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import sys
import argparse
from model import Model
import predict
class CommandLineInterface:
def __init__(self):
self.model = Model()
def parse_args(self):
parser = argparse.ArgumentParser(
prog="ft_linear_regression_cli",
description="CLI to interact with the ft_linear_regression project"
)
subparsers = parser.add_subparsers(help="sub-command help", dest="subparser_name")
parser_train = subparsers.add_parser("train", help="train the model")
parser_train.set_defaults(func=self._train)
parser_train.add_argument("-a --alpha", type=float, default=1.0, dest="alpha", help="learning rate")
parser_train.add_argument("-e --epoch", type=int, default=100, dest="epoch", help="number of iterations")
parser_predict = subparsers.add_parser("predict", help="make a predict")
parser_predict.set_defaults(func=self._predict)
parser_predict.add_argument("-x", type=int, help="mileage for which the prediction will be made")
parser_cost = subparsers.add_parser("cost", help="print model cost")
parser_cost.set_defaults(func=self._cost)
parser_plot = subparsers.add_parser("plot", help="plot data and model")
parser_plot.set_defaults(func=self._plot)
parser_plot.add_argument("-d --data", help="only plot data", action="store_true", dest="plot_data")
parser_plot.add_argument("-m --model", help="only plot model", action="store_true", dest="plot_model")
self.args = parser.parse_args(sys.argv[1:])
def _train(self):
self.model.train(self.args.alpha, self.args.epoch)
self.model.write_theta()
def _predict(self):
if self.args.x is not None:
print(self.model.hypothesis(self.args.x))
else:
predict.predict_input(self.model)
def _cost(self):
print("Cost:", self.model.cost())
def _plot(self):
if not self.args.plot_data and not self.args.plot_model:
self.model.plot()
else:
self.model.plot(self.args.plot_data, self.args.plot_model)
def exec_args(self):
self.args.func()
if __name__ == "__main__":
cli = CommandLineInterface()
cli.parse_args()
cli.exec_args()
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