Gaussians.
"""
# new figure
pl.figure()
# Gaussian parameters
params = fit[0]
# plot the histogram
plot_bars(H.T, xloc=bin_left, width=bin_width, yerr='std')
# show the Gaussians
x = np.linspace(0, 1, 100)
# first gaussian
pl.plot(x, params[0] * norm.pdf(x, params[1], params[2]), "r-", zorder=2)
pl.axvline(params[1], color='r', linestyle='--', alpha=0.6)
# second gaussian
pl.plot(x, params[3] * norm.pdf(x, params[4], params[5]), "b-", zorder=3)
pl.axvline(params[4], color='b', linestyle='--', alpha=0.6)
# dual gaussian
pl.plot(x, dual_gaussian(x, *params), "k--", alpha=0.5, zorder=1)
pl.xlim(0, 1)
pl.ylim(ymin=0)
pl.title('Dual Gaussian fit of searchlight accuracies')
if cfg.getboolean('examples', 'interactive', True):
# show the cool figures
pl.show()
"""
Gaussians.
"""
# new figure
pl.figure()
# Gaussian parameters
params = fit[0]
# plot the histogram
plot_bars(H.T, xloc=bin_left, width=bin_width, yerr='std')
# show the Gaussians
x = np.linspace(0, 1, 100)
# first gaussian
pl.plot(x, params[0] * norm.pdf(x, params[1], params[2]), "r-", zorder=2)
pl.axvline(params[1], color='r', linestyle='--', alpha=0.6)
# second gaussian
pl.plot(x, params[3] * norm.pdf(x, params[4], params[5]), "b-", zorder=3)
pl.axvline(params[4], color='b', linestyle='--', alpha=0.6)
# dual gaussian
pl.plot(x, dual_gaussian(x, *params), "k--", alpha=0.5, zorder=1)
pl.xlim(0, 1)
pl.ylim(ymin=0)
pl.title('Dual Gaussian fit of searchlight accuracies')
if cfg.getboolean('examples', 'interactive', True):
# show the cool figures
pl.show()
def plot_proj_dir(p):
pl.plot([0, p[0, 0]], [0, p[0, 1]], linewidth=3, hold=True, color='y')
pl.plot([0, p[1, 0]], [0, p[1, 1]], linewidth=3, hold=True, color='k')
def plot_proj_dir(p):
pl.plot([0, p[0,0]], [0, p[0,1]],
linewidth=3, hold=True, color='y')
pl.plot([0, p[1,0]], [0, p[1,1]],
linewidth=3, hold=True, color='k')
result = kernel.compute(data)
# In the following we draw some 2D functions at random from the
# distribution N(O,kernel) defined by each available kernel and
# plot them. These plots shows the flexibility of a given kernel
# (with default parameters) when doing interpolation. The choice
# of a kernel defines a prior probability over the function space
# used for regression/classfication with GPR/GPC.
count = 1
for k in kernel_dictionary.keys():
pl.subplot(3, 4, count)
# X = np.random.rand(size)*12.0-6.0
# X.sort()
X = np.arange(-1, 1, .02)
X = X[:, np.newaxis]
ker = kernel_dictionary[k]()
ker.compute(X, X)
print k
K = np.asarray(ker)
for i in range(10):
f = np.random.multivariate_normal(np.zeros(X.shape[0]), K)
pl.plot(X[:, 0], f, "b-")
pl.title(k)
pl.axis('tight')
count += 1
if cfg.getboolean('examples', 'interactive', True):
# show all the cool figures
pl.show()