Changeset 1810 for trunk/yat/normalizer/qQuantileNormalizer.h

Timestamp:

Feb 20, 2009, 1:52:57 AM (14 years ago)

Author:

Peter

Message:

working on documentation. refs #478

File:

• trunk/yat/normalizer/qQuantileNormalizer.h (modified) (2 diffs)

trunk/yat/normalizer/qQuantileNormalizer.h

@@ -47 +47 @@
      \brief Perform Q-quantile normalization
 
-     After a Q-quantile normalization each column has approximately
-     the same distribution of data (the Q-quantiles are the
-     same). Also, within each column the rank of an element is not
-     changed.
-
-     The normalization goes like this
-     - Data is not assumed to be sorted.
-     - Partition sorted target data in N parts. N must be 3 larger
+     Perform a Q-quantile normalization on a \a source range, after
+     which it will approximately have the same distribution of data as
+     the \a target range (the Q-quantiles are the same). The rank of
+     an element in the \a source range is not changed.
+
+     The class works also with unweighed ranges, and there is no
+     restriction that weighted \a source range requires weighted \a
+     target range or vice versa.
+
+     Normalization goes like this:
+     - Data are not assumed to be sorted.
+     - Partition sorted \a target data in N parts. N must be 3 or larger
        because of requirements from the underlying cspline fit
-     - Calculate the arithmetic mean for each part, the mean is
+     - Calculate the arithmetic (weighted) mean for each part, the mean is
        assigned to the mid point of each part.
-     - Do the above for the data to be tranformed (called source
+     - Do the above for the data to be tranformed (called \a source
        here).
-     - For each part, calculate the difference between the target and
-       the source. Now we have N differences d_i with associated rank
-       (midpoint of each part).
-     - Create a cubic spline fit to this difference vector d. The
-       resulting curve is used to recalculate all column values.
+     - For each part, calculate the difference between the \a target
+       and \a the source. Now we have \a N differences \f$ d_i \f$
+       with associated rank (midpoint of each part).
+     - Create a cubic spline fit to this difference vector \a d. The
+       resulting curve is used to recalculate all values in \a source.
        - Use the cubic spline fit for values within the cubic spline
          fit range [midpoint 1st part, midpoint last part].
        - For data outside the cubic spline fit use linear
-         extrapolation, i.e., a constant shift. d_first for points
-         below fit range, and d_last for points above fit range.
+         extrapolation, i.e., a constant shift. \f$ d_{first} \f$ for
+         points below fit range, and \f$ d_last \f$ for points above fit
+         range.
 
      \since New in yat 0.5
@@ -77 +82 @@
   public:
     /**
-       \brief Documentation please.
+       \brief Contructor
 
        \a Q is the number of parts and must be within \f$ [3,N] \f$