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Searched refs:x_ci (Results 1 – 15 of 15) sorted by relevance

/dports/math/py-seaborn/seaborn-0.11.0/seaborn/
H A Dregression.py79 x_ci="ci", scatter=True, fit_reg=True, ci=95, n_boot=1000, argument
88 self.x_ci = ci if x_ci == "ci" else x_ci
170 if self.x_ci is None:
174 if self.x_ci == "sd":
185 _ci = utils.ci(boots, self.x_ci)
457 x_ci=dedent("""\
568 x_ci="ci", scatter=True, fit_reg=True, ci=95, n_boot=1000, argument
618 x_estimator=x_estimator, x_bins=x_bins, x_ci=x_ci,
814 x_estimator=None, x_bins=None, x_ci="ci", argument
823 plotter = _RegressionPlotter(x, y, data, x_estimator, x_bins, x_ci,
/dports/misc/tvm/incubator-tvm-0.6.1/vta/python/vta/top/
H A Dvta_conv2d.py149 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
153 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
177 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
179 s[conv2d_stage].reorder(x_bo, k_o, x_j, d_j, d_i, x_co, x_i, x_bi, x_ci, k_i)
H A Dvta_conv2d_transpose.py148 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
152 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
176 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
180 s[conv2d_stage].reorder(x_bo, k_o, x_j, x_co, x_i, x_jj, d_j, d_i, x_ii, x_bi, x_ci, k_i)
H A Dvta_dense.py137 x_co, x_ci = cfg['tile_co'].apply(s, output, x_c)
138 s[output].reorder(x_bo, x_co, x_bi, x_ci)
168 s[output].pragma(x_ci, env.dma_copy)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/vta/python/vta/top/
H A Dvta_conv2d.py154 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
158 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
182 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
184 s[conv2d_stage].reorder(x_bo, k_o, x_j, d_j, d_i, x_co, x_i, x_bi, x_ci, k_i)
H A Dvta_group_conv2d.py165 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
169 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
193 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
195 s[conv2d_stage].reorder(x_bo, k_o, x_j, d_j, d_i, x_co, x_i, x_bi, x_ci, k_i)
H A Dvta_conv2d_transpose.py158 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
162 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
186 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
190 s[conv2d_stage].reorder(x_bo, k_o, x_j, x_co, x_i, x_jj, d_j, d_i, x_ii, x_bi, x_ci, k_i)
H A Dvta_dense.py138 x_co, x_ci = cfg["tile_co"].apply(s, output, x_c)
139 s[output].reorder(x_bo, x_co, x_bi, x_ci)
169 s[output].pragma(x_ci, env.dma_copy)
/dports/misc/py-tvm/incubator-tvm-0.6.1/vta/python/vta/top/
H A Dvta_conv2d.py149 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
153 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
177 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
179 s[conv2d_stage].reorder(x_bo, k_o, x_j, d_j, d_i, x_co, x_i, x_bi, x_ci, k_i)
H A Dvta_conv2d_transpose.py148 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[output].op.axis
152 s[output].reorder(x_bo, x_i0, x_co0, x_j0, x_co1, x_i1, x_j1, x_bi, x_ci)
176 x_bo, x_co, x_i, x_j, x_bi, x_ci = s[conv2d_stage].op.axis
180 s[conv2d_stage].reorder(x_bo, k_o, x_j, x_co, x_i, x_jj, d_j, d_i, x_ii, x_bi, x_ci, k_i)
H A Dvta_dense.py137 x_co, x_ci = cfg['tile_co'].apply(s, output, x_c)
138 s[output].reorder(x_bo, x_co, x_bi, x_ci)
168 s[output].pragma(x_ci, env.dma_copy)
/dports/science/gromacs/gromacs-2021.4/src/gromacs/nbnxm/
H A Dpairlist.cpp975 const real* gmx_restrict x_ci = nbl->work->iClusterData.x.data(); in makeClusterListSimple() local
1003 || (gmx::square(x_ci[i * STRIDE_XYZ + XX] in makeClusterListSimple()
1005 + gmx::square(x_ci[i * STRIDE_XYZ + YY] in makeClusterListSimple()
1007 + gmx::square(x_ci[i * STRIDE_XYZ + ZZ] in makeClusterListSimple()
1048 || (gmx::square(x_ci[i * STRIDE_XYZ + XX] in makeClusterListSimple()
1050 + gmx::square(x_ci[i * STRIDE_XYZ + YY] in makeClusterListSimple()
2366 real* x_ci = work->iSuperClusterData.x.data(); in icell_set_x() local
2371 x_ci[i * DIM + XX] = x[(ia + i) * stride + XX] + shx; in icell_set_x()
2372 x_ci[i * DIM + YY] = x[(ia + i) * stride + YY] + shy; in icell_set_x()
2373 x_ci[i * DIM + ZZ] = x[(ia + i) * stride + ZZ] + shz; in icell_set_x()
[all …]
/dports/math/py-seaborn/seaborn-0.11.0/seaborn/tests/
H A Dtest_regression.py178 nt.assert_equal(p.x_ci, 95)
180 p = lm._RegressionPlotter("x", "y", data=self.df, ci=95, x_ci=68)
182 nt.assert_equal(p.x_ci, 68)
184 p = lm._RegressionPlotter("x", "y", data=self.df, ci=95, x_ci="sd")
186 nt.assert_equal(p.x_ci, "sd")
/dports/devel/ppl/ppl-1.2/src/
H A DOctagonal_Shape_templates.hh2589 row_reference x_ci; in strong_closure_assign() local
2623 x_ci = *x_i_iter; in strong_closure_assign()
2626 vec_k[i] = x_ci[ck]; in strong_closure_assign()
2630 vec_ck[i] = x_ci[k]; in strong_closure_assign()
2830 row_reference x_ci = (i % 2 != 0) ? *(i_iter-1) : *(i_iter + 1); in incremental_strong_closure_assign() local
2847 const N& x_k_i = (i < rs_k) ? x_k[i] : x_ci[ck]; in incremental_strong_closure_assign()
2851 N& x_v_i = (i < rs_v) ? x_v[i] : x_ci[cv]; in incremental_strong_closure_assign()
2857 N& x_cv_i = (i < rs_v) ? x_cv[i] : x_ci[v]; in incremental_strong_closure_assign()
3592 typename OR_Matrix<N>::row_reference_type x_ci = *(i + 1); in add_space_dimensions_and_project() local
3595 assign_r(x_ci[ind], 0, ROUND_NOT_NEEDED); in add_space_dimensions_and_project()
H A Dppl.hh.dist73643 row_reference x_ci;
73677 x_ci = *x_i_iter;
73680 vec_k[i] = x_ci[ck];
73684 vec_ck[i] = x_ci[k];
73884 row_reference x_ci = (i % 2 != 0) ? *(i_iter-1) : *(i_iter + 1);
73901 const N& x_k_i = (i < rs_k) ? x_k[i] : x_ci[ck];
73905 N& x_v_i = (i < rs_v) ? x_v[i] : x_ci[cv];
73911 N& x_cv_i = (i < rs_v) ? x_cv[i] : x_ci[v];
74646 typename OR_Matrix<N>::row_reference_type x_ci = *(i + 1);
74649 assign_r(x_ci[ind], 0, ROUND_NOT_NEEDED);