def calculate_inter_string_salience(self):
"""Return the inter string salience.
This value represents how much the object is crying out for attention
from codelets that build structures between strings."""
if self.clamp_salience:
return 100
else:
weights = [8, 2]
values = [self.relative_importance, self.inter_string_unhappiness]
return round(toolbox.weighted_average(weights, values))
def calculate_inter_string_salience(self):
"""Return the inter string salience.
This value represents how much the object is crying out for attention
from codelets that build structures between strings."""
if self.clamp_salience:
return 100
else:
weights = [8, 2]
values = [self.relative_importance, self.inter_string_unhappiness]
return round(toolbox.weighted_average(weights, values))
def calculate_internal_strength(self):
"""Return the rule's internal strength."""
if self.has_no_change():
return 100
conceptual_depth1 = self.descriptor1.conceptual_depth
if self.relation:
conceptual_depth2 = self.relation.conceptual_depth
else:
conceptual_depth2 = self.descriptor2.conceptual_depth
conceptual_difference = abs(conceptual_depth1 - conceptual_depth2)
for obj in self.workspace.initial_string.get_objects():
if obj.is_changed:
i_object = obj
break
if i_object.correspondence:
i_object_corresponding_object = i_object.correspondence.object2
else:
i_object_corresponding_object = None
if not i_object_corresponding_object:
shared_descriptor_term = 0
else:
slipped_descriptors = []
for description in i_object_corresponding_object.relevant_descriptions(
):
slips = description.apply_slippages(
i_object_corresponding_object, self.workspace.slippages())
slipped_descriptors.append(slips.descriptor)
if self.descriptor1 in slipped_descriptors:
shared_descriptor_term = 100
else:
return 0
depth = (100 - self.descriptor1.conceptual_depth) / 10.
shared_descriptor_weight = round(depth**1.4)
weights = [18, 12, shared_descriptor_weight]
items = [
toolbox.average(conceptual_depth1, conceptual_depth2),
100 - conceptual_difference, shared_descriptor_term
]
rule_strength = round(toolbox.weighted_average(weights, items))
return min(rule_strength, 100)
def calculate_internal_strength(self):
"""Return the rule's internal strength."""
if self.has_no_change():
return 100
conceptual_depth1 = self.descriptor1.conceptual_depth
if self.relation:
conceptual_depth2 = self.relation.conceptual_depth
else:
conceptual_depth2 = self.descriptor2.conceptual_depth
conceptual_difference = abs(conceptual_depth1 - conceptual_depth2)
for obj in self.workspace.initial_string.get_objects():
if obj.is_changed:
i_object = obj
break
if i_object.correspondence:
i_object_corresponding_object = i_object.correspondence.object2
else:
i_object_corresponding_object = None
if not i_object_corresponding_object:
shared_descriptor_term = 0
else:
slipped_descriptors = []
for description in i_object_corresponding_object.relevant_descriptions():
slips = description.apply_slippages(i_object_corresponding_object,
self.workspace.slippages())
slipped_descriptors.append(slips.descriptor)
if self.descriptor1 in slipped_descriptors:
shared_descriptor_term = 100
else:
return 0
depth = (100 - self.descriptor1.conceptual_depth) / 10.
shared_descriptor_weight = round(depth ** 1.4)
weights = [18, 12, shared_descriptor_weight]
items = [toolbox.average(conceptual_depth1, conceptual_depth2),
100 - conceptual_difference,
shared_descriptor_term]
rule_strength = round(toolbox.weighted_average(weights, items))
return min(rule_strength, 100)
def calculate_internal_strength(self):
"""For now, groups based on letter category are stronger than groups
based on other facets. This should be fixed; a more general mechanism is
needed."""
if self.bond_facet == self.slipnet.plato_letter_category:
bond_facet_factor = 1
else:
bond_facet_factor = .5
related = self.slipnet.get_related_node(
self.group_category, self.slipnet.plato_bond_category)
bond_component = related.degree_of_association() * bond_facet_factor
length_component = {1: 5, 2: 20, 3: 60}.get(self.length(), 90)
bond_component_weight = bond_component**.98
length_component_weight = 100 - bond_component_weight
return toolbox.weighted_average(
(bond_component_weight, length_component_weight),
(bond_component, length_component))
def calculate_internal_strength(self):
"""For now, groups based on letter category are stronger than groups
based on other facets. This should be fixed; a more general mechanism is
needed."""
if self.bond_facet == self.slipnet.plato_letter_category:
bond_facet_factor = 1.0
else:
bond_facet_factor = .5
related = self.slipnet.get_related_node(self.group_category,
self.slipnet.plato_bond_category)
bond_component = related.degree_of_association() * bond_facet_factor
length_component = {1:5, 2:20, 3:60}.get(self.length(), 90)
bond_component_weight = bond_component ** .98
length_component_weight = 100 - bond_component_weight
return toolbox.weighted_average((bond_component_weight,
length_component_weight),
(bond_component,
length_component))
def update_total_strength(self):
"""Update the total strength."""
weights = [self.internal_strength, 100 - self.internal_strength]
values = [self.internal_strength, self.external_strength]
self.total_strength = toolbox.weighted_average(weights, values)
def update_total_strength(self):
"""Update the total strength."""
weights = [self.internal_strength, 100 - self.internal_strength]
values = [self.internal_strength, self.external_strength]
self.total_strength = toolbox.weighted_average(weights, values)