| /dports/misc/vxl/vxl-3.3.2/contrib/rpl/rrel/ |
| H A D | rrel_kernel_density_obj.cxx | 53 return -1 * kernel_density( res_begin, res_end, x, h ); in fcn()
81 double f = kernel_density( res_begin, res_end, x, h ); in best_x()
100 f1 = kernel_density( res_begin, res_end, x1, h ); in best_x()
101 f2 = kernel_density( res_begin, res_end, x2, h ); in best_x()
106 shft2( f1, f2, kernel_density( res_begin, res_end, x2, h ) ); in best_x()
110 shft2( f2, f1, kernel_density( res_begin, res_end, x1, h ) ); in best_x()
172 rrel_kernel_density_obj::kernel_density(vect_const_iter res_begin, in kernel_density() function in rrel_kernel_density_obj
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| H A D | rrel_kernel_density_obj.h | 76 double kernel_density(vect_const_iter res_begin, vect_const_iter res_end,
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| /dports/math/saga/saga-8.1.3/saga-gis/src/tools/grid/grid_gridding/ |
| H A D | Makefile.am | 22 kernel_density.cpp\
42 kernel_density.h\
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| /dports/math/octave-forge-econometrics/econometrics-1.1.2/inst/ |
| H A D | kernel_density.m | 16 ## kernel_density: multivariate kernel density estimator
19 ## dens = kernel_density(eval_points, data, bandwidth)
45 function z = kernel_density(eval_points, data, bandwidth, kernel, prewhiten, do_cv, computenodes, d… function
47 if nargin < 2; error("kernel_density: at least 2 arguments are required"); endif
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| H A D | kernel_density_cvscore.m | 19 dens = kernel_density(data, data, exp(bandwidth), true, 0, 0, chol(cov(data)), kernel);
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| H A D | kernel_example.m | 64 dens = kernel_density(grid_x, data, bandwidth, "kernel_normal", false, false, compute_nodes);
93 dens = kernel_density(eval_points, data, bandwidth, "kernel_normal", false, false, compute_nodes);
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/nonparametric/ |
| H A D | api.py | 12 from .kernel_density import \
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| H A D | _kernel_base.py | 62 from .kernel_density import KDEMultivariate
67 from .kernel_density import KDEMultivariateConditional
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| /dports/math/octave-forge-econometrics/econometrics-1.1.2/ |
| H A D | INDEX | 13 kernel_density
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| /dports/math/octave-forge-econometrics/econometrics-1.1.2/inst/private/ |
| H A D | kernel_normal.m | 16 ## kernel_normal: this function is for internal use by kernel_density
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| H A D | kernel_epanechnikov.m | 16 ## kernel_epanechnikov: this function is for internal use by kernel_density
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| H A D | kernel_density_nodes.m | 16 ## kernel_density_nodes: for internal use by kernel_density - does calculations on nodes
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/tests/ |
| H A D | test_neighbors_tree.py | 97 dens = tree.kernel_density(
230 dens_tree = tree.kernel_density(x_out[:, None], h) / n_samples
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/ |
| H A D | _kde.py | 237 log_density = self.tree_.kernel_density(
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| H A D | _binary_tree.pxi | 1503 def kernel_density(self, X, h, kernel='gaussian', member in BinaryTree
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| /dports/math/gretl/gretl-2021d/plugin/ |
| H A D | kernel.c | 299 kernel_density (const double *y, int n, double bwscale, in kernel_density() function
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| /dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/ |
| H A D | nonparametric.rst | 104 .. currentmodule:: statsmodels.nonparametric.kernel_density
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/ |
| H A D | functional.py | 7 from statsmodels.nonparametric.kernel_density import KDEMultivariate
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| /dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/examples/ |
| H A D | landing.yml | 66 target: notebooks/generated/kernel_density.html
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| /dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/release/ |
| H A D | version0.5.rst | 49 …kernel_density.KDEMultivariate>`. It supports least squares and maximum likelihood cross-validatio…
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| /dports/math/gretl/gretl-2021d/gui/ |
| H A D | library.c | 2934 int (*kernel_density) (const double *, int, double, in do_kernel() local
2956 kernel_density = gui_get_plugin_function("kernel_density"); in do_kernel()
2958 if (kernel_density != NULL) { in do_kernel()
2961 err = (*kernel_density)(y, T, bw, in do_kernel()
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| /dports/math/giacxcas/giac-1.6.0/doc/en/ |
| H A D | xcasmenu | 330 Cmds/Proba stats/Distributions/kernel_density
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| /dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/ |
| H A D | neighbors.rst | 42 core. One example is :ref:`kernel density estimation <kernel_density>`,
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| /dports/math/giacxcas/giac-1.6.0/src/ |
| H A D | optimization.cc | 3003 gen kernel_density(const vector<double> &data,double bw,double sd,int bins,double a,double b,int in… in kernel_density() function
3185 return kernel_density(ddata,bw,sd,bins,a,b,interp,x,contextptr); in _kernel_density()
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| /dports/math/giacxcas/giac-1.6.0/doc/fr/ |
| H A D | xcasmenu | 341 Cmds/Proba stats/Distributions/kernel_density
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