Beispiel #1
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 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)))
Beispiel #2
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    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)))
Beispiel #3
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 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
Beispiel #4
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 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
Beispiel #5
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 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)))
Beispiel #6
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 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))
Beispiel #9
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 def predict(self, x):
     _linear_predictor_typecheck(x, self._coeff)
     margin = _dot(x, self._coeff) + self._intercept
     return 1 if margin >= 0 else 0
Beispiel #10
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 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
Beispiel #11
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 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()))]
Beispiel #12
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 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))