def _x_gets_A_kron_B_times_sparse_v(v, M, N, row_inds, col_inds): #MVN=(N.T x M)v
if len(M.shape) == 1:
M = M[..., np.newaxis]
rc_m, cc_m = M.shape
if len(N.shape) == 1:
N = N[..., np.newaxis]
rc_n, cc_n = N.shape
nzc_v = len(row_inds)
len_c = rc_m * cc_n
if rc_m * cc_m * cc_n + cc_n * nzc_v < rc_n * cc_m * cc_n + cc_m * nzc_v:
temp = np.zeros((cc_m, cc_n))
_sampled_kronecker_products.sparse_mat_from_left(temp, v, N, row_inds, col_inds, nzc_v, cc_n)
temp = np.dot(M, temp)
return temp.reshape((len_c,), order = 'F')
else:
temp = np.zeros((rc_m, rc_n))
_sampled_kronecker_products.sparse_mat_from_right(temp, M, v, row_inds, col_inds, nzc_v, rc_m)
temp = np.dot(temp, N)
return temp.reshape((len_c,), order = 'F')
def _x_gets_A_kron_B_times_sparse_v(v, M, N, row_inds,
col_inds): #MVN=(N.T x M)v
if len(M.shape) == 1:
M = M[..., np.newaxis]
rc_m, cc_m = M.shape
if len(N.shape) == 1:
N = N[..., np.newaxis]
rc_n, cc_n = N.shape
nzc_v = len(row_inds)
len_c = rc_m * cc_n
if rc_m * cc_m * cc_n + cc_n * nzc_v < rc_n * cc_m * cc_n + cc_m * nzc_v:
temp = np.zeros((cc_m, cc_n))
_sampled_kronecker_products.sparse_mat_from_left(
temp, v, N, row_inds, col_inds, nzc_v, cc_n)
temp = np.dot(M, temp)
return temp.reshape((len_c, ), order='F')
else:
temp = np.zeros((rc_m, rc_n))
_sampled_kronecker_products.sparse_mat_from_right(
temp, M, v, row_inds, col_inds, nzc_v, rc_m)
temp = np.dot(temp, N)
return temp.reshape((len_c, ), order='F')
def sampled_vec_trick(v, M, N, row_inds_M = None, row_inds_N = None, col_inds_M = None, col_inds_N = None):
assert len(v.shape) == 1
if len(M.shape) == 1:
M = M[..., np.newaxis]
rc_m, cc_m = M.shape
if len(N.shape) == 1:
N = N[..., np.newaxis]
rc_n, cc_n = N.shape
if row_inds_N is None:
u_len = rc_m * rc_n
#row_inds_N, row_inds_M = np.unravel_index(np.arange(rc_m * rc_n), (rc_n, rc_m), order = 'F')
#Not sure why the next row is necessary
#row_inds_N, row_inds_M = np.array(row_inds_N, dtype = np.int32), np.array(row_inds_M, dtype = np.int32)
else:
u_len = len(row_inds_N)
assert len(row_inds_N) == len(row_inds_M)
assert np.min(row_inds_N) >= 0
assert np.min(row_inds_M) >= 0
assert np.max(row_inds_N) < rc_n
assert np.max(row_inds_M) < rc_m
if col_inds_N is None:
v_len = cc_m * cc_n
#col_inds_N, col_inds_M = np.unravel_index(np.arange(cc_m * cc_n), (cc_n, cc_m), order = 'F')
#Not sure why the next row is necessary
#col_inds_N, col_inds_M = np.array(col_inds_N, dtype = np.int32), np.array(col_inds_M, dtype = np.int32)
else:
v_len = len(col_inds_N)
assert len(col_inds_N) == len(col_inds_M)
assert len(col_inds_N) == v.shape[0]
assert np.min(col_inds_N) >= 0
assert np.min(col_inds_M) >= 0
assert np.max(col_inds_N) < cc_n
assert np.max(col_inds_M) < cc_m
if rc_m * v_len + cc_n * u_len < rc_n * v_len + cc_m * u_len:
if col_inds_N is None:
temp = np.dot(v.reshape((cc_n, cc_m), order = 'F'), M.T)
else:
temp = np.zeros((cc_n, rc_m), order='C')
M = np.array(M, order = 'FORTRAN')
_sampled_kronecker_products.sparse_mat_from_left(temp, v, M.T, col_inds_N, col_inds_M, v_len, rc_m)
if row_inds_N is None:
x_after = np.dot(N, temp)
x_after = x_after.reshape((u_len,), order = 'F')
else:
temp = np.array(temp, order = 'FORTRAN')
N = np.array(N, order = 'C')
x_after = np.zeros((u_len))
_sampled_kronecker_products.compute_subset_of_matprod_entries(x_after, N, temp, row_inds_N, row_inds_M, u_len, cc_n)
else:
if col_inds_N is None:
temp = np.dot(N, v.reshape((cc_n, cc_m), order = 'F'))
else:
temp = np.zeros((rc_n, cc_m), order = 'FORTRAN')
N = np.array(N, order = 'FORTRAN')
_sampled_kronecker_products.sparse_mat_from_right(temp, N, v, col_inds_N, col_inds_M, v_len, rc_n)
if row_inds_N is None:
x_after = np.dot(temp, M.T)
x_after = x_after.reshape((u_len,), order = 'F')
else:
temp = np.array(temp, order = 'C')
M = np.array(M, order = 'C')
x_after = np.zeros((u_len))
_sampled_kronecker_products.compute_subset_of_matprod_entries(x_after, temp, M.T, row_inds_N, row_inds_M, u_len, cc_m)
return x_after
def _sparse_mat_from_left(*args):
_sampled_kronecker_products.sparse_mat_from_left(*args)
def _sparse_mat_from_left(*args):
_sampled_kronecker_products.sparse_mat_from_left(*args)
def sampled_vec_trick(v,
M,
N,
row_inds_M=None,
row_inds_N=None,
col_inds_M=None,
col_inds_N=None):
assert len(v.shape) == 1
if len(M.shape) == 1:
M = M[..., np.newaxis]
rc_m, cc_m = M.shape
if len(N.shape) == 1:
N = N[..., np.newaxis]
rc_n, cc_n = N.shape
if row_inds_N == None:
u_len = rc_m * rc_n
#row_inds_N, row_inds_M = np.unravel_index(np.arange(rc_m * rc_n), (rc_n, rc_m), order = 'F')
#Not sure why the next row is necessary
#row_inds_N, row_inds_M = np.array(row_inds_N, dtype = np.int32), np.array(row_inds_M, dtype = np.int32)
else:
u_len = len(row_inds_N)
assert len(row_inds_N) == len(row_inds_M)
assert np.min(row_inds_N) >= 0
assert np.min(row_inds_M) >= 0
assert np.max(row_inds_N) < rc_n
assert np.max(row_inds_M) < rc_m
if col_inds_N == None:
v_len = cc_m * cc_n
#col_inds_N, col_inds_M = np.unravel_index(np.arange(cc_m * cc_n), (cc_n, cc_m), order = 'F')
#Not sure why the next row is necessary
#col_inds_N, col_inds_M = np.array(col_inds_N, dtype = np.int32), np.array(col_inds_M, dtype = np.int32)
else:
v_len = len(col_inds_N)
assert len(col_inds_N) == len(col_inds_M)
assert len(col_inds_N) == v.shape[0]
assert np.min(col_inds_N) >= 0
assert np.min(col_inds_M) >= 0
assert np.max(col_inds_N) < cc_n
assert np.max(col_inds_M) < cc_m
if rc_m * v_len + cc_n * u_len < rc_n * v_len + cc_m * u_len:
if col_inds_N == None:
temp = np.dot(v.reshape((cc_n, cc_m), order='F'), M.T)
else:
temp = np.zeros((cc_n, rc_m))
_sampled_kronecker_products.sparse_mat_from_left(
temp, v, M.T, col_inds_N, col_inds_M, v_len, rc_m)
if row_inds_N == None:
x_after = np.dot(N, temp)
x_after = x_after.reshape((u_len, ), order='F')
else:
x_after = np.zeros((u_len))
_sampled_kronecker_products.compute_subset_of_matprod_entries(
x_after, N, temp, row_inds_N, row_inds_M, u_len, cc_n)
else:
if col_inds_N == None:
temp = np.dot(N, v.reshape((cc_n, cc_m), order='F'))
else:
temp = np.zeros((rc_n, cc_m))
_sampled_kronecker_products.sparse_mat_from_right(
temp, N, v, col_inds_N, col_inds_M, v_len, rc_n)
if row_inds_N == None:
x_after = np.dot(temp, M.T)
x_after = x_after.reshape((u_len, ), order='F')
else:
x_after = np.zeros((u_len))
_sampled_kronecker_products.compute_subset_of_matprod_entries(
x_after, temp, M.T, row_inds_N, row_inds_M, u_len, cc_m)
return x_after
