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提交 bcb362c8 authored 作者: David Warde-Farley's avatar David Warde-Farley
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PEP8: Fix E231, whitespace after comma.

上级 467a5d6a
隐藏空白字符变更
内嵌 并排
正在显示 包含 49 行增加 和 48 行删除
theano/sparse/basic.py
浏览文件 @ bcb362c8
......@@ -114,9 +114,9 @@ def as_sparse_or_tensor_variable(x, name=None):
If we can't make a sparse variable, we try to make a tensor variable.
"""
try:
return as_sparse_variable(x,name)
return as_sparse_variable(x, name)
except (ValueError, TypeError):
return theano.tensor.as_tensor_variable(x,name)
return theano.tensor.as_tensor_variable(x, name)
def constant(x, name=None):
......@@ -124,7 +124,7 @@ def constant(x, name=None):
raise TypeError("sparse.constant must be called on a scipy.sparse.spmatrix")
try:
return SparseConstant(SparseType(format = x.format,
dtype = x.dtype), x.copy(),name=name)
dtype = x.dtype), x.copy(), name=name)
except TypeError:
raise TypeError("Could not convert %s to SparseType" % x, type(x))
......@@ -233,7 +233,7 @@ class SparseVariable(gof.Variable, _sparse_py_operators):
class SparseConstantSignature(tuple):
def __eq__(self, other):
(a, b), (x,y) = self, other
(a, b), (x, y) = self, other
return a == x \
and (b.dtype == y.dtype)\
and (type(b) == type(y))\
......@@ -241,7 +241,7 @@ class SparseConstantSignature(tuple):
and (abs(b-y).sum() < 1e-6 * b.nnz)
def __hash__(self):
(a,b) = self
(a, b) = self
return hash(type(self)) ^ hash(a) ^ hash(type(b))
......@@ -282,7 +282,7 @@ class SparseType(gof.Type):
'csr' : scipy.sparse.csr_matrix,
'csc' : scipy.sparse.csc_matrix
}
dtype_set = set(['int', 'int8', 'int16','int32', 'int64', 'float32', 'float64', 'complex64','complex128'])
dtype_set = set(['int', 'int8', 'int16', 'int32', 'int64', 'float32', 'float64', 'complex64', 'complex128'])
ndim = 2
Variable = SparseVariable
......@@ -320,23 +320,23 @@ class SparseType(gof.Type):
else:
sp = self.format_cls[self.format](value)
if str(sp.dtype) != self.dtype:
raise NotImplementedError("Expected %s dtype but got %s"%(self.dtype,str(sp.dtype)))
raise NotImplementedError("Expected %s dtype but got %s"%(self.dtype, str(sp.dtype)))
if sp.format != self.format:
raise NotImplementedError()
return sp
@staticmethod
def may_share_memory(a,b):
def may_share_memory(a, b):
# This is Fred suggestion for a quick and dirty way of checking
# aliasing .. this can potentially be further refined (ticket #374)
if _is_sparse(a) and _is_sparse(b):
return a is b
if _is_sparse(b) and isinstance(a, numpy.ndarray):
a,b=b,a
a, b = b, a
if _is_sparse(a) and isinstance(b, numpy.ndarray):
if (numpy.may_share_memory(a.data,b) or
numpy.may_share_memory(a.indices,b) or
numpy.may_share_memory(a.indptr,b)):
if (numpy.may_share_memory(a.data, b) or
numpy.may_share_memory(a.indices, b) or
numpy.may_share_memory(a.indptr, b)):
#currently we can't share memory with a.shape as it is a tuple
return True
return False
......@@ -412,7 +412,7 @@ class CSMProperties(gof.Op):
"""Extract all of .data .indices and .indptr"""
#we don't return a view of the shape, we create a new ndarray from the shape tuple.
view_map = {0:[0],1:[0],2:[0]}
view_map = {0: [0], 1: [0], 2: [0]}
kmap = None
""" WRITEME """
......@@ -472,7 +472,8 @@ def csm_shape(csm): return csm_properties(csm)[3]
class CSM(gof.Op):
"""Construct a CSC or CSR matrix from the internal representation """
view_map = {0:[0]} #should view the other inputs too, but viewing multiple inputs is not
# should view the other inputs too, but viewing multiple inputs is not
view_map = {0: [0]}
#currently supported by the destroyhandler
format = None
......@@ -568,7 +569,7 @@ class CSM(gof.Op):
def grad(self, (data, indices, indptr, shape), (g_out,)):
"""Return a gradient on the data vector"""
#unpack the data vector and wrap it as a 1d TensorType
g_data = csm_grad(self.kmap)(data, csm_data(g_out),csm_indices(g_out))
g_data = csm_grad(self.kmap)(data, csm_data(g_out), csm_indices(g_out))
return [g_data, None, None, None]
CSC = CSM('csc')
......@@ -770,7 +771,7 @@ class GetItem2d(gof.op.Op):
or numpy.isscalar(ind)):
raise NotImplementedError(
'Theano has no sparse vector' +
'Use X[a:b,c:d], X[a:b,c:c+1] or X[a:b] instead.')
'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.')
else:
raise ValueError((
'Advanced indexing is not implemented for sparse '
......@@ -956,7 +957,7 @@ class AddSD(gof.op.Op):
add_s_d = AddSD()
def add(x,y):
def add(x, y):
"""
Add two matrices, at least one of which is sparse.
"""
......@@ -967,13 +968,13 @@ def add(x,y):
y_is_sparse_variable = _is_sparse_variable(y)
assert x_is_sparse_variable or y_is_sparse_variable
if x_is_sparse_variable and y_is_sparse_variable: return add_s_s(x,y)
elif x_is_sparse_variable and not y_is_sparse_variable: return add_s_d(x,y)
elif y_is_sparse_variable and not x_is_sparse_variable: return add_s_d(y,x)
if x_is_sparse_variable and y_is_sparse_variable: return add_s_s(x, y)
elif x_is_sparse_variable and not y_is_sparse_variable: return add_s_d(x, y)
elif y_is_sparse_variable and not x_is_sparse_variable: return add_s_d(y, x)
else: raise NotImplementedError()
def sub(x,y):
def sub(x, y):
return x + (-y)
......@@ -1020,7 +1021,7 @@ class MulSD(gof.op.Op):
#upcast the tensor. Is the cast of sparse done implemented?
dtype = scalar.upcast(x.type.dtype, y.type.dtype)
if y.type.dtype != dtype:
y = tensor.cast(y,dtype)
y = tensor.cast(y, dtype)
if x.type.dtype != y.type.dtype:
raise NotImplementedError()
......@@ -1053,7 +1054,7 @@ class MulSD(gof.op.Op):
for j in xrange(0, N):
for i_idx in xrange(indptr[j], indptr[j+1]):
i = indices[i_idx]
z_data[i_idx] *= y[i,j]
z_data[i_idx] *= y[i, j]
out[0] = z
elif x.format == 'csr':
x_data = x.data
......@@ -1065,7 +1066,7 @@ class MulSD(gof.op.Op):
for i in xrange(0, M):
for j_idx in xrange(indptr[i], indptr[i+1]):
j = indices[j_idx]
z_data[j_idx] *= y[i,j]
z_data[j_idx] *= y[i, j]
out[0] = z
else:
print >> sys.stderr, "WARNING: crappy implementation of MulSD", x.format
......@@ -1078,7 +1079,7 @@ class MulSD(gof.op.Op):
mul_s_d = MulSD()
def mul(x,y):
def mul(x, y):
"""
Multiply (elementwise) two matrices, at least one of which is sparse.
"""
......@@ -1089,9 +1090,9 @@ def mul(x,y):
y_is_sparse_variable = _is_sparse_variable(y)
assert x_is_sparse_variable or y_is_sparse_variable
if x_is_sparse_variable and y_is_sparse_variable: return mul_s_s(x,y)
elif x_is_sparse_variable and not y_is_sparse_variable: return mul_s_d(x,y)
elif y_is_sparse_variable and not x_is_sparse_variable: return mul_s_d(y,x)
if x_is_sparse_variable and y_is_sparse_variable: return mul_s_s(x, y)
elif x_is_sparse_variable and not y_is_sparse_variable: return mul_s_d(x, y)
elif y_is_sparse_variable and not x_is_sparse_variable: return mul_s_d(y, x)
else: raise NotImplementedError()
......@@ -1119,17 +1120,17 @@ class StructuredDot(gof.Op):
raise NotImplementedError('non-matrix b')
if _is_sparse_variable(b):
return gof.Apply(self, [a,b], [SparseType(a.type.format,dtype_out)()])
return gof.Apply(self, [a, b], [SparseType(a.type.format, dtype_out)()])
else:
return gof.Apply(self, [a,b], [tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
return gof.Apply(self, [a, b], [tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
def perform(self, node, (a,b), (out,)):
def perform(self, node, (a, b), (out,)):
if a.shape[1] != b.shape[0]:
raise ValueError('shape mismatch in StructuredDot.perform', (a.shape, b.shape))
#variable = a.dot(b) # deprecated
variable = a * b
if isinstance(node.outputs[0].type,SparseType):
if isinstance(node.outputs[0].type, SparseType):
assert _is_sparse(variable)
out[0] = variable
return
......@@ -1138,7 +1139,7 @@ class StructuredDot(gof.Op):
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector not matrix
if variable.ndim == 1:
variable = numpy.expand_dims(variable,1)
variable = numpy.expand_dims(variable, 1)
elif variable.ndim != 2:
raise Exception('Output of structured dot should be a matrix (ndim=2)')
......@@ -1154,11 +1155,11 @@ class StructuredDot(gof.Op):
#theano._asarray function documentation.
out[0] = theano._asarray(variable, str(variable.dtype))
def grad(self, (a,b), (g_out,)):
def grad(self, (a, b), (g_out,)):
# a is sparse, b is dense, g_out is dense
# ga = g_out x b.T
# gb = a.T x g_out
return [structured_dot_grad(a, b, g_out), structured_dot(a.T,g_out)]
return [structured_dot_grad(a, b, g_out), structured_dot(a.T, g_out)]
_structured_dot = StructuredDot()
......@@ -1212,7 +1213,7 @@ class StructuredDotCSC(gof.Op):
@param a_ind: column indices of the non-null values (.indices of a scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range a_ptr[i]:a_ptr[i+1]
@param n_rows: number of rows of sparse matrix
@param b: dense matrix to perform dot product with, as in dot(a,b)
@param b: dense matrix to perform dot product with, as in dot(a, b)
@param z: return value
@param sub: TODO, not too sure, something to do with weave probably
"""
......@@ -1261,7 +1262,7 @@ class StructuredDotCSC(gof.Op):
)
{
{Py_XDECREF(%(z)s);}
npy_intp dims[] = {0,0};
npy_intp dims[] = {0, 0};
dims[0] = ((npy_int32 *)%(a_nrows)s->data)[0];
dims[1] = %(b)s->dimensions[1];
%(z)s = (PyArrayObject*) PyArray_SimpleNew(2, dims, %(typenum_z)s);
......@@ -1299,12 +1300,12 @@ class StructuredDotCSC(gof.Op):
// for m
// for n
// for k
// z[m,n] += a[m,k] * b[k,n]
// z[m, n] += a[m, k] * b[k, n]
// Here instead: Z =
// for k
// for m (sparse)
// for n
// z[m,n] += a[m,k] * b[k,n]
// z[m, n] += a[m, k] * b[k, n]
// loop over inner dimension
for (npy_int32 k = 0; k < K; ++k)
......@@ -1381,7 +1382,7 @@ class StructuredDotCSR(gof.Op):
@param a_ind: column indices of the non-null values (.indices of a scipy.csc_matrix)
@param a_ptr: a_ptr indicates col indices for col. i are in the range a_ptr[i]:a_ptr[i+1]
@param n_cols: number of columns of sparse matrix
@param b: dense matrix to perform dot product with, as in dot(a,b)
@param b: dense matrix to perform dot product with, as in dot(a, b)
@param z: return value
@param sub: TODO, not too sure, something to do with weave probably
"""
......@@ -1412,7 +1413,7 @@ class StructuredDotCSR(gof.Op):
)
{
{Py_XDECREF(%(z)s);}
npy_intp dims[] = {0,0};
npy_intp dims[] = {0, 0};
dims[0] = %(a_ptr)s->dimensions[0]-1;
dims[1] = %(b)s->dimensions[1];
%(z)s = (PyArrayObject*) PyArray_SimpleNew(2, dims, %(typenum_z)s);
......@@ -1449,12 +1450,12 @@ class StructuredDotCSR(gof.Op):
// for m
// for n
// for k
// z[m,n] += a[m,k] * b[k,n]
// z[m, n] += a[m, k] * b[k, n]
// Here instead:
// for m
// for k (sparse)
// for n
// z[m,n] += a[m,k] * b[k,n]
// z[m, n] += a[m, k] * b[k, n]
// loop over inner dimension
for (npy_int64 m = 0; m < M; ++m)
......@@ -1510,7 +1511,7 @@ def local_structured_dot(node):
def structured_dot_grad(sparse_A, dense_B, ga):
if sparse_A.type.format in ('csc','csr'):
if sparse_A.type.format in ('csc', 'csr'):
if sparse_A.type.format == 'csc':
sdgcsx = sdg_csc
......@@ -1526,10 +1527,10 @@ def structured_dot_grad(sparse_A, dense_B, ga):
#backport
#CSx = CSC if sparse_A.type.format == 'csc' else CSR
g_A_data = sdgcsx(csm_indices(sparse_A),\
g_A_data = sdgcsx(csm_indices(sparse_A), \
csm_indptr(sparse_A), dense_B, ga)
return CSx(g_A_data, csm_indices(sparse_A),\
csm_indptr(sparse_A),\
return CSx(g_A_data, csm_indices(sparse_A), \
csm_indptr(sparse_A), \
csm_shape(sparse_A))
else:
raise NotImplementedError()
......@@ -2088,7 +2089,7 @@ class UsmmCscDense(gof.Op):
)
{
{Py_XDECREF(%(zn)s);}
npy_intp dims[] = {0,0};
npy_intp dims[] = {0, 0};
dims[0] = ((npy_int32 *)%(x_nrows)s->data)[0];
dims[1] = %(y)s->dimensions[1];
%(zn)s = (PyArrayObject*) PyArray_SimpleNew(2, dims, %(typenum_zn)s);
......
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