def plot_GP_envelopes(f, x, HPD=[
.25, .5, .95], transx=None, transy=None):
"""
plot_GP_envelopes(f, x[, HPD, transx, transy])
Plots centered posterior probability envelopes for f, which is a GP instance,
which is a function of one variable.
:Arguments:
- `f`: A GaussianProcess object.
- `x`: The mesh on which to plot the envelopes.
- `HPD`: A list of values between 0 and 1 giving the probability mass
contained in each envelope.
- `transx`: Any transformation of the x-axis.
- `transy`: Any transformation of the y-axis.
"""
try:
from pymc.Matplot import centered_envelope
f_trace = f.trace()
x = x.ravel()
N = len(f_trace)
func_stacks = np.zeros((N, len(x)), dtype=float)
def identity(y):
return y
if transy is None:
transy = identity
if transx is None:
transx = identity
# Get evaluations
for i in range(N):
f = copy(f_trace[i])
func_stacks[i, :] = transy(f(transx(x)))
# Plot envelopes
HPD = np.sort(HPD)
sorted_func_stack = np.sort(func_stacks, 0)
for m in HPD[::-1]:
env = centered_envelope(sorted_func_stack, m)
# from IPython.Debugger import Pdb
# Pdb(color_scheme='LightBG').set_trace()
env.display(x, alpha=1. - m * .5, new=False)
centered_envelope(
sorted_func_stack,
0.).display(x,
alpha=1.,
new=False)
except ImportError:
six.print_('Plotter could not be imported; plotting disabled')
def plot_GP_envelopes(f, x, HPD=[.25, .5, .95], transx=None, transy=None):
"""
plot_GP_envelopes(f, x[, HPD, transx, transy])
Plots centered posterior probability envelopes for f, which is a GP instance,
which is a function of one variable.
:Arguments:
- `f`: A GaussianProcess object.
- `x`: The mesh on which to plot the envelopes.
- `HPD`: A list of values between 0 and 1 giving the probability mass
contained in each envelope.
- `transx`: Any transformation of the x-axis.
- `transy`: Any transformation of the y-axis.
"""
try:
from pymc.Matplot import centered_envelope
f_trace = f.trace()
x = x.ravel()
N = len(f_trace)
func_stacks = np.zeros((N, len(x)), dtype=float)
def identity(y):
return y
if transy is None:
transy = identity
if transx is None:
transx = identity
# Get evaluations
for i in range(N):
f = copy(f_trace[i])
func_stacks[i, :] = transy(f(transx(x)))
# Plot envelopes
HPD = np.sort(HPD)
sorted_func_stack = np.sort(func_stacks, 0)
for m in HPD[::-1]:
env = centered_envelope(sorted_func_stack, m)
# from IPython.Debugger import Pdb
# Pdb(color_scheme='LightBG').set_trace()
env.display(x, alpha=1. - m * .5, new=False)
centered_envelope(sorted_func_stack, 0.).display(x,
alpha=1.,
new=False)
except ImportError:
six.print_('Plotter could not be imported; plotting disabled')
def plot_GP_envelopes(f, x, HPD = [.25, .5, .95], transx = None, transy = None):
"""
plot_GP_envelopes(f, x[, HPD, transx, transy])
Plots centered posterior probability envelopes for f, which is a GP instance,
which is a function of one variable.
:Arguments:
- `f`: A realization-valued stochastic.
- `x`: The mesh on which to plot the envelopes.
- `HPD`: A list of values between 0 and 1 giving the probability mass
contained in each envelope.
- `transx`: Any transformation of the x-axis.
- `transy`: Any transformation of the y-axis.
"""
try:
from pymc.Matplot import centered_envelope
f_trace = f.trace()
x = x.ravel()
N = len(f_trace)
func_stacks = np.zeros((N,len(x)),dtype=float)
def identity(y):
return y
if transy is None:
transy = identity
if transx is None:
transx = identity
# Get evaluations
for i in range(N):
f = copy(f_trace[i])
func_stacks[i,:] = transy(f(transx(x)))
# Plot envelopes
HPD = np.sort(HPD)
sorted_func_stack = np.sort(func_stacks,0)
for i in range(len(HPD)-1,0,-1):
env = centered_envelope(sorted_func_stack, HPD[i])
env.display(x, alpha=1.-HPD[i],new=False)
centered_envelope(sorted_func_stack, 0.).display(x, alpha=1., new=False)
except ImportError:
print 'Plotter could not be imported; plotting disabled'
