From 6fe99473a48eb1b802bf51e24d5f44ccae3766fa Mon Sep 17 00:00:00 2001
From: rtalbi <dr_talbi@esi.dz>
Date: Mon, 20 Dec 2021 12:54:51 +0100
Subject: [PATCH] non-privacy presrerving neural networks (finished code,
started debug)
---
ML/NN/NN.cpp | 49 +++++++++++++++++++++++++++++++++++++++++--------
1 file changed, 41 insertions(+), 8 deletions(-)
diff --git a/ML/NN/NN.cpp b/ML/NN/NN.cpp
index 371c8f48..786da49e 100644
--- a/ML/NN/NN.cpp
+++ b/ML/NN/NN.cpp
@@ -221,34 +221,67 @@ void NN::train () //
}
-//todo : transform this method so that prediction happens for fa test batch
void NN::Test( ){
int counter =0;
+ int recordCounter = 0;
int size= dt->test_size;
Record * record;
+ vector<Record*> XB;
int label;
-
+ int sizeBatch=batchSize;
std::ofstream classOutput;
classOutput.open (logfile);
extTestBd = 0;
auto begin = chrono::high_resolution_clock::now();
+
+
while (counter < size) {
- counter++;
+ if (size - counter < batchSize)
+ sizeBatch = size - counter;
- label = predict(record,true);
- if(classOutput.is_open())
- {
- classOutput<<label<< endl;
+ for (recordCounter = 0; recordCounter < sizeBatch; recordCounter++) {
+ try {
+
+ record = dt->getTrainRecord();
+ XB.push_back(record);
+ extTrainBd += record->values.size() + 1;
+ counter++;
+ }
+ catch (std::exception const &e) {
+ cout << e.what() << endl;
+ }
+
+ }
+
+ vector<int> prediction = predict(XB, true);
+
+ for (int k=0; k < prediction.size();k++) {
+ int label = prediction[k];
+
+ if (classOutput.is_open()) {
+ classOutput << label << endl;
+
+ }
+ }
+
+
+ for (int i = 0; i < XB.size(); i++) {
+ delete XB[i];
}
- delete record;
+
+ XB.clear();
+
+
}
+
+
auto end = chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> duration = end - begin ;
--
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