def posterior_mean_and_cov(plot_data, curve_xs):
zero_func = VentureFunction(lambda x: 0, sp_type = sp.SPType([], t.NumberType()))
prior_mean = zero_func
prior_cov = gpexample_plugin.squared_exponential(plot_data.sigma, plot_data.l)
return gp_conditional.conditional_mean_and_cov(curve_xs, prior_mean,
prior_cov, plot_data.Xseen, plot_data.Yseen)
def __call__(self, inferrer, f_, curve_xs_, true_ys_, scatter_xs_, scatter_ys_, circles_, user_prefix_):
# Some of this is hackish
aux = [map(getNumber, p.getArray()) for p in fromStackDict(f_[0]["aux"]).getArray()]
f = f_[0]["value"]
prior_mean = f.mean
prior_cov = f.covariance
if len(aux) > 0:
Xseen, Yseen = zip(*aux)
else:
Xseen, Yseen = [], []
curve_xs = map(getNumber, fromStackDict(curve_xs_[0]).getArray())
true_ys = map(getNumber, fromStackDict(true_ys_[0]).getArray())
user_prefix = fromStackDict(user_prefix_[0]).getString()
scatter_xs = map(getNumber, fromStackDict(scatter_xs_[0]).getArray())
scatter_ys = map(getNumber, fromStackDict(scatter_ys_[0]).getArray())
circles = map(lambda a: map(getNumber, a), map(getArray, fromStackDict(circles_[0]).getArray()))
mean, cov = gp_conditional.conditional_mean_and_cov(curve_xs, prior_mean, prior_cov, Xseen, Yseen)
fig, ax = plt.subplots(1)
ax.set_xlabel("a")
ax.set_ylabel("r(a)").set_rotation(0)
ax.scatter(scatter_xs, scatter_ys, color="k", s=15)
ax.set_xlim(min(curve_xs), max(curve_xs))
for i in range(100):
ys = np.random.multivariate_normal(mean, cov)
ax.plot(curve_xs, ys, c="red", alpha=0.2, linewidth=2)
if len(true_ys) > 0:
ax.plot(curve_xs, true_ys, c="blue")
for ccoords in circles:
(x, y) = ccoords[0:2]
def to_hex_color(n):
h = hex(int(n))[2:]
padded = (6 - len(h)) * "0" + h
return clr.hex2color("#" + padded)
color = to_hex_color(ccoords[2]) if len(ccoords) > 2 else "green"
xsize, ysize = ccoords[3:5] if len(ccoords) > 3 else (0.4, 0.33)
circle = Ellipse([x, y], xsize, ysize, color=color, linewidth=5, fill=False)
circle.set_zorder(10)
ax.add_artist(circle)
date_fmt = "%Y%m%d_%H%M%S"
directory = "draw_gp_curves_callback"
def j(fname):
return os.path.join(directory, fname)
output_prefix = "%s_%s" % (user_prefix, datetime.now().strftime(date_fmt))
scatterpath = j("%s_scatter.pkl" % (output_prefix,))
print "Logging scatter data to %s" % (scatterpath,)
scatter_data = {"scatter_xs": scatter_xs, "scatter_ys": scatter_ys}
with open(scatterpath, "wb") as f:
pickle.dump(scatter_data, f)
print "Outputting to %s.png" % (j(output_prefix),)
fig.savefig("%s.png" % (j(output_prefix),), dpi=fig.dpi, bbox_inches="tight")
print "Done."
