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Try to use cimports with relative packages instead.

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This commit is contained in:
Ryan Curtin
2023-08-08 19:12:42 -04:00
parent 6b92f1199b
commit af2014ef6a
8 changed files with 128 additions and 128 deletions
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src/mlpack/bindings/python/get_arma_type.hpp
+2 -2
View File
@@ -3,8 +3,8 @@
* @author Ryan Curtin
*
* Return "mat", "col", or "row" depending on the type of the given Armadillo
* object. This is so that the correct overload of arma_numpy.numpy_to_<type>()
* can be called.
* object. This is so that the correct overload of numpy_to_<type>() can be
* called.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
src/mlpack/bindings/python/get_cython_type.hpp
+1 -1
View File
@@ -102,7 +102,7 @@ inline std::string GetCythonType(
else if (T::is_col)
type = "Col";
return "arma." + type + "[" + GetCythonType<typename T::elem_type>(d) + "]";
return type + "[" + GetCythonType<typename T::elem_type>(d) + "]";
}
template<typename T>
src/mlpack/bindings/python/mlpack/arma_numpy.pxd
+19 -19
View File
@@ -18,55 +18,55 @@ import numpy
numpy.import_array()
cimport arma
from .arma cimport Mat, Row, Col
from libcpp cimport bool
"""
Convert a numpy ndarray to a matrix.
"""
cdef arma.Mat[double]* numpy_to_mat_d(numpy.ndarray[numpy.double_t, ndim=2] X, \
bool takeOwnership) except +
cdef arma.Mat[size_t]* numpy_to_mat_s(numpy.ndarray[numpy.npy_intp, ndim=2] X, \
bool takeOwnership) except +
cdef Mat[double]* numpy_to_mat_d(numpy.ndarray[numpy.double_t, ndim=2] X, \
bool takeOwnership) except +
cdef Mat[size_t]* numpy_to_mat_s(numpy.ndarray[numpy.npy_intp, ndim=2] X, \
bool takeOwnership) except +
"""
Convert an Armadillo object to a numpy ndarray of the given type.
"""
cdef numpy.ndarray[numpy.double_t, ndim=2] mat_to_numpy_d(arma.Mat[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=2] mat_to_numpy_d(Mat[double]& X) \
except +
cdef numpy.ndarray[numpy.npy_intp, ndim=2] mat_to_numpy_s(arma.Mat[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=2] mat_to_numpy_s(Mat[size_t]& X) \
except +
"""
Convert a numpy one-dimensional ndarray to a row of the given type.
"""
cdef arma.Row[double]* numpy_to_row_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +
cdef arma.Row[size_t]* numpy_to_row_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +
cdef Row[double]* numpy_to_row_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +
cdef Row[size_t]* numpy_to_row_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +
"""
Convert an Armadillo row vector to a one-dimensional numpy ndarray of the
given type.
"""
cdef numpy.ndarray[numpy.double_t, ndim=1] row_to_numpy_d(arma.Row[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=1] row_to_numpy_d(Row[double]& X) \
except +
cdef numpy.ndarray[numpy.npy_intp, ndim=1] row_to_numpy_s(arma.Row[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=1] row_to_numpy_s(Row[size_t]& X) \
except +
"""
Convert a numpy one-dimensional ndarray to a column vector of the given type.
"""
cdef arma.Col[double]* numpy_to_col_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +
cdef arma.Col[size_t]* numpy_to_col_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +
cdef Col[double]* numpy_to_col_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +
cdef Col[size_t]* numpy_to_col_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +
"""
Convert an Armadillo column vector to a one-dimensional numpy ndarray of the
given type.
"""
cdef numpy.ndarray[numpy.double_t, ndim=1] col_to_numpy_d(arma.Col[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=1] col_to_numpy_d(Col[double]& X) \
except +
cdef numpy.ndarray[numpy.npy_intp, ndim=1] col_to_numpy_s(arma.Col[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=1] col_to_numpy_s(Col[size_t]& X) \
except +
src/mlpack/bindings/python/mlpack/arma_numpy.pyx
+68 -68
View File
@@ -23,7 +23,7 @@ import numpy
numpy.import_array()
cimport arma
from .arma cimport Mat, Row, Col
from libcpp cimport bool
import platform
@@ -39,15 +39,15 @@ cdef extern from "numpy/arrayobject.h":
cdef extern from "<mlpack/bindings/python/mlpack/arma_util.hpp>":
void SetMemState[T](T& m, int state)
size_t GetMemState[T](T& m)
double* GetMemory(arma.Mat[double]& m)
double* GetMemory(arma.Col[double]& m)
double* GetMemory(arma.Row[double]& m)
size_t* GetMemory(arma.Mat[size_t]& m)
size_t* GetMemory(arma.Col[size_t]& m)
size_t* GetMemory(arma.Row[size_t]& m)
double* GetMemory(Mat[double]& m)
double* GetMemory(Col[double]& m)
double* GetMemory(Row[double]& m)
size_t* GetMemory(Mat[size_t]& m)
size_t* GetMemory(Col[size_t]& m)
size_t* GetMemory(Row[size_t]& m)
cdef arma.Mat[double]* numpy_to_mat_d(numpy.ndarray[numpy.double_t, ndim=2] X, \
bool takeOwnership) except +:
cdef Mat[double]* numpy_to_mat_d(numpy.ndarray[numpy.double_t, ndim=2] X, \
bool takeOwnership) except +:
"""
Convert a numpy ndarray to a matrix. The memory will still be owned by numpy.
"""
@@ -58,18 +58,18 @@ cdef arma.Mat[double]* numpy_to_mat_d(numpy.ndarray[numpy.double_t, ndim=2] X, \
X = X.copy(order="C")
takeOwnership = True
cdef arma.Mat[double]* m = new arma.Mat[double](<double*> PyArray_DATA(X),
cdef Mat[double]* m = new Mat[double](<double*> PyArray_DATA(X),
PyArray_SHAPE(X)[1], PyArray_SHAPE(X)[0], isWin, False)
# Take ownership of the memory, if we need to and we are not on Windows.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Mat[double]](m[0], 0)
SetMemState[Mat[double]](m[0], 0)
return m
cdef arma.Mat[size_t]* numpy_to_mat_s(numpy.ndarray[numpy.npy_intp, ndim=2] X, \
bool takeOwnership) except +:
cdef Mat[size_t]* numpy_to_mat_s(numpy.ndarray[numpy.npy_intp, ndim=2] X, \
bool takeOwnership) except +:
"""
Convert a numpy ndarray to a matrix. The memory will still be owned by numpy.
"""
@@ -81,17 +81,17 @@ cdef arma.Mat[size_t]* numpy_to_mat_s(numpy.ndarray[numpy.npy_intp, ndim=2] X, \
X = X.copy(order="C")
takeOwnership = True
cdef arma.Mat[size_t]* m = new arma.Mat[size_t](<size_t*> PyArray_DATA(X),
cdef Mat[size_t]* m = new Mat[size_t](<size_t*> PyArray_DATA(X),
PyArray_SHAPE(X)[1], PyArray_SHAPE(X)[0], isWin, False)
# Take ownership of the memory, if we need to.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Mat[size_t]](m[0], 0)
SetMemState[Mat[size_t]](m[0], 0)
return m
cdef numpy.ndarray[numpy.double_t, ndim=2] mat_to_numpy_d(arma.Mat[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=2] mat_to_numpy_d(Mat[double]& X) \
except +:
"""
Convert an Armadillo object to a numpy ndarray.
@@ -106,13 +106,13 @@ cdef numpy.ndarray[numpy.double_t, ndim=2] mat_to_numpy_d(arma.Mat[double]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Mat[double]](X) == 0 and not isWin:
SetMemState[arma.Mat[double]](X, 1)
if GetMemState[Mat[double]](X) == 0 and not isWin:
SetMemState[Mat[double]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
cdef numpy.ndarray[numpy.npy_intp, ndim=2] mat_to_numpy_s(arma.Mat[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=2] mat_to_numpy_s(Mat[size_t]& X) \
except +:
"""
Convert an Armadillo object to a numpy ndarray.
@@ -127,14 +127,14 @@ cdef numpy.ndarray[numpy.npy_intp, ndim=2] mat_to_numpy_s(arma.Mat[size_t]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Mat[size_t]](X) == 0 and not isWin:
SetMemState[arma.Mat[size_t]](X, 1)
if GetMemState[Mat[size_t]](X) == 0 and not isWin:
SetMemState[Mat[size_t]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
cdef arma.Row[double]* numpy_to_row_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +:
cdef Row[double]* numpy_to_row_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +:
"""
Convert a numpy one-dimensional ndarray to a row. The memory will still be
owned by numpy.
@@ -147,18 +147,18 @@ cdef arma.Row[double]* numpy_to_row_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
X = X.copy(order="C")
takeOwnership = True
cdef arma.Row[double]* m = new arma.Row[double](<double*> PyArray_DATA(X),
cdef Row[double]* m = new Row[double](<double*> PyArray_DATA(X),
PyArray_SHAPE(X)[0], isWin, False)
# Transfer memory ownership, if needed.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Row[double]](m[0], 0)
SetMemState[Row[double]](m[0], 0)
return m
cdef arma.Row[size_t]* numpy_to_row_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +:
cdef Row[size_t]* numpy_to_row_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +:
"""
Convert a numpy one-dimensional ndarray to a row. The memory will still be
owned by numpy.
@@ -171,17 +171,17 @@ cdef arma.Row[size_t]* numpy_to_row_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
X = X.copy(order="C")
takeOwnership = True
cdef arma.Row[size_t]* m = new arma.Row[size_t](<size_t*> PyArray_DATA(X),
cdef Row[size_t]* m = new Row[size_t](<size_t*> PyArray_DATA(X),
PyArray_SHAPE(X)[0], isWin, False)
# Transfer memory ownership, if needed.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Row[size_t]](m[0], 0)
SetMemState[Row[size_t]](m[0], 0)
return m
cdef numpy.ndarray[numpy.double_t, ndim=1] row_to_numpy_d(arma.Row[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=1] row_to_numpy_d(Row[double]& X) \
except +:
"""
Convert an Armadillo row vector to a one-dimensional numpy ndarray.
@@ -194,13 +194,13 @@ cdef numpy.ndarray[numpy.double_t, ndim=1] row_to_numpy_d(arma.Row[double]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Row[double]](X) == 0 and not isWin:
SetMemState[arma.Row[double]](X, 1)
if GetMemState[Row[double]](X) == 0 and not isWin:
SetMemState[Row[double]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
cdef numpy.ndarray[numpy.npy_intp, ndim=1] row_to_numpy_s(arma.Row[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=1] row_to_numpy_s(Row[size_t]& X) \
except +:
"""
Convert an Armadillo row vector to a one-dimensional numpy ndarray.
@@ -214,13 +214,37 @@ cdef numpy.ndarray[numpy.npy_intp, ndim=1] row_to_numpy_s(arma.Row[size_t]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Row[size_t]](X) == 0 and not isWin:
SetMemState[arma.Row[size_t]](X, 1)
if GetMemState[Row[size_t]](X) == 0 and not isWin:
SetMemState[Row[size_t]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
cdef arma.Col[double]* numpy_to_col_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
cdef Col[double]* numpy_to_col_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
bool takeOwnership) except +:
"""
Convert a numpy one-dimensional ndarray to a column vector. The memory will
still be owned by numpy.
"""
cdef int flags = PyArray_FLAGS(X)
if not (flags & numpy.NPY_ARRAY_C_CONTIGUOUS) or \
(not (flags & numpy.NPY_ARRAY_OWNDATA) and not isWin):
# If needed, make a copy where we own the memory, except on Windows where
# we never copy.
X = X.copy(order="C")
takeOwnership = True
cdef Col[double]* m = new Col[double](<double*> PyArray_DATA(X),
PyArray_SHAPE(X)[0], isWin, False)
# Transfer memory ownership, if needed.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[Col[double]](m[0], 0)
return m
cdef Col[size_t]* numpy_to_col_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +:
"""
Convert a numpy one-dimensional ndarray to a column vector. The memory will
@@ -234,41 +258,17 @@ cdef arma.Col[double]* numpy_to_col_d(numpy.ndarray[numpy.double_t, ndim=1] X, \
X = X.copy(order="C")
takeOwnership = True
cdef arma.Col[double]* m = new arma.Col[double](<double*> PyArray_DATA(X),
cdef Col[size_t]* m = new Col[size_t](<size_t*> PyArray_DATA(X),
PyArray_SHAPE(X)[0], isWin, False)
# Transfer memory ownership, if needed.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Col[double]](m[0], 0)
SetMemState[Col[size_t]](m[0], 0)
return m
cdef arma.Col[size_t]* numpy_to_col_s(numpy.ndarray[numpy.npy_intp, ndim=1] X, \
bool takeOwnership) except +:
"""
Convert a numpy one-dimensional ndarray to a column vector. The memory will
still be owned by numpy.
"""
cdef int flags = PyArray_FLAGS(X)
if not (flags & numpy.NPY_ARRAY_C_CONTIGUOUS) or \
(not (flags & numpy.NPY_ARRAY_OWNDATA) and not isWin):
# If needed, make a copy where we own the memory, except on Windows where
# we never copy.
X = X.copy(order="C")
takeOwnership = True
cdef arma.Col[size_t]* m = new arma.Col[size_t](<size_t*> PyArray_DATA(X),
PyArray_SHAPE(X)[0], isWin, False)
# Transfer memory ownership, if needed.
if takeOwnership and not isWin:
PyArray_CLEARFLAGS(X, numpy.NPY_ARRAY_OWNDATA)
SetMemState[arma.Col[size_t]](m[0], 0)
return m
cdef numpy.ndarray[numpy.double_t, ndim=1] col_to_numpy_d(arma.Col[double]& X) \
cdef numpy.ndarray[numpy.double_t, ndim=1] col_to_numpy_d(Col[double]& X) \
except +:
"""
Convert an Armadillo column vector to a one-dimensional numpy ndarray.
@@ -281,13 +281,13 @@ cdef numpy.ndarray[numpy.double_t, ndim=1] col_to_numpy_d(arma.Col[double]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Col[double]](X) == 0 and not isWin:
SetMemState[arma.Col[double]](X, 1)
if GetMemState[Col[double]](X) == 0 and not isWin:
SetMemState[Col[double]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
cdef numpy.ndarray[numpy.npy_intp, ndim=1] col_to_numpy_s(arma.Col[size_t]& X) \
cdef numpy.ndarray[numpy.npy_intp, ndim=1] col_to_numpy_s(Col[size_t]& X) \
except +:
"""
Convert an Armadillo column vector to a one-dimensional numpy ndarray.
@@ -300,8 +300,8 @@ cdef numpy.ndarray[numpy.npy_intp, ndim=1] col_to_numpy_s(arma.Col[size_t]& X) \
output = output.copy(order="C")
# Transfer memory ownership, if needed.
if GetMemState[arma.Col[size_t]](X) == 0 and not isWin:
SetMemState[arma.Col[size_t]](X, 1)
if GetMemState[Col[size_t]](X) == 0 and not isWin:
SetMemState[Col[size_t]](X, 1)
PyArray_ENABLEFLAGS(output, numpy.NPY_ARRAY_OWNDATA)
return output
src/mlpack/bindings/python/mlpack/io.pxd
+1 -1
View File
@@ -15,7 +15,7 @@ http://www.opensource.org/licenses/BSD-3-Clause for more information.
cimport cython
from libcpp.string cimport string
from libcpp cimport bool
from params cimport Params
from .params cimport Params
cdef extern from "<mlpack/core/util/io.hpp>" namespace "mlpack" nogil:
cdef cppclass IO:
src/mlpack/bindings/python/print_input_processing.hpp
+10 -10
View File
@@ -269,7 +269,7 @@ void PrintInputProcessing(
* if param_name_tuple[0].shape[0] == 1 or
* param_name_tuple[0].shape[1] == 1:
* param_name_tuple[0].shape = (param_name_tuple[0].size,)
* param_name_mat = arma_numpy.numpy_to_mat_s(param_name_tuple[0],
* param_name_mat = numpy_to_mat_s(param_name_tuple[0],
* param_name_tuple[1])
* SetParam[mat](p, \<const string\> 'param_name', dereference(param_name_mat), True)
* p.SetPassed(\<const string\> 'param_name')
@@ -299,7 +299,7 @@ void PrintInputProcessing(
<< std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = ("
<< d.name << "_tuple[0].size,)" << std::endl;
std::cout << prefix << " " << name << "_mat = arma_numpy.numpy_to_"
std::cout << prefix << " " << name << "_mat = numpy_to_"
<< GetArmaType<T>() << "_" << GetNumpyTypeChar<T>() << "(" << name
<< "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << " SetParam[" << GetCythonType<T>(d)
@@ -319,7 +319,7 @@ void PrintInputProcessing(
<< ") < 2:" << std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = (" << name
<< "_tuple[0].shape[0], 1)" << std::endl;
std::cout << prefix << " " << name << "_mat = arma_numpy.numpy_to_"
std::cout << prefix << " " << name << "_mat = numpy_to_"
<< GetArmaType<T>() << "_" << GetNumpyTypeChar<T>() << "(" << name
<< "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << " SetParam[" << GetCythonType<T>(d)
@@ -343,7 +343,7 @@ void PrintInputProcessing(
<< name << "_tuple[0].shape[1] == 1:" << std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = ("
<< name << "_tuple[0].size,)" << std::endl;
std::cout << prefix << name << "_mat = arma_numpy.numpy_to_"
std::cout << prefix << name << "_mat = numpy_to_"
<< GetArmaType<T>() << "_" << GetNumpyTypeChar<T>() << "(" << name
<< "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << "SetParam[" << GetCythonType<T>(d)
@@ -362,7 +362,7 @@ void PrintInputProcessing(
<< std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = (" << name
<< "_tuple[0].shape[0], 1)" << std::endl;
std::cout << prefix << name << "_mat = arma_numpy.numpy_to_"
std::cout << prefix << name << "_mat = numpy_to_"
<< GetArmaType<T>() << "_" << GetNumpyTypeChar<T>() << "(" << name
<< "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << "SetParam[" << GetCythonType<T>(d)
@@ -475,7 +475,7 @@ void PrintInputProcessing(
* param_name_tuple = to_matrix_with_info(param_name)
* if len(param_name_tuple[0].shape) < 2:
* param_name_tuple[0].shape = (param_name_tuple[0].size,)
* param_name_mat = arma_numpy.numpy_to_matrix_d(param_name_tuple[0])
* param_name_mat = numpy_to_matrix_d(param_name_tuple[0])
* SetParamWithInfo[mat](p, \<const string\> 'param_name',
* dereference(param_name_mat),
* \<const cbool*\> PyArray_DATA(param_name_dims))
@@ -496,11 +496,11 @@ void PrintInputProcessing(
<< ") < 2:" << std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = (" << name
<< "_tuple[0].shape[0], 1)" << std::endl;
std::cout << prefix << " " << name << "_mat = arma_numpy.numpy_to_mat_d("
std::cout << prefix << " " << name << "_mat = numpy_to_mat_d("
<< name << "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << " " << name << "_dims = " << name
<< "_tuple[2]" << std::endl;
std::cout << prefix << " SetParamWithInfo[arma.Mat[double]](p, <const "
std::cout << prefix << " SetParamWithInfo[Mat[double]](p, <const "
<< "string> '" << d.name << "', dereference(" << name << "_mat), "
<< "<const cbool*> PyArray_DATA(" << name << "_dims))" << std::endl;
std::cout << prefix << " p.SetPassed(<const string> '" << d.name
@@ -518,11 +518,11 @@ void PrintInputProcessing(
<< ") < 2:" << std::endl;
std::cout << prefix << " " << name << "_tuple[0].shape = (" << name
<< "_tuple[0].shape[0], 1)" << std::endl;
std::cout << prefix << name << "_mat = arma_numpy.numpy_to_mat_d("
std::cout << prefix << name << "_mat = numpy_to_mat_d("
<< name << "_tuple[0], " << name << "_tuple[1])" << std::endl;
std::cout << prefix << name << "_dims = " << name << "_tuple[2]"
<< std::endl;
std::cout << prefix << "SetParamWithInfo[arma.Mat[double]](p, <const "
std::cout << prefix << "SetParamWithInfo[Mat[double]](p, <const "
<< "string> '" << d.name << "', dereference(" << name << "_mat), "
<< "<const cbool*> PyArray_DATA(" << name << "_dims))" << std::endl;
std::cout << prefix << "p.SetPassed(<const string> '" << d.name << "')"
src/mlpack/bindings/python/print_output_processing.hpp
+14 -17
View File
@@ -97,28 +97,26 @@ void PrintOutputProcessing(
/**
* This gives us code like:
*
* result = arma_numpy.mat_to_numpy_X(p.Get[mat]("name"))
* result = mat_to_numpy_X(p.Get[mat]("name"))
*
* where X indicates the type to convert to.
*/
std::cout << prefix << "result = arma_numpy." << GetArmaType<T>()
<< "_to_numpy_" << GetNumpyTypeChar<T>() << "(p.Get["
<< GetCythonType<T>(d) << "](\"" << d.name << "\"))" << std::endl;
std::cout << prefix << "result = " << GetArmaType<T>() << "_to_numpy_"
<< GetNumpyTypeChar<T>() << "(p.Get[" << GetCythonType<T>(d) << "](\""
<< d.name << "\"))" << std::endl;
}
else
{
/**
* This gives us code like:
*
* result['param_name'] =
* arma_numpy.mat_to_numpy_X(p.Get[mat]('name')
* result['param_name'] = mat_to_numpy_X(p.Get[mat]('name')
*
* where X indicates the type to convert to.
*/
std::cout << prefix << "result['" << d.name
<< "'] = arma_numpy." << GetArmaType<T>() << "_to_numpy_"
<< GetNumpyTypeChar<T>() << "(p.Get[" << GetCythonType<T>(d)
<< "]('" << d.name << "'))" << std::endl;
std::cout << prefix << "result['" << d.name << "'] = " << GetArmaType<T>()
<< "_to_numpy_" << GetNumpyTypeChar<T>() << "(p.Get["
<< GetCythonType<T>(d) << "]('" << d.name << "'))" << std::endl;
}
}
@@ -143,11 +141,11 @@ void PrintOutputProcessing(
/**
* This gives us code like:
*
* result = arma_numpy.mat_to_numpy_X(GetParamWithInfo[mat](p, 'name'))
* result = mat_to_numpy_X(GetParamWithInfo[mat](p, 'name'))
*/
std::cout << prefix << "result = arma_numpy.mat_to_numpy_"
std::cout << prefix << "result = mat_to_numpy_"
<< GetNumpyTypeChar<arma::mat>()
<< "(GetParamWithInfo[arma.Mat[double]](p, '" << d.name << "'))"
<< "(GetParamWithInfo[Mat[double]](p, '" << d.name << "'))"
<< std::endl;
}
else
@@ -155,12 +153,11 @@ void PrintOutputProcessing(
/**
* This gives us code like:
*
* result['param_name'] =
* arma_numpy.mat_to_numpy_X(GetParamWithInfo[mat](p, 'name'))
* result['param_name'] = mat_to_numpy_X(GetParamWithInfo[mat](p, 'name'))
*/
std::cout << prefix << "result['" << d.name
<< "'] = arma_numpy.mat_to_numpy_" << GetNumpyTypeChar<arma::mat>()
<< "(GetParamWithInfo[arma.Mat[double]](p, '" << d.name << "'))"
<< "'] = mat_to_numpy_" << GetNumpyTypeChar<arma::mat>()
<< "(GetParamWithInfo[Mat[double]](p, '" << d.name << "'))"
<< std::endl;
}
}
src/mlpack/bindings/python/print_pyx.cpp
+13 -10
View File
@@ -73,19 +73,22 @@ void PrintPYX(const util::BindingDetails& doc,
// First, we must generate the header comment.
// Now import all the necessary packages.
cout << "cimport arma" << endl;
cout << "cimport arma_numpy" << endl;
cout << "from io cimport IO" << endl;
cout << "from params cimport Params" << endl;
cout << "from timers cimport Timers" << endl;
cout << "from io cimport SetParam, SetParamPtr, SetParamWithInfo, "
cout << "from .arma cimport Mat, Row, Col" << endl;
cout << "from .arma_numpy cimport numpy_to_mat_d, numpy_to_mat_s, "
<< "mat_to_numpy_d, mat_to_numpy_s, numpy_to_row_d, numpy_to_row_s, "
<< "row_to_numpy_d, row_to_numpy_s, numpy_to_col_d, numpy_to_col_s, "
<< "col_to_numpy_d, col_to_numpy_s" << endl;
cout << "from .io cimport IO" << endl;
cout << "from .params cimport Params" << endl;
cout << "from .timers cimport Timers" << endl;
cout << "from .io cimport SetParam, SetParamPtr, SetParamWithInfo, "
<< "GetParamPtr" << endl;
cout << "from io cimport EnableVerbose, DisableVerbose, DisableBacktrace, "
cout << "from .io cimport EnableVerbose, DisableVerbose, DisableBacktrace, "
<< "ResetTimers, EnableTimers" << endl;
cout << "from matrix_utils import to_matrix, to_matrix_with_info" << endl;
cout << "from preprocess_json_params import process_params_out, "
cout << "from .matrix_utils import to_matrix, to_matrix_with_info" << endl;
cout << "from .preprocess_json_params import process_params_out, "
<< "process_params_in" << endl;
cout << "from serialization cimport SerializeIn, SerializeOut, "
cout << "from .serialization cimport SerializeIn, SerializeOut, "
<< "SerializeOutJSON, SerializeInJSON" << endl;
cout << endl;
cout << "import numpy as np" << endl;