def plot_mixture(dists, weights, hi=1, lo=-1, support_thresh=0.1):
"""
"""
fig = plt.figure()
ax = fig.add_subplot(111)
def mix_pdf(x):
tot = 0
for name, dist in dists.iteritems():
tot += weights[name] * dist.pdf(x)
return tot
# plot mixture
mix = plotting.plot_support(mix_pdf,
lo,
hi,
thresh=support_thresh,
style='k',
ax=ax)
for _, dist in dists.iteritems():
comp = plotting.plot_support(dist.pdf,
lo,
hi,
thresh=support_thresh,
style='k--',
ax=ax)
ax.legend((mix, comp), ('Mixture', 'Component'))
def plot_forecast(self, x, ax=None, label=None, **kwds):
"""
Plot posterior predictive distribution for input predictor values
y ~ N(Xb, s^2)
Parameters
----------
x : 1d
"""
if ax is None:
ax = plt.gca()
mode, scale = self.beta_post_params
s2 = self.sigma2
# forecast variance
rQ = np.sqrt(s2 * (np.dot(x, np.dot(scale, x)) + 1))
m = np.dot(x, self.params)
dist = stats.norm(m, scale=rQ)
plotting.plot_support(dist.pdf, m - 5 * rQ, m + 5 * rQ, ax=ax)
if label is not None:
y = dist.pdf(m)
x = m
ax.text(x, y, label, **kwds)
def plot_mixture(dists, weights, hi=1, lo=-1, support_thresh=0.1):
"""
"""
fig = plt.figure()
ax = fig.add_subplot(111)
def mix_pdf(x):
tot = 0
for name, dist in dists.iteritems():
tot += weights[name] * dist.pdf(x)
return tot
# plot mixture
mix = plotting.plot_support(mix_pdf, lo, hi,
thresh=support_thresh,
style='k',
ax=ax)
for _, dist in dists.iteritems():
comp = plotting.plot_support(dist.pdf, lo, hi,
thresh=support_thresh,
style='k--',
ax=ax)
ax.legend((mix, comp), ('Mixture', 'Component'))
def plot_forecast(self, x, ax=None, label=None, **kwds):
"""
Plot posterior predictive distribution for input predictor values
y ~ N(Xb, s^2)
Parameters
----------
x : 1d
"""
if ax is None:
ax = plt.gca()
mode, scale = self.beta_post_params
s2 = self.sigma2
# forecast variance
rQ = np.sqrt(s2 * (np.dot(x, np.dot(scale, x)) + 1))
m = np.dot(x, self.params)
dist = stats.norm(m, scale=rQ)
plotting.plot_support(dist.pdf, m - 5 * rQ, m + 5 * rQ, ax=ax)
if label is not None:
y = dist.pdf(m)
x = m
ax.text(x, y, label, **kwds)
def plot(self, style='k', ax=None, **plot_kwds):
max_sd = np.sqrt(self.variances.max())
# overkill perhaps
lo = self.means.min() - 4 * max_sd
hi = self.means.max() + 4 * max_sd
if ax is None:
ax = plt.gca()
plotting.plot_support(self.pdf, lo, hi, style=style, **plot_kwds)
def plot(self, style='k', ax=None, **plot_kwds):
max_sd = np.sqrt(self.variances.max())
# overkill perhaps
lo = self.means.min() - 4 * max_sd
hi = self.means.max() + 4 * max_sd
if ax is None:
ax = plt.gca()
plotting.plot_support(self.pdf, lo, hi, style=style,
**plot_kwds)
