def train(self):
"""
This fucntion will start the estimation. This is separated from addData.
"""
beta = scipy.empty((self.t, self.n))
b = self.initBeta
V = self.initVariance
Phi = self.Phi
S = self.Sigma
s = self.sigma
y = self.respond.data
X = self.regressors.data
beta = kalman_filter(b, V, Phi, y, X, s, S)
self.est = TimeSeriesFrame(beta, self.regressors.rheader,
self.regressors.cheader)
return self
def train(self):
"""
This fucntion will start the estimation. This is separated from addData.
"""
beta = scipy.empty((self.t, self.n))
b = self.initBeta
V = self.initVariance
Phi = self.Phi
S = self.Sigma
s = self.sigma
y = self.respond.data
X = self.regressors.data
beta = kalman_filter(b, V, Phi, y, X, s, S)
self.est = TimeSeriesFrame(beta,
self.regressors.rheader,
self.regressors.cheader)
return self
n = 7
beta = scipy.empty(scipy.shape(X), dtype=numpy.float)
b = scipy.ones((n, 1), dtype=numpy.float) / float(n)
V = scipy.identity(n, dtype=numpy.float)
Phi = scipy.identity(n, dtype=numpy.float)
S = scipy.identity(n, dtype=numpy.float)
sigma = scipy.matrix([1.0], dtype=numpy.float)
Sigma = scipy.identity(n, dtype=numpy.float)
D = scipy.ones((1, n), dtype=numpy.float)
d = scipy.matrix(1., dtype=numpy.float)
G = scipy.identity(n, dtype=numpy.float)
a = scipy.zeros((n, 1), dtype=numpy.float)
c = scipy.ones((n, 1), dtype=numpy.float)
# import code; code.interact(local=locals())
beta = clibregression.kalman_filter(b, V, Phi, y, X, sigma, Sigma, 1, D, d, G,
a, c)
#(b, V) = kalman_predict(b,V,Phi, S)
# for i in xrange(len(X)):
# beta[i] = scipy.array(b).T
# (b,V, e,K) = kalman_upd(b,V, y[i] ,X[i], sigma, Sigma, 2, D, d,G,a,c)
# (b, V) = kalman_predict(b,V,Phi, S)
# for i, (xs, ys) in enumerate(zip(X,y)):
# beta[i,:] = scipy.array(b).T
# (b,V, e,K) = kalman_upd(b,V, ys ,xs, sigma, Sigma, 2, D, d,G,a,c)
# # print "b:\n", b
# # print "V:\n", V
# # print "e:\n", e
# # print "K:\n", K
n=7 beta = scipy.empty(scipy.shape(X), dtype = numpy.float) b = scipy.ones((n,1), dtype = numpy.float)/float(n) V = scipy.identity(n, dtype = numpy.float) Phi = scipy.identity(n, dtype = numpy.float) S = scipy.identity(n, dtype = numpy.float) sigma = scipy.matrix([1.0], dtype = numpy.float) Sigma = scipy.identity(n, dtype = numpy.float) D = scipy.ones((1,n), dtype = numpy.float) d = scipy.matrix(1., dtype = numpy.float) G = scipy.identity(n, dtype = numpy.float) a = scipy.zeros((n,1), dtype = numpy.float) c = scipy.ones((n,1), dtype = numpy.float) # import code; code.interact(local=locals()) beta = clibregression.kalman_filter(b, V, Phi, y, X, sigma, Sigma, 1 ,D , d, G, a, c) #(b, V) = kalman_predict(b,V,Phi, S) # for i in xrange(len(X)): # beta[i] = scipy.array(b).T # (b,V, e,K) = kalman_upd(b,V, y[i] ,X[i], sigma, Sigma, 2, D, d,G,a,c) # (b, V) = kalman_predict(b,V,Phi, S) # for i, (xs, ys) in enumerate(zip(X,y)): # beta[i,:] = scipy.array(b).T # (b,V, e,K) = kalman_upd(b,V, ys ,xs, sigma, Sigma, 2, D, d,G,a,c) # # print "b:\n", b # # print "V:\n", V # # print "e:\n", e