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remove commented out old code
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@solomonik
solomonik committed Jan 8, 2018
1 parent 8d28df7 commit 1759542
Showing 1 changed file with 2 additions and 267 deletions.
269 changes: 2 additions & 267 deletions src_python/ctf/core.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -379,26 +379,6 @@ cdef class itensor(term):
tsr_copy = tensor(copy=self.tsr)
tsr_copy.set_all(other)
deref((<itensor>self).it) << deref(itensor(tsr_copy,self.string).it)
# if self.dtype == np.float64:
# elif self.dtype == np.float32:
# deref((<itensor>self).it) << <float>other
# elif self.dtype == np.complex128:
# deref((<itensor>self).it) << <double complex>other
# elif self.dtype == np.complex64:
# deref((<itensor>self).it) << <complex>other
# elif self.dtype == np.bool:
# deref((<itensor>self).it) << <bool>other
# elif self.dtype == np.int64:
# deref((<itensor>self).it) << <int64_t>other
# elif self.dtype == np.int32:
# deref((<itensor>self).it) << <int32_t>other
# elif self.dtype == np.int16:
# deref((<itensor>self).it) << <int16_t>other
# elif self.dtype == np.int8:
# deref((<itensor>self).it) << <int8_t>other
# else:
# raise ValueError('CTF PYTHON ERROR: bad dtype')


def __cinit__(self, tensor a, string):
self.it = new Idx_Tensor(a.dt, string.encode())
Expand Down Expand Up @@ -2165,44 +2145,13 @@ def dot(tA, tB, out=None):

A = astensor(tA)
B = astensor(tB)
#elif type(A)==tensor and type(B)!=tensor:
# ret_dtype = get_np_dtype([A.dtype, type(B)])

# if A.dtype == ret_dtype:
# temp = A
# else:
# temp = A.astype(ret_dtype)
# string = get_num_str(len(A.shape))
# ret = tensor(A.shape, dtype = ret_dtype)
# ret.i(string) << B * temp.i(string)
# return ret
#elif type(A)!=tensor and type(B)==tensor:
# ret_dtype = get_np_dtype([type(A), B.dtype])

# if ret_dtype == B.dtype:
# temp = B
# else:
# temp = B.astype(ret_dtype)
# string = get_num_str(len(A.shape))
# ret = tensor(B.shape, dtype = ret_dtype)
# ret.i(string) << A * temp.i(string)
# return ret
#elif type(A)==tensor and type(B)==tensor:
return tensordot(A, B, axes=([-1],[0]))
#else:
# return tensordot(astensor(A), astensor(B), axes=([-1],[0]))
# raise ValueError("Wrong Type")

def tensordot(tA, tB, axes=2):
A = astensor(tA)
B = astensor(tB)

# when axes equals integer
#if type(axes) == int and axes <= 0:
#ret_shape = A.shape + B.shape
#C = tensor(ret_shape, dtype = np.float64)
#C.i("abcdefg") << A.i("abcd") * B.i("efg")
#return C
if isinstance(axes, (int, np.integer)):
if axes > len(A.shape) or axes > len(B.shape):
raise ValueError("tuple index out of range")
Expand Down Expand Up @@ -2520,28 +2469,7 @@ def sum(tensor init_A, axis = None, dtype = None, out = None, keepdims = None):
return ret.reshape(np.ones(tensor.shape))
else:
return ret.read_all()[0]
#else:
# since the type is not same, we need another tensor C change the value of A and use C instead of A
# C = tensor(A.shape, dtype = dtype)
# A.convert_type(C)
# ret = tensor(ret_dim, dtype = dtype)
# ret.i("") << C.i(index_A)
# return ret
#else:
# if A.get_type() == np.bool:
# # not sure at this one
# return 0
# else:
# if dtype == A.get_type():
# ret = tensor((1,), dtype = dtype)
# ret.i("") << A.i(index_A)
# vals = ret.read([0])
# return vals[0]
# else:
# C = tensor(A.shape, dtype = dtype)
# A.convert_type(C)
# ret = tensor((1,), dtype = dtype)
# ret.i("") << C.i(index_A)


# is the axis is an integer
if isinstance(axis, (int, np.integer)):
Expand Down Expand Up @@ -2873,174 +2801,6 @@ def comp_all(tensor A, axis=None, out=None, keepdims=None):
if axis is None:
x = A.bool_sum()
return x == A.tot_size()
#if out is not None:
# if type(out) != np.ndarray:
# raise ValueError('CTF PYTHON ERROR: output must be an array')
# if out.shape != () and keepdims == False:
# raise ValueError('CTF PYTHON ERROR: output parameter has too many dimensions')
# if keepdims == True:
# dims_keep = []
# for i in range(len(A.shape)):
# dims_keep.append(1)
# dims_keep = tuple(dims_keep)
# if out.shape != dims_keep:
# raise ValueError('CTF PYTHON ERROR: output must match when keepdims = True')
#B = tensor((1,), dtype=np.bool)
#index_A = ""
#if A.get_type() == np.float64:
# all_helper[double](<ctensor*>(A.dt), <ctensor*>B.dt, index_A.encode(), "".encode())
#elif A.get_type() == np.int64:
# all_helper[int64_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), "".encode())
#elif A.get_type() == np.int32:
# all_helper[int32_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), "".encode())
#elif A.get_type() == np.int16:
# all_helper[int16_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), "".encode())
#elif A.get_type() == np.int8:
# all_helper[int8_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), "".encode())
#elif A.get_type() == np.bool:
# all_helper[bool](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), "".encode())
#if out is not None:
# if out.dtype != B.get_type():
# if keepdims == True:
# dim_keep = np.ones(len(A.shape),dtype=np.int64)
# ret = reshape(B,dim_keep)
# C = tensor((1,), dtype=out.dtype)
# B.convert_type(C)
# n, inds, vals = C.read_local()
# return vals.reshape(out.shape)
# else:
# if keepdims == True:
# dim_keep = np.ones(len(A.shape),dtype=np.int64)
# ret = reshape(B,dim_keep)
# return ret
# n, inds, vals = B.read_local()
# return vals.reshape(out.shape)
#if keepdims == True:
# dim_keep = np.ones(len(A.shape),dtype=np.int64)
# ret = reshape(B,dim_keep)
# return ret
#n, inds, vals = B.read_local()
#return vals[0]

# when the axis is not None
#dim = A.shape
#if type(axis) == int:
# if axis < 0:
# axis += len(dim)
# if axis >= len(dim) or axis < 0:
# raise ValueError("'axis' entry is out of bounds")
# dim_ret = np.delete(dim, axis)
# # print(dim_ret)
# if out is not None:
# if type(out) != np.ndarray:
# raise ValueError('CTF PYTHON ERROR: output must be an array')
# if len(dim_ret) != len(out.shape):
# raise ValueError('CTF PYTHON ERROR: output parameter dimensions mismatch')
# for i in range(len(dim_ret)):
# if dim_ret[i] != out.shape[i]:
# raise ValueError('CTF PYTHON ERROR: output parameter dimensions mismatch')
# dim_keep = None
# if keepdims == True:
# dim_keep = dim
# dim_keep[axis] = 1
# if out is not None:
# if tuple(dim_keep) != tuple(out.shape):
# raise ValueError('CTF PYTHON ERROR: output must match when keepdims = True')
# index_A = ""
# index_temp = rev_array(index_A)
# index_B = index_temp[0:axis] + index_temp[axis+1:len(dim)]
# index_B = rev_array(index_B)
# # print(index_A, " ", index_B)
# B = tensor(dim_ret, dtype=np.bool)
# if A.get_type() == np.float64:
# all_helper[double](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int64:
# all_helper[int64_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int32:
# all_helper[int32_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int16:
# all_helper[int16_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int8:
# all_helper[int8_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.bool:
# all_helper[bool](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# if out is not None:
# if out.dtype != B.get_type():
# if keepdims == True:
# C = tensor(dim_ret, dtype=out.dtype)
# B.convert_type(C)
# return reshape(C, dim_keep)
# else:
# C = tensor(dim_ret, dtype=out.dtype)
# B.convert_type(C)
# return C
# if keepdims == True:
# return reshape(B, dim_keep)
# return B
#elif type(axis) == tuple or type(axis) == np.ndarray:
# axis = np.asarray(axis, dtype=np.int64)
# dim_keep = None
# if keepdims == True:
# dim_keep = dim
# for i in range(len(axis)):
# dim_keep[axis[i]] = 1
# if out is not None:
# if tuple(dim_keep) != tuple(out.shape):
# raise ValueError('CTF PYTHON ERROR: output must match when keepdims = True')
# for i in range(len(axis.shape)):
# if axis[i] < 0:
# axis[i] += len(dim)
# if axis[i] >= len(dim) or axis[i] < 0:
# raise ValueError("'axis' entry is out of bounds")
# for i in range(len(axis.shape)):
# if np.count_nonzero(axis==axis[i]) > 1:
# raise ValueError("duplicate value in 'axis'")
# dim_ret = np.delete(dim, axis)
# if out is not None:
# if type(out) != np.ndarray:
# raise ValueError('CTF PYTHON ERROR: output must be an array')
# if len(dim_ret) != len(out.shape):
# raise ValueError('CTF PYTHON ERROR: output parameter dimensions mismatch')
# for i in range(len(dim_ret)):
# if dim_ret[i] != out.shape[i]:
# raise ValueError('CTF PYTHON ERROR: output parameter dimensions mismatch')
# B = tensor(dim_ret, dtype=np.bool)
# index_A = ""
# index_temp = rev_array(index_A)
# index_B = ""
# for i in range(len(dim)):
# if i not in axis:
# index_B += index_temp[i]
# index_B = rev_array(index_B)
# # print(" ", index_A, " ", index_B)
# if A.get_type() == np.float64:
# all_helper[double](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int64:
# all_helper[int64_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int32:
# all_helper[int32_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int16:
# all_helper[int16_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.int8:
# all_helper[int8_t](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# elif A.get_type() == np.bool:
# all_helper[bool](<ctensor*>A.dt, <ctensor*>B.dt, index_A.encode(), index_B.encode())
# if out is not None:
# if out.dtype != B.get_type():
# if keepdims == True:
# C = tensor(dim_ret, dtype=out.dtype)
# B.convert_type(C)
# return reshape(C, dim_keep)
# else:
# C = tensor(dim_ret, dtype=out.dtype)
# B.convert_type(C)
# return C
# if keepdims == True:
# return reshape(B, dim_keep)
# return B
#else:
# raise ValueError("an integer is required")
#return None

# issues:
# when the input is numpy array
Expand Down Expand Up @@ -3069,15 +2829,7 @@ def transpose(init_A, axes=None):
if axes[i] < 0:
raise ValueError("axes too negative for CTF transpose")

#all_axes = np.arange(A.ndim)
#for j in range(A.ndim):
# if j != i:
# if axes[j] < 0:
# raise ValueError("cannot have negative two negative axes for transpose")
# all_axes[j] = -1
#for j in range(A.ndim):
# if all_axes[j] != -1:
# axes[i] = j

axes_list = list(axes)
for i in range(len(axes)):
# when any elements of axes is not an integer
Expand Down Expand Up @@ -3256,23 +3008,6 @@ def svd(tensor A, rank=None):
matrix_svd(A.dt, VT.dt, S.dt, U.dt, rank)
return [U, S, VT]

# A = tensor([n, n], dtype=dtype)
# if dtype == np.float64:
# A.i("ii") << 1.0
# else:
# raise ValueError('CTF PYTHON ERROR: bad dtype')
# return A

#cdef ct f
#ef int (*cfunction) (double a, double b, double c, void *args)
#
#cdef int cfunction_cb(double a, double b, double c, void *args):
# global f
# result_from_function = (<object>f)(a, b, c, *<tuple>args)
# for k in range(fdim):
# fval[k] = fval_buffer[k]
# return 0


def match_tensor_types(first, other):
if isinstance(first, tensor):
Expand Down

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