Merge pull request #72 from trthatcher/dev

Dev

NEWS.md

@@ -1,3 +1,9 @@
+# MLKernels 0.4.0
+* Updated to support julia 0.7
+* `PairwiseFunction` renamed to `BaseFunction` and no longer exported
+* `HyperParameters` deprecated
+* `MemoryLayout` types (`ColumnMajor` & `RowMajor`) deprecated and replaced with `Val(:row)` and `Val(:col)`
+
 # MLKernels 0.3.0
 * Updated to support julia 0.6
 

README.md

@@ -6,16 +6,16 @@
 
 #### Summary
 
-**MLKernels.jl** is a Julia package for Mercer kernel functions (or the 
-covariance functions used in Gaussian processes) that are used in the kernel 
-methods of machine learning. This package provides a flexible datatype for 
+**MLKernels.jl** is a Julia package for Mercer kernel functions (or the
+covariance functions used in Gaussian processes) that are used in the kernel
+methods of machine learning. This package provides a flexible datatype for
 representing and constructing machine learning kernels as well as an efficient
-set of methods to compute or approximate kernel matrices. The package has no 
+set of methods to compute or approximate kernel matrices. The package has no
 dependencies beyond base Julia.
 
 #### Documentation
 
-Full [documentation](http://mlkernels.readthedocs.org/en/latest/) is available 
+Full [documentation](http://mlkernels.readthedocs.org/en/latest/) is available
 on **Read the Docs**.
 
 #### Visualization
@@ -26,23 +26,23 @@ mapping data from the original feature space to the new feature space.
 Non-linearly separable data may be linearly separable in the transformed space.
 For example, the following data set is not linearly separable:
 
-<p align="center"><img alt="Feature Space" src="doc/images/kerneltrick/featurespace.png"  /></p>
+<p align="center"><img alt="Feature Space" src="docs/images/featurespace.png"  /></p>
 
 Using a Polynomial Kernel of degree 2, the points are mapped to a 3-dimensional
 space where a plane can be used to linearly separate the data:
 
-<p align="center"><img alt="Transformed Data" src="doc/images/kerneltrick/hilbertspace.png"  /></p>
+<p align="center"><img alt="Transformed Data" src="docs/images/hilbertspace.png"  /></p>
 
 Explicitly, the Polynomial Kernel of degree 2 maps the data to a cone in
 3-dimensional space. The intersecting hyperplane forms a conic section with the
 cone:
 
-<p align="center"><img alt="Transformed Data" src="doc/images/kerneltrick/kernelgeometry.png"  /></p>
+<p align="center"><img alt="Transformed Data" src="docs/images/kernelgeometry.png"  /></p>
 
 When translated back to the original feature space, the conic section
 corresponds to a circle which can be used to perfectly separate the data:
 
-<p align="center"><img alt="Separating Hyperplane" src="doc/images/kerneltrick/featurespaceseparated.png"  /></p>
+<p align="center"><img alt="Separating Hyperplane" src="docs/images/featurespaceseparated.png"  /></p>
 
 The above plots were generated using
-[PyPlot.jl](https://github.com/stevengj/PyPlot.jl). 
+[PyPlot.jl](https://github.com/stevengj/PyPlot.jl).