/dports/math/py-seaborn/seaborn-0.11.0/seaborn/ |
H A D | regression.py | 79 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,
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/dports/misc/tvm/incubator-tvm-0.6.1/vta/python/vta/top/ |
H A D | vta_conv2d.py | 149 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)
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H A D | vta_conv2d_transpose.py | 148 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)
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H A D | vta_dense.py | 137 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)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/vta/python/vta/top/ |
H A D | vta_conv2d.py | 154 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)
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H A D | vta_group_conv2d.py | 165 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)
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H A D | vta_conv2d_transpose.py | 158 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)
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H A D | vta_dense.py | 138 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)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/vta/python/vta/top/ |
H A D | vta_conv2d.py | 149 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)
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H A D | vta_conv2d_transpose.py | 148 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)
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H A D | vta_dense.py | 137 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)
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/dports/science/gromacs/gromacs-2021.4/src/gromacs/nbnxm/ |
H A D | pairlist.cpp | 975 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 …]
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/dports/math/py-seaborn/seaborn-0.11.0/seaborn/tests/ |
H A D | test_regression.py | 178 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")
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/dports/devel/ppl/ppl-1.2/src/ |
H A D | Octagonal_Shape_templates.hh | 2589 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()
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H A D | ppl.hh.dist | 73643 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);
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