def is_met(self, response, global_variables, local_variables, event_counter):
variables_both = Variables.join(global_variables, local_variables)
if self.condition is None:
ismet = True
elif self.condition_is_behavior:
ismet = (self.condition == response)
else:
ismet, err = ParseUtil.evaluate(self.condition, variables_both, event_counter,
ParseUtil.PHASE_LINE)
if err:
raise ParseException(self.lineno, err)
if type(ismet) is not bool:
raise ParseException(self.lineno, f"Condition '{self.condition}' is not a boolean expression.")
if ismet:
label = self._goto_if_met(variables_both)
if label is None: # In "ROW1(0.1),ROW2(0.3)", goto_if_met returns None with prob. 0.6
ismet = False
else:
label = None
return ismet, label
def _perform_action(self, lineno, action):
"""
Sets a variable (x:3) or count_reset(event).
"""
omit_learn = False
# No action to perform
if len(action) == 0:
pass
# Setting a variable
elif action.count(':') == 1 or action.count('=') == 1:
if action.count('=') == 1:
sep = '='
else:
sep = ':'
var_name, value_str = ParseUtil.split1_strip(action, sep=sep)
variables_join = Variables.join(self.global_variables, self.local_variables)
value, err = ParseUtil.evaluate(value_str, variables_join)
if err:
raise ParseException(lineno, err)
err = self.local_variables.set(var_name, value, self.parameters)
if err:
raise ParseException(lineno, err)
# count_reset
elif action.startswith("count_reset(") and action.endswith(")"):
event = action[12:-1]
self.event_counter.reset_count(event)
# omit_learn
elif action == "@omit_learn":
omit_learn = True
else:
raise ParseException(lineno, "Internal error.") # Should have been caught during Pase.parse()
return omit_learn
def next_stimulus(self, response, ignore_response_increment=False, preceeding_help_lines=None, omit_learn=None):
# if not self.is_parsed:
# raise Exception("Internal error: Cannot call Phase.next_stimulus" +
# " before Phase.parse().")
if preceeding_help_lines is None:
preceeding_help_lines = list()
if not ignore_response_increment:
# if not self.is_first_line:
if response is not None:
self.event_counter.increment_count(response)
self.event_counter.increment_count_line(response)
self.event_counter.set_last_response(response)
if self.first_stimulus_presented:
variables_both = Variables.join(self.global_variables, self.local_variables)
if self.stop_condition.is_met(variables_both, self.event_counter):
return None, None, preceeding_help_lines, None
if self.is_first_line:
# assert(response is None)
rowlbl = self.first_label
self.is_first_line = False
omit_learn = True # Since response is None, there is no learning (updating of v-values) to do
else:
rowlbl, omit_learn = self.curr_lineobj.next_line(response, self.global_variables, self.local_variables,
self.event_counter)
self.prev_linelabel = self.curr_lineobj.label
self._make_current_line(rowlbl)
stimulus = self.phase_lines[rowlbl].stimulus
if stimulus is not None:
for element, intensity in stimulus.items():
if type(intensity) is str: # element[var] where var is a (local) variable
variables_both = Variables.join(self.global_variables, self.local_variables)
stimulus[element], err = ParseUtil.evaluate(intensity, variables=variables_both)
if err:
raise ParseException(self.phase_lines[rowlbl].lineno, err)
if rowlbl != self.prev_linelabel:
self.event_counter.reset_count_line()
self.event_counter.line_label = rowlbl
self.event_counter.increment_count(rowlbl)
self.event_counter.increment_count_line(rowlbl)
if stimulus is None: # Help line
action = self.phase_lines[rowlbl].action
lineno = self.phase_lines[rowlbl].lineno
self._perform_action(lineno, action)
preceeding_help_lines.append(rowlbl)
next_stimulus_out = self.next_stimulus(response, ignore_response_increment=True,
preceeding_help_lines=preceeding_help_lines,
omit_learn=omit_learn)
stimulus, rowlbl, preceeding_help_lines, omit_learn_help = next_stimulus_out
omit_learn = (omit_learn or omit_learn_help)
else:
for stimulus_element in stimulus:
self.event_counter.increment_count(stimulus_element)
self.event_counter.increment_count_line(stimulus_element)
self.first_stimulus_presented = True
return stimulus, rowlbl, preceeding_help_lines, omit_learn
