Ejemplo n.º 1
0
 def test_normalisation(self):
     r = np.random.rand(100,3)
     
     n = norm_vect(r)
     d = dot_product(n,n)
     
     ones = np.ones((100))
     
     assert_almost_equal(d, ones, 8, err_msg="Your vectors don't normalise too well")
Ejemplo n.º 2
0
    def test_block_vect(self):
        r = np.random.rand(100, 3)

        d = dot_product(r, r)

        r_sqar = np.power(r[:,:], 2)
        r_sum = np.sum(r_sqar[:,:], axis=1)

        assert_equal(np.shape(d), np.shape(r_sum), err_msg="The shape of the final dot product is not right")

        assert_almost_equal(d[:], r_sum[:], 8, err_msg="The dot_product sum does not add up (funny, huh?)")
Ejemplo n.º 3
0
    def test_sum_vect(self):
        r = np.array(([[1,2,3]]))

        d = dot_product(r,r)

        assert_equal(d, 14, err_msg="sum in dot_product is broken")
Ejemplo n.º 4
0
    def _dim_vect(self,r):
        d = dot_product(r,r)

        assert_equal( d, 4, err_msg="dot_product is in 1 dimension broken")