Python networkx.minimum_cut() Examples

def generate_constrs(self, m_: Model):
        xf, cp, Gl = m_.translate(self.x), CutPool(), nx.DiGraph()
        Ar = [(i, j) for (i, j) in Arcs if xf[i][j] and xf[i][j].x >= 1e-4]
        for (u, v) in Ar:
            Gl.add_edge(u, v, capacity=xf[u][v].x)
        for (u, v) in F:
            val, (S, NS) = nx.minimum_cut(Gl, u, v)
            if val <= 0.99:
                aInS = [(xf[i][j], xf[i][j].x) for (i, j) in Ar if i in S and j in S]
                if sum(f for v, f in aInS) >= (len(S) - 1) + 1e-4:
                    cut = xsum(1.0 * v for v, fm in aInS) <= len(S) - 1
                    cp.add(cut)
                    if len(cp.cuts) > 32:
                        for cut in cp.cuts:
                            m_ += cut
                        return
        for cut in cp.cuts:
            m_ += cut 

def generate_constrs(self, model: Model):
        xf, V_, cp, G = model.translate(self.x), self.V, CutPool(), nx.DiGraph()
        for (u, v) in [(k, l) for (k, l) in product(V_, V_) if k != l and xf[k][l]]:
            G.add_edge(u, v, capacity=xf[u][v].x)
        for (u, v) in F:
            val, (S, NS) = nx.minimum_cut(G, u, v)
            if val <= 0.99:
                aInS = [(xf[i][j], xf[i][j].x)
                        for (i, j) in product(V_, V_) if i != j and xf[i][j] and i in S and j in S]
                if sum(f for v, f in aInS) >= (len(S)-1)+1e-4:
                    cut = xsum(1.0*v for v, fm in aInS) <= len(S)-1
                    cp.add(cut)
                    if len(cp.cuts) > 256:
                        for cut in cp.cuts:
                            model += cut
                        return
        for cut in cp.cuts:
            model += cut 

def generate_constrs(self, model: Model):
        G = nx.DiGraph()
        r = [(v, v.x) for v in model.vars if v.name.startswith('x(')]
        U = [int(v.name.split('(')[1].split(',')[0]) for v, f in r]
        V = [int(v.name.split(')')[0].split(',')[1]) for v, f in r]
        cp = CutPool()
        for i in range(len(U)):
            G.add_edge(U[i], V[i], capacity=r[i][1])
        for (u, v) in F:
            if u not in U or v not in V:
                continue
            val, (S, NS) = nx.minimum_cut(G, u, v)
            if val <= 0.99:
                arcsInS = [(v, f) for i, (v, f) in enumerate(r)
                           if U[i] in S and V[i] in S]
                if sum(f for v, f in arcsInS) >= (len(S)-1)+1e-4:
                    cut = xsum(1.0*v for v, fm in arcsInS) <= len(S)-1
                    cp.add(cut)
                    if len(cp.cuts) > 256:
                        for cut in cp.cuts:
                            model += cut
                        return
        for cut in cp.cuts:
            model += cut
        return 

def hypothesis_errors(infr, pos_subgraph, neg_edges):
        if not nx.is_connected(pos_subgraph):
            raise AssertionError('Not connected' + repr(pos_subgraph))
        infr.print(
            'Find hypothesis errors in {} nodes with {} neg edges'.format(
                len(pos_subgraph), len(neg_edges)), 3)

        pos_edges = list(pos_subgraph.edges())

        neg_weight = infr._mincut_edge_weights(neg_edges)
        pos_weight = infr._mincut_edge_weights(pos_edges)

        capacity = 'weight'
        nx.set_edge_attributes(pos_subgraph, name=capacity, values=ut.dzip(pos_edges, pos_weight))

        # Solve a multicut problem for multiple pairs of terminal nodes.
        # Running multiple min-cuts produces a k-factor approximation
        maybe_error_edges = set([])
        for (s, t), join_weight in zip(neg_edges, neg_weight):
            cut_weight, parts = nx.minimum_cut(pos_subgraph, s, t,
                                               capacity=capacity)
            cut_edgeset = nxu.edges_cross(pos_subgraph, *parts)
            if join_weight < cut_weight:
                join_edgeset = {(s, t)}
                chosen = join_edgeset
                hypothesis = POSTV
            else:
                chosen = cut_edgeset
                hypothesis = NEGTV
            for edge in chosen:
                if edge not in maybe_error_edges:
                    maybe_error_edges.add(edge)
                    yield (edge, hypothesis) 

def get_single_mask(imgA_t, imgB_t,
                    imgA_t_1=None, imgB_t_1=None, smask_old=None):
    h, w = imgA_t.shape[:2]
    L2_NORM_t = np.linalg.norm(imgA_t - imgB_t, axis=2)
    if smask_old is None:
        G = build_graph(h, w, [L2_NORM_t])
        cut_value, partition = nx.minimum_cut(G, str(h * w), str(h * w + 1))
    else:
        L2_NORM_AA = np.linalg.norm(imgA_t - imgA_t_1, axis=2)
        L2_NORM_BB = np.linalg.norm(imgB_t - imgB_t_1, axis=2)
        L2_NORM_AB = np.linalg.norm(imgA_t - imgB_t_1, axis=2)
        L2_NORM_BA = np.linalg.norm(imgB_t - imgA_t_1, axis=2)
        G = build_graph(h, w, [L2_NORM_t, L2_NORM_AA +
                               L2_NORM_BB + L2_NORM_AB + L2_NORM_BA],
                        smask_old)
        cut_value, partition = nx.minimum_cut(
            G, str(2 * h * w), str(2 * h * w + 1))

    reachable, non_reachable = partition
    mask = np.zeros((h * w,))
    reachable = np.int32(list(reachable))
    mask[reachable[reachable < h * w]] = 1
    mask = mask.reshape((h, w))
    mask_color = np.zeros((h, w, 3))
    mask_color[:, :, 0] = mask
    mask_color[:, :, 1] = mask
    mask_color[:, :, 2] = mask

    return mask_color 

def generate_constrs(self, model: Model):
        G = nx.DiGraph()
        r = [(v, v.x) for v in model.vars if v.name.startswith("x(")]
        U = [v.name.split("(")[1].split(",")[0] for v, f in r]
        V = [v.name.split(")")[0].split(",")[1] for v, f in r]
        N = list(set(U + V))
        cp = CutPool()
        for i in range(len(U)):
            G.add_edge(U[i], V[i], capacity=r[i][1])
        for (u, v) in product(N, N):
            if u == v:
                continue
            val, (S, NS) = nx.minimum_cut(G, u, v)
            if val <= 0.99:
                arcsInS = [
                    (v, f) for i, (v, f) in enumerate(r) if U[i] in S and V[i] in S
                ]
                if sum(f for v, f in arcsInS) >= (len(S) - 1) + 1e-4:
                    cut = xsum(1.0 * v for v, fm in arcsInS) <= len(S) - 1
                    cp.add(cut)
                    if len(cp.cuts) > 256:
                        for cut in cp.cuts:
                            model.add_cut(cut)
                        return
        for cut in cp.cuts:
            model.add_cut(cut) 

def compare_flows_and_cuts(G, s, t, solnFlows, solnValue, capacity='capacity'):
    for flow_func in flow_funcs:
        R = flow_func(G, s, t, capacity)
        # Test both legacy and new implementations.
        flow_value = R.graph['flow_value']
        flow_dict = build_flow_dict(G, R)
        assert_equal(flow_value, solnValue, msg=msg.format(flow_func.__name__))
        validate_flows(G, s, t, flow_dict, solnValue, capacity, flow_func)
        # Minimum cut
        cut_value, partition = nx.minimum_cut(G, s, t, capacity=capacity,
                                              flow_func=flow_func)
        validate_cuts(G, s, t, solnValue, partition, capacity, flow_func) 

def test_minimum_cut_no_cutoff(self):
        G = self.G
        for flow_func in flow_funcs:
            assert_raises(nx.NetworkXError, nx.minimum_cut, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0)
            assert_raises(nx.NetworkXError, nx.minimum_cut_value, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0) 

def compare_flows_and_cuts(G, s, t, solnFlows, solnValue, capacity='capacity'):
    for flow_func in flow_funcs:
        R = flow_func(G, s, t, capacity)
        # Test both legacy and new implementations.
        flow_value = R.graph['flow_value']
        flow_dict = build_flow_dict(G, R)
        assert_equal(flow_value, solnValue, msg=msg.format(flow_func.__name__))
        validate_flows(G, s, t, flow_dict, solnValue, capacity, flow_func)
        # Minimum cut
        cut_value, partition = nx.minimum_cut(G, s, t, capacity=capacity,
                                              flow_func=flow_func)
        validate_cuts(G, s, t, solnValue, partition, capacity, flow_func) 

def test_minimum_cut_no_cutoff(self):
        G = self.G
        for flow_func in flow_funcs:
            assert_raises(nx.NetworkXError, nx.minimum_cut, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0)
            assert_raises(nx.NetworkXError, nx.minimum_cut_value, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0) 

def compare_flows_and_cuts(G, s, t, solnFlows, solnValue, capacity='capacity'):
    for flow_func in flow_funcs:
        R = flow_func(G, s, t, capacity)
        # Test both legacy and new implementations.
        flow_value = R.graph['flow_value']
        flow_dict = build_flow_dict(G, R)
        assert_equal(flow_value, solnValue, msg=msg.format(flow_func.__name__))
        validate_flows(G, s, t, flow_dict, solnValue, capacity, flow_func)
        # Minimum cut
        cut_value, partition = nx.minimum_cut(G, s, t, capacity=capacity,
                                              flow_func=flow_func)
        validate_cuts(G, s, t, solnValue, partition, capacity, flow_func) 

def test_minimum_cut_no_cutoff(self):
        G = self.G
        for flow_func in flow_funcs:
            assert_raises(nx.NetworkXError, nx.minimum_cut, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0)
            assert_raises(nx.NetworkXError, nx.minimum_cut_value, G, 'x', 'y',
                          flow_func=flow_func, cutoff=1.0)