import numpy as np
import matplotlib.pylab as plt
import padasip as pa

# creation of data
N = 500
x = np.random.normal(0, 1, (N, 4)) # input matrix
v = np.random.normal(0, 0.1, N) # noise
d = 2*x[:,0] + 0.1*x[:,1] - 4*x[:,2] + 0.5*x[:,3] + v # target

# identification
f = pa.filters.FilterRLS(n=4, mu=0.1, w="random")
y, e, w = f.run(d, x)
print(d)
print(x)



# show results
plt.figure(figsize=(15,9))
plt.subplot(211);plt.title("Adaptation")
plt.xlabel("samples - k")
plt.plot(d,"b", label="d - target")
plt.plot(y,"g", label="y - output")
plt.legend()
plt.subplot(212)
plt.title("Filter error")
plt.xlabel("samples - k")
plt.plot(10*np.log10(e**2),"r", label="e - error [dB]")
plt.legend()
plt.tight_layout()
plt.show()