def __init__(self, label_a="a", label_b="b"):
self.label_a = label_a
self.label_b = label_b
self.Ea = MeanVariance()
self.Eb = MeanVariance()
self.Edadb = Expectation()
self.R = None
self.R_needs_update = True
self.num_samples = 0
self.last_a = None
self.last_b = None
class PredictionStats:
@contract(label_a="str", label_b="str")
def __init__(self, label_a="a", label_b="b"):
self.label_a = label_a
self.label_b = label_b
self.Ea = MeanVariance()
self.Eb = MeanVariance()
self.Edadb = Expectation()
self.R = None
self.R_needs_update = True
self.num_samples = 0
self.last_a = None
self.last_b = None
@contract(a="array,shape(x)", b="array,shape(x)", dt="float,>0")
def update(self, a, b, dt=1.0):
self.Ea.update(a, dt)
self.Eb.update(b, dt)
da = a - self.Ea.get_mean()
db = b - self.Eb.get_mean()
self.Edadb.update(da * db, dt)
self.num_samples += dt
self.R_needs_update = True
self.last_a = a
self.last_b = b
def get_correlation(self):
""" Returns the correlation between the two streams. """
if self.R_needs_update:
std_a = self.Ea.get_std_dev()
std_b = self.Eb.get_std_dev()
p = std_a * std_b
zeros = p == 0
p[zeros] = 1
R = self.Edadb() / p
R[zeros] = np.NAN
self.R = R
self.R_needs_update = False
return self.R