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utils.py
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utils.py
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from __future__ import division
import numpy as np
# generate a sine wave
def gen_signal(t, f, fs):
return np.sin(2. * np.pi * f / fs * t)
# generate sum of signals
def sum_signals(*args):
return sum(args)
# sigmoid function
def sigmoid(x):
return 1.0/(1.0 + np.exp(-x))
# normalize to +/- 1
def normalize(x):
# normalize to +/- 1
return x / max(abs(x))
# scale (usually to 0-1)
def scale(x, a, b):
return (x - x.min()) * (b - a) / (x.max() - x.min()) + a
# take only a small portion of the signal - one frame
def get_first_frame(x, fs, msec=50.):
n = fs / 1000. * msec
return x[0:n], n
def mse(x, y):
return ((x-y)**2).mean()
# repmat signal and add noise
def make_observation_matrix(x, N=1000, std=0.01):
L = len(x)
X = np.matlib.repmat(x, N, 1)
noise = np.random.normal(0, std, (N, L))
return X + noise
def make_test_signal(x, std=0.01):
return x + np.random.normal(0, std, len(x))
class Signal(object):
def __init__(self, signal, samples=None):
if samples:
self.x = np.array(signal)
self.x = np.array(signal)
def normalize(self):
return self.x / max(abs(self.x))
def scale(self, a, b):
return (self.x - self.x.min()) * (b - a) / (self.x.max() - self.x.min()) + a
def mse(self, est):
return ((self.x - est)**2).mean()
if __name__ == "__main__":
x = Signal([1,2,3,4,5])
x.normalize()
print x.orig
print x.x