def plotLocallyWeightedLines(inputs,lines,lines_weighted,ax,n_samples_per_dim,activations_normalized=[],activations=[]):
"""Plots locally weighted lines, whilst being smart about the dimensionality of input data."""
n_dims = len(numpy.atleast_1d(inputs[0]))
if (n_dims==1):
#line_handles = ax.plot(inputs,lines, '-', color='#cccccc')
line_handles = ax.plot(inputs,lines_weighted, '-', color='red')
y_lim_min = min(lines_weighted) - 0.2*(max(lines_weighted)-min(lines_weighted))
y_lim_max = max(lines_weighted) + 0.2*(max(lines_weighted)-min(lines_weighted))
ax.set_ylim(y_lim_min,y_lim_max)
if (len(activations_normalized)>0):
ax_two = ax.twinx()
line_handles_bfs = plotBasisFunctions(inputs,activations,ax_two,n_samples_per_dim)
plt.setp(line_handles_bfs,color='#aaffaa')
line_handles_bfs = plotBasisFunctions(inputs,activations_normalized,ax_two,n_samples_per_dim)
plt.setp(line_handles_bfs,color='green')
for tl in ax_two.get_yticklabels():
tl.set_color('green')
ax_two.set_ylim(-2.0,3.0)
for ii in xrange(len(activations_normalized[0])):
active = activations_normalized[:,ii]>(max(activations_normalized[:,ii])*0.75)
ax.plot(inputs[active],lines[active,ii], '-',color='#aaaaaa',linewidth='3')
elif (n_dims==2):
n_lines = len(lines[0])
# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.Set3
colors = [colormap(i) for i in numpy.linspace(0, 0.9, n_lines)]
inputs_0_on_grid = numpy.reshape(inputs[:,0],n_samples_per_dim)
inputs_1_on_grid = numpy.reshape(inputs[:,1],n_samples_per_dim)
if (len(activations)>0):
for i_line in range(n_lines):
cur_color = colors[i_line]
cur_line = lines[:,i_line];
cur_activations = activations[:,i_line];
cur_line[cur_activations<0.4] = numpy.nan # Make plotting easier by leaving out small numbers
line_on_grid = numpy.reshape(cur_line,n_samples_per_dim)
line_handles = ax.plot_wireframe(inputs_0_on_grid,inputs_1_on_grid,line_on_grid,linewidth=2,rstride=1, cstride=1, color=cur_color)
line_on_grid = numpy.reshape(lines_weighted,n_samples_per_dim)
line_handles = ax.plot_wireframe(inputs_0_on_grid,inputs_1_on_grid,line_on_grid,linewidth=1,rstride=1, cstride=1, color="#333333")
else:
print 'Cannot plot input data with a dimensionality of '+str(n_dims)+'.'
line_handles = []
return line_handles
def plotLocallyWeightedLines(inputs,
lines,
ax,
n_samples_per_dim,
activations=None,
activations_unnormalized=None):
"""Plots locally weighted lines, whilst being smart about the dimensionality of input data."""
line_handles = []
n_dims = len(numpy.atleast_1d(inputs[0]))
if (n_dims == 1):
if not activations is None:
ax_two = ax.twinx()
# Plot basis functions
if not activations_unnormalized is None:
line_handles_bfs = plotBasisFunctions(
inputs, activations_unnormalized, ax_two,
n_samples_per_dim)
plt.setp(line_handles_bfs, color='#aaffaa')
line_handles_bfs = plotBasisFunctions(inputs, activations, ax_two,
n_samples_per_dim)
plt.setp(line_handles_bfs, color='green')
for tl in ax_two.get_yticklabels():
tl.set_color('green')
ax_two.set_ylim(-2.0, 3.0)
# Plot line segements
n_basis_functions = len(numpy.atleast_1d(activations[0]))
if n_basis_functions == 1:
active = activations > (max(activations) * 0.001)
line_handles = ax.plot(inputs[active],
lines[active],
'--',
color='#aaaaaa',
linewidth=0.5)
else:
for ii in range(n_basis_functions):
active = activations[:, ii] > (max(activations[:, ii]) *
0.0001)
line_handles = ax.plot(inputs[active],
lines[active, ii],
'--',
color='#aaaaaa',
linewidth=1)
elif (n_dims == 2):
inputs_0_on_grid = numpy.reshape(inputs[:, 0], n_samples_per_dim)
inputs_1_on_grid = numpy.reshape(inputs[:, 1], n_samples_per_dim)
n_lines = len(lines[0])
# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.Set1
shuffler = numpy.linspace(0, 0.9, n_lines)
random.shuffle(shuffler)
colors = [colormap(i) for i in shuffler]
if (len(activations) > 0):
min_val = numpy.amax(lines)
for i_line in range(n_lines):
cur_color = colors[i_line]
cur_line = lines[:, i_line]
cur_activations = activations[:, i_line]
cur_line[
cur_activations <
0.25] = numpy.nan # Make plotting easier by leaving out small numbers
line_on_grid = numpy.reshape(cur_line, n_samples_per_dim)
line_handles = ax.plot_wireframe(inputs_0_on_grid,
inputs_1_on_grid,
line_on_grid,
linewidth=0.5,
rstride=1,
cstride=1,
color=cur_color)
if numpy.nanmin(cur_line) < min_val:
min_val = numpy.nanmin(cur_line)
level = numpy.mean(
[numpy.amin(activations),
numpy.amax(activations)])
for i_line in range(n_lines):
cur_color = colors[i_line]
cur_activations = activations[:, i_line]
acts_on_grid = numpy.reshape(cur_activations,
n_samples_per_dim)
cset = ax.contour(inputs_0_on_grid,
inputs_1_on_grid,
acts_on_grid, [level],
zdir='z',
offset=min_val,
colors=[cur_color])
#line_on_grid = numpy.reshape(lines_weighted,n_samples_per_dim)
#line_handles = ax.plot_wireframe(inputs_0_on_grid,inputs_1_on_grid,line_on_grid,linewidth=1,rstride=1, cstride=1, color="#333333")
else:
print('Cannot plot input data with a dimensionality of ' +
str(n_dims) + '.')
line_handles = []
return line_handles
def plotLocallyWeightedLines(inputs,lines,ax,n_samples_per_dim,activations=None,activations_unnormalized=None):
"""Plots locally weighted lines, whilst being smart about the dimensionality of input data."""
line_handles = []
n_dims = len(numpy.atleast_1d(inputs[0]))
if (n_dims==1):
if not activations is None:
ax_two = ax.twinx()
# Plot basis functions
if not activations_unnormalized is None:
line_handles_bfs = plotBasisFunctions(inputs,activations_unnormalized,ax_two,n_samples_per_dim)
plt.setp(line_handles_bfs,color='#aaffaa')
line_handles_bfs = plotBasisFunctions(inputs,activations,ax_two,n_samples_per_dim)
plt.setp(line_handles_bfs,color='green')
for tl in ax_two.get_yticklabels():
tl.set_color('green')
ax_two.set_ylim(-2.0,3.0)
# Plot line segements
n_basis_functions = len(numpy.atleast_1d(activations[0]));
if n_basis_functions==1:
active = activations>(max(activations)*0.001)
line_handles = ax.plot(inputs[active],lines[active], '--',color='#aaaaaa',linewidth=0.5)
else:
for ii in xrange(n_basis_functions):
active = activations[:,ii]>(max(activations[:,ii])*0.0001)
line_handles = ax.plot(inputs[active],lines[active,ii], '--',color='#aaaaaa',linewidth=1)
elif (n_dims==2):
inputs_0_on_grid = numpy.reshape(inputs[:,0],n_samples_per_dim)
inputs_1_on_grid = numpy.reshape(inputs[:,1],n_samples_per_dim)
n_lines = len(lines[0])
# Have a look at the colormaps here and decide which one you'd like:
# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
colormap = plt.cm.Set1
shuffler = numpy.linspace(0, 0.9, n_lines)
random.shuffle(shuffler)
colors = [colormap(i) for i in shuffler]
if (len(activations)>0):
min_val = numpy.amax(lines)
for i_line in range(n_lines):
cur_color = colors[i_line]
cur_line = lines[:,i_line];
cur_activations = activations[:,i_line];
cur_line[cur_activations<0.25] = numpy.nan # Make plotting easier by leaving out small numbers
line_on_grid = numpy.reshape(cur_line,n_samples_per_dim)
line_handles = ax.plot_wireframe(inputs_0_on_grid,inputs_1_on_grid,line_on_grid,linewidth=0.5,rstride=1, cstride=1, color=cur_color)
if numpy.nanmin(cur_line)<min_val:
min_val = numpy.nanmin(cur_line)
level = numpy.mean([numpy.amin(activations), numpy.amax(activations)])
for i_line in range(n_lines):
cur_color = colors[i_line]
cur_activations = activations[:,i_line];
acts_on_grid = numpy.reshape(cur_activations,n_samples_per_dim)
cset = ax.contour(inputs_0_on_grid, inputs_1_on_grid, acts_on_grid, [level], zdir='z', offset=min_val, colors=[cur_color])
#line_on_grid = numpy.reshape(lines_weighted,n_samples_per_dim)
#line_handles = ax.plot_wireframe(inputs_0_on_grid,inputs_1_on_grid,line_on_grid,linewidth=1,rstride=1, cstride=1, color="#333333")
else:
print 'Cannot plot input data with a dimensionality of '+str(n_dims)+'.'
line_handles = []
return line_handles
