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dpnp.mean() to desc #851

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Merged
merged 1 commit into from
Aug 16, 2021
Merged

dpnp.mean() to desc #851

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2 changes: 1 addition & 1 deletion dpnp/dpnp_algo/dpnp_algo.pxd
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Original file line number Diff line number Diff line change
Expand Up @@ -347,7 +347,7 @@ cpdef dparray dpnp_transpose(dpnp_descriptor array1, axes=*)
Statistics functions
"""
cpdef dpnp_descriptor dpnp_cov(dpnp_descriptor array1)
cpdef dparray dpnp_mean(dparray a, axis)
cpdef object dpnp_mean(dpnp_descriptor a, axis)
cpdef dpnp_descriptor dpnp_min(dpnp_descriptor a, axis)


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37 changes: 23 additions & 14 deletions dpnp/dpnp_algo/dpnp_algo_statistics.pyx
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Expand Up @@ -194,29 +194,35 @@ cpdef dparray dpnp_max(dparray input, axis):
return _dpnp_max(input, axis_, output_shape)


cpdef dparray _dpnp_mean(dparray input):
cpdef utils.dpnp_descriptor _dpnp_mean(utils.dpnp_descriptor input):
cdef shape_type_c input_shape = input.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(input.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MEAN, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
cdef dparray result = dparray((1,), dtype=result_type)
cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,), kernel_data.return_type, None)

cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr

# stub for interface support
cdef shape_type_c axis
cdef Py_ssize_t axis_size = 0

func(input.get_data(), result.get_data(), < size_t * > input_shape.data(), input.ndim, < size_t * > axis.data(), axis_size)
func(input.get_data(),
result.get_data(),
< size_t * > input_shape.data(),
input.ndim,
< size_t * > axis.data(),
axis_size)

return result


cpdef dparray dpnp_mean(dparray input, axis):
cpdef object dpnp_mean(utils.dpnp_descriptor input, axis):
cdef shape_type_c output_shape

if axis is None:
return _dpnp_mean(input)
return _dpnp_mean(input).get_pyobj()

cdef long size_input = input.size
cdef shape_type_c shape_input = input.shape
Expand All @@ -235,10 +241,9 @@ cpdef dparray dpnp_mean(dparray input, axis):
axis_ = axis

if axis_ is None:
output_shape = dparray(1, dtype=dpnp.int64)
output_shape[0] = 1
output_shape.push_back(1)
else:
output_shape = dparray(len(shape_input) - len(axis_), dtype=dpnp.int64)
output_shape = (0, ) * (len(shape_input) - len(axis_))
ind = 0
for id, shape_axis in enumerate(shape_input):
if id not in axis_:
Expand Down Expand Up @@ -295,7 +300,7 @@ cpdef dparray dpnp_mean(dparray input, axis):
for i, result_axis_val in enumerate(result_axis):
result_offset += (output_shape_offsets[i] * result_axis_val)

input_elem = input.item(source_idx)
input_elem = input.get_pyobj().item(source_idx)
if axis_ is None:
if result_array[0] is None:
result_array[0] = input_elem
Expand All @@ -317,22 +322,26 @@ cpdef dparray dpnp_mean(dparray input, axis):
return dpnp_result_array / del_


cpdef dparray dpnp_median(utils.dpnp_descriptor array1):
cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1):
cdef shape_type_c x1_shape = array1.shape
cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype)

cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_MEDIAN, param1_type, param1_type)

result_type = dpnp_DPNPFuncType_to_dtype(< size_t > kernel_data.return_type)
cdef dparray result = dparray((1,), dtype=result_type)
cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,), kernel_data.return_type, None)

cdef custom_statistic_1in_1out_func_ptr_t func = <custom_statistic_1in_1out_func_ptr_t > kernel_data.ptr

# stub for interface support
cdef shape_type_c axis
cdef Py_ssize_t axis_size = 0

func(array1.get_data(), result.get_data(), < size_t * > x1_shape.data(), array1.ndim, < size_t * > axis.data(), axis_size)
func(array1.get_data(),
result.get_data(),
< size_t * > x1_shape.data(),
array1.ndim,
< size_t * > axis.data(),
axis_size)

return result

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4 changes: 2 additions & 2 deletions dpnp/dpnp_iface_statistics.py
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Expand Up @@ -432,7 +432,7 @@ def mean(x1, axis=None, **kwargs):
if x1_desc.size == 0:
pass
else:
result_obj = dpnp_mean(x1, axis=axis)
result_obj = dpnp_mean(x1_desc, axis)
result = dpnp.convert_single_elem_array_to_scalar(result_obj)

return result
Expand Down Expand Up @@ -482,7 +482,7 @@ def median(x1, axis=None, out=None, overwrite_input=False, keepdims=False):
elif keepdims:
pass
else:
result_obj = dpnp_median(x1_desc)
result_obj = dpnp_median(x1_desc).get_pyobj()
result = dpnp.convert_single_elem_array_to_scalar(result_obj)

return result
Expand Down