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)
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)
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)
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)
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 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 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)
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)
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)
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)
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
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
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
