def main(): train_data, test_data = read_file(train_file_name), read_file( test_file_name) cleaned_train_data, cleaned_test_data = preform_pre_processing( train_data), preform_pre_processing(test_data) print('========== Question 1 ==========') run_q1(cleaned_train_data, cleaned_test_data) print('========== Question 2 ==========') run_q2(cleaned_train_data, cleaned_test_data)
#!/usr/bin/env python import matplotlib.pyplot as plt from waveform import Waveform import data_reader import sys if __name__ == '__main__': w = data_reader.read_file(sys.argv[-1]) peaks = w.ft_peaks() fx, fy = w.abs_dft() plt.plot(fx, fy) for peak in peaks: plt.plot(fx[peak], fy[peak], marker='o', color='r') plt.show()
import spacy from spacy.util import minibatch, compounding import sys import data_reader import argparse import ast parser = argparse.ArgumentParser(description='Proccess excel file and train data') parser.add_argument('-f') parser.add_argument('-sh') parser.add_argument('-m') parser.add_argument('-o') args = vars(parser.parse_args()) # training data TRAIN_DATA = data_reader.read_file(args['f'], args['sh']) @plac.annotations( model=("Model name. Defaults to blank 'en' model.", "option", "m", str), file=("File of excel.", "option", "f", str), sheet=("Sheetname of excel.", "option", "sh", str), output_dir=("Optional output directory", "option", "o", Path), n_iter=("Number of training iterations", "option", "n", int)) def main(model=None, output_dir=None, n_iter=100, file=None, sheet=None): """Load the model, set up the pipeline and train the entity recognizer.""" if model is not None: nlp = spacy.load(model) # load existing spaCy model print("Loaded model '%s'" % model) else: nlp = spacy.blank('en') # create blank Language class
bbpg = player.random_poisson('BB') points = player.score(opg, hpg, hbpg, bbpg, wpg, erpg, lpg, spg, kpg) else: tb = player.random_poisson('TB') r = player.random_poisson('R') bb = player.random_poisson('BB') rbi = player.random_poisson('RBI') cs = player.random_poisson('CS') sb = player.random_poisson('SB') k = player.random_poisson('K') points = player.score(tb, r, bb, rbi, cs, sb, k) return (points) players = data_reader.read_file('./2017Players.csv') for p in players: p.points_per_game() pitchers = data_reader.read_file('./2017Pitchers.csv', True) for p in pitchers: p.points_per_game() players = [x for x in players if not x.pitcher] pitchers.sort(key=lambda x: x.ppg, reverse=True) players.sort(key=lambda x: x.ppg, reverse=True) my_team = Team() other_team = Team() teams = [my_team, other_team] no_plays = [x for x in players + pitchers if x.sim_plays < 50] while len(no_plays) > 0: topPosPlayers = players[:250]
from waveform import Waveform import data_reader if __name__ == '__main__': waveforms = [] xs = [] ys = [] for trial in ['Trial_6']: datafiles = sorted(os.listdir(os.path.join(sys.argv[-1], trial)), key=lambda item: (int(item.partition(' ')[0]) if item[0].isdigit() else float('inf'), item)) print(datafiles) for datum in datafiles: w = data_reader.read_file(os.path.join(sys.argv[-1], trial, datum)) waveforms.append(w) for w in waveforms: x, y = w.abs_dft() peaks = w.ft_peaks() xs.append(x[peaks[0]]) ys.append(y[peaks[0]]) #plt.plot(x, y) #plt.plot(x[peaks[0]], y[peaks[0]], marker='o', color='r') #plt.show() sorted_x = sorted(xs) sorted_y = [x for _, x in sorted(zip(ys, xs))] plt.plot(sorted_x, sorted_y)
#!/usr/bin/env python import matplotlib.pyplot as plt import data_reader import sys if __name__ == '__main__': data_reader.read_file(sys.argv[-1]).plot() plt.show()