87 lines
1.9 KiB
Python
87 lines
1.9 KiB
Python
import pandas as pd
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import numpy as np
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import seaborn as sb
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import scipy
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import matplotlib.pyplot as plt
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uy = pd.read_csv('data/m.csv')
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print(uy)
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u = uy['u']
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u = u[1:len(u)]
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y = uy['y']
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y = y[1:len(y)]
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plt.plot(u)
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plt.xlabel("$ \mathit{k}T $")
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plt.title("Signal u")
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plt.savefig('res/signal_u.png', dpi=300)
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plt.clf()
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# ----
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plt.plot(y)
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plt.xlabel("$ \mathit{k}T $")
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plt.title("Signal y")
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plt.savefig('res/signal_y.png', dpi=300)
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plt.clf()
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correlation=scipy.stats.pearsonr(uy['u'], uy['y'])
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print(correlation)
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p = sb.lmplot(
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data=uy,
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x='u',
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y='y',
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fit_reg=True,
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order=2,
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)
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p.set(title='Correlation UY')
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p.savefig('res/uy_correlation.png', dpi=300)
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plt.clf()
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plt.close()
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theta = pd.read_csv('data/theta.csv', header=None)
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# theta = np.transpose(theta) # -> no need to transpose.
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th_columns = ["$\hat{a}_1 $", "$\hat{a}_2 $", "$\hat{b}_1$", "$\hat{b}_2$"]
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for i in theta:
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plt.plot(theta[i], label=th_columns[i])
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plt.locator_params(axis='x', nbins=12)
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plt.legend()
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plt.xlabel("$\mathit{k}T$")
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plt.title("ARX Theta (θ) Parameter Estimation using RLSq")
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plt.savefig('res/theta.png', dpi=300)
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plt.clf()
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error = pd.read_csv('data/estimate_error.csv', header=None)
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plt.plot(error, label="$\hat{e}(\mathit{k}T)$")
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plt.locator_params(axis='x', nbins=12)
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plt.legend()
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plt.xlabel("$\mathit{k}T$")
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plt.title("Error of ARX Theta (θ) Parameter Estimation using RLSq")
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plt.savefig('res/error.png', dpi=300)
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plt.clf()
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explicitTheta = pd.read_csv('data/explicit_theta.csv', header=None)
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eTx = explicitTheta[0:4]
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eTy = explicitTheta[4:8]
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eTxy = [eTx, eTy]
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explicitTheta = np.array(eTxy)
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print(explicitTheta)
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explicitError = pd.read_csv('data/explicit_error.csv', header=None)
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plt.plot(explicitError, label="$ê(\mathit{k}T)$")
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plt.locator_params(axis='x', nbins=12)
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plt.legend()
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plt.xlabel("$\mathit{k}T$")
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plt.title("Error of ARX Theta (θ) Parameter Estimation using ELSq")
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plt.savefig('res/explicit_error.png', dpi=300)
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plt.clf()
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