def test_addition():
from diagram.diagram import MTBDD, EVBDD
mat1 = np.array([[1, 7, 0], [0, -1, 0], [2, 8, 1], [1, 5, 0], [1, 5, 0], [1, 5, 0], [1, 15, 0]], dtype=float)
print mat1 + mat1
print('MTBDD:')
mtbdd = MTBDD()
node1 = mtbdd.create(mat1, 0)
node2 = mtbdd.create(mat1, 0)
from diagram.operations.computations import add_diagrams
node3 = add_diagrams(node1, node2)
print node3.to_matrix(7, True)
print('EVBDD:')
evbdd = EVBDD()
node4 = evbdd.create(mat1, 0)
node5 = evbdd.create(mat1, 0)
from diagram.operations.computations import diagram_sum
node6 = add_diagrams(node4, node5)
print node6.to_matrix(7, True)
print('summing the diagrams:')
print mat1
print diagram_sum(node1)
print diagram_sum(node4)
print np.sum(mat1)
print node4.nodes
node6.plot('ev-add')
node3.plot('mt-add')
def test_power_method_sparse(iterations, episodes, max_size, sparsity):
for ep in range(episodes):
# initialization
vec_size = np.random.random_integers(10, high=max_size)
vec = np.random.rand(vec_size, 1)
mat = np.multiply(np.random.rand(vec_size, vec_size), np.random.rand(vec_size, vec_size) > sparsity) + \
np.random.rand(vec_size, vec_size) * np.identity(vec_size)
for it in range(iterations):
# multiplying
vec = np.dot(mat, vec)
# normalizing
vec /= np.linalg.norm(vec)
# creating the BDD representations
from diagram.diagram import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
node_mt = mtbdd.create(
np.round(config.precision_elements / np.max(vec) * vec,
config.precision_round), 0)
node_ev = evbdd.create(
np.round(config.precision_elements / np.max(vec) * vec,
config.precision_round), 0)
c_mt = node_mt.complexity()
c_ev = node_ev.complexity()
# storing the complexity
store_results('power-sparse', 'mat', ep, vec_size)
store_results('power-sparse', 'mtbdd', ep, c_mt)
store_results('power-sparse', 'evbdd', ep, c_ev)
def test_addition():
from diagram.diagram import MTBDD, EVBDD
mat1 = np.array([[1, 7, 0], [0, -1, 0], [2, 8, 1], [1, 5, 0], [1, 5, 0],
[1, 5, 0], [1, 15, 0]],
dtype=float)
print mat1 + mat1
print('MTBDD:')
mtbdd = MTBDD()
node1 = mtbdd.create(mat1, 0)
node2 = mtbdd.create(mat1, 0)
from diagram.operations.computations import add_diagrams
node3 = add_diagrams(node1, node2)
print node3.to_matrix(7, True)
print('EVBDD:')
evbdd = EVBDD()
node4 = evbdd.create(mat1, 0)
node5 = evbdd.create(mat1, 0)
from diagram.operations.computations import diagram_sum
node6 = add_diagrams(node4, node5)
print node6.to_matrix(7, True)
print('summing the diagrams:')
print mat1
print diagram_sum(node1)
print diagram_sum(node4)
print np.sum(mat1)
print node4.nodes
node6.plot('ev-add')
node3.plot('mt-add')
def test_new_implementation():
from diagram.diagram import MTBDD
mat1 = np.array([[1, 2, 0], [0, 3, 0], [0, 4, 1], [1, 5, 0], [1, 5, 0], [1, 5, 0], [1, 5, 0]])
mtbdd = MTBDD()
node = mtbdd.create(mat1, 0)
print node.to_matrix(7)
print node.get_subdiagrams(5)
def test_new_implementation():
from diagram.diagram import MTBDD
mat1 = np.array([[1, 2, 0], [0, 3, 0], [0, 4, 1], [1, 5, 0], [1, 5, 0],
[1, 5, 0], [1, 5, 0]])
mtbdd = MTBDD()
node = mtbdd.create(mat1, 0)
print node.to_matrix(7)
print node.get_subdiagrams(5)
def test_multiplication():
from diagram.diagram import MTBDD, EVBDD
mat1 = np.array([[1, 7, 0], [0, -1, 0], [2, 8, 1], [1, 5, 0], [1, 5, 0],
[1, 5, 0], [1, 15, 0]],
dtype=float)
print mat1
print 'Scalar Multiplication: ~~~~~~~~~~~~~~'
print mat1 * 2.5
print('MTBDD:')
from diagram.operations.computations import scalar_multiply_diagram, elementwise_multiply_diagrams_evbdd, \
elementwise_multiply_diagrams_mtbdd, multiply_by_column_vector, multiply_diagram
mtbdd = MTBDD()
node1 = mtbdd.create(mat1, 0)
node2 = scalar_multiply_diagram(node1, 2.5)
print node2.to_matrix(7, True)
print('EVBDD:')
evbdd = EVBDD()
node4 = evbdd.create(mat1, 0)
node5 = scalar_multiply_diagram(node4, 2.5)
print node5.to_matrix(7, True)
print 'Elementwise Multiplication: ~~~~~~~~~'
print np.multiply(mat1, mat1)
print('MTBDD:')
node3 = elementwise_multiply_diagrams_mtbdd(node1, node1)
# node3.plot('mtbdd')
print node3.to_matrix(7, True)
print('EVBDD:')
node6 = elementwise_multiply_diagrams_evbdd(node4, node4)
print node6.to_matrix(7, True)
# node6.plot('evbdd')
vec1 = np.array([1.0, 2.0, 3.0])
vnode_mt = mtbdd.create(vec1, 0)
vnode_ev = evbdd.create(vec1, 0)
print('Column-vector multiplication')
print('Reference')
print(np.dot(mat1, vec1))
print('MTBDD')
node7 = multiply_by_column_vector(node1, vnode_mt)
print(node7.to_matrix(7, True))
print('EVBDD')
node8 = multiply_by_column_vector(node4, vnode_ev)
print(node8.to_matrix(7, True))
print('Matrix-multiplication')
mat2 = np.array([[1, -2, 0], [2, -3, 0], [3, -4, -1]])
print('Reference')
print(np.dot(mat1, mat2))
print('MTBDD')
node11 = mtbdd.create(mat2, 0)
node9 = multiply_diagram(node1, node11, mat1.shape[0])
print(node9.to_matrix(3, True))
print('EVBDD')
node12 = evbdd.create(mat2, 0)
node10 = multiply_diagram(node4, node12, mat1.shape[0])
print(node10.to_matrix(7, True))
def plot_graphs():
# mat1 = np.array([[1, 4], [1, 4], [1, 4], [1, 4]])
mat1 = np.random.random((4, 4))
from diagram.binary import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
node1 = mtbdd.create(mat1, 0)
node4 = evbdd.create(mat1, 0)
# node1.plot('mt-st-r')
node4.plot('random')
def plot_graphs():
# mat1 = np.array([[1, 4], [1, 4], [1, 4], [1, 4]])
mat1 = np.random.random((4, 4))
from diagram.binary import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
node1 = mtbdd.create(mat1, 0)
node4 = evbdd.create(mat1, 0)
# node1.plot('mt-st-r')
node4.plot('random')
def test_reduction():
from diagram.binary import MTBDD, EVBDD
mat1 = np.array([[1, 2, 0], [0, 1, 0], [2, 4, 1], [1, 5, 0], [1, 5, 0],
[1, 5, 0], [1, 5, 0]],
dtype=float)
print mat1
print('MTBDD:')
mtbdd = MTBDD()
node = mtbdd.create(mat1, 0)
# import code; code.interact(local=dict(locals().items() + globals().items()))
# print node.to_matrix(7)
# print node.leaves
node1 = mtbdd.create(mat1, 0)
reduced = mtbdd.reduce(node1)
# import code; code.interact(local=dict(locals().items() + globals().items()))
red = reduced.leaves
print node.to_matrix(7)
print len(node.leaves)
print reduced.to_matrix(7)
print len(red)
print('EVBDD:')
mtbdd = EVBDD()
node = mtbdd.create(mat1, 0)
node1 = mtbdd.create(mat1, 0)
reduced = mtbdd.reduce(node1)
red = reduced.leaves
print node.to_matrix(7)
print len(node.leaves)
print reduced.to_matrix(7)
print len(red)
def test_multiplication():
from diagram.diagram import MTBDD, EVBDD
mat1 = np.array([[1, 7, 0], [0, -1, 0], [2, 8, 1], [1, 5, 0], [1, 5, 0], [1, 5, 0], [1, 15, 0]], dtype=float)
print mat1
print 'Scalar Multiplication: ~~~~~~~~~~~~~~'
print mat1 * 2.5
print('MTBDD:')
from diagram.operations.computations import scalar_multiply_diagram, elementwise_multiply_diagrams_evbdd, \
elementwise_multiply_diagrams_mtbdd, multiply_by_column_vector, multiply_diagram
mtbdd = MTBDD()
node1 = mtbdd.create(mat1, 0)
node2 = scalar_multiply_diagram(node1, 2.5)
print node2.to_matrix(7, True)
print('EVBDD:')
evbdd = EVBDD()
node4 = evbdd.create(mat1, 0)
node5 = scalar_multiply_diagram(node4, 2.5)
print node5.to_matrix(7, True)
print 'Elementwise Multiplication: ~~~~~~~~~'
print np.multiply(mat1, mat1)
print('MTBDD:')
node3 = elementwise_multiply_diagrams_mtbdd(node1, node1)
# node3.plot('mtbdd')
print node3.to_matrix(7, True)
print('EVBDD:')
node6 = elementwise_multiply_diagrams_evbdd(node4, node4)
print node6.to_matrix(7, True)
# node6.plot('evbdd')
vec1 = np.array([1.0, 2.0, 3.0])
vnode_mt = mtbdd.create(vec1, 0)
vnode_ev = evbdd.create(vec1, 0)
print('Column-vector multiplication')
print('Reference')
print(np.dot(mat1, vec1))
print('MTBDD')
node7 = multiply_by_column_vector(node1, vnode_mt)
print(node7.to_matrix(7, True))
print('EVBDD')
node8 = multiply_by_column_vector(node4, vnode_ev)
print(node8.to_matrix(7, True))
print('Matrix-multiplication')
mat2 = np.array([[1, -2, 0], [2, -3, 0], [3, -4, -1]])
print('Reference')
print(np.dot(mat1, mat2))
print('MTBDD')
node11 = mtbdd.create(mat2, 0)
node9 = multiply_diagram(node1, node11, mat1.shape[0])
print(node9.to_matrix(3, True))
print('EVBDD')
node12 = evbdd.create(mat2, 0)
node10 = multiply_diagram(node4, node12, mat1.shape[0])
print(node10.to_matrix(7, True))
def general_tests():
from diagram.diagram import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
mat1 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
node1 = mtbdd.create(mat1, 0)
node4 = evbdd.create(mat1, 0)
node1.plot('mt-st-r')
node4.plot('ev-st-r')
node1 = mtbdd.create(mat1, 0, False)
node4 = evbdd.create(mat1, 0, False)
node1.plot('mt-st-nr')
node4.plot('ev-st-nr')
def test_reduction():
from diagram.binary import MTBDD, EVBDD
mat1 = np.array([[1, 2, 0], [0, 1, 0], [2, 4, 1], [1, 5, 0], [1, 5, 0], [1, 5, 0], [1, 5, 0]], dtype=float)
print mat1
print('MTBDD:')
mtbdd = MTBDD()
node = mtbdd.create(mat1, 0)
# import code; code.interact(local=dict(locals().items() + globals().items()))
# print node.to_matrix(7)
# print node.leaves
node1 = mtbdd.create(mat1, 0)
reduced = mtbdd.reduce(node1)
# import code; code.interact(local=dict(locals().items() + globals().items()))
red = reduced.leaves
print node.to_matrix(7)
print len(node.leaves)
print reduced.to_matrix(7)
print len(red)
print('EVBDD:')
mtbdd = EVBDD()
node = mtbdd.create(mat1, 0)
node1 = mtbdd.create(mat1, 0)
reduced = mtbdd.reduce(node1)
red = reduced.leaves
print node.to_matrix(7)
print len(node.leaves)
print reduced.to_matrix(7)
print len(red)
def general_tests():
from diagram.diagram import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
mat1 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
node1 = mtbdd.create(mat1, 0)
node4 = evbdd.create(mat1, 0)
node1.plot('mt-st-r')
node4.plot('ev-st-r')
node1 = mtbdd.create(mat1, 0, False)
node4 = evbdd.create(mat1, 0, False)
node1.plot('mt-st-nr')
node4.plot('ev-st-nr')
def test_power_method(iterations, episodes, max_size):
for ep in range(episodes):
# initialization
vec_size = np.random.random_integers(10, high=max_size)
vec = np.random.rand(vec_size, 1)
mat = np.random.rand(vec_size, vec_size)
for it in range(iterations):
# multiplying
vec = np.dot(mat, vec)
# normalizing
vec /= np.linalg.norm(vec)
# creating the BDD representations
from diagram.diagram import MTBDD, EVBDD
mtbdd = MTBDD()
evbdd = EVBDD()
node_mt = mtbdd.create(np.round(config.precision_elements/np.max(vec)*vec, config.precision_round), 0)
node_ev = evbdd.create(np.round(config.precision_elements/np.max(vec)*vec, config.precision_round), 0)
c_mt = node_mt.complexity()
c_ev = node_ev.complexity()
# storing the complexity
store_results('power', 'mat', ep, vec_size)
store_results('power', 'mtbdd', ep, c_mt)
store_results('power', 'evbdd', ep, c_ev)