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71 lines
1.9 KiB
71 lines
1.9 KiB
#include "DecisionTree.h"
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#include "RF.h"
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// __AQ_NO_SESSION__
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#include "../server/table.h"
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#include "aquery.h"
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DecisionTree *dt = nullptr;
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RandomForest *rf = nullptr;
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__AQEXPORT__(bool)
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newtree(int height, long f, ColRef<int> X, double forget, long maxf, long noclasses, Evaluation e, long r, long rb)
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{
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if (X.size != f)
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return false;
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int *X_cpy = (int *)malloc(f * sizeof(int));
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memcpy(X_cpy, X.container, f);
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if (maxf < 0)
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maxf = f;
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dt = new DecisionTree(f, X_cpy, forget, maxf, noclasses, e);
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rf = new RandomForest(height, f, X_cpy, forget, noclasses, e)
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return true;
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}
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// size_t pt = 0;
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// __AQEXPORT__(bool) fit(ColRef<ColRef<double>> X, ColRef<int> y){
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// if(X.size != y.size)return 0;
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// double** data = (double**)malloc(X.size*sizeof(double*));
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// long* result = (long*)malloc(y.size*sizeof(long));
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// for(long i=0; i<X.size; i++){
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// data[i] = X.container[i].container;
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// result[i] = y.container[i];
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// }
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// data[pt] = (double*)malloc(X.size*sizeof(double));
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// for(uint32_t j=0; j<X.size; j++){
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// data[pt][j]=X.container[j];
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// }
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// result[pt] = y;
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// pt ++;
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// return 1;
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// }
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__AQEXPORT__(bool)
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fit(vector_type<vector_type<double>> v, vector_type<long> res)
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{
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double **data = (double **)malloc(v.size * sizeof(double *));
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for (int i = 0; i < v.size; ++i)
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data[i] = v.container[i].container;
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// dt->fit(data, res.container, v.size);
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rf->fit(data, res.container, v.size);
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return true;
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}
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__AQEXPORT__(vectortype_cstorage)
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predict(vector_type<vector_type<double>> v)
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{
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int *result = (int *)malloc(v.size * sizeof(int));
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for (long i = 0; i < v.size; i++)
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//result[i] = dt->Test(v.container[i].container, dt->DTree);
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result[i] = rf->Test(v.container, rf->DTrees);
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auto container = (vector_type<int> *)malloc(sizeof(vector_type<int>));
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container->size = v.size;
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container->capacity = 0;
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container->container = result;
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auto ret = vectortype_cstorage{.container = container, .size = 1, .capacity = 0};
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return ret;
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}
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