def test_squared_distance(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 3
lil[3, 0] = 2
dv = array([1., 2., 3., 4.])
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
self.assertEquals(15.0, _squared_distance(lil, dv))
self.assertEquals(15.0, _squared_distance(lil, sv))
self.assertEquals(15.0, _squared_distance(dv, lil))
self.assertEquals(15.0, _squared_distance(sv, lil))
def test_squared_distance(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 3
lil[3, 0] = 2
dv = array([1., 2., 3., 4.])
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
self.assertEquals(15.0, _squared_distance(lil, dv))
self.assertEquals(15.0, _squared_distance(lil, sv))
self.assertEquals(15.0, _squared_distance(dv, lil))
self.assertEquals(15.0, _squared_distance(sv, lil))
def test_squared_distance(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = array([1., 2., 3., 4.])
lst = [4, 3, 2, 1]
self.assertEquals(15.0, _squared_distance(sv, dv))
self.assertEquals(25.0, _squared_distance(sv, lst))
self.assertEquals(20.0, _squared_distance(dv, lst))
self.assertEquals(15.0, _squared_distance(dv, sv))
self.assertEquals(25.0, _squared_distance(lst, sv))
self.assertEquals(20.0, _squared_distance(lst, dv))
self.assertEquals(0.0, _squared_distance(sv, sv))
self.assertEquals(0.0, _squared_distance(dv, dv))
self.assertEquals(0.0, _squared_distance(lst, lst))
def test_squared_distance(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = array([1., 2., 3., 4.])
lst = [4, 3, 2, 1]
self.assertEquals(15.0, _squared_distance(sv, dv))
self.assertEquals(25.0, _squared_distance(sv, lst))
self.assertEquals(20.0, _squared_distance(dv, lst))
self.assertEquals(15.0, _squared_distance(dv, sv))
self.assertEquals(25.0, _squared_distance(lst, sv))
self.assertEquals(20.0, _squared_distance(lst, dv))
self.assertEquals(0.0, _squared_distance(sv, sv))
self.assertEquals(0.0, _squared_distance(dv, dv))
self.assertEquals(0.0, _squared_distance(lst, lst))
def predict(self, x):
"""Find the cluster to which x belongs in this model."""
best = 0
best_distance = float("inf")
for i in range(0, len(self.centers)):
distance = _squared_distance(x, self.centers[i])
if distance < best_distance:
best = i
best_distance = distance
return best
def predict(self, x):
"""Find the cluster to which x belongs in this model."""
best = 0
best_distance = float("inf")
for i in range(0, len(self.centers)):
distance = _squared_distance(x, self.centers[i])
if distance < best_distance:
best = i
best_distance = distance
return best
