61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
import numpy as np # helps with the math
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import matplotlib.pyplot as plt # to plot error during training
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from data.get_data import pull_training_data
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from data.db_connect import Database
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from data.build_weather import get_weather, get_sun_and_moon_phase
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from data.stats_importer import Importer
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# input data
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inputs = np.array([[0, 0, 1, 0],
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[0, 0, 1, 1],
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[0, 0, 0, 0],
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[1, 1, 0, 0],
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[1, 1, 1, 1],
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[1, 1, 0, 1]])
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# output data
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outputs = np.array([[0], [0], [0], [1], [1], [1]])
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if __name__ == '__main__':
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#db_file = "./database/baseball.db"
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#db_conn = Database(db_file)
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#pull_training_data(db_conn, "20240602", 0, "BAL12")
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print(get_sun_and_moon_phase(39.283889, -76.621667, "20240602"))
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"""
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build_db_path = "./data/sql/build_db.sql"
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fill_parks_path = "./data/sql/prefill_parks.sql"
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fill_teams_path = "./data/sql/prefill_teams.sql"
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db_conn.run_sql_file(build_db_path)
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db_conn.run_sql_file(fill_parks_path)
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db_conn.run_sql_file(fill_teams_path)
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imp = Importer(db_conn)
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imp.parse_all_data("./data/stats/to_import", "./data/stats/imported/")
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"""
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"""
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else:
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# create neural network
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NN = NeuralNetwork(inputs, outputs)
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# train neural network
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NN.train()
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# create two new examples to predict
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example = np.array([[1, 1, 1, 0]])
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example_2 = np.array([[0, 0, 1, 1]])
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# print the predictions for both examples
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print(NN.predict(example), ' - Correct: ', example[0][0])
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print(NN.predict(example_2), ' - Correct: ', example_2[0][0])
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# plot the error over the entire training duration
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plt.figure(figsize=(15,5))
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plt.plot(NN.epoch_list, NN.error_history)
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plt.xlabel('Epoch')
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plt.ylabel('Error')
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plt.savefig('plot.png')
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""" |