===========
NumPy C-API
===========
::
unsigned int
PyArray_GetNDArrayCVersion(void )
Included at the very first so not auto-grabbed and thus not labeled.
::
int
PyArray_SetNumericOps(PyObject *dict)
Set internal structure with number functions that all arrays will use
::
PyObject *
PyArray_GetNumericOps(void )
Get dictionary showing number functions that all arrays will use
::
int
PyArray_INCREF(PyArrayObject *mp)
For object arrays, increment all internal references.
::
int
PyArray_XDECREF(PyArrayObject *mp)
Decrement all internal references for object arrays.
(or arrays with object fields)
::
void
PyArray_SetStringFunction(PyObject *op, int repr)
Set the array print function to be a Python function.
::
PyArray_Descr *
PyArray_DescrFromType(int type)
Get the PyArray_Descr structure for a type.
::
PyObject *
PyArray_TypeObjectFromType(int type)
Get a typeobject from a type-number -- can return NULL.
New reference
::
char *
PyArray_Zero(PyArrayObject *arr)
Get pointer to zero of correct type for array.
::
char *
PyArray_One(PyArrayObject *arr)
Get pointer to one of correct type for array
::
PyObject *
PyArray_CastToType(PyArrayObject *arr, PyArray_Descr *dtype, int
is_f_order)
For backward compatibility
Cast an array using typecode structure.
steals reference to dtype --- cannot be NULL
This function always makes a copy of arr, even if the dtype
doesn't change.
::
int
PyArray_CastTo(PyArrayObject *out, PyArrayObject *mp)
Cast to an already created array.
::
int
PyArray_CastAnyTo(PyArrayObject *out, PyArrayObject *mp)
Cast to an already created array. Arrays don't have to be "broadcastable"
Only requirement is they have the same number of elements.
::
int
PyArray_CanCastSafely(int fromtype, int totype)
Check the type coercion rules.
::
npy_bool
PyArray_CanCastTo(PyArray_Descr *from, PyArray_Descr *to)
leaves reference count alone --- cannot be NULL
PyArray_CanCastTypeTo is equivalent to this, but adds a 'casting'
parameter.
::
int
PyArray_ObjectType(PyObject *op, int minimum_type)
Return the typecode of the array a Python object would be converted to
Returns the type number the result should have, or NPY_NOTYPE on error.
::
PyArray_Descr *
PyArray_DescrFromObject(PyObject *op, PyArray_Descr *mintype)
new reference -- accepts NULL for mintype
::
PyArrayObject **
PyArray_ConvertToCommonType(PyObject *op, int *retn)
This function is only used in one place within NumPy and should
generally be avoided. It is provided mainly for backward compatibility.
The user of the function has to free the returned array.
::
PyArray_Descr *
PyArray_DescrFromScalar(PyObject *sc)
Return descr object from array scalar.
New reference
::
PyArray_Descr *
PyArray_DescrFromTypeObject(PyObject *type)
::
npy_intp
PyArray_Size(PyObject *op)
Compute the size of an array (in number of items)
::
PyObject *
PyArray_Scalar(void *data, PyArray_Descr *descr, PyObject *base)
Get scalar-equivalent to a region of memory described by a descriptor.
::
PyObject *
PyArray_FromScalar(PyObject *scalar, PyArray_Descr *outcode)
Get 0-dim array from scalar
0-dim array from array-scalar object
always contains a copy of the data
unless outcode is NULL, it is of void type and the referrer does
not own it either.
steals reference to outcode
::
void
PyArray_ScalarAsCtype(PyObject *scalar, void *ctypeptr)
Convert to c-type
no error checking is performed -- ctypeptr must be same type as scalar
in case of flexible type, the data is not copied
into ctypeptr which is expected to be a pointer to pointer
::
int
PyArray_CastScalarToCtype(PyObject *scalar, void
*ctypeptr, PyArray_Descr *outcode)
Cast Scalar to c-type
The output buffer must be large-enough to receive the value
Even for flexible types which is different from ScalarAsCtype
where only a reference for flexible types is returned
This may not work right on narrow builds for NumPy unicode scalars.
::
int
PyArray_CastScalarDirect(PyObject *scalar, PyArray_Descr
*indescr, void *ctypeptr, int outtype)
Cast Scalar to c-type
::
PyObject *
PyArray_ScalarFromObject(PyObject *object)
Get an Array Scalar From a Python Object
Returns NULL if unsuccessful but error is only set if another error occurred.
Currently only Numeric-like object supported.
::
PyArray_VectorUnaryFunc *
PyArray_GetCastFunc(PyArray_Descr *descr, int type_num)
Get a cast function to cast from the input descriptor to the
output type_number (must be a registered data-type).
Returns NULL if un-successful.
::
PyObject *
PyArray_FromDims(int NPY_UNUSED(nd) , int *NPY_UNUSED(d) , int
NPY_UNUSED(type) )
Deprecated, use PyArray_SimpleNew instead.
::
PyObject *
PyArray_FromDimsAndDataAndDescr(int NPY_UNUSED(nd) , int
*NPY_UNUSED(d) , PyArray_Descr
*descr, char *NPY_UNUSED(data) )
Deprecated, use PyArray_NewFromDescr instead.
::
PyObject *
PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int
min_depth, int max_depth, int flags, PyObject
*context)
Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
Steals a reference to newtype --- which can be NULL
::
PyObject *
PyArray_EnsureArray(PyObject *op)
This is a quick wrapper around
PyArray_FromAny(op, NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)
that special cases Arrays and PyArray_Scalars up front
It *steals a reference* to the object
It also guarantees that the result is PyArray_Type
Because it decrefs op if any conversion needs to take place
so it can be used like PyArray_EnsureArray(some_function(...))
::
PyObject *
PyArray_EnsureAnyArray(PyObject *op)
::
PyObject *
PyArray_FromFile(FILE *fp, PyArray_Descr *dtype, npy_intp num, char
*sep)
Given a ``FILE *`` pointer ``fp``, and a ``PyArray_Descr``, return an
array corresponding to the data encoded in that file.
The reference to `dtype` is stolen (it is possible that the passed in
dtype is not held on to).
The number of elements to read is given as ``num``; if it is < 0, then
then as many as possible are read.
If ``sep`` is NULL or empty, then binary data is assumed, else
text data, with ``sep`` as the separator between elements. Whitespace in
the separator matches any length of whitespace in the text, and a match
for whitespace around the separator is added.
For memory-mapped files, use the buffer interface. No more data than
necessary is read by this routine.
::
PyObject *
PyArray_FromString(char *data, npy_intp slen, PyArray_Descr
*dtype, npy_intp num, char *sep)
Given a pointer to a string ``data``, a string length ``slen``, and
a ``PyArray_Descr``, return an array corresponding to the data
encoded in that string.
If the dtype is NULL, the default array type is used (double).
If non-null, the reference is stolen.
If ``slen`` is < 0, then the end of string is used for text data.
It is an error for ``slen`` to be < 0 for binary data (since embedded NULLs
would be the norm).
The number of elements to read is given as ``num``; if it is < 0, then
then as many as possible are read.
If ``sep`` is NULL or empty, then binary data is assumed, else
text data, with ``sep`` as the separator between elements. Whitespace in
the separator matches any length of whitespace in the text, and a match
for whitespace around the separator is added.
::
PyObject *
PyArray_FromBuffer(PyObject *buf, PyArray_Descr *type, npy_intp
count, npy_intp offset)
::
PyObject *
PyArray_FromIter(PyObject *obj, PyArray_Descr *dtype, npy_intp count)
steals a reference to dtype (which cannot be NULL)
::
PyObject *
PyArray_Return(PyArrayObject *mp)
Return either an array or the app
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数据收集与预处理:收集大规模的音乐数据和用户行为数据。音乐数据可以包括音频特征(如频谱图)、元数据(如歌手、流派、专辑等)、歌词等。用户行为数据可以包括听歌历史、喜好标记、播放列表等。然后对数据进行清洗、去重和特征提取。 特征表示:将音乐数据和用户行为数据转换成适合神经网络模型输入的表示形式。对于音乐数据,可以使用音频特征的向量表示,而对于用户行为数据,可以使用用户的ID或者其行为序列的编码表示。 神经网络模型设计:设计适合音乐推荐任务的神经网络模型。这可能包括基于图神经网络的模型、序列模型(如RNN、LSTM、GRU)、自编码器等。模型的设计应考虑到音乐和用户的复杂关系,以及模型的可解释性和训练效率。 模型训练:使用大规模的数据集对设计好的神经网络模型进行训练。可以采用反向传播算法和优化器来最小化推荐误差。在训练过程中,可以采用一些技巧如正则化、批归一化等来提高模型的泛化能力。 模型评估与调优:使用一些评价指标如准确率、召回率、F1 分数等来评估模型的性能。根据评估结果对模型进行调优,可能包括调整模型结构、调整超参数、采用不同的损失函数等。 模型部署与应用:将训练好的模型部
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