49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import csv
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import numpy as np
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from sklearn.svm import SVR
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import matplotlib.pyplot as plt
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dates = []
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prices = []
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def get_data(filename):
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with open(filename, 'r') as csvfile:
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csvFileReader = csv.reader(csvfile)
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next(csvFileReader)
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for row in csvFileReader:
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dates.append(int(row[0].split('-')[1]))
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prices.append(float(row[1]))
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return
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def predict_prices(dates, prices, x):
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dates = np.reshape(dates, (len(dates), 1))
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svr_lin = SVR(kernel='linear', C=1e3)
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svr_poly = SVR(kernel='poly', C=1e3, degree = 2)
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svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
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svr_lin.fit(dates, prices)
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svr_poly.fit(dates, prices)
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svr_rbf.fit(dates, prices)
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plt.scatter(dates, prices, color='black', label='Data')
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plt.plot(dates, svr_lin.predict(dates), color='green', label='linear model')
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plt.plot(dates, svr_poly.predict(dates), color='blue', label='Polynomial model')
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plt.plot(dates, svr_rbf.predict(dates), color='red', label='RBF model')
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plt.xlabel('Date')
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plt.ylabel('Price')
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plt.title('Support Vector Regression')
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plt.legend()
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plt.show()
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return svr_lin.predict(x)[0], svr_poly.predict(x)[0], svr_rbf.predict(x)[0]
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get_data('static/akcjeWIG40.csv')
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print(dates)
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print(prices)
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predicted_price = predict_prices(dates, prices, 29)
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print(predicted_price)
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