diff --git a/src/IrisDatasetTest.cpp b/src/IrisDatasetTest.cpp
index 3fcaa42..af6ef35 100644
--- a/src/IrisDatasetTest.cpp
+++ b/src/IrisDatasetTest.cpp
@@ -21,14 +21,22 @@ INITIALIZE_EASYLOGGINGPP
 // Disclaimer: This is NOT an example of good machine learning practices
 //              regarding training/testing dataset partitioning.
 
+const int input_size = 4;
+const int number_classes = 3;
+#if defined(_WIN32)
 const char *iris_dataset = "../../data/iris.data";
-const std::array<std::string, 3> class_names =
+const std::string iris_mlp_weights = "../../data/iris.mlp";
+#else
+const char *iris_dataset = "./data/iris.data";
+const std::string iris_mlp_weights = "./data/iris.mlp";
+#endif
+const std::array<std::string, number_classes> class_names =
 { "Iris-setosa", "Iris-versicolor", "Iris-virginica" };
 
 
 bool load_data(int *samples,
-               std::vector<double>  *input,
-               std::vector<double> *iris_class) {
+  std::vector<double>  *input,
+  std::vector<double> *iris_class) {
   // Load the iris data-set. 
   FILE *in = fopen(iris_dataset, "r");
   if (!in) {
@@ -46,32 +54,41 @@ bool load_data(int *samples,
   LOG(INFO) << "Loading " << (*samples)
     << " data points from " << iris_dataset << ".";
   // Allocate memory for input and output data.
-  input->resize((*samples) * 4);
-  iris_class->resize((*samples) * 3);
+  input->resize((*samples) * input_size);
+  iris_class->resize((*samples) * number_classes);
 
   // Read the file into our arrays. 
   int i, j;
   for (i = 0; i < (*samples); ++i) {
-    double *p = &((*input)[0]) + i * 4;
-    double *c = &((*iris_class)[0]) + i * 3;
-    c[0] = c[1] = c[2] = 0.0;
+    double *p = &((*input)[0]) + i * input_size;
+    double *c = &((*iris_class)[0]) + i * number_classes;
+    for (int k = 0; k < number_classes; k++) {
+      c[i] = 0.0;
+    }
 
     fgets(line, 1024, in);
 
     char *split = strtok(line, ",");
     for (j = 0; j < 4; ++j) {
       p[j] = atof(split);
-      split = strtok(0, ",");
+      split = strtok(NULL, ",");
     }
 
-    split[strlen(split) - 1] = 0;
+    if (strlen(split) >= 1 && split[strlen(split) - 1] == '\n')
+      split[strlen(split) - 1] = '\0';
+    if (strlen(split) >= 2 && split[strlen(split) - 2] == '\r')
+      split[strlen(split) - 2] = '\0';
+
     if (strcmp(split, class_names[0].c_str()) == 0) {
       c[0] = 1.0;
-    } else if (strcmp(split, class_names[1].c_str()) == 0) {
+    }
+    else if (strcmp(split, class_names[1].c_str()) == 0) {
       c[1] = 1.0;
-    } else if (strcmp(split, class_names[2].c_str()) == 0) {
+    }
+    else if (strcmp(split, class_names[2].c_str()) == 0) {
       c[2] = 1.0;
-    } else {
+    }
+    else {
       LOG(ERROR) << "Unknown iris_class " << split
         << ".";
       return false;
@@ -106,7 +123,7 @@ int main(int argc, char *argv[]) {
     for (int i = 0; i < 3; i++)
       training_set_output.push_back(*(&(iris_class[0]) + j * 3 + i));
     training_set.emplace_back(std::move(training_set_input),
-                              std::move(training_set_output));
+      std::move(training_set_output));
   }
   std::vector<TrainingSample> training_sample_set_with_bias(std::move(training_set));
   //set up bias
@@ -126,11 +143,11 @@ int main(int argc, char *argv[]) {
     LOG(INFO) << "Training for " << loops << " loops over data.";
     my_mlp.Train(training_sample_set_with_bias, .01, loops, 0.10, false);
 
-    my_mlp.SaveMLPNetwork(std::string("../../data/iris.mlp"));
+    my_mlp.SaveMLPNetwork(iris_mlp_weights);
   }
   //Destruction/Construction of a MLP object to show off saving and loading a trained model
   {
-    MLP my_mlp(std::string("../../data/iris.mlp"));
+    MLP my_mlp(iris_mlp_weights);
 
     int correct = 0;
     for (int j = 0; j < samples; ++j) {
@@ -141,9 +158,11 @@ int main(int argc, char *argv[]) {
 
       if (iris_class[j * 3 + 0] == 1.0 && class_id == 0) {
         ++correct;
-      } else if (iris_class[j * 3 + 1] == 1.0  && class_id == 1) {
+      }
+      else if (iris_class[j * 3 + 1] == 1.0  && class_id == 1) {
         ++correct;
-      } else if (iris_class[j * 3 + 2] == 1.0 && class_id == 2) {
+      }
+      else if (iris_class[j * 3 + 2] == 1.0 && class_id == 2) {
         ++correct;
       }
     }
diff --git a/src/MLP.cpp b/src/MLP.cpp
index 558ca44..cd76f15 100644
--- a/src/MLP.cpp
+++ b/src/MLP.cpp
@@ -28,7 +28,7 @@ MLP::MLP(const std::vector<uint64_t> & layers_nodes,
             constant_weight_init);
 };
 
-MLP::MLP(std::string & filename) {
+MLP::MLP(const std::string & filename) {
   LoadMLPNetwork(filename);
 }
 
@@ -58,7 +58,7 @@ void MLP::CreateMLP(const std::vector<uint64_t> & layers_nodes,
   }
 };
 
-void MLP::SaveMLPNetwork(std::string & filename)const {
+void MLP::SaveMLPNetwork(const std::string & filename)const {
   FILE * file;
   file = fopen(filename.c_str(), "wb");
   fwrite(&m_num_inputs, sizeof(m_num_inputs), 1, file);
@@ -71,7 +71,7 @@ void MLP::SaveMLPNetwork(std::string & filename)const {
   }
   fclose(file);
 };
-void MLP::LoadMLPNetwork(std::string & filename) {
+void MLP::LoadMLPNetwork(const std::string & filename) {
   m_layers_nodes.clear();
   m_layers.clear();
 
diff --git a/src/MLP.h b/src/MLP.h
index 783cc05..d4ecbea 100644
--- a/src/MLP.h
+++ b/src/MLP.h
@@ -24,11 +24,11 @@ public:
       const std::vector<std::string> & layers_activfuncs,
       bool use_constant_weight_init = false,
       double constant_weight_init = 0.5);
-  MLP(std::string & filename);
+  MLP(const std::string & filename);
   ~MLP();
 
-  void SaveMLPNetwork(std::string & filename)const;
-  void LoadMLPNetwork(std::string & filename);
+  void SaveMLPNetwork(const std::string & filename)const;
+  void LoadMLPNetwork(const std::string & filename);
 
   void GetOutput(const std::vector<double> &input,
                  std::vector<double> * output,