Dziala makePrediction na 1 time-slice, podpinam pairwiseFactor

server-predictor-tests/server-predictor-tests/PairwiseModelR.cs

@@ -49,8 +49,10 @@ public void init()
                     Logger.Instance.log("Error initializing R.NET engine");
                     throw (e);
                 }
+                Logger.Instance.log("Correctly initialized R.NET engine");
             }
-            Logger.Instance.log("Correctly initialized R.NET engine");
+            engine.Evaluate("source('general.r')");
+
         }
         ArrayList pairwiseFactors;
         private string p;
@@ -63,8 +65,6 @@ public void initPairwiseCalculation(string data_file_name)
             catch (Exception e)
             {
             }
-
-
         }
 
 

server-predictor-tests/server-predictor-tests/SingularModelR.cs

@@ -20,7 +20,8 @@ public static REngine engine
             get;
             set;
         }//engine to R
-        private string m_trained_model_file; //saved trained model
+        private string m_trained_model_file; //sed trained model
+        private string m_traffic_test_file;
         private string m_source_file;
         private string m_alg_name;
         private int m_ahead;
@@ -36,6 +37,7 @@ public SingularModelR(string source_file = "model0b.r", string alg_name = "model
             m_source_file = source_file; m_alg_name = alg_name;
             m_trained_model_file = "data/" + m_name + ".trained_model.RDa"; //setting up used file paths
             m_predict_var_name = "predict_options_" + m_name;
+            m_traffic_test_file = "data/traffic_test." + m_name + ".txt";
 
 
             isTrained = false;
@@ -66,8 +68,9 @@ public void init(int look_behind = 30, int link_count = 20,
                     Logger.Instance.log("Error initializing R.NET engine");
                     throw (e);
                 }
+                Logger.Instance.log("Correctly initialized R.NET engine");
             }
-            Logger.Instance.log("Correctly initialized R.NET engine");
+
 
             try
             {
@@ -83,11 +86,16 @@ public void init(int look_behind = 30, int link_count = 20,
 
                 SingularModelR.engine.Evaluate(m_predict_var_name + "= " + m_alg_name + ".getDefaultPredictOptions()");
 
-                SingularModelR.engine.Evaluate(m_predict_var_name+"= " + look_behind.ToString());
+                SingularModelR.engine.Evaluate(m_predict_var_name+"$look_behind= " + look_behind.ToString());
                 SingularModelR.engine.Evaluate(m_predict_var_name+"$link_count= " + link_count.ToString());
                 SingularModelR.engine.Evaluate(m_predict_var_name + "$look_behind_prediction= " + look_behind_prediction.ToString());
                 SingularModelR.engine.Evaluate(m_predict_var_name + "$look_behind_prediction_self= " + look_behind_prediction_self.ToString());
                 SingularModelR.engine.Evaluate(m_predict_var_name + "$ahead= " + ahead.ToString());
+                SingularModelR.engine.Evaluate(m_predict_var_name + "$name=\"" + m_name.ToString()+"\"");
+                SingularModelR.engine.Evaluate("model0b.prepareDependList(file_name=\""
+                    + m_traffic_test_file +
+                    "\",predict_options=" + m_predict_var_name+")");
+                //SingularModelR.engine.Evaluate(m_predict_var_name + "$name= " + this.m_name.ToString());
                 Logger.Instance.log("predict_options set correctly");
 
                 SingularModelR.engine.Evaluate(m_alg_name + ".calculateGloballyGraph()");
@@ -172,6 +180,7 @@ public double[] predict(double[] X_input)
                 Logger.Instance.log_error("Error while predicting " + e.ToString());
                 throw (e);
             }
+            //0 is dummy !!
             double[] Y_output = new double[Y.Length + 1];
             Y.AsNumeric().CopyTo(Y_output, Y.Length,  0,  1);
             //Console.WriteLine(Y.First().ToString());

server-predictor-tests/server-predictor-tests/bin/Debug/general.r

@@ -161,6 +161,8 @@ model0b.getDependList<-function(i, predict_options){
 
 model0b.prepareDependList<-function(file_name = "data/traffic_test.txt", predict_options){
     ###1. Read 100 lines and feed into get_corr_list
+	print("Prepare depend list")
+	print(file_name)
     data.sample = read.clump(file_name, 100, 1)
     depend_list = general.get_depend_list(data.sample)
     save(depend_list, file = paste("data/model0b.",predict_options$name,".depend_list.RDa",sep=""))

server-predictor-tests/server-predictor-tests/bin/Debug/model0b.r

@@ -46,7 +46,7 @@ model0b.getDefaultPredictOptions <- function(){
 look_behind = 30,             #number of steps to include into prediction as it is in THE DATA  #quite global 
 look_behind_prediction_self=30,  #number of steps to look behind when considering the link to predict   
 look_behind_prediction = 10,  #-,- other links
-name = "Cars", #algorithm name for naming purposes
+name = "smrCars", #algorithm name for naming purposes
 ahead = 2, #number of steps to predict
 ahead_data = 10, #actual number of steps registered in the file 
 link_count=20, #number of all links int he network to predict for (every link will separately predict for it subset of dependency list)
@@ -282,25 +282,24 @@ model0b.applyModel0b<-function(model0b, test_data, predict_options){
     }
     predicted = predicted * (1.0/model0b$normalize_factor_y)
     ## !!!!!!! REMOVE THIS LINE !!!!!!!!!!!#
-    return(sum(predicted))
+    return(c(predicted))
 }
 
 ### Perform whole (required) prediction ###
 model0b.makePrediction <- function(link_models, x, predict_options){
-   y = matrix(0, ncol = predict_options$link_count, nrow = 1)
+   y = matrix(0, ncol = predict_options$link_count, nrow = predict_options$ahead)
 
-
    depend_list = model0b.getCompleteDependList(predict_options) 
 
-
-   for(i in 1:predict_options$link_count){
+     for(i in 1:predict_options$link_count){
         indices = c()
         for(j in 1:length(depend_list[[i]])){
-            indices = c(indices, ((depend_list[[i]][j]-1)*predict_options$look_behind+1):(depend_list[[i]][j]*predict_options$look_behind))
+		            indices = c(indices, ((depend_list[[i]][j]-1)*predict_options$look_behind+1):(depend_list[[i]][j]*predict_options$look_behind))
         }
         y[,i] = model0b.applyModel0b(link_models[[i]], as.vector(x[indices]), predict_options) 
    } 
 
+	##note: sligh change - Y1+1 Y1+2 Y2+1 Y2+2 format
    return(y)
 }
 
@@ -382,6 +381,8 @@ model0b.test<-function(){
         y_collected = rbind(y_collected, model0b.makePrediction(model0b.model, data_transform$samples[i,], predict_options))
     }
 
+    print(y_collected)
+
     load("data/model0b.link.general.data.RDa")