本文整理汇总了Python中scipy.sparse.coo_matrix方法的典型用法代码示例。如果您正苦于以下问题:Python sparse.coo_matrix方法的具体用法?Python sparse.coo_matrix怎么用?Python sparse.coo_matrix使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.sparse
的用法示例。
在下文中一共展示了sparse.coo_matrix方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: transform
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def transform(self, X):
"""Encode categorical columns into sparse matrix with one-hot-encoding.
Args:
X (pandas.DataFrame): categorical columns to encode
Returns:
(scipy.sparse.coo_matrix): sparse matrix encoding categorical
variables into dummy variables
"""
for i, col in enumerate(X.columns):
X_col = self._transform_col(X[col], i)
if X_col is not None:
if i == 0:
X_new = X_col
else:
X_new = sparse.hstack((X_new, X_col))
logger.debug('{} --> {} features'.format(
col, self.label_encoder.label_maxes[i])
)
return X_new
示例2: create_inverse_degree_matrix
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def create_inverse_degree_matrix(edges):
"""
Creating an inverse degree matrix from an edge list.
:param edges: Edge list.
:return D_1: Inverse degree matrix.
"""
graph = nx.from_edgelist(edges)
ind = range(len(graph.nodes()))
degs = [1.0/graph.degree(node) for node in range(graph.number_of_nodes())]
D_1 = sparse.coo_matrix((degs, (ind, ind)),
shape=(graph.number_of_nodes(),
graph.number_of_nodes()),
dtype=np.float32)
return D_1
示例3: vnucele_coo_coulomb
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def vnucele_coo_coulomb(sv, **kw):
"""
Computes the matrix elements defined by
Vne = f(r) sum_a Z_a/|r-R_a| g(r)
Args:
sv : (System Variables), this must have arrays of coordinates and species, etc
Returns:
matrix elements
"""
from numpy import einsum, dot
from scipy.sparse import coo_matrix
g = sv.build_3dgrid_ae(**kw)
ca2o = sv.comp_aos_den(g.coords)
vnuc = sv.comp_vnuc_coulomb(g.coords)
vnuc_w = g.weights*vnuc
cb2vo = einsum('co,c->co', ca2o, vnuc_w)
vne = dot(ca2o.T,cb2vo)
return coo_matrix(vne)
示例4: get_da2cc_sparse
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def get_da2cc_sparse(self, dtype=np.float64, sparseformat=coo_matrix):
""" Returns Conversion Coefficients as sparse COO matrix """
nfdp,nfap = self.dpc2s[-1],self.c2s[-1]
nnz = self.get_da2cc_nnz()
irow,icol,data = zeros(nnz, dtype=int),zeros(nnz, dtype=int), zeros(nnz, dtype=dtype) # Start to construct coo matrix
inz = 0
for atom, [sd,fd,pt] in enumerate(zip(self.dpc2s,self.dpc2s[1:],self.dpc2t)):
if pt!=1: continue
for d in range(sd,fd):
irow[inz],icol[inz],data[inz] = d,d,1.0
inz+=1
for atom, [sd,fd,pt,spp] in enumerate(zip(self.dpc2s,self.dpc2s[1:],self.dpc2t,self.dpc2sp)):
if pt==1: continue
inf = self.bp2info[spp]
for c,ls,lf in zip(inf.cc2a, inf.cc2s, inf.cc2s[1:]):
for d in range(sd,fd):
for a in range(self.c2s[c],self.c2s[c+1]):
irow[inz],icol[inz],data[inz] = d,a,inf.cc[d-sd,a-self.c2s[c]+ls]
inz+=1
return sparseformat((data,(irow,icol)), dtype=dtype, shape=(nfdp, nfap))
示例5: get_degree_directed
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def get_degree_directed(graph):
graph = spsp.coo_matrix(graph)
out_degree_vector = np.zeros(graph.shape[0], dtype=np.float64)
out_weighted_degree_vector = np.zeros(graph.shape[0], dtype=np.float64)
in_degree_vector = np.zeros(graph.shape[0], dtype=np.float64)
in_weighted_degree_vector = np.zeros(graph.shape[0], dtype=np.float64)
for i, j, d in zip(graph.row, graph.col, graph.data):
out_degree_vector[i] += 1.0
in_degree_vector[j] += 1.0
out_weighted_degree_vector[i] += d
in_weighted_degree_vector[j] += d
total_degree_vector = out_degree_vector + in_degree_vector
total_weighted_degree_vector = out_weighted_degree_vector + in_weighted_degree_vector
return out_degree_vector,\
out_weighted_degree_vector, \
in_degree_vector, \
in_weighted_degree_vector, \
total_degree_vector,\
total_weighted_degree_vector
示例6: randomedge_sampler
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def randomedge_sampler(self, percent, normalization, cuda):
"""
Randomly drop edge and preserve percent% edges.
"""
"Opt here"
if percent >= 1.0:
return self.stub_sampler(normalization, cuda)
nnz = self.train_adj.nnz
perm = np.random.permutation(nnz)
preserve_nnz = int(nnz*percent)
perm = perm[:preserve_nnz]
r_adj = sp.coo_matrix((self.train_adj.data[perm],
(self.train_adj.row[perm],
self.train_adj.col[perm])),
shape=self.train_adj.shape)
r_adj = self._preprocess_adj(normalization, r_adj, cuda)
fea = self._preprocess_fea(self.train_features, cuda)
return r_adj, fea
示例7: degree_sampler
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def degree_sampler(self, percent, normalization, cuda):
"""
Randomly drop edge wrt degree (high degree, low probility).
"""
if percent >= 0:
return self.stub_sampler(normalization, cuda)
if self.degree_p is None:
degree_adj = self.train_adj.multiply(self.degree)
self.degree_p = degree_adj.data / (1.0 * np.sum(degree_adj.data))
# degree_adj = degree_adj.multi degree_adj.sum()
nnz = self.train_adj.nnz
preserve_nnz = int(nnz * percent)
perm = np.random.choice(nnz, preserve_nnz, replace=False, p=self.degree_p)
r_adj = sp.coo_matrix((self.train_adj.data[perm],
(self.train_adj.row[perm],
self.train_adj.col[perm])),
shape=self.train_adj.shape)
r_adj = self._preprocess_adj(normalization, r_adj, cuda)
fea = self._preprocess_fea(self.train_features, cuda)
return r_adj, fea
示例8: test_cast
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def test_cast():
m = spsp.coo_matrix(([1, 1], ([0, 1], [1, 2])), (4, 4))
g = dgl.DGLGraph(m, readonly=True)
gsrc, gdst = g.edges(order='eid')
ndata = F.randn((4, 5))
edata = F.randn((2, 4))
g.ndata['x'] = ndata
g.edata['y'] = edata
hg = dgl.as_heterograph(g, 'A', 'AA')
assert hg.ntypes == ['A']
assert hg.etypes == ['AA']
assert hg.canonical_etypes == [('A', 'AA', 'A')]
assert hg.number_of_nodes() == 4
assert hg.number_of_edges() == 2
hgsrc, hgdst = hg.edges(order='eid')
assert F.array_equal(gsrc, hgsrc)
assert F.array_equal(gdst, hgdst)
g2 = dgl.as_immutable_graph(hg)
assert g2.number_of_nodes() == 4
assert g2.number_of_edges() == 2
g2src, g2dst = hg.edges(order='eid')
assert F.array_equal(g2src, gsrc)
assert F.array_equal(g2dst, gdst)
示例9: create_test_heterograph2
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def create_test_heterograph2(index_dtype):
plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
wishes_nx = nx.DiGraph()
wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
wishes_nx.add_edge('u0', 'g1', id=0)
wishes_nx.add_edge('u2', 'g0', id=1)
develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
g = dgl.heterograph({
('user', 'follows', 'user'): [(0, 1), (1, 2)],
('user', 'plays', 'game'): plays_spmat,
('user', 'wishes', 'game'): wishes_nx,
('developer', 'develops', 'game'): develops_g,
})
return g
示例10: create_test_heterograph3
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def create_test_heterograph3(index_dtype):
plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
wishes_nx = nx.DiGraph()
wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
wishes_nx.add_edge('u0', 'g1', id=0)
wishes_nx.add_edge('u2', 'g0', id=1)
follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows', _restrict_format='coo')
plays_g = dgl.bipartite(
[(0, 0), (1, 0), (2, 1), (1, 1)], 'user', 'plays', 'game', _restrict_format='coo')
wishes_g = dgl.bipartite([(0, 1), (2, 0)], 'user', 'wishes', 'game', _restrict_format='coo')
develops_g = dgl.bipartite(
[(0, 0), (1, 1)], 'developer', 'develops', 'game', _restrict_format='coo')
g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])
return g
示例11: test_pickling_heterograph
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def test_pickling_heterograph():
# copied from test_heterograph.create_test_heterograph()
plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
wishes_nx = nx.DiGraph()
wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
wishes_nx.add_edge('u0', 'g1', id=0)
wishes_nx.add_edge('u2', 'g0', id=1)
follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows')
plays_g = dgl.bipartite(plays_spmat, 'user', 'plays', 'game')
wishes_g = dgl.bipartite(wishes_nx, 'user', 'wishes', 'game')
develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])
g.nodes['user'].data['u_h'] = F.randn((3, 4))
g.nodes['game'].data['g_h'] = F.randn((2, 5))
g.edges['plays'].data['p_h'] = F.randn((4, 6))
new_g = _reconstruct_pickle(g)
_assert_is_identical_hetero(g, new_g)
示例12: test_pickling_heterograph_index_compatibility
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def test_pickling_heterograph_index_compatibility():
plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
wishes_nx = nx.DiGraph()
wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
wishes_nx.add_edge('u0', 'g1', id=0)
wishes_nx.add_edge('u2', 'g0', id=1)
follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows')
plays_g = dgl.bipartite(plays_spmat, 'user', 'plays', 'game')
wishes_g = dgl.bipartite(wishes_nx, 'user', 'wishes', 'game')
develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])
with open("tests/compute/hetero_pickle_old.pkl", "rb") as f:
gi = pickle.load(f)
f.close()
new_g = dgl.DGLHeteroGraph(gi, g.ntypes, g.etypes)
_assert_is_identical_hetero(g, new_g)
示例13: mmread
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def mmread(source):
"""
Reads the contents of a Matrix Market file-like 'source' into a matrix.
Parameters
----------
source : str or file-like
Matrix Market filename (extensions .mtx, .mtz.gz)
or open file-like object.
Returns
-------
a : ndarray or coo_matrix
Dense or sparse matrix depending on the matrix format in the
Matrix Market file.
"""
return MMFile().read(source)
# -----------------------------------------------------------------------------
示例14: _transform_col
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def _transform_col(self, x, i):
"""Encode one categorical column into sparse matrix with one-hot-encoding.
Args:
x (pandas.Series): a categorical column to encode
i (int): column index
Returns:
(scipy.sparse.coo_matrix): sparse matrix encoding a categorical
variable into dummy variables
"""
labels = self.label_encoder._transform_col(x, i)
label_max = self.label_encoder.label_maxes[i]
# build row and column index for non-zero values of a sparse matrix
index = np.array(range(len(labels)))
i = index[labels > 0]
j = labels[labels > 0] - 1 # column index starts from 0
if len(i) > 0:
return sparse.coo_matrix((np.ones_like(i), (i, j)),
shape=(x.shape[0], label_max))
else:
# if there is no non-zero value, return no matrix
return None
示例15: normalize_adjacency
# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import coo_matrix [as 别名]
def normalize_adjacency(edges):
"""
Method to calculate a sparse degree normalized adjacency matrix.
:param edges: Edge list of graph.
:return A: Normalized adjacency matrix.
"""
D_1 = create_inverse_degree_matrix(edges)
index_1 = [edge[0] for edge in edges] + [edge[1] for edge in edges]
index_2 = [edge[1] for edge in edges] + [edge[0] for edge in edges]
values = [1.0 for edge in edges] + [1.0 for edge in edges]
A = sparse.coo_matrix((values, (index_1, index_2)),
shape=D_1.shape,
dtype=np.float32)
A = A.dot(D_1)
return A