Python scipy.where方法代码示例

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def getLeadingEigenvector(A, normalized=True, lanczosVecs = 15, maxiter = 1000):
        """Compute normalized leading eigenvector of a given matrix A.

        @param A: sparse matrix for which leading eigenvector will be computed
        @param normalized: wheter or not to normalize. Default is C{True}
        @param lanczosVecs: number of Lanczos vectors to be used in the approximate
            calculation of eigenvectors and eigenvalues. This maps to the ncv parameter 
            of scipy's underlying function eigs. 
        @param maxiter: scaling factor for the number of iterations to be used in the 
            approximate calculation of eigenvectors and eigenvalues. The number of iterations 
            passed to scipy's underlying eigs function will be n*maxiter where n is the 
            number of rows/columns of the Laplacian matrix.
        """

        if _sparse.issparse(A) == False:
            raise TypeError("A must be a sparse matrix")

        # NOTE: ncv sets additional auxiliary eigenvectors that are computed
        # NOTE: in order to be more confident to find the one with the largest
        # NOTE: magnitude, see https://github.com/scipy/scipy/issues/4987
        w, pi = _sla.eigs( A, k=1, which="LM", ncv=lanczosVecs, maxiter=maxiter)
        pi = pi.reshape(pi.size,)
        if normalized:
            pi /= sum(pi)
        return pi 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def getAlgebraicConnectivity(self, lanczosVecs = 15, maxiter = 20):
        """
        Returns the algebraic connectivity of the higher-order network.    
        
        @param lanczosVecs: number of Lanczos vectors to be used in the approximate
            calculation of eigenvectors and eigenvalues. This maps to the ncv parameter 
            of scipy's underlying function eigs. 
        @param maxiter: scaling factor for the number of iterations to be used in the 
            approximate calculation of eigenvectors and eigenvalues. The number of iterations 
            passed to scipy's underlying eigs function will be n*maxiter where n is the
            number of rows/columns of the Laplacian matrix.         
        """
    
        Log.add('Calculating algebraic connectivity ... ', Severity.INFO)

        L = self.getLaplacianMatrix()
        # NOTE: ncv sets additional auxiliary eigenvectors that are computed
        # NOTE: in order to be more confident to find the one with the largest
        # NOTE: magnitude, see https://github.com/scipy/scipy/issues/4987
        w = _sla.eigs( L, which="SM", k=2, ncv=lanczosVecs, return_eigenvectors=False, maxiter = maxiter )
        evals_sorted = _np.sort(_np.absolute(w))

        Log.add('finished.', Severity.INFO)

        return _np.abs(evals_sorted[1]) 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def get_concentration_functions(composition_table_dict):

    meta = composition_table_dict["meta"]
    composition_table = Table.from_dict(composition_table_dict["data"])
    elements = [col for col in composition_table.columns if col not in meta]
    x = composition_table["X"].values
    y = composition_table["Y"].values
    cats = composition_table["X"].unique()
    concentration, conc, d, y_c, functions = {}, {}, {}, {}, RecursiveDict()

    for el in elements:
        concentration[el] = to_numeric(composition_table[el].values) / 100.0
        conc[el], d[el], y_c[el] = {}, {}, {}

        if meta["X"] == "category":
            for i in cats:
                k = "{:06.2f}".format(float(i))
                y_c[el][k] = to_numeric(y[where(x == i)])
                conc[el][k] = to_numeric(concentration[el][where(x == i)])
                d[el][k] = interp1d(y_c[el][k], conc[el][k])

            functions[el] = lambda a, b, el=el: d[el][a](b)

        else:
            functions[el] = interp2d(float(x), float(y), concentration[el])

    return functions 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def mycount_garrick(V):
	f=s.zeros((n_bin, n_bin, n_bin))
	Vsum=V[:,s.newaxis]+V[s.newaxis,:] # matrix with the sum of the volumes in the bins
	for k in xrange(1,(n_bin-1)):
		f[:,:,k]=s.where((Vsum<=V[k+1]) & (Vsum>=V[k]), (V[k+1]-Vsum)/(V[k+1]-V[k]),\
		f[:,:,k] )
		f[:,:,k]=s.where((Vsum<=V[k]) & (Vsum>=V[k-1]),(Vsum-V[k-1])/(V[k]-V[k-1]),\
		f[:,:,k])
	
	return f 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def pixSeedfillBinary(self, Imask, Iseed):
        Iseedfill = copy.deepcopy(Iseed)
        s = ones((3, 3))
        Ijmask, k = ndimage.label(Imask, s)
        Ijmask2 = Ijmask * Iseedfill
        A = list(unique(Ijmask2))
        A.remove(0)
        for i in range(0, len(A)):
            x, y = where(Ijmask == A[i])
            Iseedfill[x, y] = 1
        return Iseedfill 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def pagerank(graph_matrix, n_iter=100, alpha=0.9, tol=1e-6):
    n_nodes = graph_matrix.shape[0]
    n_edges_per_node = graph_matrix.sum(axis=1)
    n_edges_per_node = np.array(n_edges_per_node).flatten()

    np.seterr(divide='ignore')
    normilize_vector = np.where((n_edges_per_node != 0),
                                1. / n_edges_per_node, 0)
    np.seterr(divide='warn')

    normilize_matrix = sp.spdiags(normilize_vector, 0,
                                  *graph_matrix.shape, format='csr')

    graph_proba_matrix = normilize_matrix * graph_matrix

    teleport_proba = np.repeat(1. / n_nodes, n_nodes)
    is_dangling, = scipy.where(normilize_vector == 0)

    x_current = teleport_proba
    for _ in range(n_iter):
        x_previous = x_current.copy()

        dangling_total_proba = sum(x_current[is_dangling])
        x_current = (
            x_current * graph_proba_matrix +
            dangling_total_proba * teleport_proba
        )
        x_current = alpha * x_current + (1 - alpha) * teleport_proba

        error = np.abs(x_current - x_previous).mean()
        if error < tol:
            break

    else:
        print("PageRank didn't converge")

    return x_current 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def getFiedlerVectorSparse(self, normalized = True, lanczosVecs = 15, maxiter = 20):
        """Returns the (sparse) Fiedler vector of the higher-order network. The Fiedler 
        vector can be used for a spectral bisectioning of the network.
     
        Note that sparse linear algebra for eigenvalue problems with small eigenvalues 
        is problematic in terms of numerical stability. Consider using the dense version
        of this method in this case. Note also that the sparse Fiedler vector might be scaled by 
        a factor (-1) compared to the dense version.
          
        @param normalized: whether (default) or not to normalize the fiedler vector.
          Normalization is done such that the sum of squares equals one in order to
          get reasonable values as entries might be positive and negative.
        @param lanczosVecs: number of Lanczos vectors to be used in the approximate
            calculation of eigenvectors and eigenvalues. This maps to the ncv parameter 
            of scipy's underlying function eigs. 
        @param maxiter: scaling factor for the number of iterations to be used in the 
            approximate calculation of eigenvectors and eigenvalues. The number of iterations 
            passed to scipy's underlying eigs function will be n*maxiter where n is the 
            number of rows/columns of the Laplacian matrix.
        """
    
        # NOTE: The transposed matrix is needed to get the "left" eigenvectors
        L = self.getLaplacianMatrix()

        # NOTE: ncv sets additional auxiliary eigenvectors that are computed
        # NOTE: in order to be more confident to find the one with the largest
        # NOTE: magnitude, see https://github.com/scipy/scipy/issues/4987
        maxiter = maxiter*L.get_shape()[0]
        w = _sla.eigs( L, k=2, which="SM", ncv=lanczosVecs, return_eigenvectors=False, maxiter=maxiter )
    
        # compute a sparse LU decomposition and solve for the eigenvector 
        # corresponding to the second largest eigenvalue
        n = L.get_shape()[0]
        b = _np.ones(n)
        evalue = _np.sort(_np.abs(w))[1]
        A = (L[1:n,:].tocsc()[:,1:n] - _sparse.identity(n-1).multiply(evalue))
        b[1:n] = A[0,:].toarray()
    
        lu = _sla.splu(A)
        b[1:n] = lu.solve(b[1:n])

        if normalized:
            b /= np.sqrt(np.inner(b, b))
        return b 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def gen_feature_nodearray(xi, feature_max=None):
	if feature_max:
		assert(isinstance(feature_max, int))

	xi_shift = 0 # ensure correct indices of xi
	if scipy and isinstance(xi, tuple) and len(xi) == 2\
			and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
		index_range = xi[0] + 1 # index starts from 1
		if feature_max:
			index_range = index_range[scipy.where(index_range <= feature_max)]
	elif scipy and isinstance(xi, scipy.ndarray):
		xi_shift = 1
		index_range = xi.nonzero()[0] + 1 # index starts from 1
		if feature_max:
			index_range = index_range[scipy.where(index_range <= feature_max)]
	elif isinstance(xi, (dict, list, tuple)):
		if isinstance(xi, dict):
			index_range = xi.keys()
		elif isinstance(xi, (list, tuple)):
			xi_shift = 1
			index_range = range(1, len(xi) + 1)
		index_range = filter(lambda j: xi[j-xi_shift] != 0, index_range)

		if feature_max:
			index_range = filter(lambda j: j <= feature_max, index_range)
		index_range = sorted(index_range)
	else:
		raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)')

	ret = (feature_node*(len(index_range)+2))()
	ret[-1].index = -1 # for bias term
	ret[-2].index = -1

	if scipy and isinstance(xi, tuple) and len(xi) == 2\
			and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
		for idx, j in enumerate(index_range):
			ret[idx].index = j
			ret[idx].value = (xi[1])[idx]
	else:
		for idx, j in enumerate(index_range):
			ret[idx].index = j
			ret[idx].value = xi[j - xi_shift]

	max_idx = 0
	if len(index_range) > 0:
		max_idx = index_range[-1]
	return ret, max_idx 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def laplacian_matrix(G, nodelist=None, weight='weight'):
    """Return the Laplacian matrix of G.

    The graph Laplacian is the matrix L = D - A, where
    A is the adjacency matrix and D is the diagonal matrix of node degrees.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    L : SciPy sparse matrix
      The Laplacian matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges weights are summed.

    See Also
    --------
    to_numpy_matrix
    normalized_laplacian_matrix
    """
    import scipy.sparse
    if nodelist is None:
        nodelist = G.nodes()
    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  format='csr')
    n,m = A.shape
    diags = A.sum(axis=1)
    D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format='csr')
    return  D - A 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def laplacian_matrix(G, nodelist=None, weight='weight'):
    """Returns the Laplacian matrix of G.

    The graph Laplacian is the matrix L = D - A, where
    A is the adjacency matrix and D is the diagonal matrix of node degrees.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    L : SciPy sparse matrix
      The Laplacian matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges weights are summed.

    See Also
    --------
    to_numpy_matrix
    normalized_laplacian_matrix
    """
    import scipy.sparse
    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  format='csr')
    n, m = A.shape
    diags = A.sum(axis=1)
    D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format='csr')
    return D - A 

# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import where [as 别名]
def laplacian_matrix(G, nodelist=None, weight='weight'):
    """Return the Laplacian matrix of G.

    The graph Laplacian is the matrix L = D - A, where
    A is the adjacency matrix and D is the diagonal matrix of node degrees.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list, optional
       The rows and columns are ordered according to the nodes in nodelist.
       If nodelist is None, then the ordering is produced by G.nodes().

    weight : string or None, optional (default='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    L : SciPy sparse matrix
      The Laplacian matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges weights are summed.

    See Also
    --------
    to_numpy_matrix
    normalized_laplacian_matrix
    """
    import scipy.sparse
    if nodelist is None:
        nodelist = list(G)
    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  format='csr')
    n, m = A.shape
    diags = A.sum(axis=1)
    D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format='csr')
    return D - A