def unify_dir(in_path,out_path):
all_files=os.listdir(in_path)
def is_file(f):
return os.path.isfile(os.path.join(in_path,f))
dataset_paths=filter(is_file ,all_files)
dataset_paths=[in_path+ path for path in dataset_paths]
print(dataset_paths)
new_dataset=dataset.annotated_to_dataset(dataset_paths[0])
for i in range(1,len(dataset_paths)):
partial_dataset=dataset.annotated_to_dataset(dataset_paths[i])
new_dataset=unify(new_dataset,partial_dataset)
return new_dataset
print(cf_matrix)
def show_error(dataset,clf):
y_true, y_pred = dataset.y, clf.predict(dataset.X)
result=(y_pred==y_true)
for i,y in enumerate(result):
if(not y):
label=y_true[i]
label_pred=y_pred[i]
person=dataset.anno[i]
print(str(i)+" "+str(label)+" "+str(label_pred)+" "+str(person))
def parse_args(args):
if(len(args)>1):
random=int(args[1])
random=bool(random)
else:
random=False
return random
if __name__ == "__main__":
random=parse_args(sys.argv)
if(random):
in_path="../af/cascade4/full_dataset"#"../af/result/full_dataset"
dataset=dataset.annotated_to_dataset(in_path)#labeled_to_dataset(in_path)
random_eval(dataset)
else:
train_path="../af/cascade4/full_dataset_train"
test_path="../af/cascade4/full_dataset_test"
determistic_eval(train_path,test_path,False)
