[mlpack-git] master: Add implementation of the weight initialization method by T. Kathirvalavakumar and S. Subavathi. (13da3da)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40

>---------------------------------------------------------------

commit 13da3da7bcf85d8577a192a101aa77b9ca705bb4
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date:   Wed Dec 31 22:08:49 2014 +0100

    Add implementation of the weight initialization method by T. Kathirvalavakumar and S. Subavathi.


>---------------------------------------------------------------

13da3da7bcf85d8577a192a101aa77b9ca705bb4
 .../kathirvalavakumar_subavathi_init.hpp           | 94 ++++++++++++++++++++++
 1 file changed, 94 insertions(+)

diff --git a/src/mlpack/methods/ann/init_rules/kathirvalavakumar_subavathi_init.hpp b/src/mlpack/methods/ann/init_rules/kathirvalavakumar_subavathi_init.hpp
new file mode 100644
index 0000000..dcac6b2
--- /dev/null
+++ b/src/mlpack/methods/ann/init_rules/kathirvalavakumar_subavathi_init.hpp
@@ -0,0 +1,94 @@
+/**
+ * @file kathirvalavakumar_subavathi_init.hpp
+ * @author Marcus Edel
+ *
+ * Definition and implementation of the initialization method by T.
+ * Kathirvalavakumar and S. Subavathi. This initialization rule is based on
+ * sensitivity analysis using cauchy’s inequality.
+ *
+ * For more information, see the following paper.
+ *
+ * @code
+ * @inproceedings{KathirvalavakumarJILSA2011,
+ *   title={A New Weight Initialization Method Using Cauchy’s Inequality Based
+ *   on Sensitivity Analysis},
+ *   author={T. Kathirvalavakumar and S. Subavathi},
+ *   booktitle={Journal of Intelligent Learning Systems and Applications,
+ *   Vol. 3 No. 4},
+ *   year={2011}
+ * }
+ * @endcode
+ */
+#ifndef __MLPACK_METHOS_ANN_INIT_RULES_KATHIRVALAVAKUMAR_SUBAVATHI_INIT_HPP
+#define __MLPACK_METHOS_ANN_INIT_RULES_KATHIRVALAVAKUMAR_SUBAVATHI_INIT_HPP
+
+#include <mlpack/core.hpp>
+#include <mlpack/methods/ann/activation_functions/logistic_function.hpp>
+
+#include "random_init.hpp"
+
+namespace mlpack {
+namespace ann /** Artificial Neural Network. */ {
+
+/**
+ * This class is used to initialize the weight matrix with the method proposed
+ * by T. Kathirvalavakumar and S. Subavathi. The method is based on sensitivity
+ * analysis using using cauchy’s inequality. The method is defined by
+ *
+ * @f[
+ * \={s} &=& f^{-1}(\={t})
+ * \Theta^{1}_{p} \le \={s} \sqrt{\frac{3}{I\sum_{i=1}^{I}(x_{ip}^2)}}
+ * \Theta^1 = min(\Theta_{p}^{1}); p=1,2,..,P
+ * -\Theta^{1} \le w_{i}^{1} \le \Theta^{1} \\
+ * @f]
+ *
+ * Where I is the number of inputs including the bias, p refers the pattern
+ * considered in training, f is the transfer function and \={s} is the active
+ * region in which the derivative of the activation function is greater than 4%
+ * of the maximum derivatives.
+ *
+ * @tparam MatType Type of matrix (should be arma::mat or arma::spmat).
+ */
+template<typename MatType = arma::mat>
+class KathirvalavakumarSubavathiInitialization
+{
+ public:
+  /**
+   * Initialize the random initialization rule with the given values.
+   *
+   * @param data The input patterns.
+   * @param s Parameter that defines the active region.
+   */
+  KathirvalavakumarSubavathiInitialization(const MatType& data, const double s)
+      : data(data), s(s) { }
+
+  /**
+   * Initialize the elements of the specified weight matrix with the
+   * Kathirvalavakumar-Subavathi method.
+   *
+   * @param W Weight matrix to initialize.
+   * @param n_rows Number of rows.
+   * @return n_cols Number of columns.
+   */
+  void Initialize(MatType& W, const size_t n_rows, const size_t n_cols)
+  {
+    arma::rowvec b = s * arma::sqrt(3 / (n_rows * sum(data + data)));
+    double theta = b.min();
+
+    RandomInitialization<MatType> randomInit(-theta, theta);
+    randomInit.Initialize(W, n_rows, n_cols);
+  }
+
+ private:
+  //! The input patterns.
+  MatType data;
+
+  //! Parameter that defines the active region.
+  const double s;
+}; // class KathirvalavakumarSubavathiInitialization
+
+
+}; // namespace ann
+}; // namespace mlpack
+
+#endif