bd6871fa3f9c39227a02cf3e9a37e5515d27d900,benchmarks/benchmarks/sparse_csgraph_matching.py,MaximumBipartiteMatching,setup,#MaximumBipartiteMatching#Any#Any#,15

Before Change

    param_names = ["n", "density"]

    def setup(self, n, density):
        graph = scipy.sparse.rand(n, n, density=density,
                                  format="csr", random_state=42)
        self.graph = graph

    def time_maximum_bipartite_matching(self, n, density):
        maximum_bipartite_matching(self.graph)

After Change

        // Create random sparse matrices. Note that we could use
        // scipy.sparse.rand for this purpose, but simply using np.random and
        // disregarding duplicates is quite a bit faster.
        np.random.seed(42)
        d = np.random.randint(0, n, size=(int(n*n*density), 2))
        graph = scipy.sparse.csr_matrix((np.ones(len(d)), (d[:, 0], d[:, 1])),
                                        shape=(n, n))
        self.graph = graph

Instances