add matrix triple product

code/scipy/linalg/linalg.c

@@ -7,6 +7,7 @@
  * The MIT License (MIT)
  *
  * Copyright (c) 2021 Vikas Udupa
+ *               2024 Zoltán Vörös
  *
 */
 
@@ -32,6 +33,7 @@
 
 #if ULAB_MAX_DIMS > 1
 
+#if ULAB_SCIPY_LINALG_HAS_SOLVE_TRIANGULAR
 //| def solve_triangular(A: ulab.numpy.ndarray, b: ulab.numpy.ndarray, lower: bool) -> ulab.numpy.ndarray:
 //|    """
 //|    :param ~ulab.numpy.ndarray A: a matrix
@@ -146,6 +148,9 @@ static mp_obj_t solve_triangular(size_t n_args, const mp_obj_t *pos_args, mp_map
 }
 
 MP_DEFINE_CONST_FUN_OBJ_KW(linalg_solve_triangular_obj, 2, solve_triangular);
+#endif /* ULAB_SCIPY_LINALG_HAS_SOLVE_TRIANGULAR */
+
+#if ULAB_SCIPY_LINALG_HAS_CHO_SOLVE
 
 //| def cho_solve(L: ulab.numpy.ndarray, b: ulab.numpy.ndarray) -> ulab.numpy.ndarray:
 //|    """
@@ -255,7 +260,591 @@ static mp_obj_t cho_solve(mp_obj_t _L, mp_obj_t _b) {
 
 MP_DEFINE_CONST_FUN_OBJ_2(linalg_cho_solve_obj, cho_solve);
 
-#endif
+#endif /* ULAB_SCIPY_LINALG_HAS_CHO_SOLVE */
+
+#if ULAB_SCIPY_LINALG_HAS_SVD
+
+//| def svd(a: ulab.numpy.ndarray) -> (ulab.numpy.ndarray, ulab.numpy.ndarray, ulab.numpy.ndarray):
+//|    """
+//|    :param ~ulab.numpy.ndarray a: matrix whose singular-value decomposition is requested
+//|    :return: tuple of (U, s, Vh) matrices such that a = U s Vh^*. 
+//|
+//|    Calculate singular-value decomposition of a"""
+//|    ...
+//|
+
+static void scipy_linalg_svd22(mp_float_t A00, mp_float_t A01, mp_float_t A10, mp_float_t A11, 
+                        mp_float_t *U, mp_float_t *S, mp_float_t *V) {
+    // adapted from https://github.com/openmv/apriltag/blob/master/common/svd22.c
+
+    mp_float_t B0 = A00 + A11;
+    mp_float_t B1 = A00 - A11;
+    mp_float_t B2 = A01 + A10;
+    mp_float_t B3 = A01 - A10;
+
+    mp_float_t PminusT = atan2(B3, B0);
+    mp_float_t PplusT = atan2(B2, B1);
+
+    mp_float_t P = (PminusT + PplusT) / 2;
+    mp_float_t T = (-PminusT + PplusT) / 2;
+
+    mp_float_t CP = cos(P), SP = sin(P);
+    mp_float_t CT = cos(T), ST = sin(T);
+
+    U[0] = CT;
+    U[1] = -ST;
+    U[2] = ST;
+    U[3] = CT;
+
+    V[0] = CP;
+    V[1] = -SP;
+    V[2] = SP;
+    V[3] = CP;
+
+    // C0 = e + f. There are two ways to compute C0; we pick the one
+    // that is better conditioned.
+    mp_float_t CPmT = cos(P - T), SPmT = sin(P - T);
+    mp_float_t C0 = 0;
+    if(MICROPY_FLOAT_C_FUN(fabs)(CPmT) > MICROPY_FLOAT_C_FUN(fabs)(SPmT)) {
+        C0 = B0 / CPmT;
+    } else {
+        C0 = B3 / SPmT;
+    }
+
+    // C1 = e - f. There are two ways to compute C1; we pick the one
+    // that is better conditioned.
+    mp_float_t CPpT = cos(P+T), SPpT = sin(P+T);
+    mp_float_t C1 = 0;
+    if(MICROPY_FLOAT_C_FUN(fabs)(CPpT) > MICROPY_FLOAT_C_FUN(fabs)(SPpT)) {
+        C1 = B1 / CPpT;
+    } else {
+        C1 = B2 / SPpT;
+    }
+
+    // e and f are the singular values
+    mp_float_t e = (C0 + C1) / 2;
+    mp_float_t f = (C0 - C1) / 2;
+
+    if(e < 0) {
+        e = -e;
+        U[0] = -U[0];
+        U[2] = -U[2];
+    }
+
+    if(f < 0) {
+        f = -f;
+        U[1] = -U[1];
+        U[3] = -U[3];
+    }
+
+    // sort singular values.
+    if(e > f) {
+        // already in big-to-small order
+        S[0] = e;
+        S[1] = f;
+    } else {
+        // P = [ 0 1 ; 1 0 ]
+        // USV' = (UP)(PSP)(PV')
+        //      = (UP)(PSP)(VP)'
+        //      = (UP)(PSP)(P'V')'
+        S[0] = f;
+        S[1] = e;
+
+        // exchange columns of U and V
+        double tmp[2];
+        tmp[0] = U[0];
+        tmp[1] = U[2];
+        U[0] = U[1];
+        U[2] = U[3];
+        U[1] = tmp[0];
+        U[3] = tmp[1];
+
+        tmp[0] = V[0];
+        tmp[1] = V[2];
+        V[0] = V[1];
+        V[2] = V[3];
+        V[1] = tmp[0];
+        V[3] = tmp[1];
+    }
+}
+
+// find the index of the off-diagonal element with the largest mag
+static inline size_t linalg_max_index(mp_float_t *A, size_t row, size_t maxcol) {
+    // in ulab, columns are the second index of matrices, 
+    // so we can simply walk along the axis, as if it was 
+    // a linear array; simply pass *A as A[row * columns]
+
+    size_t maxi = 0;
+    mp_float_t maxv = -1.0;
+    mp_float_t v;
+
+    for(size_t i = 0; i < maxcol; i++) {
+        if(i == row) {
+            continue;
+        }
+        v = MICROPY_FLOAT_C_FUN(fabs)(A[i]);
+        if(v > maxv) {
+            maxi = i;
+            maxv = v;
+        }
+    }
+
+    return maxi;
+}
+
+static mp_obj_t linalg_svd(mp_obj_t _a) {
+
+    if(!mp_obj_is_type(_a, &ulab_ndarray_type)) {
+        mp_raise_TypeError(MP_ERROR_TEXT("input matrix must be an ndarray"));
+    }
+
+    ndarray_obj_t *A = MP_OBJ_TO_PTR(_a);
+
+    if(A->ndim != 2) {
+        mp_raise_TypeError(MP_ERROR_TEXT("input must be a 2D array"));
+    }
+
+    #if ULAB_SUPPORTS_COMPLEX
+    if(A->dtype == NDARRAY_COMPLEX) {
+        mp_raise_TypeError(MP_ERROR_TEXT("input matrix must be real"));
+    }
+    #endif
+
+    mp_float_t (*get_A_element)(void *) = ndarray_get_float_function(A->dtype);
+    uint8_t *a = (uint8_t *)A->array;
+    
+    size_t ncolumns = A->shape[ULAB_MAX_DIMS - 1];
+    size_t nrows = A->shape[ULAB_MAX_DIMS - 2];
+
+    mp_float_t *b = m_new(mp_float_t, nrows * ncolumns);
+    
+    // copy data from a to b
+    for(size_t i = 0; i < ncolumns; i++) {
+        for(size_t j = 0; j < nrows; j++) {
+            *b++ = get_A_element(a);
+            a += A->strides[ULAB_MAX_DIMS - 1];
+        }
+        a -= A->strides[ULAB_MAX_DIMS - 1] * ncolumns;
+        a += A->strides[ULAB_MAX_DIMS - 2];
+    }
+
+    b -= nrows * ncolumns;
+
+    // LS: cumulative left-handed transformations
+    mp_float_t *LS = m_new0(mp_float_t, nrows * nrows);
+    // start with the unit matrix
+    for(size_t i = 0; i < nrows; i++) {
+        LS[i * (nrows + 1)] = 1.0; /* LS[i, i] */
+    }
+
+    // RS: cumulative right-handed transformations
+    mp_float_t *RS = m_new0(mp_float_t, ncolumns * ncolumns);
+    // start with the unit matrix
+    for(size_t i = 0; i < ncolumns; i++) {
+        RS[i * (ncolumns + 1)] = 1.0; /* RS[i, i] */
+    }
+
+    for(size_t hhidx = 0; hhidx < nrows; hhidx++)  {
+        if(hhidx < ncolumns) {
+            size_t vlen = nrows - hhidx;
+
+            mp_float_t *v = m_new0(mp_float_t, vlen);
+
+            mp_float_t mag2 = 0.0;
+            for(size_t i = 0; i < vlen; i++) {
+                v[i] = b[(hhidx + i) * ncolumns + hhidx]; /* B[hhidx + i, hhidx] */
+                mag2 += v[i] * v[i];
+            }
+
+            mp_float_t oldv0 = v[0];
+            if(oldv0 < 0) {
+                v[0] -= MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+            } else {
+                v[0] += MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+            }
+
+            mag2 += -oldv0 * oldv0 + v[0] * v[0];
+
+            // normalize v
+            double mag = MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+
+            if(mag == 0.0) {
+                continue;
+            }
+
+            for(size_t i = 0; i < vlen; i++) {
+                v[i] /= mag;
+            }
+
+            // Q = I - 2vv'
+            mp_float_t *Q = m_new0(mp_float_t, nrows * nrows);
+            for(size_t i = 0; i < nrows; i++) {
+                Q[i * (ncolumns + 1)] = 1.0;
+            }
+
+            for(size_t i = 0; i < vlen; i++) {
+                for(size_t j = 0; j < vlen; j++) {
+                    Q[(i + hhidx) * nrows +  (j + hhidx)] -= 2*v[i]*v[j]; /* Q[i + hhidx, j + hhidx] */
+                }
+            }
+
+            // LS = matd_op("F*M", LS, Q);
+            // Implementation: take each row of LS, compute dot product with n,
+            // subtract n (scaled by dot product) from it.
+            for(size_t i = 0; i < nrows; i++) {
+                mp_float_t dot = 0.0;
+                for(size_t j = 0; j < vlen; j++) {
+                    dot += LS[i * nrows + (hhidx + j)] * v[j]; /* U[i, hhidx + j] */
+                }
+                for(size_t j = 0; j < vlen; j++) {
+                    LS[i * nrows + (hhidx + j)] -= 2 * dot * v[j]; /* U[i, hhidx + j] */
+                }
+            }
+
+            //  B = matd_op("M*F", Q, B); 
+            // should be Q', but Q is symmetric.
+            for(size_t i = 0; i < ncolumns; i++) {
+                mp_float_t dot = 0.0;
+                for(size_t j = 0; j < vlen; j++) {
+                    dot += b[(hhidx + j) * ncolumns + i] * v[j]; /* B[hhidx + j, i] */
+                }
+                for(size_t j = 0; j < vlen; j++) {
+                    b[(hhidx + j) * ncolumns + i] -= 2*dot*v[j]; /* B[hhidx + j, i] */
+                }
+            }
+            m_del(mp_float_t, v, vlen);
+            m_del(mp_float_t, Q, nrows * nrows);
+        }
+
+        if(hhidx + 2 < ncolumns) {
+            size_t vlen = ncolumns - hhidx - 1;
+            mp_float_t *v = m_new0(mp_float_t, vlen);
+
+            mp_float_t mag2 = 0.0;
+            for(size_t i = 0; i < vlen; i++) {
+                v[i] = b[hhidx * ncolumns + hhidx + i + 1];
+                mag2 += v[i] * v[i];
+            }
+
+            mp_float_t oldv0 = v[0];
+            if(oldv0 < 0) {
+                v[0] -= MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+            } else {
+                v[0] += MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+            }
+
+            mag2 += -oldv0 * oldv0 + v[0] * v[0];
+
+            // compute magnitude of ([1 0 0..]+v)
+            mp_float_t mag = MICROPY_FLOAT_C_FUN(sqrt)(mag2);
+
+            // this case can occur when the vectors are already perpendicular
+            if(mag == 0.0) {
+                continue;
+            }
+
+            for(size_t i = 0; i < vlen; i++) {
+                v[i] /= mag;
+            }
+
+            for(size_t i = 0; i < ncolumns; i++) { // this is the number of rows, but RS is of size ncolumns * ncolumns
+                mp_float_t dot = 0.0;
+                for(size_t j = 0; j < vlen; j++) {
+                    dot += RS[i * ncolumns +  hhidx + 1 + j] * v[j]; /* V[i, hhidx + 1 + j] */
+                }
+                for(size_t j = 0; j < vlen; j++) {
+                    RS[i * ncolumns +  hhidx + 1 + j] -= 2 * dot * v[j]; /* V[i, hhidx + 1 + j] */
+                }
+            }
+
+            //   B = matd_op("F*M", B, Q); // should be Q', but Q is symmetric.
+            for(size_t i = 0; i < nrows; i++) {
+                mp_float_t dot = 0.0;
+                for(size_t j = 0; j < vlen; j++) {
+                    dot += b[i * ncolumns + hhidx + 1 + j] * v[j]; /* B[i, hhidx + 1 + j] */
+                }
+                for(size_t j = 0; j < vlen; j++) {
+                    b[i * ncolumns + hhidx + 1 + j] -= 2 * dot * v[j]; /* B[i, hhidx + 1 + j] */
+                }
+            }
+            m_del(mp_float_t, v, vlen);
+        }
+    }
+
+    mp_float_t maxv; // maximum non-zero value being reduced this iteration
+    size_t *maxrowidx = m_new0(size_t, ncolumns);
+
+    for(size_t i = 2; i < ncolumns; i++) {
+        maxrowidx[i] = linalg_max_index(b + (i * ncolumns), i, ncolumns); /* B[i, ...] */
+    }
+    size_t lastmaxi = 0;
+    size_t lastmaxj = 1;
+
+    for(size_t iterations = 0; iterations < SCIPY_LINALG_SVD_MAXITERATIONS; iterations++) {
+        if(ncolumns < 2) {
+            break;
+        }
+        int32_t maxi = -1;
+        int32_t maxj;
+        maxv = -1;
+
+        // now, EVERY row also had columns lastmaxi and lastmaxj modified.
+        for(size_t rowi = 0; rowi < ncolumns; rowi++) {
+
+            // the magnitude of the largest off-diagonal element
+            // in this row.
+            mp_float_t thismaxv;
+
+            if((rowi == lastmaxi) || (rowi == lastmaxj)) {
+                maxrowidx[rowi] = linalg_max_index(b + rowi * ncolumns, rowi, ncolumns); /* B[rowi, ...] */
+                thismaxv = MICROPY_FLOAT_C_FUN(fabs)(b[rowi * ncolumns + maxrowidx[rowi]]); /* B[rowi, maxrowidx[rowi]] */
+                goto endrowi;
+            }
+
+            if((maxrowidx[rowi] == lastmaxi) || (maxrowidx[rowi] == lastmaxj)) {
+                maxrowidx[rowi] = linalg_max_index(b + rowi * ncolumns, rowi, ncolumns); /* B[rowi, ... ]*/
+                thismaxv = MICROPY_FLOAT_C_FUN(fabs)(b[rowi * ncolumns + maxrowidx[rowi]]); /* B[rowi, maxrowidx[rowi]] */
+                goto endrowi;
+            }
+
+            thismaxv = MICROPY_FLOAT_C_FUN(fabs)(b[rowi * ncolumns + maxrowidx[rowi]]); /* B[rowi, maxrowidx[rowi]] */
+
+            // check column lastmaxi. Is it now the maximum?
+            if(lastmaxi != rowi) {
+                mp_float_t v = MICROPY_FLOAT_C_FUN(fabs)(b[rowi * ncolumns + maxrowidx[rowi]]); /* B[rowi, maxrowidx[rowi]] */
+                if (v > thismaxv) {
+                    thismaxv = v;
+                    maxrowidx[rowi] = lastmaxi;
+                }
+            }
+
+            // check column lastmaxj
+            if(lastmaxj != rowi) {
+                mp_float_t v = MICROPY_FLOAT_C_FUN(fabs)(b[rowi * ncolumns + lastmaxj]); /* B[rowi, lastmaxj] */
+                if(v > thismaxv) {
+                    thismaxv = v;
+                    maxrowidx[rowi] = lastmaxj;
+                }
+            }
+
+            // does this row have the largest value we've seen so far?
+            endrowi:
+                if(thismaxv > maxv) {
+                    maxv = thismaxv;
+                    maxi = rowi;
+                }
+        }
+
+        assert(maxi >= 0);
+        maxj = maxrowidx[maxi];
+
+        // save these for the next iteration.
+        lastmaxi = maxi;
+        lastmaxj = maxj;
+
+        if(maxv < SCIPY_LINALG_EPSILON) {
+            break;
+        }
+        // Solve the 2x2 SVD problem for the matrix
+        // [ A00 A01 ]
+        // [ A10 A11 ]
+        mp_float_t A00 = b[maxi * ncolumns + maxi]; /* B[maxi, maxi] */
+        mp_float_t A01 = b[maxi * ncolumns + maxj]; /* B[maxi, maxj] */
+        mp_float_t A10 = b[maxj * ncolumns + maxi]; /* B[maxj, maxi] */
+        mp_float_t A11 = b[maxj * ncolumns + maxj]; /* B[maxj, maxj] */
+
+        // we should probably move this out of the loop...
+        mp_float_t *u = m_new(mp_float_t, 4);
+        mp_float_t *s = m_new(mp_float_t, 2);
+        mp_float_t *v = m_new(mp_float_t, 4);
+
+        scipy_linalg_svd22(A00, A01, A10, A11, u, s, v);
+
+        //  LS = matd_op("F*M", LS, QL);
+        for(size_t i = 0; i < nrows; i++) {
+            mp_float_t vi = LS[i * nrows + maxi]; /* U[i, maxi] */
+            mp_float_t vj = LS[i * nrows + maxj]; /* U[i, maxj] */
+
+            LS[i * nrows + maxi] = u[0] * vi + u[2] * vj;  /* U[i, maxi] */
+            LS[i * nrows + maxj] = u[1] * vi + u[3] * vj;  /* U[i, maxj] */
+        }
+
+        //  RS = matd_op("F*M", RS, QR); // remember we'll transpose RS.
+        for(size_t i = 0; i < ncolumns; i++) { // this is the nunber of rows in RS, for it is of size columns * columns
+            mp_float_t vi = RS[i * ncolumns + maxi]; /* V[i, maxi] */
+            mp_float_t vj = RS[i * ncolumns + maxj]; /* V[i, maxj] */
+
+            RS[i * ncolumns + maxi] = v[0] * vi + v[2] * vj; /* V[i, maxi] */
+            RS[i * ncolumns + maxj] = v[1] * vi + v[3] * vj; /* V[i, maxj] */
+        }
+
+        // B = matd_op("M'*F*M", QL, B, QR);
+        // The QL matrix mixes rows of B.
+        for(size_t i = 0; i < ncolumns; i++) {
+            mp_float_t vi = b[maxi * ncolumns + i]; /* B[maxi, i] */
+            mp_float_t vj = b[maxj * ncolumns + i]; /* B[maxj, i] */
+
+            b[maxi * ncolumns + i] = u[0] * vi + u[2] * vj; /* B[maxi, i] */
+            b[maxj * ncolumns + i] = u[1] * vi + u[3] * vj; /* B[maxj, i] */
+        }
+
+        // The QR matrix mixes columns of B.
+        for(size_t i = 0; i < nrows; i++) {
+            mp_float_t vi = b[i * ncolumns + maxi]; /* B[i, maxi] */
+            mp_float_t vj = b[i * ncolumns + maxj]; /* B[i, maxj] */
+
+            b[i * ncolumns + maxi] = v[0] * vi + v[2] * vj; /* B[i, maxi] */
+            b[i * ncolumns + maxj] = v[1] * vi + v[3] * vj; /* B[i, maxj] */
+        }
+
+        m_del(mp_float_t, u, 4);
+        m_del(mp_float_t, s, 2);
+        m_del(mp_float_t, v, 4);
+    }
+
+    m_del(size_t, maxrowidx, ncolumns);
+
+    size_t *idxs = m_new(size_t, ncolumns);
+    mp_float_t *vals = m_new(mp_float_t, ncolumns);
+
+    for(size_t i = 0; i < ncolumns; i++) {
+        idxs[i] = i;
+        vals[i] = b[i * ncolumns + i]; /* B[i, i] */
+    }
+
+    // Bubble sort
+    uint8_t changed;
+    do {
+        changed = 0;
+
+        for(size_t i = 0; i + 1 < ncolumns; i++) {
+            if(MICROPY_FLOAT_C_FUN(fabs)(vals[i+1]) > MICROPY_FLOAT_C_FUN(fabs)(vals[i])) {
+                size_t tmpi = idxs[i];
+                idxs[i] = idxs[i + 1];
+                idxs[i + 1] = tmpi;
+
+                mp_float_t tmpv = vals[i];
+                vals[i] = vals[i + 1];
+                vals[i + 1] = tmpv;
+
+                changed = 1;
+            }
+        }
+    } while (changed);
+
+    mp_float_t *LP = m_new(mp_float_t, nrows * nrows);
+    // start with the unit matrix
+    for(size_t i = 0; i < nrows; i++) {
+        LP[i * (nrows + 1)] = 1.0; /* LP[i, i] */
+    }
+
+    mp_float_t *RP = m_new(mp_float_t, ncolumns * ncolumns);
+    // start with the unit matrix
+    for(size_t i = 0; i < ncolumns; i++) {
+        RP[i * (ncolumns + 1)] = 1.0; /* RP[i, i] */
+    }
+
+    for(size_t i = 0; i < ncolumns; i++) {
+        LP[idxs[i] * ncolumns + idxs[i]] = 0.0; /* LP[idxs[i], idxs[i]] */
+        RP[idxs[i] * nrows + idxs[i]] = 0.0; /* RP[idxs[i], idxs[i]] */
+
+        LP[idxs[i] * ncolumns + i] = vals[i] < 0 ? -1.0 : 1.0;
+        RP[idxs[i] * nrows + i] = 1.0;
+    }
+
+    m_del(size_t, idxs, ncolumns);
+    m_del(mp_float_t, vals, ncolumns);
+
+    // we've factored:
+    // LP*(something)*RP'
+
+    // // solve for (something)
+    // B = matd_op("M'*F*M", LP, B, RP);
+
+    ndarray_obj_t *S = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, nrows, ncolumns), NDARRAY_FLOAT);
+    mp_float_t *s = (mp_float_t *)S->array;
+
+    // B * RP
+    mp_float_t *x = m_new(mp_float_t, nrows * ncolumns);
+    for(size_t i = 0; i < nrows; i++) {
+        for(size_t j = 0; j < ncolumns; j++) {
+            mp_float_t tmp = 0.0;
+            for(size_t k = 0; k < ncolumns; k++) {
+                tmp += b[i * nrows + k] * RP[k * nrows + j]; /* B[i, k] * RP[k, j] */
+            }
+            x[i * nrows + j] = tmp; /* x[i, j] */ 
+        }
+    }
+    
+    // S = LS' * x = LS' * (B * RP)
+    for(size_t i = 0; i < nrows; i++) {
+        for(size_t j = 0; j < nrows; j++) {
+            mp_float_t tmp = 0.0;
+            for(size_t k = 0; k < nrows; k++) {
+                tmp += LS[k * nrows + i] * x[k * nrows + j]; /* LS[k, i] * x[k, j] */
+            }
+            s[i * nrows + j] = tmp; /* S[i, j] */ 
+        }
+    }
+
+    m_del(mp_float_t, x, nrows * ncolumns);
+    m_del(mp_float_t, b, nrows * ncolumns);
+
+    // LS = matd_op("F*M", LS, LP);
+    ndarray_obj_t *U = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, nrows, nrows), NDARRAY_FLOAT);
+    mp_float_t *u = (mp_float_t *)U->array;
+
+    for(size_t i = 0; i < nrows; i++) {
+        for(size_t j = 0; j < nrows; j++) {
+            mp_float_t tmp = 0.0;
+            for(size_t k = 0; k < nrows; k++) {
+                tmp += LS[i * nrows + k] * LP[k * nrows + j]; /* LS[i, k] * LP[k, j] */
+            }
+            u[i * nrows + j] = tmp; /* U[i, j] */ 
+        }
+    }
+
+    m_del(mp_float_t, LS, nrows * nrows);
+    m_del(mp_float_t, LP, nrows * nrows);
+
+    // RS = matd_op("F*M", RS, RP);
+    ndarray_obj_t *V = ndarray_new_dense_ndarray(2, ndarray_shape_vector(0, 0, ncolumns, ncolumns), NDARRAY_FLOAT);
+    mp_float_t *v = (mp_float_t *)V->array;
+    for(size_t i = 0; i < ncolumns; i++) {
+        for(size_t j = 0; j < ncolumns; j++) {
+            mp_float_t tmp = 0.0;
+            for(size_t k = 0; k < ncolumns; k++) {
+                tmp += RS[i * ncolumns + k] * RP[k * ncolumns + j]; /* RS[i, k] * RP[k, j] */
+            }
+            v[i * ncolumns + j] = tmp; /* V[i, j] */ 
+        }
+    }
+
+    m_del(mp_float_t, RS, ncolumns * ncolumns);
+    m_del(mp_float_t, RP, ncolumns * ncolumns);
+
+    // make S exactly diagonal
+    for(size_t i = 0; i < ncolumns; i++) {
+        for(size_t j = 0; j < nrows; j++) {
+            if(i != j) {
+                s[i * ncolumns + j] = 0.0; /* B[i, j] */
+            }
+        }
+    }
+
+    mp_obj_t *items = m_new(mp_obj_t, 3);
+    items[0] = U;
+    items[1] = S;
+    items[2] = V;
+    
+    mp_obj_t tuple = mp_obj_new_tuple(3, items);
+    m_del(mp_obj_t, items, 3);
+    return tuple;
+}
+
+MP_DEFINE_CONST_FUN_OBJ_1(linalg_svd_obj, linalg_svd);
+
+#endif /* ULAB_SCIPY_LINALG_HAS_SVD */
 
 static const mp_rom_map_elem_t ulab_scipy_linalg_globals_table[] = {
     { MP_ROM_QSTR(MP_QSTR___name__), MP_ROM_QSTR(MP_QSTR_linalg) },
@@ -266,6 +855,9 @@ static const mp_rom_map_elem_t ulab_scipy_linalg_globals_table[] = {
         #if ULAB_SCIPY_LINALG_HAS_CHO_SOLVE
         { MP_ROM_QSTR(MP_QSTR_cho_solve), MP_ROM_PTR(&linalg_cho_solve_obj) },
         #endif
+        #if ULAB_SCIPY_LINALG_HAS_SVD
+        { MP_ROM_QSTR(MP_QSTR_svd), MP_ROM_PTR(&linalg_svd_obj) },
+        #endif
     #endif
 };
 
@@ -278,4 +870,6 @@ const mp_obj_module_t ulab_scipy_linalg_module = {
 #if CIRCUITPY_ULAB
 MP_REGISTER_MODULE(MP_QSTR_ulab_dot_scipy_dot_linalg, ulab_scipy_linalg_module);
 #endif
-#endif
+
+#endif /* ULAB_MAX_DIMS > 1 */
+#endif /* ULAB_SCIPY_HAS_LINALG_MODULE */

code/scipy/linalg/linalg.h

@@ -13,9 +13,21 @@
 #ifndef _SCIPY_LINALG_
 #define _SCIPY_LINALG_
 
+
+#define SCIPY_LINALG_SVD_MAXITERATIONS                  1UL << 10
+
+#ifndef SCIPY_LINALG_EPSILON
+#if MICROPY_FLOAT_IMPL == MICROPY_FLOAT_IMPL_FLOAT
+#define SCIPY_LINALG_EPSILON                            MICROPY_FLOAT_CONST(1.2e-7)
+#elif MICROPY_FLOAT_IMPL == MICROPY_FLOAT_IMPL_DOUBLE
+#define SCIPY_LINALG_EPSILON                            MICROPY_FLOAT_CONST(2.3e-16)
+#endif
+#endif /* SCIPY_LINALG_EPSILON */
+
 extern const mp_obj_module_t ulab_scipy_linalg_module;
 
 MP_DECLARE_CONST_FUN_OBJ_KW(linalg_solve_triangular_obj);
 MP_DECLARE_CONST_FUN_OBJ_2(linalg_cho_solve_obj);
+MP_DECLARE_CONST_FUN_OBJ_1(linalg_svd_obj);
 
 #endif /* _SCIPY_LINALG_ */

code/ulab.c

@@ -33,7 +33,7 @@
 #include "user/user.h"
 #include "utils/utils.h"
 
-#define ULAB_VERSION 6.5.2
+#define ULAB_VERSION 6.6.0
 #define xstr(s) str(s)
 #define str(s) #s
 

code/ulab.h

@@ -728,6 +728,10 @@
 #define ULAB_SCIPY_LINALG_HAS_SOLVE_TRIANGULAR  (1)
 #endif
 
+#ifndef ULAB_SCIPY_LINALG_HAS_SVD
+#define ULAB_SCIPY_LINALG_HAS_SVD           (1)
+#endif
+
 #ifndef ULAB_SCIPY_HAS_SIGNAL_MODULE
 #define ULAB_SCIPY_HAS_SIGNAL_MODULE        (1)
 #endif