Merge pull request #3398 from AdarshSantoria/cf

Update outdated codes in cf.md and datasetmapper.md

HISTORY.md

@@ -1,5 +1,8 @@
 ### mlpack ?.?.?
 ###### ????-??-??
+
+  * Update outdated code in tutorials (#3398).
+
   * Bugfix for non-square convolution kernels (#3376).
 
   * Fix a few missing includes in `<mlpack.hpp>` (#3374).

doc/tutorials/cf.md

@@ -255,7 +255,7 @@ extern size_t rank;
 // Build the CF object and perform the decomposition.
 // The constructor takes a default-constructed factorizer, which, by default,
 // is of type NMFALSFactorizer.
-CF cf(data, NMFALSFactorizer(), neighborhood, rank);
+CF cf(data, NMFPolicy(), neighborhood, rank);
 
 // Store the results in this object.
 arma::Mat<size_t> recommendations;
@@ -270,12 +270,15 @@ mlpack provides a number of existing factorizers which can be used in place of
 the default `NMFALSFactorizer` (which is non-negative matrix factorization with
 alternating least squares update rules).  These include:
 
- - `SVDBatchFactorizer`
- - `SVDCompleteIncrementalFactorizer`
- - `SVDIncompleteIncrementalFactorizer`
- - `NMFALSFactorizer`
- - `RegularizedSVD`
- - `QUIC_SVD`
+ - `BatchSVDPolicy`
+ - `SVDCompletePolicy`
+ - `SVDIncompletePolicy`
+ - `NMFPolicy`
+ - `RegSVDPolicy`
+ - `QuicSVDPolicy`
+ - `BiasSVDPolicy`
+ - `SVDPlusPlusPolicy`
+ - `RandomizedSVDPolicy`
 
 The `AMF` class has many other possibilities than those listed here; it is a
 framework for alternating matrix factorization techniques.  See the `AMF` class
@@ -297,7 +300,7 @@ extern size_t neighborhood;
 extern size_t rank;
 
 // Build the CF object and perform the decomposition.
-CF cf(data, RegularizedSVD(), neighborhood, rank);
+CFType cf(data, RegSVDPolicy(), neighborhood, rank);
 
 // Store the results in this object.
 arma::Mat<size_t> recommendations;
@@ -330,7 +333,7 @@ extern size_t rank;
 // Build the CF object and perform the decomposition.
 // The constructor takes a default-constructed factorizer, which, by default,
 // is of type NMFALSFactorizer.
-CF cf(data, NMFALSFactorizer(), neighborhood, rank);
+CF cf(data, NMFPolicy(), neighborhood, rank);
 
 const double prediction = cf.Predict(12, 50); // User 12, item 50.
 ```
@@ -356,7 +359,7 @@ extern size_t rank;
 // Build the CF object and perform the decomposition.
 // The constructor takes a default-constructed factorizer, which, by default,
 // is of type NMFALSFactorizer.
-CF cf(data, NMFALSFactorizer(), neighborhood, rank);
+CF cf(data, NMFPolicy(), neighborhood, rank);
 
 // References to W and H matrices.
 const arma::mat& W = cf.W();

doc/tutorials/datasetmapper.md

@@ -43,7 +43,7 @@ function.
 using namespace mlpack;
 
 arma::mat data;
-data::DatasetMapper info;
+data::DatasetInfo info;
 data::Load("dataset.csv", data, info);
 ```
 
@@ -155,8 +155,8 @@ std::cout << info.UnmapString(1, 2) << "\n";
 This will print:
 
 ```
-T
-F
+True
+False
 ```
 
 ### `UnmapValue()`
@@ -168,8 +168,8 @@ The `UnmapValue()` function has the signature `UnmapValue(const std::string
   - `dimension` is the dimension in which you want to find the mapped value
 
 ```c++
-std::cout << info.UnmapValue("T", 2) << "\n";
-std::cout << info.UnmapValue("F", 2) << "\n";
+std::cout << info.UnmapValue("True", 2) << "\n";
+std::cout << info.UnmapValue("False", 2) << "\n";
 ```
 
 will produce:

src/mlpack/methods/cf/cf.hpp

@@ -283,6 +283,8 @@ class CFType
   };
 }; // class CFType
 
+typedef CFType<> CF;
+
 } // namespace mlpack
 
 // Include implementation of templated functions.