add qr implementation
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Zoltán Vörös
parent
90cb0959b9
commit
a3ce0ce29a
4 changed files with 143 additions and 2 deletions
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@ -364,7 +364,140 @@ static mp_obj_t linalg_norm(size_t n_args, const mp_obj_t *pos_args, mp_map_t *k
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}
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MP_DEFINE_CONST_FUN_OBJ_KW(linalg_norm_obj, 1, linalg_norm);
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// MP_DEFINE_CONST_FUN_OBJ_1(linalg_norm_obj, linalg_norm);
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#if ULAB_MAX_DIMS > 1
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//| def qr(m: ulab.numpy.ndarray) -> Tuple[ulab.numpy.ndarray, ulab.numpy.ndarray]:
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//| """
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//| :param m: a matrix
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//| :return tuple (Q, R):
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//|
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//| Computes the QR decomposition of a matrix"""
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//| ...
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//|
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static mp_obj_t linalg_qr(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
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static const mp_arg_t allowed_args[] = {
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{ MP_QSTR_, MP_ARG_REQUIRED | MP_ARG_OBJ, { .u_rom_obj = mp_const_none } },
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{ MP_QSTR_mode, MP_ARG_OBJ, { .u_rom_obj = MP_ROM_QSTR(MP_QSTR_complete) } },
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};
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mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
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mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
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if(!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type)) {
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mp_raise_TypeError(translate("operation is defined for ndarrays only"));
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}
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ndarray_obj_t *source = MP_OBJ_TO_PTR(args[0].u_obj);
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if(source->ndim != 2) {
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mp_raise_ValueError(translate("operation is defined for 2D arrays only"));
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}
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size_t m = source->shape[ULAB_MAX_DIMS - 2]; // rows
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size_t n = source->shape[ULAB_MAX_DIMS - 1]; // columns
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ndarray_obj_t *Q = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, m, m), NDARRAY_FLOAT);
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ndarray_obj_t *R = ndarray_new_dense_ndarray(2, source->shape, NDARRAY_FLOAT);
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mp_float_t *qarray = (mp_float_t *)Q->array;
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mp_float_t *rarray = (mp_float_t *)R->array;
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// simply copy the entries of source to a float array
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mp_float_t (*func)(void *) = ndarray_get_float_function(source->dtype);
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uint8_t *sarray = (uint8_t *)source->array;
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for(size_t i = 0; i < m; i++) {
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for(size_t j = 0; j < n; j++) {
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*rarray++ = func(sarray);
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sarray += source->strides[ULAB_MAX_DIMS - 1];
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}
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sarray -= n * source->strides[ULAB_MAX_DIMS - 1];
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sarray += source->strides[ULAB_MAX_DIMS - 2];
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}
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rarray -= m * n;
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// start with the unit matrix
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for(size_t i = 0; i < m; i++) {
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qarray[i * (m + 1)] = 1.0;
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}
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for(size_t j = 0; j < n; j++) { // columns
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for(size_t i = m - 1; i > j; i--) { // rows
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mp_float_t c, s;
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// Givens matrix: note that numpy uses a strange form of the rotation
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// [[c s],
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// [s -c]]
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if(MICROPY_FLOAT_C_FUN(fabs)(rarray[i * n + j]) < LINALG_EPSILON) { // r[i, j]
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c = (rarray[(i - 1) * n + j] >= 0.0) ? 1.0 : -1.0; // r[i-1, j]
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s = 0.0;
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} else if(MICROPY_FLOAT_C_FUN(fabs)(rarray[(i - 1) * n + j]) < LINALG_EPSILON) { // r[i-1, j]
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c = 0.0;
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s = (rarray[i * n + j] >= 0.0) ? -1.0 : 1.0; // r[i, j]
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} else {
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mp_float_t t, u;
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if(MICROPY_FLOAT_C_FUN(fabs)(rarray[(i - 1) * n + j]) > MICROPY_FLOAT_C_FUN(fabs)(rarray[i * n + j])) { // r[i-1, j], r[i, j]
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t = rarray[i * n + j] / rarray[(i - 1) * n + j]; // r[i, j]/r[i-1, j]
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u = MICROPY_FLOAT_C_FUN(sqrt)(1 + t * t);
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c = -1.0 / u;
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s = c * t;
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} else {
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t = rarray[(i - 1) * n + j] / rarray[i * n + j]; // r[i-1, j]/r[i, j]
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u = MICROPY_FLOAT_C_FUN(sqrt)(1 + t * t);
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s = -1.0 / u;
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c = s * t;
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}
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}
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mp_float_t r1, r2;
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// update R: multiply with the rotation matrix from the left
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for(size_t k = 0; k < n; k++) {
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r1 = rarray[(i - 1) * n + k]; // r[i-1, k]
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r2 = rarray[i * n + k]; // r[i, k]
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rarray[(i - 1) * n + k] = c * r1 + s * r2; // r[i-1, k]
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rarray[i * n + k] = s * r1 - c * r2; // r[i, k]
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}
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// update Q: multiply with the transpose of the rotation matrix from the right
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for(size_t k = 0; k < m; k++) {
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r1 = qarray[k * m + (i - 1)];
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r2 = qarray[k * m + i];
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qarray[k * m + (i - 1)] = c * r1 + s * r2;
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qarray[k * m + i] = s * r1 - c * r2;
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}
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}
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}
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mp_obj_tuple_t *tuple = MP_OBJ_TO_PTR(mp_obj_new_tuple(2, NULL));
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GET_STR_DATA_LEN(args[1].u_obj, mode, len);
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if(memcmp(mode, "complete", 8) == 0) {
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tuple->items[0] = MP_OBJ_FROM_PTR(Q);
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tuple->items[1] = MP_OBJ_FROM_PTR(R);
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} else if(memcmp(mode, "reduced", 7) == 0) {
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size_t k = MAX(m, n) - MIN(m, n);
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ndarray_obj_t *q = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, m, m - k), NDARRAY_FLOAT);
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ndarray_obj_t *r = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, m - k, n), NDARRAY_FLOAT);
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mp_float_t *qa = (mp_float_t *)q->array;
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mp_float_t *ra = (mp_float_t *)r->array;
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for(size_t i = 0; i < m; i++) {
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memcpy(qa, qarray, (m - k) * q->itemsize);
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qa += (m - k);
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qarray += m;
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}
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for(size_t i = 0; i < m - k; i++) {
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memcpy(ra, rarray, n * r->itemsize);
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ra += n;
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rarray += n;
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}
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tuple->items[0] = MP_OBJ_FROM_PTR(q);
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tuple->items[1] = MP_OBJ_FROM_PTR(r);
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} else {
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mp_raise_ValueError(translate("mode must be complete, or reduced"));
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}
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return tuple;
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}
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MP_DEFINE_CONST_FUN_OBJ_KW(linalg_qr_obj, 1, linalg_qr);
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#endif
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STATIC const mp_rom_map_elem_t ulab_linalg_globals_table[] = {
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{ MP_OBJ_NEW_QSTR(MP_QSTR___name__), MP_OBJ_NEW_QSTR(MP_QSTR_linalg) },
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@ -381,6 +514,9 @@ STATIC const mp_rom_map_elem_t ulab_linalg_globals_table[] = {
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#if ULAB_LINALG_HAS_INV
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{ MP_ROM_QSTR(MP_QSTR_inv), (mp_obj_t)&linalg_inv_obj },
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#endif
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#if ULAB_LINALG_HAS_QR
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{ MP_ROM_QSTR(MP_QSTR_qr), (mp_obj_t)&linalg_qr_obj },
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#endif
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#endif
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#if ULAB_LINALG_HAS_NORM
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{ MP_ROM_QSTR(MP_QSTR_norm), (mp_obj_t)&linalg_norm_obj },
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@ -23,4 +23,5 @@ MP_DECLARE_CONST_FUN_OBJ_1(linalg_det_obj);
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MP_DECLARE_CONST_FUN_OBJ_1(linalg_eig_obj);
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MP_DECLARE_CONST_FUN_OBJ_1(linalg_inv_obj);
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MP_DECLARE_CONST_FUN_OBJ_KW(linalg_norm_obj);
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MP_DECLARE_CONST_FUN_OBJ_KW(linalg_qr_obj);
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#endif
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@ -33,7 +33,7 @@
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#include "user/user.h"
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#include "utils/utils.h"
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#define ULAB_VERSION 3.2.0
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#define ULAB_VERSION 3.3.0
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#define xstr(s) str(s)
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#define str(s) #s
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#define ULAB_VERSION_STRING xstr(ULAB_VERSION) xstr(-) xstr(ULAB_MAX_DIMS) xstr(D)
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@ -371,6 +371,10 @@
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#define ULAB_LINALG_HAS_NORM (1)
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#endif
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#ifndef ULAB_LINALG_HAS_QR
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#define ULAB_LINALG_HAS_QR (1)
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#endif
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// the FFT module; functions of the fft module still have
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// to be defined separately
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#ifndef ULAB_NUMPY_HAS_FFT_MODULE
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