def cost_test(n=100, k=5):
    # check that cost.max() is equal to the data variance
    np.random.seed(0)
    x = np.random.randn(n, 2)
    G = knn(x, k)
    u, cost = ward_segment(G, x)
    assert_true(np.abs(cost.max() / (n * np.var(x, 0).sum()) - 1) < 1e-6)
Пример #2
0
def cost_test(n=100, k=5):
    # check that cost.max() is equal to the data variance
    np.random.seed(0)
    x = np.random.randn(n, 2)
    G = knn(x, k)
    u, cost =  ward_segment(G, x)
    assert_true(np.abs(cost.max()/(n*np.var(x,0).sum()) - 1) < 1e-6)
def cost_test(n=100, k=5):
    """
    check that cost.max() is equal to the data variance
    """
    np.random.seed(0)
    x = np.random.randn(n, 2)
    G = knn(x, k)
    u, cost = ward_segment(G, x)
    print cost.max() / n, np.var(x, 0).sum()
    assert np.abs(cost.max() / (n * np.var(x, 0).sum()) - 1) < 1.0e-6
def wards_test_3():
    # Check ward_segment
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[:int(0.3 * n)] += 10
    x[int(0.8 * n):] -= 10
    G = knn(x, k)
    u, cost = ward_segment(G, x, qmax=2)
    assert_equal(u.max(), 2)
def wards_test_basic(n=100, k=5):
    # Basic check of ward's segmentation algorithm
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7 * n)] += 3
    G = knn(x, k)
    u, cost = ward_segment(G, x, qmax=2)
    v = np.zeros(n)
    v[:int(0.7 * n)] = 1
    w = np.absolute(u - v)
    assert_equal(np.sum(w * (1 - w)), 0)
def wardf_test(n=100, k=5):
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7 * n)] += 3
    G = knn(x, 5)
    F = field_from_graph_and_data(G, x)
    u, cost = ward_field_segment(F, qmax=2)
    v = np.zeros(n)
    v[:int(0.7 * n)] = 1
    w = np.absolute(u - v)
    assert_equal(np.sum(w * (1 - w)), 0)
Пример #7
0
def ward_test_more(n=100, k=5, verbose=0):
    # Check that two implementations give the same result
    np.random.seed(0)
    X = randn(n,2)
    X[:np.ceil(n/3)] += 5
    G = knn(X, 5)
    u,c = ward_segment(G, X, stop=-1, qmax=1, verbose=verbose)
    u1,c = ward_segment(G, X, stop=-1, qmax=k, verbose=verbose)
    u,c = ward_quick_segment(G, X, stop=-1, qmax=1, verbose=verbose)
    u2,c = ward_quick_segment(G, X, stop=-1, qmax=k, verbose=verbose)
    assert_equal(np.sum(u1==u2), n)
Пример #8
0
def alg_test_3(n=100,k=5):
    # Check that we obtain the correct solution in a simplistic case
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7*n)] += 3
    G = knn(x, k)
    u, cost = average_link_graph_segment(G, qmax=2)
    v = np.zeros(n)
    v[:int(0.7*n)]=1
    w = np.absolute(u-v)
    assert_true(np.sum(w*(1-w))==0)
def ward_test_more(n=100, k=5, verbose=0):
    # Check that two implementations give the same result
    np.random.seed(0)
    X = randn(n, 2)
    X[:int(math.ceil(n / 3))] += 5
    G = knn(X, 5)
    u, c = ward_segment(G, X, stop=-1, qmax=1, verbose=verbose)
    u1, c = ward_segment(G, X, stop=-1, qmax=k, verbose=verbose)
    u, c = ward_quick_segment(G, X, stop=-1, qmax=1, verbose=verbose)
    u2, c = ward_quick_segment(G, X, stop=-1, qmax=k, verbose=verbose)
    assert_equal(np.sum(u1 == u2), n)
Пример #10
0
def wards_test_3():
    # Check ward_segment
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n,2)
    x[:int(0.3*n)] += 10
    x[int(0.8*n):] -= 10
    G = knn(x,k)
    u,cost = ward_segment(G, x, qmax=2)
    assert_equal(u.max(), 2)
Пример #11
0
def wards_test_basic(n=100,k=5):
    # Basic check of ward's segmentation algorithm
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7*n)] += 3
    G = knn(x, k)
    u,cost =  ward_segment(G, x, qmax=2)
    v = np.zeros(n)
    v[:int(0.7*n)]=1
    w = np.absolute(u-v)
    assert_equal(np.sum(w*(1-w)), 0)
def alg_test_3(n=100, k=5):
    # Check that we obtain the correct solution in a simplistic case
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7 * n)] += 3
    G = knn(x, k)
    u, cost = average_link_graph_segment(G, qmax=2)
    v = np.zeros(n)
    v[:int(0.7 * n)] = 1
    w = np.absolute(u - v)
    assert_true(np.sum(w * (1 - w)) == 0)
Пример #13
0
def wardf_test(n=100,k=5):
    np.random.seed(0)
    x = np.random.randn(n,2)
    x[:int(0.7*n)] += 3
    G = knn(x, 5)
    F = field_from_graph_and_data(G, x)
    u, cost = ward_field_segment(F, qmax=2)
    v = np.zeros(n)
    v[:int(0.7*n)]=1
    w = np.absolute(u-v)
    assert_equal(np.sum(w*(1-w)), 0)
def wardq_test_2():
    # Do we handle case of graph with too many connected components?
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[:int(0.3 * n)] += 10
    x[int(0.8 * n):] -= 10
    G = knn(x, k)
    t = ward_quick(G, x)
    u = t.split(2)
    assert_equal(u.max(), 2)
def wardq_test_basic(n=100, k=5):
    # Basic check of ward's algorithm
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7 * n)] += 3
    G = knn(x, k)
    t = ward_quick(G, x)
    u = t.split(2)
    v = np.zeros(n)
    v[:int(0.7 * n)] = 1
    w = np.absolute(u - v)
    assert_true(np.sum(w * (1 - w)) == 0)
Пример #16
0
def wards_test_3():
    """Check that ward_segment
    """
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[: int(0.3 * n)] += 10
    x[int(0.8 * n) :] -= 10
    G = knn(x, k)
    u, cost = ward_segment(G, x, qmax=2)
    assert u.max() == 2
def alg_test_2():
    # Do we handle case of graph with too many connected components?
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[:int(0.3 * n)] += 10
    x[int(0.8 * n):] -= 10
    G = knn(x, k)
    t = average_link_graph(G)
    u = t.split(2)
    assert_true(u.max() == 2)
Пример #18
0
def wardq_test_2():
    # Do we handle case of graph with too many connected components?
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[:int(0.3*n)] += 10
    x[int(0.8*n):] -= 10
    G = knn(x, k)
    t = ward_quick(G, x)
    u = t.split(2)
    assert_equal(u.max(), 2)
Пример #19
0
def wardq_test_basic(n=100,k=5):
    # Basic check of ward's algorithm
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[:int(0.7*n)] += 3
    G = knn(x, k)
    t = ward_quick(G, x)
    u = t.split(2)
    v = np.zeros(n)
    v[:int(0.7*n)]=1
    w = np.absolute(u-v)
    assert_true(np.sum(w*(1-w))==0)
Пример #20
0
def alg_test_2():
    # Do we handle case of graph with too many connected components?
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[:int(0.3*n)] += 10
    x[int(0.8*n):] -= 10
    G = knn(x, k)
    t = average_link_graph(G)
    u = t.split(2)
    assert_true(u.max()==2)
Пример #21
0
def alg_test_basic(n=100, k=5):
    """
    Check that we obtain the correct solution in a simplistic case 
    """
    np.random.seed(0)
    x = np.random.randn(n, 2)
    x[: int(0.7 * n)] += 3
    G = knn(x, k)
    t = average_link_graph(G)
    u = t.split(2)
    v = np.zeros(n)
    v[: int(0.7 * n)] = 1
    w = np.absolute(u - v)
    assert np.sum(w * (1 - w)) == 0
Пример #22
0
def wardq_test_2():
    """
    Check that we hasnle the case of a graph with too many
    connected components
    """
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[: int(0.3 * n)] += 10
    x[int(0.8 * n) :] -= 10
    G = knn(x, k)
    t = ward_quick(G, x)
    u = t.split(2)
    assert u.max() == 2
Пример #23
0
def alg_test_2():
    """
    Check that we hasnle the case of a graph with too many
    connected components
    """
    np.random.seed(0)
    n = 100
    k = 5
    x = np.random.randn(n, 2)
    x[: int(0.3 * n)] += 10
    x[int(0.8 * n) :] -= 10
    G = knn(x, k)
    t = average_link_graph(G)
    u = t.split(2)
    assert u.max() == 2