Fix XSpode
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bayesnet/classifiers/.XSPODE.h.swp
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BIN
bayesnet/classifiers/.XSPODE.h.swp
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@@ -264,7 +264,7 @@ namespace bayesnet {
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normalize(probs);
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return probs;
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}
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std::vector<std::vector<double>> XSpode::predict_proba(const std::vector<std::vector<int>>& test_data)
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std::vector<std::vector<double>> XSpode::predict_proba(std::vector<std::vector<int>>& test_data)
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{
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int test_size = test_data[0].size();
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int sample_size = test_data.size();
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@@ -390,14 +390,22 @@ namespace bayesnet {
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torch::Tensor XSpode::predict(torch::Tensor& X)
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{
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auto X_ = TensorUtils::to_matrix(X);
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auto result = predict(X_);
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return TensorUtils::to_tensor(result);
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auto result_v = predict(X_);
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torch::Tensor result;
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for (int i = 0; i < result_v.size(); ++i) {
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result.index_put_({ i, "..." }, torch::tensor(result_v[i], torch::kInt32));
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}
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return result;
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}
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torch::Tensor XSpode::predict_proba(torch::Tensor& X)
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{
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auto X_ = TensorUtils::to_matrix(X);
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auto result = predict_proba(X_);
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return TensorUtils::to_tensor<double>(result);
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auto result_v = predict_proba(X_);
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torch::Tensor result;
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for (int i = 0; i < result_v.size(); ++i) {
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result.index_put_({ i, "..." }, torch::tensor(result_v[i], torch::kDouble));
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}
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return result;
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}
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torch::Tensor XSpode::predict(torch::Tensor& X)
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{
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@@ -18,7 +18,7 @@ namespace bayesnet {
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public:
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explicit XSpode(int spIndex);
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std::vector<double> predict_proba(const std::vector<int>& instance) const;
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std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>& test_data);
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std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
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int predict(const std::vector<int>& instance) const;
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void normalize(std::vector<double>& v) const;
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std::string to_string() const;
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