Merge pull request #501 from barak/master

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doc/tutorials/amf/amf.txt

@@ -72,14 +72,14 @@ the threshold or the number of iterations goes beyond the threshold, positive
 termination signal is passed to AMF.
 
 In SimpleToleranceTermination, termination criterion is met when increase in
-residue value drops below the given tolerance. To accomodate spikes, certain
+residue value drops below the given tolerance. To accommodate spikes, certain
 number of successive residue drops are accepted. Secondary termination criterion
 terminates algorithm when iteration count goes beyond the threshold.
 
 ValidationRMSETermination divids the data into 2 sets, training set and
 validation set. Entries of validation set are nullifed in the input matrix.
 Termination criterion is met when increase in validation set RMSe value drops
-below the given tolerance. To accomodate spikes certain number of successive
+below the given tolerance. To accommodate spikes certain number of successive
 validation RMSE drops are accepted. This upper imit on successive drops can be
 adjusted with reverseStepCount. Secondary termination criterion terminates
 algorithm when iteration count goes above the threshold. Though this termination
@@ -101,7 +101,7 @@ The AMF class comes with 2 initialization policies
 
 RandomInitialization initializes matrices W and H with random uniform distribution
 while RandomAcolInitialization initializes the W matrix by averaging p randomly
-chosen columns of V.  In case of RandomAcolIntialization, p is a template parameter.
+chosen columns of V.  In case of RandomAcolInitialization, p is a template parameter.
 
 To implement their own initialization policy, users need to define the following
 function in their class.
@@ -160,7 +160,7 @@ int main()
 }
 @endcode
 
-NMFALSFactorizer uses SimpleResidueTermination which is most prefered with
+NMFALSFactorizer uses SimpleResidueTermination which is most preferred with
 Non-Negative Matrix factorizers. Initialization of W and H in NMFALSFactorizer
 is random. The Apply function returns the residue obtained by comparing the
 constructed matrix W * H with the original matrix V.

src/mlpack/core/tree/ballbound_impl.hpp

@@ -256,7 +256,7 @@ BallBound<VecType, TMetricType>::operator|=(const MatType& data)
     if (dist > radius)
     {
       // Move towards the new point and increase the radius just enough to
-      // accomodate the new point.
+      // accommodate the new point.
       arma::vec diff = data.col(i) - center;
       center += ((dist - radius) / (2 * dist)) * diff;
       radius = 0.5 * (dist + radius);

src/mlpack/core/tree/cosine_tree/cosine_tree.cpp

@@ -325,7 +325,7 @@ void CosineTree::ColumnSamplesLS(std::vector<size_t>& sampledIndices,
     cDistribution(i+1) = cDistribution(i) + l2NormsSquared(i) / frobNormSquared;
   }
 
-  // Intialize sizes of the 'sampledIndices' and 'probabilities' vectors.
+  // Initialize sizes of the 'sampledIndices' and 'probabilities' vectors.
   sampledIndices.resize(numSamples);
   probabilities.zeros(numSamples);
 

src/mlpack/methods/amf/init_rules/average_init.hpp

@@ -2,7 +2,7 @@
  * @file averge_init.hpp
  * @author Sumedh Ghaisas
  *
- * Intialization rule for Alternating Matrix Factorization.
+ * Initialization rule for Alternating Matrix Factorization.
  */
 #ifndef __MLPACK_METHODS_AMF_AVERAGE_INIT_HPP
 #define __MLPACK_METHODS_AMF_AVERAGE_INIT_HPP
@@ -61,7 +61,7 @@ class AverageInitialization
 
     avgV = sqrt(((avgV / (n * m)) - min) / r);
 
-    // Intialize to random values.
+    // Initialize to random values.
     W.randu(n, r);
     H.randu(r, m);
 

src/mlpack/methods/amf/init_rules/random_acol_init.hpp

@@ -2,7 +2,7 @@
  * @file random_acol_init.hpp
  * @author Mohan Rajendran
  *
- * Intialization rule for Alternating Matrix Factorization.
+ * Initialization rule for Alternating Matrix Factorization.
  */
 #ifndef __MLPACK_METHODS_LMF_RANDOM_ACOL_INIT_HPP
 #define __MLPACK_METHODS_LMF_RANDOM_ACOL_INIT_HPP

src/mlpack/methods/amf/init_rules/random_init.hpp

@@ -2,7 +2,7 @@
  * @file random_init.hpp
  * @author Mohan Rajendran
  *
- * Intialization rule for alternating matrix forization (AMF). This simple
+ * Initialization rule for alternating matrix forization (AMF). This simple
  * initialization is performed by assigning a random matrix to W and H.
  */
 #ifndef __MLPACK_METHODS_LMF_RANDOM_INIT_HPP
@@ -41,7 +41,7 @@ class RandomInitialization
     const size_t n = V.n_rows;
     const size_t m = V.n_cols;
 
-    // Intialize to random values.
+    // Initialize to random values.
     W.randu(n, r);
     H.randu(r, m);
   }

src/mlpack/methods/amf/termination_policies/simple_tolerance_termination.hpp

@@ -15,7 +15,7 @@ namespace amf {
 /**
  * This class implements residue tolerance termination policy. Termination
  * criterion is met when increase in residue value drops below the given tolerance.
- * To accomodate spikes certain number of successive residue drops are accepted.
+ * To accommodate spikes certain number of successive residue drops are accepted.
  * This upper imit on successive drops can be adjusted with reverseStepCount.
  * Secondary termination criterion terminates algorithm when iteration count
  * goes above the threshold.

src/mlpack/methods/amf/termination_policies/validation_RMSE_termination.hpp

@@ -19,7 +19,7 @@ namespace amf
  * The input data matrix is divided into 2 sets, training set and validation set.
  * Entries of validation set are nullifed in the input matrix. Termination
  * criterion is met when increase in validation set RMSe value drops below the
- * given tolerance. To accomodate spikes certain number of successive validation
+ * given tolerance. To accommodate spikes certain number of successive validation
  * RMSE drops are accepted. This upper imit on successive drops can be adjusted
  * with reverseStepCount. Secondary termination criterion terminates algorithm
  * when iteration count goes above the threshold.

src/mlpack/methods/lsh/lsh_search.hpp

@@ -185,8 +185,8 @@ class LSHSearch
    * standard hash.
    *
    * This function does not have any parameters and relies on parameters which
-   * are private members of this class, intialized during the class
-   * intialization.
+   * are private members of this class, initialized during the class
+   * initialization.
    */
   void BuildHash();
 

src/mlpack/methods/softmax_regression/softmax_regression_function.cpp

@@ -20,7 +20,7 @@ SoftmaxRegressionFunction::SoftmaxRegressionFunction(
     lambda(lambda),
     fitIntercept(fitIntercept)
 {
-  // Intialize the parameters to suitable values.
+  // Initialize the parameters to suitable values.
   initialPoint = InitializeWeights();
 
   // Calculate the label matrix.

src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp

@@ -142,7 +142,7 @@ class SparseAutoencoderFunction
  private:
   //! The matrix of data points.
   const arma::mat& data;
-  //! Intial parameter vector.
+  //! Initial parameter vector.
   arma::mat initialPoint;
   //! Size of the visible layer.
   size_t visibleSize;

src/mlpack/tests/cosine_tree_test.cpp

@@ -48,7 +48,7 @@ BOOST_AUTO_TEST_CASE(CosineTreeNoSplit)
  */
 BOOST_AUTO_TEST_CASE(CosineNodeCosineSplit)
 {
-  // Intialize constants required for the test.
+  // Initialize constants required for the test.
   const size_t numRows = 500;
   const size_t numCols = 1000;