PLSSVM - Parallel Least Squares Support Vector Machine
2.0.0
A Least Squares Support Vector Machine implementation using different backends.
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Calculate the w
vector to speed up the prediction of the labels for data points using the linear kernel function.
More...
#include <predict_kernel.hpp>
Public Types | |
using | real_type = T |
The type of the data. | |
Public Member Functions | |
device_kernel_w_linear (real_type *w_d, const real_type *data_d, const real_type *data_last_d, const real_type *alpha_d, const kernel_index_type num_data_points, const kernel_index_type num_features) | |
Construct a new device kernel generating the w vector used to speedup the prediction when using the linear kernel function. More... | |
void | operator() (::sycl::id< 1 > index) const |
Function call operator overload performing the actual calculation. More... | |
Calculate the w
vector to speed up the prediction of the labels for data points using the linear kernel function.
Supports multi-GPU execution.
T | the type of the data |
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inline |
Construct a new device kernel generating the w
vector used to speedup the prediction when using the linear kernel function.
Currently only single GPU execution is supported.
[out] | w_d | the w vector to assemble |
[in] | data_d | the one-dimension support vector matrix |
[in] | data_last_d | the last row of the support vector matrix |
[in] | alpha_d | the previously calculated weight for each data point |
[in] | num_data_points | the total number of support vectors |
[in] | num_features | the number of features per support vector |
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inline |
Function call operator overload performing the actual calculation.
[in] | index | the sycl::id identifying an instance of the functor executing at each point in a sycl::range |