Add IRIS dataset usage example.

Add LoadModel and SaveModel methods.

src/IrisDatasetTest.cpp

@@ -0,0 +1,157 @@
+//============================================================================
+// Name : Main.cpp
+// Author : David Nogueira
+//============================================================================
+#include "MLP.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <iostream>
+#include <sstream>
+#include <fstream>
+#include <vector>
+#include <array>
+#include <algorithm>
+#include "microunit.h"
+#include "easylogging++.h"
+
+INITIALIZE_EASYLOGGINGPP
+
+
+// Example illustrating practical use of this MLP lib.
+// Disclaimer: This is NOT an example of good machine learning practices
+//              regarding training/testing dataset partitioning.
+
+const char *iris_dataset = "../../data/iris.data";
+const std::array<std::string, 3> class_names =
+{ "Iris-setosa", "Iris-versicolor", "Iris-virginica" };
+
+
+bool load_data(int *samples,
+               std::vector<double>  *input,
+               std::vector<double> *iris_class) {
+  // Load the iris data-set. 
+  FILE *in = fopen(iris_dataset, "r");
+  if (!in) {
+    LOG(ERROR) << "Could not open file: " << iris_dataset << ".";
+    return false;
+  }
+
+  // Loop through the data to get a count.
+  char line[1024];
+  while (!feof(in) && fgets(line, 1024, in)) {
+    ++(*samples);
+  }
+  fseek(in, 0, SEEK_SET);
+
+  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);
+
+  // 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;
+
+    fgets(line, 1024, in);
+
+    char *split = strtok(line, ",");
+    for (j = 0; j < 4; ++j) {
+      p[j] = atof(split);
+      split = strtok(0, ",");
+    }
+
+    split[strlen(split) - 1] = 0;
+    if (strcmp(split, class_names[0].c_str()) == 0) {
+      c[0] = 1.0;
+    } else if (strcmp(split, class_names[1].c_str()) == 0) {
+      c[1] = 1.0;
+    } else if (strcmp(split, class_names[2].c_str()) == 0) {
+      c[2] = 1.0;
+    } else {
+      LOG(ERROR) << "Unknown iris_class " << split
+        << ".";
+      return false;
+    }
+  }
+
+  fclose(in);
+  return true;
+}
+
+
+int main(int argc, char *argv[]) {
+  LOG(INFO) << "Train MLP with IRIS dataset using backpropagation.";
+  int samples = 0;
+  std::vector<double> input;
+  std::vector<double> iris_class;
+
+  // Load the data from file.
+  if (!load_data(&samples, &input, &iris_class)) {
+    LOG(ERROR) << "Error processing input file.";
+    return -1;
+  }
+
+  std::vector<TrainingSample> training_set;
+  for (int j = 0; j < samples; ++j) {
+    std::vector<double> training_set_input;
+    std::vector<double> training_set_output;
+    training_set_input.reserve(4);
+    for (int i = 0; i < 4; i++)
+      training_set_input.push_back(*(&(input[0]) + j * 4 + i));
+    training_set_output.reserve(3);
+    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::vector<TrainingSample> training_sample_set_with_bias(std::move(training_set));
+  //set up bias
+  for (auto & training_sample_with_bias : training_sample_set_with_bias) {
+    training_sample_with_bias.AddBiasValue(1);
+  }
+
+  {
+    /* 4 inputs + 1 bias.
+    * 1 hidden layer(s) of 4 neurons.
+    * 3 outputs (1 per iris_class)
+    */
+    MLP my_mlp({ 4 + 1, 4 ,3 }, { "sigmoid", "linear" }, false);
+
+
+    int loops = 5000;
+
+
+    // Train the network with backpropagation.
+    LOG(INFO) << "Training for " << loops << " loops over data.";
+    my_mlp.UpdateMiniBatch(training_sample_set_with_bias, .01, loops, 0.10, false);
+
+    my_mlp.SaveMLPNetwork(std::string("../../data/iris.mlp"));
+  }
+  //Destruction/Construction of a MLP object to show off saving and loading a trained model
+  {
+    MLP my_mlp(std::string("../../data/iris.mlp"));
+
+    int correct = 0;
+    for (int j = 0; j < samples; ++j) {
+      std::vector<double> guess;
+      my_mlp.GetOutput(training_sample_set_with_bias[j].input_vector(), &guess);
+      size_t class_id;
+      my_mlp.GetOutputClass(guess, &class_id);
+
+      if (iris_class[j * 3 + 0] == 1.0 && class_id == 0) {
+        ++correct;
+      } 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) {
+        ++correct;
+      }
+    }
+    LOG(INFO) << correct << "/" << samples
+      << " (" << ((double)correct / samples * 100.0) << "%).";
+  }
+  return 0;
+}