def main():
data = numpy.float_(
[[0, 0, 0, 0, 0],
[
1.74120000000000, 4.07812000000000, -0.0927036000000,
41.7888000000000, 41.7888000000000
],
[
7.75309000000000, 16.2466000000000, 3.03956000000000,
186.074000000000, 186.074000000000
],
[
2.85493000000000, 3.25380000000000, 2.50559000000000,
68.5184000000000, 68.5184000000000
],
[
5.81414000000000, 8.14015000000000, 3.22950000000000,
139.539000000000, 139.539000000000
],
[
2.57927000000000, 2.63399000000000, 2.46802000000000,
61.9026000000000, 61.9026000000000
]])
assignments, means, counts = kmedoids(Matrix(data),
4) # clusters the data in 4 groups.
print("assignments", list(assignments))
print("means", list(means))
print("counts", list(counts))
def test_insert_1d():
matrix = Matrix()
id1 = matrix.insert(numpy.float_([1.0, 2.0]))
assert len(matrix) == 1
assert id1 == 0
id2 = matrix.insert(numpy.float_([2.0, 3.0]))
assert len(matrix) == 2
assert id2 == 1
def test_insert_2d():
matrix = Matrix()
ids = list(matrix.insert(numpy.float_([[1], [2], [3], [4]])))
assert len(matrix) == 4
assert ids == [0, 1, 2, 3]
ids = list(matrix.insert(numpy.float_([[1], [2], [3], [4]])))
assert len(matrix) == 8
assert ids == [4, 5, 6, 7]
def main():
data = numpy.float_([
[0, 0, 0, 0, 0],
[1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000],
[7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000],
[2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000],
[5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000],
[2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000]
])
matrix = Matrix(data)
print(len(matrix))
print(matrix(1, 3))
def main():
data = numpy.float_([
[0, 0, 0, 0, 0],
[1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000],
[7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000],
[2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000],
[5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000],
[2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000]
])
assignments, exemplars, counts = AffProp()(Matrix(data))
print('assignments', list(assignments))
print('exemplars', list(exemplars))
print('counts', list(counts))
def main():
data = numpy.float_([
[0, 0, 0, 0, 0],
[1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000],
[7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000],
[2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000],
[5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000],
[2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000]
])
distance_matrix = Matrix(data)
[assignments, seeds, counts] = dbscan(distance_matrix, 64.0, 1)
print(list(assignments))
print(list(seeds))
print(list(counts))
def test_create_1d():
data = numpy.float_([1.0, 2.0])
matrix = Matrix(data)
assert len(matrix) == 1
def test_create_empty():
matrix = Matrix()
assert len(matrix) == 0
def test_create_2d():
matrix = Matrix(numpy.float_([[1], [2], [3]]))
assert len(matrix) == 3