def extract(self):
logger.info('extracting csvs from shared drive')
csv_reader = CSVReader(Config.directory)
csv_reader.targets = [Target(*params) for params in Config.targets]
received, shipped, history, picked = csv_reader.read()
received.to_csv('data/received.csv')
shipped.to_csv('data/shipped.csv')
history.to_csv('data/history.csv')
picked.to_csv('data/picked.csv')
return received, shipped, history, picked
from linear_regression import LinearRegression
from train_test_split import TrainTestSplit
import matplotlib.pyplot as plt
from csv_reader import CSVReader
f = CSVReader()
f.read(csv="autos_prepared.csv")
x_train, x_test, y_train, y_test = TrainTestSplit().split(x=f.data["powerPS"],
y=f.data["price"])
model = LinearRegression()
model.train(x=x_train, y=y_train)
print("Fehler |", model.error)
print("Durchschnittsfehler |", model.avg_error)
print("a |", model.a)
print("b |", model.b)
print("Bestimmtheitsmaß |", model.score(xs=x_test, ys=y_test))
predicted = model.predict(x_test)
plt.scatter(x_train, y_train)
plt.scatter(x_test, y_test, color="green")
plt.plot(x_test, predicted, color="red")
plt.show()
from os import listdir
from os.path import join,dirname,realpath,isdir
import json
from csv_reader import CSVReader
from body_box_extractor import BodyBoxExtractor
ROOT_DIRECTORY = join(dirname(realpath(sys.argv[0])), '..', '..', 'res')
data_directories = [f for f in listdir(ROOT_DIRECTORY) if isdir(join(ROOT_DIRECTORY, f))]
csv_reader = CSVReader(ROOT_DIRECTORY)
body_box_extractor = BodyBoxExtractor('', None)
check_results = {}
for data_directory in data_directories:
try:
type_json_file = open(join(ROOT_DIRECTORY, data_directory, 'type.json'))
type_data = json.load(type_json_file)
csv_reader.set_file(join(data_directory, data_directory+'.csv'))
skeleton_data = {'raw_data': csv_reader.read()}
data = body_box_extractor.process(skeleton_data)
check_results[data_directory] = type_data
check_results[data_directory]['has_shoulder'] = all(abs(data['body_box']['shoulder_left']['3d']['mean']) > 0.001) and all(abs(data['body_box']['shoulder_left']['3d']['mean']) > 0.001)
check_results[data_directory]['has_hip'] = all(abs(data['body_box']['hip_left']['3d']['mean']) > 0.001) and all(abs(data['body_box']['hip_right']['3d']['mean']) > 0.001)
except IOError:
continue
selected_signs = [data_name for data_name,data in check_results.items() if data['has_hip'] and data['oneOrTwo'] is 1 and data['handRecognized']]
print sorted(selected_signs)
ROOT_DIRECTORY = join(dirname(realpath(sys.argv[0])), '..', '..', 'res')
data_directories = [
f for f in listdir(ROOT_DIRECTORY) if isdir(join(ROOT_DIRECTORY, f))
]
csv_reader = CSVReader(ROOT_DIRECTORY)
body_box_extractor = BodyBoxExtractor('', None)
check_results = {}
for data_directory in data_directories:
try:
type_json_file = open(join(ROOT_DIRECTORY, data_directory,
'type.json'))
type_data = json.load(type_json_file)
csv_reader.set_file(join(data_directory, data_directory + '.csv'))
skeleton_data = {'raw_data': csv_reader.read()}
data = body_box_extractor.process(skeleton_data)
check_results[data_directory] = type_data
check_results[data_directory]['has_shoulder'] = all(
abs(data['body_box']['shoulder_left']['3d']['mean']) > 0.001
) and all(
abs(data['body_box']['shoulder_left']['3d']['mean']) > 0.001)
check_results[data_directory]['has_hip'] = all(
abs(data['body_box']['hip_left']['3d']['mean']) > 0.001) and all(
abs(data['body_box']['hip_right']['3d']['mean']) > 0.001)
except IOError:
continue
selected_signs = [
