def print_script(self, evaluation):
"""Print an R script that shows the movement of the lens in this
particular simulation."""
assert evaluation in ("true positive", "false positive",
"true negative", "false negative",
"succeeded", "failed")
print "# %s at %d, %s\n" % (self.scene.filename,
self.visited_positions[0], evaluation)
# Some R functions for plotting.
rtools.print_set_window_division(1, 1)
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(self.scene.fvalues, show_grid=True)
xs = self.visited_positions
ys = [ float(i) / max(10, len(self.visited_positions))
for i in range(0, len(self.visited_positions)) ]
rtools.print_plot_point_pairs(xs, ys, 25, "blue", "blue", True)
result = self.last_position()
if result >= 0:
print "segments(%d, 0.0, %d, 1.0)" % (result, result)
print "\n# Plot me!\n"
def print_R_script(scene, tree, classifier, step_size):
print "# " + scene.filename + "\n"
# Some R functions for plotting.
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues)
# The correct classifications.
classes = [ "left" if classifier(scene, lens_pos) else "right"
for lens_pos in range(2 * step_size, scene.step_count) ]
# What we actually get.
results = []
for lens_pos in range(2 * step_size, scene.step_count):
initial_positions = featuresfirststep.first_three_lens_pos(
lens_pos, step_size)
first, second, third = scene.get_focus_values(initial_positions)
norm_lens_pos = float(lens_pos) / (scene.step_count - 1)
evaluator = featuresfirststep.firststep_feature_evaluator(
first, second, third, norm_lens_pos)
evaluation = evaluatetree.evaluate_tree(tree, evaluator)
results.append(evaluation)
rtools.print_classification_points(classes, results, ["left", "right"])
print "# Plot me!\n"
def print_R_script(scene):
print "# " + scene.filename + "\n"
# Some R functions for plotting.
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues)
# Axis to indicate that the bottom points mean coarse and
# the top points means fine.
print "axis(2, at=0, labels=\"coarse\", padj=-2)"
print "axis(2, at=1.0, labels=\"fine\", padj=-2)"
# Points to plot
results = simulate(scene)
xs, ys = ([ a for a, _ in results ], [ b for _, b in results ]) # unzip
# Indicate the correct classes (coarse or fine) and the predicted classes.
# The predicted classes is slightly offset to avoid overlapping.
rtools.print_plot_point_pairs(xs, ys, 22, "black", "blue", False, 0.3, 0.5)
# In the title, indicate how many steps are used.
print ("title(main=paste(\"coarse steps:\", %d, "
"\"fine steps:\", %d, "
"\"total steps:\", %d))"
% (ys.count(0), ys.count(1), len(ys)))
print "# Plot me!\n"
def print_R_script(scene, tree, step_size):
print "# " + scene.filename + "\n"
# Some R functions for plotting.
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues)
classes = []
results = []
for lens_pos in range(2 * step_size, scene.step_count):
# The correct classification.
go_left = featuresfirststep.nearest_on_left(scene, lens_pos)
initial_positions = featuresfirststep.first_three_lens_pos(
lens_pos, step_size)
if go_left:
initial_positions.reverse()
last_pos = initial_positions[-1]
if scene.distance_to_closest_left_peak(last_pos) <= 15:
classes.append("fine")
else:
classes.append("coarse")
else:
last_pos = initial_positions[-1]
if scene.distance_to_closest_right_peak(last_pos) <= 15:
classes.append("fine")
else:
classes.append("coarse")
# The classification obtained by evaluating the decision tree.
first, second, third = scene.get_focus_values(initial_positions)
norm_lens_pos = float(lens_pos) / (scene.step_count - 1)
evaluator = featuresfirststep.firststep_feature_evaluator(
first, second, third, norm_lens_pos)
evaluation = evaluatetree.evaluate_tree(tree, evaluator)
results.append(evaluation)
rtools.print_classification_points(classes, results, ["coarse", "fine"])
print "# Plot me!\n"
def print_R_script(scene, lens_positions, instances):
assert len(lens_positions) == len(instances)
print "# " + scene.filename + "\n"
xs_continue = []
ys_continue = []
xs_turn_peak = []
ys_turn_peak = []
xs_backtrack = []
ys_backtrack = []
for lens_pos, (_, classif, weight) in zip(lens_positions, instances):
if classif == Action.CONTINUE:
xs_continue.append(lens_pos)
ys_continue.append(weight)
if classif == Action.TURN_PEAK:
xs_turn_peak.append(lens_pos)
ys_turn_peak.append(weight)
if classif == Action.BACKTRACK:
xs_backtrack.append(lens_pos)
ys_backtrack.append(weight)
# Some R functions for plotting.
rtools.print_set_window_division(1, 1)
print "library(scales)" # for alpha blending
rtools.print_plot_focus_measures(scene.fvalues, (-0.1, 1))
rtools.print_plot_point_pairs(xs_continue,
ys_continue, 25, "black", "black")
rtools.print_plot_point_pairs(xs_turn_peak,
ys_turn_peak, 25, "green", "green")
rtools.print_plot_point_pairs(xs_backtrack,
ys_backtrack, 25, "red", "red")
print "\n# Plot me!\n"