def test_dot(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 1
lil[3, 0] = 2
dv = array([1., 2., 3., 4.])
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
mat = array([[1., 2., 3., 4.], [1., 2., 3., 4.], [1., 2., 3., 4.],
[1., 2., 3., 4.]])
self.assertEquals(10.0, _dot(lil, dv))
self.assertTrue(array_equal(array([3., 6., 9., 12.]), _dot(lil, mat)))
def test_dot(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 1
lil[3, 0] = 2
dv = array([1.0, 2.0, 3.0, 4.0])
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
mat = array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]])
self.assertEquals(10.0, _dot(lil, dv))
self.assertTrue(array_equal(array([3.0, 6.0, 9.0, 12.0]), _dot(lil, mat)))
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
if margin > 0:
prob = 1 / (1 + exp(-margin))
else:
prob = 1 - 1 / (1 + exp(margin))
return 1 if prob > 0.5 else 0
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
if margin > 0:
prob = 1 / (1 + exp(-margin))
else:
prob = 1 - 1 / (1 + exp(margin))
return 1 if prob > 0.5 else 0
def test_dot(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = array([1.0, 2.0, 3.0, 4.0])
lst = [1, 2, 3, 4]
mat = array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]])
self.assertEquals(10.0, _dot(sv, dv))
self.assertTrue(array_equal(array([3.0, 6.0, 9.0, 12.0]), _dot(sv, mat)))
self.assertEquals(30.0, _dot(dv, dv))
self.assertTrue(array_equal(array([10.0, 20.0, 30.0, 40.0]), _dot(dv, mat)))
self.assertEquals(30.0, _dot(lst, dv))
self.assertTrue(array_equal(array([10.0, 20.0, 30.0, 40.0]), _dot(lst, mat)))
def test_dot(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = array([1., 2., 3., 4.])
lst = [1, 2, 3, 4]
mat = array([[1., 2., 3., 4.], [1., 2., 3., 4.], [1., 2., 3., 4.],
[1., 2., 3., 4.]])
self.assertEquals(10.0, _dot(sv, dv))
self.assertTrue(array_equal(array([3., 6., 9., 12.]), _dot(sv, mat)))
self.assertEquals(30.0, _dot(dv, dv))
self.assertTrue(array_equal(array([10., 20., 30., 40.]), _dot(dv,
mat)))
self.assertEquals(30.0, _dot(lst, dv))
self.assertTrue(
array_equal(array([10., 20., 30., 40.]), _dot(lst, mat)))
def predict(self, x):
"""Return the most likely class for a data vector x"""
return self.labels[numpy.argmax(self.pi +
_dot(x, self.theta.transpose()))]
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
return ('1', abs(margin)) if margin >= 0 else ('0', abs(margin))
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
return 1 if margin >= 0 else 0
def predict(self, x):
"""Predict the value of the dependent variable given a vector x"""
"""containing values for the independent variables."""
_linear_predictor_typecheck(x, self._coeff)
return _dot(x, self._coeff) + self._intercept
def predict(self, x):
"""Return the most likely class for a data vector x"""
return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
return 1 if margin >= 0 else 0
def predict(self, x):
_linear_predictor_typecheck(x, self._coeff)
margin = _dot(x, self._coeff) + self._intercept
return ('1', abs(margin)) if margin >= 0 else ('0', abs(margin))
