Esempio n. 1
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 def test_fmpt(self):
     k = self.p.shape[0]
     obs = ergodic.fmpt(self.p).flatten().tolist()[0]
     exp = np.matrix([[2.5, 4., 3.33333333], [2.66666667, 5.,
                                              2.66666667], [3.33333333, 4., 2.5]])
     exp = exp.flatten().tolist()[0]
     for i in range(k):
         self.assertAlmostEqual(exp[i], obs[i])
Esempio n. 2
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    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
Esempio n. 3
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    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