Python numpy.diag函数代码示例

def _dpmm(coords, alpha, null_density, dof, prior_precision, prior_h0,
          subjects, sampling_coords=None, n_iter=1000, burnin=100,
          co_clust=False):
    """Apply the dpmm analysis to compute clusters from regions coordinates
    """
    from nipy.algorithms.clustering.imm import MixedIMM

    dim = coords.shape[1]
    migmm = MixedIMM(alpha, dim)
    migmm.set_priors(coords)
    migmm.set_constant_densities(
        null_dens=null_density, prior_dens=null_density)
    migmm._prior_dof = dof
    migmm._prior_scale = np.diag(prior_precision[0] / dof)
    migmm._inv_prior_scale_ = [np.diag(dof * 1. / (prior_precision[0]))]
    migmm.sample(coords, null_class_proba=prior_h0, niter=burnin, init=False,
                 kfold=subjects)

    # sampling
    like, pproba, co_clustering = migmm.sample(
        coords, null_class_proba=prior_h0, niter=n_iter, kfold=subjects,
        sampling_points=sampling_coords, co_clustering=True)

    if co_clust:
        return like, 1 - pproba, co_clustering
    else:
        return like, 1 - pproba

    def grad_EVzxVzxT_by_hyper_exact(self, EVzxVzxT_list_this, Z, A, B, hyperno):

        P = Z.shape[0]
        R = Z.shape[1]
        N = A.shape[0]

        if hyperno != 0:
            return EVzxVzxT_list_this * 0

        alpha = self.length_scale * self.length_scale

        I = np.identity(R)
        S = np.diag(B[0, :] * B[0, :])
        Sinv = np.diag(1 / B[0, :] * B[0, :])
        C = I * alpha
        Cinv = I * (1 / alpha)
        CinvSinv = 2 * Cinv + Sinv
        CinvSinv_inv = np.diag(1 / CinvSinv.diagonal())

        dC = self.length_scale * I
        dCinv = -Cinv.dot(dC).dot(Cinv)
        dCinvSinv = 2 * dCinv
        dCinvSinv_inv = -CinvSinv_inv.dot(dCinvSinv).dot(CinvSinv_inv)

        S1 = (
            dCinv
            - dCinv.dot(CinvSinv_inv).dot(Cinv)
            - Cinv.dot(dCinvSinv_inv).dot(Cinv)
            - Cinv.dot(CinvSinv_inv).dot(dCinv)
        )
        S2 = -Sinv.dot(dCinvSinv_inv).dot(Sinv)
        S3 = Sinv.dot(dCinvSinv_inv).dot(Cinv) + Sinv.dot(CinvSinv_inv).dot(dCinv)
        S4 = dCinv.dot(CinvSinv_inv).dot(Cinv) + Cinv.dot(dCinvSinv_inv).dot(Cinv) + Cinv.dot(CinvSinv_inv).dot(dCinv)

        T1s = np.tile(Z.dot(S1).dot(Z.T).diagonal(), [P, 1])
        T1 = np.tile(T1s, [N, 1, 1])
        T2s = T1s.T
        T2 = np.tile(T2s, [N, 1, 1])
        T3 = np.tile(Z.dot(S4).dot(Z.T), [N, 1, 1])
        T4 = np.tile(A.dot(S2).dot(A.T).diagonal(), [P, 1]).T
        T4 = np.expand_dims(T4, axis=2)
        T4 = np.repeat(T4, P, axis=2)
        T5 = A.dot(S3).dot(Z.T)
        T5 = np.expand_dims(T5, axis=2)
        T5 = np.repeat(T5, P, axis=2)
        T6 = np.swapaxes(T5, 1, 2)

        SCinvI = 2 * Cinv.dot(S) + I
        SCinvI_inv = np.diag(1 / SCinvI.diagonal())
        (temp, logDetSCinvI) = np.linalg.slogdet(SCinvI)
        detSCinvI = np.exp(logDetSCinvI)
        dDetSCinvI = -0.5 * np.power(detSCinvI, -0.5) * SCinvI_inv.dot(2 * dCinv).dot(S).trace()

        expTerm = EVzxVzxT_list_this / np.power(detSCinvI, -0.5)

        res = EVzxVzxT_list_this * (-0.5 * T1 - 0.5 * T2 + T3 - 0.5 * T4 + T5 + T6) + dDetSCinvI * expTerm

        res = np.sum(res, axis=0)

        return res

def Haffine_from_points(fp, tp):
    '''计算仿射变换的单应性矩阵H，使得tp是由fp经过仿射变换得到的'''
    if fp.shape != tp.shape:
        raise RuntimeError('number of points do not match')

    # 对点进行归一化
    # 映射起始点
    m = numpy.mean(fp[:2], axis=1)
    maxstd = numpy.max(numpy.std(fp[:2], axis=1)) + 1e-9
    C1 = numpy.diag([1/maxstd, 1/maxstd, 1])
    C1[0, 2] = -m[0] / maxstd
    C1[1, 2] = -m[1] / maxstd
    fp_cond = numpy.dot(C1, fp)

    # 映射对应点
    m = numpy.mean(tp[:2], axis=1)
    maxstd = numpy.max(numpy.std(tp[:2], axis=1)) + 1e-9
    C2 = numpy.diag([1/maxstd, 1/maxstd, 1])
    C2[0, 2] = -m[0] / maxstd
    C2[1, 2] = -m[1] / maxstd
    tp_cond = numpy.dot(C2, tp)

    # 因为归一化之后点的均值为0，所以平移量为0
    A = numpy.concatenate((fp_cond[:2], tp_cond[:2]), axis=0)
    U, S, V = numpy.linalg.svd(A.T)
    # 创建矩阵B和C
    tmp = V[:2].T
    B = tmp[:2]
    C = tmp[2:4]

    tmp2 = numpy.concatenate((numpy.dot(C, numpy.linalg.pinv(B)), numpy.zeros((2, 1))), axis=1)
    H = numpy.vstack((tmp2, [0, 0, 1]))

    H = numpy.dot(numpy.linalg.inv(C2), numpy.dot(H, C1))  # 反归一化
    return H / H[2, 2]  # 归一化，然后返回

 def objective_gradient(self, X, J=None, return_K=False):
     """
     Computes MPF objective gradient on input data X given coupling
     strengths J.
     
     Parameters
     ----------
     X : numpy array
         (M, N)-dim array of binary input patterns of length N,
         where N is the number of nodes in the network
     J : numpy array, optional
         Coupling matrix of size N x N, where N denotes the number
         of nodes in the network (default None)
     return_K : bool, optional
         Flag wether to return K (default False)
     
     Returns
     -------
     dJ [, K] : numpy array [, numpy array]
         Update to coupling matrix J [and K if return_K is True]
     """
     if J is None:
         J = self._J
         J[np.eye(self._N, dtype=bool)] = -2 * self._theta
     X = np.atleast_2d(X)
     M, N = X.shape
     S = 2 * X - 1
     Kfull = np.exp(-S * np.dot(X, J.T) + .5 * np.diag(J)[None, :])
     dJ = -np.dot(X.T, Kfull * S) + .5 * np.diag(Kfull.sum(0))
     if self._symmetric is True:
         dJ = .5 * (dJ + dJ.T)
     if return_K:
         return Kfull.sum() / M, dJ / M
     else:
         return dJ / M

def svdUpdate(U, S, V, a, b):
    """
    Update SVD of an (m x n) matrix `X = U * S * V^T` so that
    `[X + a * b^T] = U' * S' * V'^T`
    and return `U'`, `S'`, `V'`.
    
    `a` and `b` are (m, 1) and (n, 1) rank-1 matrices, so that svdUpdate can simulate 
    incremental addition of one new document and/or term to an already existing 
    decomposition.
    """
    rank = U.shape[1]
    m = U.T * a
    p = a - U * m
    Ra = numpy.sqrt(p.T * p)
    assert float(Ra) > 1e-10
    P = (1.0 / float(Ra)) * p
    n = V.T * b
    q = b - V * n
    Rb = numpy.sqrt(q.T * q)
    assert float(Rb) > 1e-10
    Q = (1.0 / float(Rb)) * q

    K = numpy.matrix(numpy.diag(list(numpy.diag(S)) + [0.0])) + numpy.bmat("m ; Ra") * numpy.bmat(" n; Rb").T
    u, s, vt = numpy.linalg.svd(K, full_matrices=False)
    tUp = numpy.matrix(u[:, :rank])
    tVp = numpy.matrix(vt.T[:, :rank])
    tSp = numpy.matrix(numpy.diag(s[:rank]))
    Up = numpy.bmat("U P") * tUp
    Vp = numpy.bmat("V Q") * tVp
    Sp = tSp
    return Up, Sp, Vp

    def psi(self, x, y):
        # x is unaries
        # y is a labeling
        ## unary features:
        gx, gy = np.ogrid[:x.shape[0], :x.shape[1]]
        selected_unaries = x[gx, gy, y]
        unaries_acc = np.sum(x[gx, gy, y])
        unaries_acc = np.bincount(y.ravel(), selected_unaries.ravel(),
                minlength=self.n_states)

        ##accumulated pairwise
        #make one hot encoding
        labels = np.zeros((y.shape[0], y.shape[1], self.n_states),
                dtype=np.int)
        gx, gy = np.ogrid[:y.shape[0], :y.shape[1]]
        labels[gx, gy, y] = 1
        # vertical edges
        vert = np.dot(labels[1:, :, :].reshape(-1, self.n_states).T,
                labels[:-1, :, :].reshape(-1, self.n_states))
        # horizontal edges
        horz = np.dot(labels[:, 1:, :].reshape(-1, self.n_states).T, labels[:,
            :-1, :].reshape(-1, self.n_states))
        pw = vert + horz
        pw = pw + pw.T - np.diag(np.diag(pw))
        feature = np.hstack([unaries_acc, pw[np.tri(self.n_states,
            dtype=np.bool)]])
        return feature

def get_eq_from_eig(m):   
    ''' get the equilibrium frequencies from the matrix. the eq freqs are the left eigenvector corresponding to eigenvalue of 0. 
        Code here is largely taken from Bloom. See here - https://github.com/jbloom/phyloExpCM/blob/master/src/submatrix.py, specifically in the fxn StationaryStates
    '''
    (w, v) = linalg.eig(m, left=True, right=False)
    max_i = 0
    max_w = w[max_i]
    for i in range(1, len(w)):
        if w[i] > max_w:
            max_w = w[i]
            max_i = i
    assert( abs(max_w) < ZERO ), "Maximum eigenvalue is not close to zero."
    max_v = v[:,max_i]
    max_v /= np.sum(max_v)
    eq_freqs = max_v.real # these are the stationary frequencies
    
    # SOME SANITY CHECKS
    assert np.allclose(np.zeros(61), np.dot(eq_freqs, m)) # should be true since eigenvalue of zero
    pi_inv = np.diag(1.0 / eq_freqs)
    s = np.dot(m, pi_inv)
    assert np.allclose(m, np.dot(s, np.diag(eq_freqs)), atol=ZERO, rtol=1e-5), "exchangeability and equilibrium does not recover matrix"
    
    # And for some impressive overkill, double check pi_i*q_ij = pi_j*q_ji
    for i in range(61):
        pi_i = eq_freqs[i]
        for j in range(61):
            pi_j = eq_freqs[j]
            forward  = pi_i * m[i][j] 
            backward = pi_j * m[j][i]
            assert(abs(forward - backward) < ZERO), "Detailed balance violated."    
    return eq_freqs

    def test_diags_vs_diag(self):
        # Check that
        #
        #    diags([a, b, ...], [i, j, ...]) == diag(a, i) + diag(b, j) + ...
        #

        np.random.seed(1234)

        for n_diags in [1, 2, 3, 4, 5, 10]:
            n = 1 + n_diags//2 + np.random.randint(0, 10)

            offsets = np.arange(-n+1, n-1)
            np.random.shuffle(offsets)
            offsets = offsets[:n_diags]

            diagonals = [np.random.rand(n - abs(q)) for q in offsets]

            mat = construct.diags(diagonals, offsets)
            dense_mat = sum([np.diag(x, j) for x, j in zip(diagonals, offsets)])

            assert_array_almost_equal_nulp(mat.todense(), dense_mat)

            if len(offsets) == 1:
                mat = construct.diags(diagonals[0], offsets[0])
                dense_mat = np.diag(diagonals[0], offsets[0])
                assert_array_almost_equal_nulp(mat.todense(), dense_mat)

def R_from_homography(H, f1, f2):
    K1 = np.diag([f1, f1, 1])
    K2 = np.diag([f2, f2, 1])
    K2inv = np.linalg.inv(K2)
    R = K2inv.dot(H).dot(K1)
    R = project_to_rotation_matrix(R)
    return R

def genGraph(S_actual, S_est, S_previous, empCov_set, nodeID, e1, e2, e3, e4, display = False):
    D = np.where(S_est != 0)[0].shape[0]
    T = np.where(S_actual != 0)[0].shape[0]
    TandD = float(np.where(np.logical_and(S_actual,S_est) == True)[0].shape[0])
    P = TandD/D
    R = TandD/T
    offDiagDiff = S_actual - S_est
    offDiagDiff = offDiagDiff - np.diag(np.diag(offDiagDiff))
    S_diff = (S_est - S_previous)  
    S_diff = S_diff - np.diag(np.diag(S_diff))
    ind = (S_diff < 1e-2) & (S_diff > - 1e-2)
    S_diff[ind] = 0    
    K = np.count_nonzero(S_diff)
    e1.append( alg.norm(offDiagDiff, 'fro'))
    e2.append(2* P*R/(P+R))
    
    
    K = float(np.where(np.logical_and((S_est>0) != (S_previous>0), S_est>0) == True)[0].shape[0])
    e3.append(-np.log(alg.det(S_est)) + np.trace(np.dot(S_est, empCov_set[nodeID])) + K)
    e4.append(alg.norm(S_est -  S_previous, 'fro'))
    
    display = False
    if display == True:
        if (nodeID >timeShift -10) and (nodeID < timeShift + 10):
            print 'nodeID = ', nodeID
            print 'S_true = ', S_actual,'\nS_est', S_est
#            print 'S_error = ',S_actual - S_est, '\n its Fro error = ', alg.norm(S_actual - S_est, 'fro')
            print 'D = ',D,'T = ', T,'TandD = ', TandD,'K = ', K,'P = ', P,'R = ', R,'Score = ', 2* P*R/(P+R)
            
    return e1, e2, e3, e4

def integration_recruitment(MA, S):
    '''
    Input Module-Allegiance "MA" and community strucutre "S"
    Output Integration and Recruitment
    '''


    # transform S to a column vector

    if min(S) == 1:
        S = S-1
    if np.shape(S)[0] == 1:
        S = S.T
    MA = np.double(MA)
    num_node = len(S)
    num_cl = max(S)+1
    H = np.zeros(shape=(num_node, num_cl), dtype = np.double)
    for i in range(num_cl):
        H[:,i] = (S==i)
    D_H = (H.T).dot(H)

    recruitment = np.zeros(shape = (num_cl, num_cl))
    integration = np.zeros(shape = (num_cl, num_cl))

    D_H_Inv = linalg.inv(D_H)
    recruitment = D_H_Inv.dot(H.T).dot(MA).dot(H).dot(D_H_Inv)
    D = np.diag(np.diag(recruitment))
    D_Inv_Sqr = np.power(D, -0.5)
    integration = D_Inv_Sqr.dot(recruitment).dot(D_Inv_Sqr)
    return (integration,recruitment)

def sig_lmc(C, A):
    '''
    This a function that using Lumped Markov chain to calculate
    the significance of clusters in a give communinity structure.
    refer to "Piccardi 2011 in PloS one".
    Here we normalize the original definition of persistence by
    the size of the corresponding cluster to get a better
    INPUT:
        "A" is a N-by-N weighted adjacency matrix
        "C" is a N-by-1 partition(cluster) vector
    OUTPUT:
        normalized persistence probability of all clusters
    '''
    '''
    Transition Matrix
    '''
    C = np.asarray(C)
    A = np.double(A)
    P = np.linalg.solve(np.diag(np.sum(A,axis = 1)),A)
    [eval, evec] = linalg.eigs(P.T, 1)
    if min(evec)<0:
        evec = -evec
    pi = np.double(evec.T)
    num_node = np.double(np.shape(A)[0])
    cl_label = np.double(np.unique(C))
    num_cl = len(cl_label)
    H = np.zeros((num_node, num_cl),dtype = np.double)
    for i in range(num_cl):
        H[:, i] = np.double((C==cl_label[i]))

    # Transition matrix of the lumped Markov chain

    Q = np.dot(np.dot(np.dot(np.linalg.solve(np.diag(np.dot(pi,H).flatten()),H.T),np.diag(pi.flatten())),P),H)
    persistence = np.multiply(np.divide(np.diag(Q), np.sum(H,axis = 0)),np.sum(H))
    return persistence

    def initialize_hypers(self, W):
        mu_0 = W.mean(axis=(0,1))
        sigma_0 = np.diag(W.var(axis=(0,1)))

        # Set the global cov
        nu_0 = self._cov_model.nu_0
        self._cov_model.sigma_0 = sigma_0 * (nu_0 - self.B - 1)

        # Set the mean
        for c1 in xrange(self.C):
            for c2 in xrange(self.C):
                self._gaussians[c1][c2].mu_0 = mu_0
                self._gaussians[c1][c2].sigma = self._cov_model.sigma_0
                self._gaussians[c1][c2].resample()

        if self.special_case_self_conns:
            W_self = W[np.arange(self.N), np.arange(self.N)]
            self._self_gaussian.mu_0 = W_self.mean(axis=0)
            self._self_gaussian.sigma_0 = np.diag(W_self.var(axis=0))
            self._self_gaussian.resample()

        # Cluster the neurons based on their rows and columns
        from sklearn.cluster import KMeans
        features = np.hstack((W[:,:,0], W[:,:,0].T))
        km = KMeans(n_clusters=self.C)
        km.fit(features)
        self.c = km.labels_.astype(np.int)

        print "Initial c: ", self.c

    def generate_time_of_use_periods(self):
        """
        time of use periods will be described by NxM indicator matricies

        """
        N = const.DAILY_UNITS
        quarters = self.generate_quarter_hours()
        
        peak_indicator = [1 if ( (t >= const.PEAK_TIME_RANGE[0]) & (t < const.PEAK_TIME_RANGE[1])) else 0 for t in quarters]

        part_peak_indicator = [1 if ( (t >= const.PART_PEAK_TIME_RANGE[0][0]) and (t < const.PART_PEAK_TIME_RANGE[0][1])
                                        or t >= const.PART_PEAK_TIME_RANGE[1][0]) and (t < const.PART_PEAK_TIME_RANGE[1][1]) else 0 for t in quarters]

        off_peak_indicator = [1 if ( (t >= const.OFF_PEAK_TIME_RANGE[0][0]) and (t < const.OFF_PEAK_TIME_RANGE[0][1])
                                        or t >= const.OFF_PEAK_TIME_RANGE[1][0]) and (t < const.OFF_PEAK_TIME_RANGE[1][1]) else 0 for t in quarters]

        peak_day = np.diag(peak_indicator)
        part_peak = np.diag(part_peak_indicator)
        off_peak_weekday = np.diag(off_peak_indicator) 

        off_peak_weekend_off = np.zeros([N,N]) # used for peak, part_peak
        off_peak_weekend_on  = np.diag([1]*N) # used for off_peak

        # each of these will block_diag 5 week day indicators and 2 weekend indicators
        self.peak_mat = block_diag(peak_day, peak_day, peak_day, peak_day, peak_day,
                                        off_peak_weekend_off, off_peak_weekend_off)
        self.part_peak_mat = block_diag(part_peak, part_peak, part_peak, part_peak, part_peak,
                                        off_peak_weekend_off,off_peak_weekend_off)
        self.off_peak_mat = block_diag(off_peak_weekday, off_peak_weekday, off_peak_weekday, off_peak_weekday, off_peak_weekday,
                                        off_peak_weekend_on, off_peak_weekend_on)
        self.all_peak_mat = np.eye(self.horizon)

def compute_matrix_c(clean_labels, noisy_labels):
    cm = confusion_matrix(clean_labels, noisy_labels)
    cm -= np.diag(np.diag(cm))
    cm = cm * 1.0 / cm.sum(axis=1, keepdims=True)
    cm = cm.T
    L = len(cm)
    alpha = 1.0 / (L - 1)
    C = np.zeros((L, L))
    for j in xrange(L):
        f = cm[:, j].ravel()
        f = zip(f, xrange(L))
        f.sort(reverse=True)
        best_lik = -np.inf
        best_i = -1
        for i in xrange(L + 1):
            c = np.zeros((L,))
            for k in xrange(0, i):
                c[k] = f[k][0]
            if c.sum() > 0:
                c /= c.sum()
            lik = 0
            for k in xrange(0, i):
                lik += f[k][0] * np.log(c[k])
            for k in xrange(i, L):
                lik += f[k][0] * np.log(alpha)
            if lik >= best_lik:
                best_lik = lik
                best_i = i
            if i < L and f[i][0] == 0:
                break
        for k in xrange(0, best_i):
            C[f[k][1], j] = f[k][0]
    return C / C.sum(axis=0)