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Fix bug in topology classes #176

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Jul 23, 2018
Merged

Fix bug in topology classes #176

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2 changes: 1 addition & 1 deletion pyswarms/backend/topology/pyramid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ def compute_gbest(self, swarm):
# Obtain best cost and position
best_cost = np.min(swarm.pbest_cost[best_neighbor])
best_pos = swarm.pbest_pos[
np.argmin(swarm.pbest_cost[best_neighbor])
best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
]
except AttributeError:
msg = "Please pass a Swarm class. You passed {}".format(
Expand Down
7 changes: 2 additions & 5 deletions pyswarms/backend/topology/random.py
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Expand Up @@ -74,7 +74,7 @@ def compute_gbest(self, swarm, k):
# Obtain best cost and position
best_cost = np.min(swarm.pbest_cost[best_neighbor])
best_pos = swarm.pbest_pos[
np.argmin(swarm.pbest_cost[best_neighbor])
best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
]

except AttributeError:
Expand Down Expand Up @@ -169,9 +169,6 @@ def __compute_neighbors(self, swarm, k):
graph needs edges to change it to a connected
graph.

.. note:: If the graph isn't connected, it is possible that the
PSO algorithm does not find the best position within
the swarm.

Parameters
----------
Expand Down Expand Up @@ -206,7 +203,7 @@ def __compute_neighbors(self, swarm, k):
# Generate connected graph.
while connected_components(adj_matrix, directed=False, return_labels=False) != 1:
for i, j in itertools.product(range(swarm.n_particles), repeat=2):
if dist_matrix[i][j] == 0:
if dist_matrix[i][j] == np.inf:
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This is the bug in the Random class right? 👍

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That's right 👍

adj_matrix[i][j] = 1

return adj_matrix
4 changes: 2 additions & 2 deletions pyswarms/backend/topology/ring.py
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Expand Up @@ -72,15 +72,15 @@ def compute_gbest(self, swarm, p, k):
if k == 1:
# The minimum index is itself, no mapping needed.
best_neighbor = swarm.pbest_cost[self.neighbor_idx][:, np.newaxis].argmin(
axis=1
axis=0
)
else:
idx_min = swarm.pbest_cost[self.neighbor_idx].argmin(axis=1)
best_neighbor = self.neighbor_idx[np.arange(len(self.neighbor_idx)), idx_min]
# Obtain best cost and position
best_cost = np.min(swarm.pbest_cost[best_neighbor])
best_pos = swarm.pbest_pos[
np.argmin(swarm.pbest_cost[best_neighbor])
best_neighbor[np.argmin(swarm.pbest_cost[best_neighbor])]
]
except AttributeError:
msg = "Please pass a Swarm class. You passed {}".format(
Expand Down
52 changes: 37 additions & 15 deletions tests/backend/topology/conftest.py
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Expand Up @@ -15,20 +15,42 @@
def swarm():
"""A contrived instance of the Swarm class at a certain timestep"""
attrs_at_t = {
"position": np.array([[5, 5, 5], [3, 3, 3], [1, 1, 1]]),
"velocity": np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]),
"current_cost": np.array([2, 2, 2]),
"pbest_cost": np.array([1, 2, 3]),
"pbest_pos": np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
"best_cost": 1,
"best_pos": np.array([1, 1, 1]),
"options": {"c1": 0.5, "c2": 1, "w": 2},
"position": np.array([[9.95838686e-01, 5.87433429e-04, 6.49113772e-03],
[1.00559609e+00, 3.96477697e-02, 7.67205397e-01],
[2.87990950e-01, -3.64932609e-02, 1.89750725e-02],
[1.11646877e+00, 3.12037361e-03, 1.97885369e-01],
[8.96117216e-01, -9.79602053e-03, -1.66139336e-01],
[9.90423669e-01, 1.99307974e-03, -1.23386797e-02],
[2.06800701e-01, -1.67869387e-02, 1.14268810e-01],
[4.21786494e-01, 2.58755510e-02, 6.62254843e-01],
[9.90350831e-01, 3.81575154e-03, 8.80833545e-01],
[9.94353749e-01, -4.85086205e-02, 9.85313500e-03]]),
"velocity": np.array([[2.09076818e-02, 2.04936403e-03, 1.06761248e-02],
[1.64940497e-03, 5.67924469e-03, 9.74902301e-02],
[1.50445516e-01, 9.11699158e-03, 1.51474794e-02],
[2.94238740e-01, 5.71545680e-04, 1.54122294e-02],
[4.10430034e-02, 6.51847479e-04, 6.25109226e-02],
[6.71076116e-06, 1.89615516e-04, 4.65023770e-03],
[4.76081378e-02, 4.24416089e-03, 7.11856172e-02],
[1.33832808e-01, 1.81818698e-02, 1.16947941e-01],
[1.22849955e-03, 1.55685312e-03, 1.67819003e-02],
[5.60617396e-03, 4.31819608e-02, 2.52217220e-02]]),
"current_cost": np.array([1.07818462, 5.5647911, 19.6046078, 14.05300016, 3.72597614, 1.01169386,
16.51846203, 32.72262829, 3.80274901, 1.05237138]),
"pbest_cost": np.array([1.00362006, 2.39151041, 2.55208424, 5.00176207, 1.04510827, 1.00025284,
6.31216654, 2.53873121, 2.00530884, 1.05237138]),
"pbest_pos": np.array([[9.98033031e-01, 4.97392619e-03, 3.07726256e-03],
[1.00665809e+00, 4.22504014e-02, 9.84334657e-01],
[1.12159389e-02, 1.11429739e-01, 2.86388193e-02],
[1.64059236e-01, 6.85791237e-03, -2.32137604e-02],
[9.93740665e-01, -6.16501403e-03, -1.46096578e-02],
[9.90438476e-01, 2.50379538e-03, 1.87405987e-05],
[1.12301876e-01, 1.77099784e-03, 1.45382457e-01],
[4.41204876e-02, 4.84059652e-02, 1.05454822e+00],
[9.89348409e-01, -1.31692358e-03, 9.88291764e-01],
[9.99959923e-01, -5.32665972e-03, -1.53685870e-02]]),
"best_cost": 1.0002528364353296,
"best_pos": np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05]),
"options": {'c1': 0.5, 'c2': 0.3, 'w': 0.9},
}
return Swarm(**attrs_at_t)


@pytest.fixture(scope="module")
def k():
"""Default neighbor number"""
_k = 1
return _k
8 changes: 4 additions & 4 deletions tests/backend/topology/test_pyramid.py
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Expand Up @@ -13,11 +13,11 @@
def test_compute_gbest_return_values(swarm, static):
"""Test if compute_gbest() gives the expected return values"""
topology = Pyramid(static=static)
expected_cost = 1
expected_pos = np.array([1, 2, 3])
expected_cost = 1.0002528364353296
expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
pos, cost = topology.compute_gbest(swarm)
assert cost == expected_cost
assert (pos == expected_pos).all()
assert cost == pytest.approx(expected_cost)
assert (pos == pytest.approx(expected_pos))


@pytest.mark.parametrize("static", [True, False])
Expand Down
21 changes: 16 additions & 5 deletions tests/backend/topology/test_random.py
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Expand Up @@ -15,10 +15,10 @@ def test_update_gbest_neighborhood(swarm, k, static):
"""Test if update_gbest_neighborhood gives the expected return values"""
topology = Random(static=static)
pos, cost = topology.compute_gbest(swarm, k=k)
expected_pos = np.array([1, 2, 3])
expected_cost = 1
assert (pos == expected_pos).all()
assert cost == expected_cost
expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
expected_cost = 1.0002528364353296
assert cost == pytest.approx(expected_cost)
assert (pos == pytest.approx(expected_pos))


@pytest.mark.parametrize("static", [True, False])
Expand Down Expand Up @@ -57,16 +57,27 @@ def test_compute_neighbors_return_values(swarm, k, static):


@pytest.mark.parametrize("static", [True, False])
@pytest.mark.parametrize("k", [1])
def test_compute_neighbors_adjacency_matrix(swarm, k, static):
"""Test if __compute_neighbors() gives the expected matrix"""
np.random.seed(1)
topology = Random(static=static)
adj_matrix = topology._Random__compute_neighbors(swarm, k)
comparison_matrix = np.array([[1, 1, 0], [1, 1, 1], [0, 1, 1]])
comparison_matrix = np.array([[1, 1, 1, 0, 1, 0, 0, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 0, 0, 0, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 1, 0, 1, 0, 1, 1],
[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
[0, 1, 1, 1, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 0, 1, 1, 1, 0, 0, 1]])
assert np.allclose(adj_matrix, comparison_matrix, atol=1e-8)


@pytest.mark.parametrize("static", [True, False])
@pytest.mark.parametrize("k", [1])
def test_neighbor_idx(swarm, k, static):
"""Test if the neighbor_idx attribute is assigned"""
topology = Random(static=static)
Expand Down
10 changes: 5 additions & 5 deletions tests/backend/topology/test_ring.py
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Expand Up @@ -16,10 +16,10 @@ def test_update_gbest_neighborhood(swarm, p, k, static):
"""Test if update_gbest_neighborhood gives the expected return values"""
topology = Ring(static=static)
pos, cost = topology.compute_gbest(swarm, p=p, k=k)
expected_pos = np.array([1, 2, 3])
expected_cost = 1
assert (pos == expected_pos).all()
assert cost == expected_cost
expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
expected_cost = 1.0002528364353296
assert cost == pytest.approx(expected_cost)
assert (pos == pytest.approx(expected_pos))


@pytest.mark.parametrize("static", [True, False])
Expand Down Expand Up @@ -50,7 +50,7 @@ def test_compute_position_return_values(swarm, bounds, static):
@pytest.mark.parametrize("static", [True, False])
@pytest.mark.parametrize("k", [1, 2, 3])
@pytest.mark.parametrize("p", [1, 2])
def test_neighbor_idx(swarm, static, p , k):
def test_neighbor_idx(swarm, static, p, k):
"""Test if the neighbor_idx attribute is assigned"""
topology = Ring(static=static)
p = topology.compute_gbest(swarm, p=p, k=k)
Expand Down
8 changes: 4 additions & 4 deletions tests/backend/topology/test_star.py
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Expand Up @@ -12,11 +12,11 @@
def test_compute_gbest_return_values(swarm):
"""Test if compute_gbest() gives the expected return values"""
topology = Star()
expected_cost = 1
expected_pos = np.array([1, 2, 3])
expected_cost = 1.0002528364353296
expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
pos, cost = topology.compute_gbest(swarm)
assert cost == expected_cost
assert (pos == expected_pos).all()
assert cost == pytest.approx(expected_cost)
assert (pos == pytest.approx(expected_pos))


@pytest.mark.parametrize("clamp", [None, (0, 1), (-1, 1)])
Expand Down