Commit 710e6bba authored by Fize Jacques's avatar Fize Jacques
Browse files

Add an early Get Started section to the README

parent 3e181588
......@@ -21,13 +21,51 @@ $ python3 setup.py install
## Get Started
### Graph input format
For now, every algorithms class is composed with a static method called `compare()`. `compare()` takes
two arguments :
In `Gmatch4py`, algorithms manipulate `networkx.Graph`, a complete graph model that
comes with a large spectrum of parser to load your graph from various inputs : `*.graphml,*.gexf,..` (check [here](https://networkx.github.io/documentation/stable/reference/readwrite/index.html) to see all the format accepted)
### Use Gmatch4py
If you want to use algorithms like *graph edit distances*, here is an example:
```{python}
# Gmatch4py use networkx graph
import networkx as nx
# import the GED using the munkres algorithm
from gmatch4py.ged.graph_edit_dist import GraphEditDistance
```
In this example, we use generated graphs using `networkx` helpers:
```{python}
g1=nx.complete_bipartite_graph(5,4)
g2=nx.complete_bipartite_graph(6,4)
```
All graph matching algorithms in `Gmatch4py work this way:
* Each algorithm is associated with an object, each object having its specific parameters. In this case, the parameters are the edit costs (delete a vertex, add a vertex, ...)
* Each object is associated with a `compare()` function with two parameters. First parameter is **a list of the graphs** you want to **compare**, i.e. measure the distance/similarity (depends on the algorithm). Then, you can specify a sample of graphs to be compared to all the other graphs. To this end, the second parameter should be **a list containing the indices** of these graphs (based on the first parameter list). If you rather compute the distance/similarity **between all graphs**, just use the `None` value.
```{python}
ged=GraphEditDistance(1,1,1,1) # all edit costs are equal to 1
result=ged.compare([g1,g2],None)
print(result)
```
The output is a similarity/distance matrix :
```
Out[10]:
array([[0., 7.],
[7., 0.]])
```
This output result is "raw", if you wish to have normalized results in terms of distance (or similarity) you can use :
```
ged.similarity(result)
# or
ged.distance(result)
```
* An array containing the graphs to compare
* An array containing indexes of graph you want to compare. Set to `None`, if you want to
measure the similarity/distance between every graphs.
## List of algorithms
......
......@@ -37,21 +37,16 @@ def makeExtension(extName):
extNames = scandir("gmatch4py")
# and build up the set of Extension objects
extensions = cythonize([makeExtension(name) for name in extNames])
setup(
name="GMatch4py",
description="A module for graph matching",
packages=["gmatch4py","gmatch4py.helpers"],
ext_modules=extensions,
cmdclass={'build_ext': build_ext},
setup_requires=["numpy","networkx","networkit"],
install_requires=["numpy","networkx","networkit"],
setup_requires=["numpy","networkx","networkit","scipy"],
install_requires=["numpy","networkx","networkit","scipy"],
version="0.1"
)
#Clean cpp and compiled file
......
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