[mlpack-git] master: 1 : add read only function FeatureSize 2 : add function InitializeWeights to avoid the case when RVO fail (e5c2e71)

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

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

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/8a8b708650f72c2aecbd9b4a12c8b16b4e0a3508...dc2c5c68dc4bfcdd2075b1a0fd2d641fce651669

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

commit e5c2e71d69da76dcef187e93f6afae94fb9bc9cd
Author: stereomatchingkiss <stereomatchingkiss at gmail.com>
Date:   Wed Sep 30 11:47:19 2015 +0800

    1 : add read only function FeatureSize
    2 : add function InitializeWeights to avoid the case when RVO fail


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

e5c2e71d69da76dcef187e93f6afae94fb9bc9cd
 .../softmax_regression/softmax_regression.hpp      |  4 ++++
 .../softmax_regression_function.cpp                | 26 ++++++++++++++--------
 .../softmax_regression_function.hpp                | 17 +++++++++++++-
 .../softmax_regression/softmax_regression_impl.hpp |  5 +++--
 4 files changed, 40 insertions(+), 12 deletions(-)

diff --git a/src/mlpack/methods/softmax_regression/softmax_regression.hpp b/src/mlpack/methods/softmax_regression/softmax_regression.hpp
index fa7142a..8875421 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression.hpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression.hpp
@@ -161,6 +161,10 @@ class SoftmaxRegression
   //! Get the model parameters.
   const arma::mat& Parameters() const { return parameters; }
 
+  //! Gets the features size of the training data
+  size_t FeatureSize() const
+  { return parameters.n_rows; }
+
   /**
    * Serialize the SoftmaxRegression model. 
    */
diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp b/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp
index 9482e86..a3417af 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp
@@ -41,19 +41,27 @@ InitializeWeights(const size_t featureSize,
                   const size_t numClasses,
                   const bool fitIntercept)
 {    
-    // Initialize values to 0.005 * r. 'r' is a matrix of random values taken from
-    // a Gaussian distribution with mean zero and variance one.
-    // If the fitIntercept flag is true, parameters.col(0) is the intercept.
     arma::mat parameters;
-    if (fitIntercept)
-      parameters.randn(numClasses, featureSize + 1);
-    else
-      parameters.randn(numClasses, featureSize);
-    parameters = 0.005 * parameters;
-
+    InitializeWeights(parameters, featureSize, numClasses, fitIntercept);
     return parameters;
 }
 
+void SoftmaxRegressionFunction::
+InitializeWeights(arma::mat &weights,
+                  const size_t featureSize,
+                  const size_t numClasses,
+                  const bool fitIntercept)
+{
+  // Initialize values to 0.005 * r. 'r' is a matrix of random values taken from
+  // a Gaussian distribution with mean zero and variance one.
+  // If the fitIntercept flag is true, parameters.col(0) is the intercept.
+  if (fitIntercept)
+    weights.randn(numClasses, featureSize + 1);
+  else
+    weights.randn(numClasses, featureSize);
+  weights *= 0.005;
+}
+
 /**
  * This is equivalent to applying the indicator function to the training
  * labels. The output is in the form of a matrix, which leads to simpler
diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp b/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp
index 9cf7dbd..01fe6d7 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp
@@ -49,6 +49,20 @@ class SoftmaxRegressionFunction
                                            const bool fitIntercept = false);
 
   /**
+   * Initialize Softmax Regression weights(trainable parameters) with
+   * the given parameters.
+   * @paaram weights weights want to initialize
+   * @param featureSize The features size of the training set
+   * @param numClasses Number of classes for classification.
+   * @param fitIntercept Intercept term flag.
+   * @return weights after initialize
+   */
+  static void InitializeWeights(arma::mat &weights,
+                                const size_t featureSize,
+                                const size_t numClasses,
+                                const bool fitIntercept = false);
+
+  /**
    * Constructs the ground truth label matrix with the passed labels.
    *
    * @param labels Labels associated with the training data.
@@ -98,7 +112,8 @@ class SoftmaxRegressionFunction
   size_t NumClasses() const { return numClasses; }
 
   //! Gets the features size of the training data
-  size_t FeatureSize() const { return data.n_rows; }
+  size_t FeatureSize() const
+  { return initialPoint.n_rows; }
 
   //! Sets the regularization parameter.
   double& Lambda() { return lambda; }
diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp b/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp
index 122524f..43f6e01 100644
--- a/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp
+++ b/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp
@@ -22,8 +22,9 @@ SoftmaxRegression(const size_t inputSize,
     lambda(0.0001),
     fitIntercept(fitIntercept)
 {  
-  parameters = SoftmaxRegressionFunction::InitializeWeights(inputSize, numClasses,
-                                                            fitIntercept);
+   SoftmaxRegressionFunction::InitializeWeights(parameters,
+                                                inputSize, numClasses,
+                                                fitIntercept);
 }
 
 template<template<typename> class OptimizerType>