def _calc(self, y, w, classes, k):
# lag markov
ly = pysal.lag_spatial(w, y)
npm = np.matrix
npa = np.array
if self.fixed:
l_classes = pysal.Quantiles(ly.flatten(), k=k).yb
l_classes.shape = ly.shape
else:
l_classes = npa([
pysal.Quantiles(ly[:, i], k=k).yb for i in np.arange(self.cols)
])
l_classes = l_classes.transpose()
l_classic = Markov(l_classes)
T = np.zeros((k, k, k))
n, t = y.shape
for t1 in range(t - 1):
t2 = t1 + 1
for i in range(n):
T[l_classes[i, t1], classes[i, t1], classes[i, t2]] += 1
P = np.zeros_like(T)
F = np.zeros_like(T) # fmpt
ss = np.zeros_like(T[0])
for i, mat in enumerate(T):
row_sum = mat.sum(axis=1)
row_sum = row_sum + (row_sum == 0)
p_i = np.matrix(np.diag(1. / row_sum) * np.matrix(mat))
#print i
#print mat
#print p_i
ss[i] = steady_state(p_i).transpose()
try:
F[i] = fmpt(p_i)
except:
#pylint; "No exception type(s) specified"
print "Singlular fmpt matrix for class ", i
P[i] = p_i
return T, P, ss, F
def _calc(self, y, w, classes, k):
# lag markov
ly = pysal.lag_spatial(w, y)
npm = np.matrix
npa = np.array
if self.fixed:
l_classes = pysal.Quantiles(ly.flatten(), k=k).yb
l_classes.shape = ly.shape
else:
l_classes = npa([pysal.Quantiles(
ly[:, i], k=k).yb for i in np.arange(self.cols)])
l_classes = l_classes.transpose()
l_classic = Markov(l_classes)
T = np.zeros((k, k, k))
n, t = y.shape
for t1 in range(t - 1):
t2 = t1 + 1
for i in range(n):
T[l_classes[i, t1], classes[i, t1], classes[i, t2]] += 1
P = np.zeros_like(T)
F = np.zeros_like(T) # fmpt
ss = np.zeros_like(T[0])
for i, mat in enumerate(T):
row_sum = mat.sum(axis=1)
row_sum = row_sum + (row_sum == 0)
p_i = np.matrix(np.diag(1. / row_sum) * np.matrix(mat))
#print i
#print mat
#print p_i
ss[i] = steady_state(p_i).transpose()
try:
F[i] = fmpt(p_i)
except:
#pylint; "No exception type(s) specified"
print "Singlular fmpt matrix for class ", i
P[i] = p_i
return T, P, ss, F
def test_steady_state(self):
obs = ergodic.steady_state(self.p).tolist()
exp = np.matrix([[0.4], [0.2], [0.4]]).tolist()
k = self.p.shape[0]
for i in range(k):
self.assertAlmostEqual(exp[i][0], obs[i][0])
