class PerformanceRecord:
def __init__(self, possibleRewards):
self.confusionMatrix = ConfusionMatrix(possibleRewards)
self.accumulatedReward = 0
self.noVisitedStates = -1 # to exclude start and end
self.predictedReward = 0
def recordMove(self, currentStateReward, nextStatePredictedReward):
self.noVisitedStates += 1
self.accumulatedReward += currentStateReward
self.confusionMatrix.addObservation(currentStateReward, self.predictedReward)
self.predictedReward = nextStatePredictedReward
def print_order_confusion_matrix_for_feature(self, out_file=sys.stdout):
# build and print a confusion matrix for svo
print("\nComparing results with gold standard from WALS for " + self.label + ":", file=out_file)
confusion_matrix = ConfusionMatrix(self.label, self.possibilities)
num_reported = 0
for code in self.feature_dictionary:
our_value = self.feature_dictionary[code]
num_instances = self.feature_instances_dictionary[code]
if num_instances >= self.min_num_instances:
try:
wals_value = self.wals_gold_standard.get_value_from_iso_language_id(code)
confusion_matrix.add_label(wals_value, our_value)
num_reported += 1
except KeyError:
pass
# print("No matching SVO WALS data for language " + code)
print("Num Languages Compared: " + str(num_reported), file=out_file)
if num_reported > 0:
print(confusion_matrix.print_matrix(), file=out_file)
def __init__(self, possibleRewards): self.confusionMatrix = ConfusionMatrix(possibleRewards) self.accumulatedReward = 0 self.noVisitedStates = -1 # to exclude start and end self.predictedReward = 0
feature_dictionary[language_code]="Mixed type"
if ratio<=.20:
feature_dictionary[language_code]="Tense-aspect prefixes"
if feature_dictionary[language_code]!=wals_value:
output.write(language_code+"\n")
output.write("Our value "+feature_dictionary[language_code]+" WALS "+wals_value+"\n")
for igt in igt_list:
printIGT(igt,output)
print(language_code,prefix_count,suffix_count,affix_count,no_affix_count,wals_value)
print(str(lang_count),"languages classified")
#find the accuracy
possibilities = ["Tense-aspect prefixes","Tense-aspect suffixes","Mixed type","No tense-aspect inflection","Tense-aspect tone"]
confusion_matrix = ConfusionMatrix("Generated and WALS", possibilities)
correct=0
for code in feature_dictionary:
our_value = feature_dictionary[code]
try:
wals_code = wals_dictionary.iso_to_wals[code]
wals_value = wals.feature_dictionary[wals_code]
confusion_matrix.addLabel(wals_value, our_value)
except KeyError:
print("No matching WALS data for language " + code)
print(confusion_matrix.printMatrix())
print("baseline accuracy:"+str(baseline_correct/lang_count))
output.close()