Merge pull request #11 from birm/style-1

Fix Style Build

Kaggle/DigitRecognizer/src/DigitRecognizer.cpp

@@ -79,7 +79,7 @@ int main()
       train.n_cols - 1) / 255.0;
   const mat validX = valid.submat(1, 0, valid.n_rows - 1,
       valid.n_cols - 1) / 255.0;
-  
+
   const int ITERATIONS_PER_CYCLE = trainX.n_cols;
   // According to NegativeLogLikelihood output layer of NN, labels should
   // specify class of a data point and be in the interval from 1 to

Kaggle/DigitRecognizerBatchNorm/src/DigitRecognizerBatchNorm.cpp

@@ -3,7 +3,8 @@
  * solving Digit Recognizer problem from Kaggle website.
  *
  * The full description of a problem as well as datasets for training
- * and testing are available here https://www.kaggle.com/c/digit-recognizer using BatchNorm
+ * and testing are available here https://www.kaggle.com/c/digit-recognizer
+ * using BatchNorm
  *
  * mlpack is free software; you may redistribute it and/or modify it under the
  * terms of the 3-clause BSD license.  You should have received a copy of the
@@ -97,15 +98,15 @@ int main() {
   // data and PRelU layer. Parameters specify the number of input features
   // and number of neurons in the next layer.
   model.Add<Linear<> >(trainX.n_rows, H1);
-  // The first PReLU activation layer. parameter can be set as constructor argument.
+  // The first PReLU activation layer. parameter can be set as constructor arg.
   model.Add<PReLU<> >();
-  // BatchNorm layer applied after PReLU activation as it gives better results practically.
+  // BatchNorm layer applied after PReLU activation as it gives better results.
   model.Add<BatchNorm<> >(H1);
   // Intermediate layer between PReLU activation layers.
   model.Add<Linear<> >(H1, H2);
   // The second PReLU layer.
   model.Add<PReLU<> >();
-  //Second BatchNorm layer
+  // Second BatchNorm layer
   model.Add<BatchNorm<> >(H2);
   // Intermediate layer.
   model.Add<Linear<> >(H2, 10);
@@ -136,7 +137,6 @@ int main() {
   // Cycles for monitoring the process of a solution.
   for (int i = 0; i <= CYCLES; i++)
   {
-
     // Train neural network. If this is the first iteration, weights are
     // random, using current values as starting point otherwise.
     model.Train(trainX, trainY, optimizer);

Kaggle/DigitRecognizerCNN/src/DigitRecognizerCNN.cpp

@@ -116,8 +116,7 @@ int main()
       0,  // Padding width.
       0,  // Padding height.
       28, // Input width.
-      28  // Input height.
-      );
+      28); // Input Height
 
   // Add first ReLU.
   model.Add<LeakyReLU<> >();
@@ -128,8 +127,7 @@ int main()
       2,  // Height of field.
       2,  // Stride along width.
       2,  // Stride along height.
-      true
-      );
+      true);
 
   // Add the second convolution layer.
   model.Add<Convolution<> >(
@@ -142,8 +140,7 @@ int main()
       0,  // Padding width.
       0,  // Padding height.
       12, // Input width.
-      12  // Input height.
-      );
+      12); // Input Height
 
   // Add the second ReLU.
   model.Add<LeakyReLU<> >();
@@ -223,6 +220,6 @@ int main()
 
   // Saving results into Kaggle compatibe CSV file.
   save("Kaggle/results.csv", "ImageId,Label", testPred);
-  cout << "Results were saved to Kaggle/results.csv. This file can be uploaded to "
-      << "https://www.kaggle.com/c/digit-recognizer/submissions." << endl;
+  cout << "Results were saved to Kaggle/results.csv. This file can be uploaded "
+      << "to https://www.kaggle.com/c/digit-recognizer/submissions." << endl;
 }

Kaggle/kaggle_utils.hpp

@@ -71,20 +71,20 @@ double accuracy(arma::Row<size_t> predLabels, const arma::mat& realY)
 void save(const std::string filename, std::string header,
   const arma::Row<size_t>& predLabels)
 {
-	std::ofstream out(filename);
-	out << header << std::endl;
-	for (size_t j = 0; j < predLabels.n_cols; ++j)
-	{
-	  // j + 1 because Kaggle indexes start from 1
-	  // pred - 1 because 1st class is 0, 2nd class is 1 and etc.
-		out << j + 1 << "," << std::round(predLabels(j)) - 1;
+  std::ofstream out(filename);
+  out << header << std::endl;
+  for (size_t j = 0; j < predLabels.n_cols; ++j)
+  {
+    // j + 1 because Kaggle indexes start from 1
+    // pred - 1 because 1st class is 0, 2nd class is 1 and etc.
+    out << j + 1 << "," << std::round(predLabels(j)) - 1;
     // to avoid an empty line in the end of the file
-		if (j < predLabels.n_cols - 1)
-		{
-		  out << std::endl;
-		}
-	}
-	out.close();
+    if (j < predLabels.n_cols - 1)
+    {
+      out << std::endl;
+    }
+  }
+  out.close();
 }
 
 #endif

LSTM/TimeSeries-Multivariate/src/LSTMTimeSeriesMultivariate.cpp

@@ -14,7 +14,7 @@
  */
 
 /*
-NOTE: the data need to be sorted by date in ascending order! The RNN learns from 
+NOTE: the data need to be sorted by date in ascending order! The RNN learns from
 oldest to newest!
 
 date  close  volume  open  high  low
@@ -101,7 +101,8 @@ void SaveResults(const string filename,
   // The prediction results are the (high, low) for the next day and come from
   // the last slice from the prediction.
   flatDataAndPreds.rows(flatDataAndPreds.n_rows - 2,
-      flatDataAndPreds.n_rows - 1) = predictions.slice(predictions.n_slices - 1);
+      flatDataAndPreds.n_rows - 1) = predictions.slice(
+          predictions.n_slices - 1);
 
   scale.InverseTransform(flatDataAndPreds, flatDataAndPreds);
 
@@ -193,10 +194,10 @@ int main()
   size_t inputSize = 5, outputSize = 2;
 
   // Split the dataset into training and validation sets.
-  arma::mat trainData = dataset.submat(arma::span(),arma::span(0, (1 - RATIO) *
+  arma::mat trainData = dataset.submat(arma::span(), arma::span(0, (1 - RATIO) *
       dataset.n_cols));
-  arma::mat testData = dataset.submat(arma::span(), arma::span((1 - RATIO) * dataset.n_cols,
-      dataset.n_cols - 1));
+  arma::mat testData = dataset.submat(arma::span(),
+      arma::span((1 - RATIO) * dataset.n_cols, dataset.n_cols - 1));
 
   // Number of epochs for training.
   const int EPOCHS = 150;
@@ -255,13 +256,13 @@ int main()
         BATCH_SIZE, // Batch size. Number of data points that are used in each
                     // iteration.
         trainData.n_cols * EPOCHS, // Max number of iterations.
-        1e-8,// Tolerance.
+        1e-8, // Tolerance.
         true, // Shuffle.
         AdamUpdate(1e-8, 0.9, 0.999)); // Adam update policy.
 
     // Instead of terminating based on the tolerance of the objective function,
-    // we'll depend on the maximum number of iterations, and terminate early using
-    // the EarlyStopAtMinLoss callback.
+    // we'll depend on the maximum number of iterations, and terminate early
+    // using the EarlyStopAtMinLoss callback.
     optimizer.Tolerance() = -1;
 
     cout << "Training ..." << endl;
@@ -305,6 +306,6 @@ int main()
   SaveResults(predFile, predOutP, scale, testX);
 
   // Use this on Windows in order to keep the console window open.
-  //cout << "Ready!" << endl;
-  //getchar();
+  // cout << "Ready!" << endl;
+  // getchar();
 }

LSTM/TimeSeries-Univariate/src/LSTMTimeSeriesUnivariate.cpp

@@ -1,5 +1,5 @@
 /**
- * An example of using Recurrent Neural Network (RNN) 
+ * An example of using Recurrent Neural Network (RNN)
  * to make forcasts on a time series of number of kilowatt-hours used in a
  * residential home over a 3.5 month period, 25 November 2011 to 17 March 2012,
  * which we aim to solve using a simple LSTM neural network. Electricity usage
@@ -16,7 +16,7 @@
  */
 
 /*
-NOTE: the data need to be sorted by date in ascending order! The RNN learns from 
+NOTE: the data need to be sorted by date in ascending order! The RNN learns from
 oldest to newest!
 
 DateTime,Consumption kWh,Off-peak,Mid-peak,On-peak
@@ -72,7 +72,7 @@ void CreateTimeSeriesData(InputDataType dataset,
                           LabelType& y,
                           const size_t rho)
 {
-  for(size_t i = 0; i < dataset.n_cols - rho; i++)
+  for (size_t i = 0; i < dataset.n_cols - rho; i++)
   {
     X.subcube(arma::span(), arma::span(i), arma::span()) =
         dataset.submat(arma::span(), arma::span(i, i + rho - 1));
@@ -98,7 +98,8 @@ void SaveResults(const string& filename,
   // The prediction result is the energy consumption for the next hour and comes
   // from the last slice of the prediction.
   flatDataAndPreds.rows(flatDataAndPreds.n_rows - 1,
-      flatDataAndPreds.n_rows - 1) = predictions.slice(predictions.n_slices - 1);
+      flatDataAndPreds.n_rows - 1) = predictions.slice(
+          predictions.n_slices - 1);
 
   scale.InverseTransform(flatDataAndPreds, flatDataAndPreds);
   // We need to remove the last column because it was not used for training
@@ -188,10 +189,10 @@ int main()
   dataset = dataset.submat(1, 1, 1, dataset.n_cols - 1);
 
   // Split the dataset into training and validation sets.
-  arma::mat trainData = dataset.submat(arma::span(),arma::span(0, (1 - RATIO) *
+  arma::mat trainData = dataset.submat(arma::span(), arma::span(0, (1 - RATIO) *
       dataset.n_cols));
-  arma::mat testData = dataset.submat(arma::span(), arma::span((1 - RATIO) * dataset.n_cols,
-      dataset.n_cols - 1));
+  arma::mat testData = dataset.submat(arma::span(),
+      arma::span((1 - RATIO) * dataset.n_cols, dataset.n_cols - 1));
 
   // Number of iterations per cycle.
   const int EPOCHS = 150;
@@ -243,15 +244,15 @@ int main()
     // Set parameters for the Stochastic Gradient Descent (SGD) optimizer.
     SGD<AdamUpdate> optimizer(
         STEP_SIZE, // Step size of the optimizer.
-        BATCH_SIZE, // Batch size. Number of data points that are used in each iteration.
+        BATCH_SIZE, // Batch size. Number of data points used per iteration.
         trainData.n_cols * EPOCHS, // Max number of iterations.
         1e-8, // Tolerance.
         true, // Shuffle.
         AdamUpdate(1e-8, 0.9, 0.999)); // Adam update policy.
 
     // Instead of terminating based on the tolerance of the objective function,
-    // we'll depend on the maximum number of iterations, and terminate early using
-    // the EarlyStopAtMinLoss callback.
+    // we'll depend on the maximum number of iterations, and terminate early
+    // using the EarlyStopAtMinLoss callback.
     optimizer.Tolerance() = -1;
 
     cout << "Training ..." << endl;
@@ -296,6 +297,6 @@ int main()
   SaveResults(predFile, predOutP, scale, testX);
 
   // Use this on Windows in order to keep the console window open.
-  //cout << "Ready!" << endl;
-  //getchar();
+  // cout << "Ready!" << endl;
+  // getchar();
 }