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Improve the efficiency of SVMRegressor code (#3054)

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Prabhat 2020-02-21 13:14:11 +00:00 committed by GitHub
parent 92b8a7a2be
commit 752d74928c
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1 changed files with 9 additions and 16 deletions
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25
onnxruntime/core/providers/cpu/ml/svmregressor.cc
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@ -45,31 +45,24 @@ Status SVMRegressor<T>::Compute(OpKernelContext* ctx) const {
Tensor* Y = ctx->Output(0, TensorShape({N, 1})); // this op outputs for one target only
const auto* x_data = X->template Data<T>();
int64_t current_weight_0;
float sum;
for (int64_t n = 0; n < N; n++) { //for each example
int64_t current_weight_0 = n * stride;
std::vector<float> scores;
float sum = 0.f;
current_weight_0 = n * stride;
sum = 0.f;
if (mode_ == SVM_TYPE::SVM_SVC) {
for (int64_t j = 0; j < vector_count_; j++) {
float val1 = kernel_dot(x_data, current_weight_0, support_vectors_, feature_count_ * j, feature_count_, get_kernel_type());
float val2 = coefficients_[j];
float val3 = val1 * val2;
sum += val3;
sum += kernel_dot(x_data, current_weight_0, support_vectors_, feature_count_ * j,
feature_count_, get_kernel_type()) * coefficients_[j];
}
sum += rho_[0];
} else if (mode_ == SVM_TYPE::SVM_LINEAR) { //liblinear
sum = kernel_dot(x_data, current_weight_0, coefficients_, 0, feature_count_, get_kernel_type());
sum = kernel_dot(x_data, current_weight_0, coefficients_, 0,
feature_count_, get_kernel_type());
sum += rho_[0];
}
if (one_class_ && sum > 0) {
Y->template MutableData<float>()[n] = 1.f;
} else if (one_class_) {
Y->template MutableData<float>()[n] = -1.f;
} else {
Y->template MutableData<float>()[n] = sum;
}
Y->template MutableData<float>()[n] = one_class_ ? (sum > 0 ? 1.f : -1.f) : sum;
}
return Status::OK();