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micropython-ulab/code/numpy/transform.c
Zoltán Vörös cb3f4b3898 add compress function
2022-01-02 12:46:12 +01:00

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/*
* This file is part of the micropython-ulab project,
*
* https://github.com/v923z/micropython-ulab
*
* The MIT License (MIT)
*
* Copyright (c) 2019-2021 Zoltán Vörös
*
*/
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include "py/obj.h"
#include "py/runtime.h"
#include "py/misc.h"
#include "../ulab.h"
#include "../ulab_tools.h"
#include "carray/carray_tools.h"
#include "transform.h"
#if ULAB_NUMPY_HAS_COMPRESS
static mp_obj_t transform_compress(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
static const mp_arg_t allowed_args[] = {
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, { .u_rom_obj = mp_const_none } },
{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, { .u_rom_obj = mp_const_none } },
{ MP_QSTR_axis, MP_ARG_KW_ONLY | MP_ARG_OBJ, { .u_rom_obj = mp_const_none } },
};
mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
mp_obj_t condition = args[0].u_obj;
ndarray_obj_t *ndarray = MP_OBJ_TO_PTR(args[1].u_obj);
uint8_t *array = (uint8_t *)ndarray->array;
mp_obj_t axis = args[2].u_obj;
size_t len = MP_OBJ_SMALL_INT_VALUE(mp_obj_len_maybe(condition));
int8_t ax, shift_ax;
if(axis != mp_const_none) {
ax = tools_get_axis(axis, ndarray->ndim);
shift_ax = ULAB_MAX_DIMS - ndarray->ndim + ax;
}
if(((axis == mp_const_none) && (len != ndarray->len)) ||
((axis != mp_const_none) && (len != ndarray->shape[shift_ax]))) {
mp_raise_ValueError(translate("wrong length of condition array"));
}
size_t true_count = 0;
mp_obj_iter_buf_t iter_buf;
mp_obj_t item, iterable = mp_getiter(condition, &iter_buf);
while((item = mp_iternext(iterable)) != MP_OBJ_STOP_ITERATION) {
if(mp_obj_is_true(item)) {
true_count++;
}
}
iterable = mp_getiter(condition, &iter_buf);
ndarray_obj_t *result = NULL;
uint8_t *rarray = NULL;
size_t *shape = m_new(size_t, ULAB_MAX_DIMS);
memcpy(shape, ndarray->shape, ULAB_MAX_DIMS * sizeof(size_t));
size_t *rshape = m_new(size_t, ULAB_MAX_DIMS);
memcpy(rshape, ndarray->shape, ULAB_MAX_DIMS * sizeof(size_t));
int32_t *strides = m_new(int32_t, ULAB_MAX_DIMS);
memcpy(strides, ndarray->strides, ULAB_MAX_DIMS * sizeof(int32_t));
int32_t *rstrides = m_new(int32_t, ULAB_MAX_DIMS);
if(axis == mp_const_none) {
result = ndarray_new_linear_array(true_count, ndarray->dtype);
rarray = (uint8_t *)result->array;
memset(rstrides, 0, ndarray->ndim * sizeof(int32_t));
rstrides[ULAB_MAX_DIMS - 1] = ndarray->itemsize;
rshape[ULAB_MAX_DIMS - 1] = 0;
} else {
rshape[shift_ax] = true_count;
result = ndarray_new_dense_ndarray(ndarray->ndim, rshape, ndarray->dtype);
rarray = (uint8_t *)result->array;
SWAP(size_t, shape[shift_ax], shape[ULAB_MAX_DIMS - 1]);
SWAP(size_t, rshape[shift_ax], rshape[ULAB_MAX_DIMS - 1]);
SWAP(int32_t, strides[shift_ax], strides[ULAB_MAX_DIMS - 1]);
memcpy(rstrides, result->strides, ULAB_MAX_DIMS * sizeof(int32_t));
SWAP(int32_t, rstrides[shift_ax], rstrides[ULAB_MAX_DIMS - 1]);
}
#if ULAB_MAX_DIMS > 3
size_t i = 0;
do {
#endif
#if ULAB_MAX_DIMS > 2
size_t j = 0;
do {
#endif
#if ULAB_MAX_DIMS > 1
size_t k = 0;
do {
#endif
size_t l = 0;
if(axis != mp_const_none) {
iterable = mp_getiter(condition, &iter_buf);
}
do {
item = mp_iternext(iterable);
if(mp_obj_is_true(item)) {
memcpy(rarray, array, ndarray->itemsize);
rarray += rstrides[ULAB_MAX_DIMS - 1];
}
array += strides[ULAB_MAX_DIMS - 1];
l++;
} while(l < shape[ULAB_MAX_DIMS - 1]);
#if ULAB_MAX_DIMS > 1
array -= strides[ULAB_MAX_DIMS - 1] * shape[ULAB_MAX_DIMS - 1];
array += strides[ULAB_MAX_DIMS - 2];
rarray -= rstrides[ULAB_MAX_DIMS - 1] * rshape[ULAB_MAX_DIMS - 1];
rarray += rstrides[ULAB_MAX_DIMS - 2];
k++;
} while(k < shape[ULAB_MAX_DIMS - 2]);
#endif
#if ULAB_MAX_DIMS > 2
array -= strides[ULAB_MAX_DIMS - 2] * shape[ULAB_MAX_DIMS - 2];
array += strides[ULAB_MAX_DIMS - 3];
rarray -= rstrides[ULAB_MAX_DIMS - 2] * rshape[ULAB_MAX_DIMS - 2];
rarray += rstrides[ULAB_MAX_DIMS - 3];
j++;
} while(j < shape[ULAB_MAX_DIMS - 3]);
#endif
#if ULAB_MAX_DIMS > 3
array -= strides[ULAB_MAX_DIMS - 3] * shape[ULAB_MAX_DIMS - 3];
array += strides[ULAB_MAX_DIMS - 4];
rarray -= rstrides[ULAB_MAX_DIMS - 2] * rshape[ULAB_MAX_DIMS - 2];
rarray += rstrides[ULAB_MAX_DIMS - 3];
i++;
} while(i < shape[ULAB_MAX_DIMS - 4]);
#endif
return result;
}
MP_DEFINE_CONST_FUN_OBJ_KW(transform_compress_obj, 2, transform_compress);
#endif /* ULAB_NUMPY_HAS_COMPRESS */
#if ULAB_MAX_DIMS > 1
#if ULAB_NUMPY_HAS_DOT
//| def dot(m1: ulab.numpy.ndarray, m2: ulab.numpy.ndarray) -> Union[ulab.numpy.ndarray, _float]:
//| """
//| :param ~ulab.numpy.ndarray m1: a matrix, or a vector
//| :param ~ulab.numpy.ndarray m2: a matrix, or a vector
//|
//| Computes the product of two matrices, or two vectors. In the letter case, the inner product is returned."""
//| ...
//|
mp_obj_t transform_dot(mp_obj_t _m1, mp_obj_t _m2) {
// TODO: should the results be upcast?
// This implements 2D operations only!
if(!mp_obj_is_type(_m1, &ulab_ndarray_type) || !mp_obj_is_type(_m2, &ulab_ndarray_type)) {
mp_raise_TypeError(translate("arguments must be ndarrays"));
}
ndarray_obj_t *m1 = MP_OBJ_TO_PTR(_m1);
ndarray_obj_t *m2 = MP_OBJ_TO_PTR(_m2);
COMPLEX_DTYPE_NOT_IMPLEMENTED(m1->dtype)
COMPLEX_DTYPE_NOT_IMPLEMENTED(m2->dtype)
uint8_t *array1 = (uint8_t *)m1->array;
uint8_t *array2 = (uint8_t *)m2->array;
mp_float_t (*func1)(void *) = ndarray_get_float_function(m1->dtype);
mp_float_t (*func2)(void *) = ndarray_get_float_function(m2->dtype);
if(m1->shape[ULAB_MAX_DIMS - 1] != m2->shape[ULAB_MAX_DIMS - m2->ndim]) {
mp_raise_ValueError(translate("dimensions do not match"));
}
uint8_t ndim = MIN(m1->ndim, m2->ndim);
size_t shape1 = m1->ndim == 2 ? m1->shape[ULAB_MAX_DIMS - m1->ndim] : 1;
size_t shape2 = m2->ndim == 2 ? m2->shape[ULAB_MAX_DIMS - 1] : 1;
size_t *shape = NULL;
if(ndim == 2) { // matrix times matrix -> matrix
shape = ndarray_shape_vector(0, 0, shape1, shape2);
} else { // matrix times vector -> vector, vector times vector -> vector (size 1)
shape = ndarray_shape_vector(0, 0, 0, shape1);
}
ndarray_obj_t *results = ndarray_new_dense_ndarray(ndim, shape, NDARRAY_FLOAT);
mp_float_t *rarray = (mp_float_t *)results->array;
for(size_t i=0; i < shape1; i++) { // rows of m1
for(size_t j=0; j < shape2; j++) { // columns of m2
mp_float_t dot = 0.0;
for(size_t k=0; k < m1->shape[ULAB_MAX_DIMS - 1]; k++) {
// (i, k) * (k, j)
dot += func1(array1) * func2(array2);
array1 += m1->strides[ULAB_MAX_DIMS - 1];
array2 += m2->strides[ULAB_MAX_DIMS - m2->ndim];
}
*rarray++ = dot;
array1 -= m1->strides[ULAB_MAX_DIMS - 1] * m1->shape[ULAB_MAX_DIMS - 1];
array2 -= m2->strides[ULAB_MAX_DIMS - m2->ndim] * m2->shape[ULAB_MAX_DIMS - m2->ndim];
array2 += m2->strides[ULAB_MAX_DIMS - 1];
}
array1 += m1->strides[ULAB_MAX_DIMS - m1->ndim];
array2 = m2->array;
}
if((m1->ndim * m2->ndim) == 1) { // return a scalar, if product of two vectors
return mp_obj_new_float(*(--rarray));
} else {
return MP_OBJ_FROM_PTR(results);
}
}
MP_DEFINE_CONST_FUN_OBJ_2(transform_dot_obj, transform_dot);
#endif
#endif
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