
c_1 = (k_1 * l) ** 2
k_2 = 0.03
c_2 = (k_2 * l) ** 2
#Numerator of SSIM
num_ssim = (2 * img_mat_mu_12 + c_1) * (2 * img_mat_sigma_12 + c_2)
#Denominator of SSIM
den_ssim = (img_mat_mu_1_sq + img_mat_mu_2_sq + c_1) * \
(img_mat_sigma_1_sq + img_mat_sigma_2_sq + c_2)
#SSIM
ssim_map = num_ssim / den_ssim
index = numpy.average(ssim_map)
return index
def norm (obj):
return sqrt(obj.x*obj.x + obj.y*obj.y + obj.z*obj.z)
sampler_names_short = ["SM","AM-FS","AM-LS","KAMH-LS"]
sampler_names = ["StandardMetropolis","AdaptiveMetropolis","AdaptiveMetropolisLearnScale","KameleonWindowLearnScale"]
colours = ['blue', 'red', 'magenta', 'green']
ii=0
for sampler_name in sampler_names:
filename = directory+sampler_name+"_mmds.bin"
f = open(filename,"r")
upto, mmds, mean_dist = load(f)
trials=shape(mean_dist)[1]
figure(1)
if which_plot == "mean":
stds = std(mean_dist,1)/sqrt(trials)
means = mean(mean_dist,1)
if which_plot == "mmd":
stds = std(mmds,1)/sqrt(trials)
means = mean(mmds,1)
zscore=1.28
yerr = zscore*stds
if highlight == "SM":
condition = sampler_name == "StandardMetropolis"
elif highlight == "AM":
condition = sampler_name == "AdaptiveMetropolis" or sampler_name == "AdaptiveMetropolisLearnScale"
elif highlight == "KAMH":
condition = sampler_name == "KameleonWindowLearnScale"
else:
condition = True
def find_mode_newton(self, return_full=False):
"""
Newton search for mode of p(y|f)p(f)
from GP book, algorithm 3.1, added step size
"""
K = self.gp.K
if self.newton_start is None:
f = zeros(len(K))
else:
f = self.newton_start
if return_full:
steps = [f]
iteration = 0
norm_difference = inf
objective_value = -inf
while iteration < self.newton_max_iterations and norm_difference > self.newton_epsilon:
# from GP book, algorithm 3.1, added step size
# scale log_lik_grad_vector and K^-1 f = a
w = -self.gp.likelihood.log_lik_hessian_vector(self.gp.y, f)
w_sqrt = sqrt(w)
# diag(w_sqrt).dot(K.dot(diag(w_sqrt))) == (K.T*w_sqrt).T*w_sqrt
L = cholesky(eye(len(K)) + (K.T * w_sqrt).T * w_sqrt)
b = f * w + self.newton_step * \
self.gp.likelihood.log_lik_grad_vector(self.gp.y, f)
# a=b-diag(w_sqrt).dot(inv(eye(len(K)) + (K.T*w_sqrt).T*w_sqrt).dot(diag(w_sqrt).dot(K.dot(b))))
a = (w_sqrt * (K.dot(b)))
a = solve_triangular(L, a, lower=True)
a = solve_triangular(L.T, a, lower=False)
a = w_sqrt * a
a = b - a
f_new = K.dot(self.newton_step * a)
# convergence stuff and next iteration
objective_value_new = -0.5 * a.T.dot(f) + \
sum(self.gp.likelihood.log_lik_vector(self.gp.y, f))
norm_difference = norm(f - f_new)
if objective_value_new > objective_value:
f = f_new
if return_full:
steps.append(f)
else:
self.newton_step /= 2
iteration += 1
objective_value = objective_value_new
self.computed = True
if return_full:
return f, L, asarray(steps)
else:
return f
def fixed_data(self):
"""
Uses some fixed data from the ToyModel and pre-computes all matrices.
Results are asserted to be correct (against Matlab implementation) and
output files can be used to test the KernelBP implementation.
"""
model = ToyModel()
graph = model.get_moralised_graph()
# one observation at node 4
observations = {4: 0.0}
# directed edges for kernel BP implementation
edges = model.extract_edges(observations)
print "graph:", graph
print "observations:", observations
print "edges:", edges
# we sample the data jointly, so edges will share data along vertices
joint_data = {}
joint_data[1] = [-0.274722354853981, 0.044011207316815, 0.073737451640458]
joint_data[2] = [-0.173264814517908, 0.213918664844409, 0.123246012188621]
joint_data[3] = [-0.348879413536605, -0.081766464397055, -0.117171083361484]
joint_data[4] = [-0.014012058355118, -0.145789276405117, -0.317649695308685]
joint_data[5] = [-0.291794859908481, 0.260902212951398, -0.276258182225143]
# generate data in format that works for the dense matrix class, i.e., a pair of
# points for every edge
data = {}
for edge in edges:
# only sample once per undirected edge
inverse_edge = (edge[1], edge[0])
data[edge] = (joint_data[edge[0]], joint_data[edge[1]])
data[inverse_edge] = (joint_data[edge[1]], joint_data[edge[0]])
# Gaussian kernel used in matlab files
kernel = GaussianKernel(sigma=sqrt(0.15))
# use the example class for dense matrix data that can be stored in memory
precomputer = PrecomputeDenseMatrixKernelBP(
graph, edges, data, observations, kernel, reg_lambda=0.1, output_filename=self.output_filename
)
precomputer.precompute()
# go through all the files and make sure they contain the correct matrices
# files created by matlab implementation
filenames = [
"1->2->3_non_obs_kernel.txt",
"1->2->4_non_obs_kernel.txt",
"1->3->2_non_obs_kernel.txt",
"1->3->4_non_obs_kernel.txt",
"1->3->5_non_obs_kernel.txt",
"2->1->3_non_obs_kernel.txt",
"2->3->1_non_obs_kernel.txt",
"2->3->4_non_obs_kernel.txt",
"2->3->5_non_obs_kernel.txt",
"2->4->3_non_obs_kernel.txt",
"3->1->2_non_obs_kernel.txt",
"3->2->1_non_obs_kernel.txt",
"3->2->4_non_obs_kernel.txt",
"3->4->2_non_obs_kernel.txt",
"4->2->1_non_obs_kernel.txt",
"4->2->3_non_obs_kernel.txt",
"4->3->1_non_obs_kernel.txt",
"4->3->2_non_obs_kernel.txt",
"4->3->5_non_obs_kernel.txt",
"5->3->1_non_obs_kernel.txt",
"5->3->2_non_obs_kernel.txt",
"5->3->4_non_obs_kernel.txt",
"3->4_obs_kernel.txt",
"2->4_obs_kernel.txt",
"1->2_L_s.txt",
"1->3_L_s.txt",
"2->1_L_s.txt",
"2->3_L_s.txt",
"2->4_L_s.txt",
"2->4_L_t.txt",
"3->1_L_s.txt",
"3->2_L_s.txt",
"3->4_L_s.txt",
"3->4_L_t.txt",
"3->5_L_s.txt",
"5->3_L_s.txt",
]
# from matlab implementation
matrices = {
"2->1->3_non_obs_kernel.txt": asarray(
[[1.000000, 0.712741, 0.667145], [0.712741, 1.000000, 0.997059], [0.667145, 0.997059, 1.000000]]
),
"3->1->2_non_obs_kernel.txt": asarray(
[[1.000000, 0.712741, 0.667145], [0.712741, 1.000000, 0.997059], [0.667145, 0.997059, 1.000000]]
),
"1->2->3_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"1->2->4_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"3->2->1_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"3->2->4_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"4->2->1_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"4->2->3_non_obs_kernel.txt": asarray(
[[1.000000, 0.606711, 0.745976], [0.606711, 1.000000, 0.972967], [0.745976, 0.972967, 1.000000]]
),
"1->3->2_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"1->3->4_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"1->3->5_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"2->3->1_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"2->3->4_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"2->3->5_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"4->3->1_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"4->3->2_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"4->3->5_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"5->3->1_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"5->3->2_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"5->3->4_non_obs_kernel.txt": asarray(
[[1.000000, 0.788336, 0.836137], [0.788336, 1.000000, 0.995830], [0.836137, 0.995830, 1.000000]]
),
"2->4->3_non_obs_kernel.txt": asarray(
[[1.000000, 0.943759, 0.735416], [0.943759, 1.000000, 0.906238], [0.735416, 0.906238, 1.000000]]
),
"3->4->2_non_obs_kernel.txt": asarray(
[[1.000000, 0.943759, 0.735416], [0.943759, 1.000000, 0.906238], [0.735416, 0.906238, 1.000000]]
),
"2->4_obs_kernel.txt": asarray([[0.999346], [0.931603], [0.714382]]),
"2->4_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.578476, 0.874852, 0.000000], [0.711260, 0.641846, 0.426782]]
),
"2->4_L_t.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.899839, 0.538785, 0.000000], [0.701191, 0.510924, 0.589311]]
),
"3->4_obs_kernel.txt": asarray([[0.999346], [0.931603], [0.714382]]),
"3->4_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.751649, 0.731453, 0.000000], [0.797226, 0.542204, 0.412852]]
),
"3->4_L_t.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.899839, 0.538785, 0.000000], [0.701191, 0.510924, 0.589311]]
),
"2->1_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.578476, 0.874852, 0.000000], [0.711260, 0.641846, 0.426782]]
),
"3->1_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.751649, 0.731453, 0.000000], [0.797226, 0.542204, 0.412852]]
),
"1->2_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.679572, 0.798863, 0.000000], [0.636098, 0.706985, 0.442211]]
),
"3->2_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.751649, 0.731453, 0.000000], [0.797226, 0.542204, 0.412852]]
),
"1->3_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.679572, 0.798863, 0.000000], [0.636098, 0.706985, 0.442211]]
),
"2->3_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.578476, 0.874852, 0.000000], [0.711260, 0.641846, 0.426782]]
),
"5->3_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.344417, 0.990645, 0.000000], [0.952696, 0.054589, 0.435191]]
),
"3->5_L_s.txt": asarray(
[[1.048809, 0.000000, 0.000000], [0.751649, 0.731453, 0.000000], [0.797226, 0.542204, 0.412852]]
),
}
assert len(filenames) == len(matrices)
for filename in filenames:
self.assertTrue(self.assert_file_matrix(self.output_folder + filename, matrices[filename]))
