예제 #1
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    def test_procrustes2(self):
        """procrustes disparity should not depend on order of matrices"""
        m1, m3, disp13 = procrustes(self.data1, self.data3)
        m3_2, m1_2, disp31 = procrustes(self.data3, self.data1)
        assert_almost_equal(disp13, disp31)

        # try with 3d, 8 pts per
        rand1 = array([[2.61955202, 0.30522265, 0.55515826],
                       [0.41124708, -0.03966978, -0.31854548],
                       [0.91910318, 1.39451809, -0.15295084],
                       [2.00452023, 0.50150048, 0.29485268],
                       [0.09453595, 0.67528885, 0.03283872],
                       [0.07015232, 2.18892599, -1.67266852],
                       [0.65029688, 1.60551637, 0.80013549],
                       [-0.6607528, 0.53644208, 0.17033891]])

        rand3 = array([[0.0809969, 0.09731461, -0.173442],
                       [-1.84888465, -0.92589646, -1.29335743],
                       [0.67031855, -1.35957463, 0.41938621],
                       [0.73967209, -0.20230757, 0.52418027],
                       [0.17752796, 0.09065607, 0.29827466],
                       [0.47999368, -0.88455717, -0.57547934],
                       [-0.11486344, -0.12608506, -0.3395779],
                       [-0.86106154, -0.28687488, 0.9644429]])
        res1, res3, disp13 = procrustes(rand1, rand3)
        res3_2, res1_2, disp31 = procrustes(rand3, rand1)
        assert_almost_equal(disp13, disp31)
예제 #2
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 def test_procrustes2(self):
     """procrustes disparity should not depend on order of matrices"""
     m1, m3, disp13 = procrustes(self.data1, self.data3)
     m3_2, m1_2, disp31 = procrustes(self.data3, self.data1)
     assert_almost_equal(disp13, disp31)
     
     # try with 3d, 8 pts per
     rand1 = array([[ 2.61955202,  0.30522265,  0.55515826],
     [ 0.41124708, -0.03966978, -0.31854548],
     [ 0.91910318,  1.39451809, -0.15295084],
     [ 2.00452023,  0.50150048,  0.29485268],
     [ 0.09453595,  0.67528885,  0.03283872],
     [ 0.07015232,  2.18892599, -1.67266852],
     [ 0.65029688,  1.60551637,  0.80013549],
     [-0.6607528 ,  0.53644208,  0.17033891]])
     
     rand3 = array([[ 0.0809969 ,  0.09731461, -0.173442  ],
     [-1.84888465, -0.92589646, -1.29335743],
     [ 0.67031855, -1.35957463,  0.41938621],
     [ 0.73967209, -0.20230757,  0.52418027],
     [ 0.17752796,  0.09065607,  0.29827466],
     [ 0.47999368, -0.88455717, -0.57547934],
     [-0.11486344, -0.12608506, -0.3395779 ],
     [-0.86106154, -0.28687488,  0.9644429 ]])
     res1, res3, disp13 = procrustes(rand1,rand3)
     res3_2, res1_2, disp31 = procrustes(rand3, rand1)
     assert_almost_equal(disp13, disp31)
예제 #3
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    def test_summarize_pcoas(self):
        """summarize_pcoas works
        """
        master_pcoa = [['1', '2', '3'], \
            array([[-1.0, 0.0, 1.0], [2.0, 4.0, -4.0]]), \
            array([.76, .24])]
        jn1 = [['1', '2', '3'], \
            array([[1.2, 0.1, -1.2],[-2.5, -4.0, 4.5]]), \
            array([0.80, .20])]
        jn2 = [['1', '2', '3'], \
            array([[-1.4, 0.05, 1.3],[2.6, 4.1, -4.7]]), \
            array([0.76, .24])]
        jn3 = [['1', '2', '3'], \
            array([[-1.5, 0.05, 1.6],[2.4, 4.0, -4.8]]), \
            array([0.84, .16])]
        jn4 = [['1', '2', '3'], \
            array([[-1.5, 0.05, 1.6],[2.4, 4.0, -4.8]]), \
            array([0.84, .16])]
        support_pcoas = [jn1, jn2, jn3, jn4]
        #test with the ideal_fourths option
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, 'ideal_fourths',
                            apply_procrustes=False)
        self.assertEqual(m_names, ['1', '2', '3'])
        assert_almost_equal(matrix_average[(0, 0)], -1.4)
        assert_almost_equal(matrix_average[(0, 1)], 0.0125)
        assert_almost_equal(matrix_low[(0, 0)], -1.5)
        assert_almost_equal(matrix_high[(0, 0)], -1.28333333)
        assert_almost_equal(matrix_low[(0, 1)], -0.0375)
        assert_almost_equal(matrix_high[(0, 1)], 0.05)
        assert_almost_equal(eigval_average[0], 0.81)
        assert_almost_equal(eigval_average[1], 0.19)
        #test with the IQR option
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, method='IQR',
                            apply_procrustes=False)
        assert_almost_equal(matrix_low[(0, 0)], -1.5)
        assert_almost_equal(matrix_high[(0, 0)], -1.3)

        #test with procrustes option followed by sdev
        m, m1, msq = procrustes(master_pcoa[1], jn1[1])
        m, m2, msq = procrustes(master_pcoa[1], jn2[1])
        m, m3, msq = procrustes(master_pcoa[1], jn3[1])
        m, m4, msq = procrustes(master_pcoa[1], jn4[1])
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, method='sdev',
                            apply_procrustes=True)

        x = array([m1[0, 0], m2[0, 0], m3[0, 0], m4[0, 0]])
        self.assertEqual(x.mean(), matrix_average[0, 0])
        self.assertEqual(-x.std(ddof=1) / 2, matrix_low[0, 0])
        self.assertEqual(x.std(ddof=1) / 2, matrix_high[0, 0])
예제 #4
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    def test_summarize_pcoas(self):
        """summarize_pcoas works
        """
        master_pcoa = [['1', '2', '3'], \
            array([[-1.0, 0.0, 1.0], [2.0, 4.0, -4.0]]), \
            array([.76, .24])]
        jn1 = [['1', '2', '3'], \
            array([[1.2, 0.1, -1.2],[-2.5, -4.0, 4.5]]), \
            array([0.80, .20])]
        jn2 = [['1', '2', '3'], \
            array([[-1.4, 0.05, 1.3],[2.6, 4.1, -4.7]]), \
            array([0.76, .24])]
        jn3 = [['1', '2', '3'], \
            array([[-1.5, 0.05, 1.6],[2.4, 4.0, -4.8]]), \
            array([0.84, .16])]
        jn4 = [['1', '2', '3'], \
            array([[-1.5, 0.05, 1.6],[2.4, 4.0, -4.8]]), \
            array([0.84, .16])]
        support_pcoas = [jn1, jn2, jn3, jn4]
        #test with the ideal_fourths option
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, 'ideal_fourths',
                            apply_procrustes=False)
        self.assertEqual(m_names, ['1', '2', '3'])
        assert_almost_equal(matrix_average[(0,0)], -1.4)
        assert_almost_equal(matrix_average[(0,1)], 0.0125)
        assert_almost_equal(matrix_low[(0,0)], -1.5)
        assert_almost_equal(matrix_high[(0,0)], -1.28333333)
        assert_almost_equal(matrix_low[(0,1)], -0.0375)
        assert_almost_equal(matrix_high[(0,1)], 0.05)
        assert_almost_equal(eigval_average[0], 0.81)
        assert_almost_equal(eigval_average[1], 0.19)
        #test with the IQR option
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, method='IQR',
                            apply_procrustes=False)
        assert_almost_equal(matrix_low[(0,0)], -1.5)
        assert_almost_equal(matrix_high[(0,0)], -1.3)

        #test with procrustes option followed by sdev
        m, m1, msq = procrustes(master_pcoa[1],jn1[1])
        m, m2, msq = procrustes(master_pcoa[1],jn2[1])
        m, m3, msq = procrustes(master_pcoa[1],jn3[1])
        m, m4, msq = procrustes(master_pcoa[1],jn4[1])
        matrix_average, matrix_low, matrix_high, eigval_average, m_names = \
            summarize_pcoas(master_pcoa, support_pcoas, method='sdev',
                            apply_procrustes=True)

        x = array([m1[0,0],m2[0,0],m3[0,0],m4[0,0]])
        self.assertEqual(x.mean(),matrix_average[0,0])
        self.assertEqual(-x.std(ddof=1)/2,matrix_low[0,0])
        self.assertEqual(x.std(ddof=1)/2,matrix_high[0,0])
예제 #5
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파일: util.py 프로젝트: jacksonchen/emperor
def summarize_pcoas(master_pcoa, support_pcoas, method='IQR', apply_procrustes=True):
    """returns the average PCoA vector values for the support pcoas

    Also returns the ranges as calculated with the specified method.
    The choices are:
        IQR: the Interquartile Range
        ideal fourths: Ideal fourths method as implemented in scipy
    """
    if apply_procrustes:
        # perform procrustes before averaging
        support_pcoas = [list(sp) for sp in support_pcoas]
        master_pcoa = list(master_pcoa)
        for i, pcoa in enumerate(support_pcoas):
            master_std, pcoa_std, m_squared = procrustes(master_pcoa[1],pcoa[1])
            support_pcoas[i][1] = pcoa_std
        master_pcoa[1] = master_std

    m_matrix = master_pcoa[1]
    m_eigvals = master_pcoa[2]
    m_names = master_pcoa[0]
    jn_flipped_matrices = []
    all_eigvals = []
    for rep in support_pcoas:
        matrix = rep[1]
        eigvals = rep[2]
        all_eigvals.append(eigvals)
        jn_flipped_matrices.append(_flip_vectors(matrix, m_matrix))
    matrix_average, matrix_low, matrix_high = _compute_jn_pcoa_avg_ranges(\
            jn_flipped_matrices, method)
    #compute average eigvals
    all_eigvals_stack = vstack(all_eigvals)
    eigval_sum = numpy_sum(all_eigvals_stack, axis=0)
    eigval_average = eigval_sum / float(len(all_eigvals))
    return matrix_average, matrix_low, matrix_high, eigval_average, m_names
예제 #6
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    def test_procrustes(self):
        """tests procrustes' ability to match two matrices.
        
        the second matrix is a rotated, shifted, scaled, and mirrored version
        of the first, in two dimensions only
        """
        # can shift, mirror, and scale an 'L'?
        a, b, disparity = procrustes(self.data1, self.data2)
        assert_almost_equal(b, a)
        assert_almost_equal(disparity, 0.)

        # if first mtx is standardized, leaves first mtx unchanged?
        m4, m5, disp45 = procrustes(self.data4, self.data5)
        assert_almost_equal(m4, self.data4)

        # at worst, data3 is an 'L' with one point off by .5
        m1, m3, disp13 = procrustes(self.data1, self.data3)
        self.assertTrue(disp13 < .5**2)
예제 #7
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    def test_procrustes(self):
        """tests procrustes' ability to match two matrices.
        
        the second matrix is a rotated, shifted, scaled, and mirrored version
        of the first, in two dimensions only
        """
        # can shift, mirror, and scale an 'L'?
        a,b,disparity = procrustes(self.data1, self.data2)
        assert_almost_equal(b, a)
        assert_almost_equal(disparity,0.)
        
        # if first mtx is standardized, leaves first mtx unchanged?
        m4, m5, disp45 = procrustes(self.data4, self.data5)
        assert_almost_equal(m4, self.data4)

        # at worst, data3 is an 'L' with one point off by .5
        m1, m3, disp13 = procrustes(self.data1, self.data3)
        self.assertTrue(disp13 < .5**2)
예제 #8
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def summarize_pcoas(master_pcoa,
                    support_pcoas,
                    method='IQR',
                    apply_procrustes=True):
    """returns the average PCoA vector values for the support pcoas

    Also returns the ranges as calculated with the specified method.
    The choices are:
        IQR: the Interquartile Range
        ideal fourths: Ideal fourths method as implemented in scipy
    """
    if apply_procrustes:
        # perform procrustes before averaging
        support_pcoas = [list(sp) for sp in support_pcoas]
        master_pcoa = list(master_pcoa)
        for i, pcoa in enumerate(support_pcoas):
            master_std, pcoa_std, m_squared = procrustes(
                master_pcoa[1], pcoa[1])
            support_pcoas[i][1] = pcoa_std
        master_pcoa[1] = master_std

    m_matrix = master_pcoa[1]
    m_eigvals = master_pcoa[2]
    m_names = master_pcoa[0]
    jn_flipped_matrices = []
    all_eigvals = []
    for rep in support_pcoas:
        matrix = rep[1]
        eigvals = rep[2]
        all_eigvals.append(eigvals)
        jn_flipped_matrices.append(_flip_vectors(matrix, m_matrix))
    matrix_average, matrix_low, matrix_high = _compute_jn_pcoa_avg_ranges(\
            jn_flipped_matrices, method)
    #compute average eigvals
    all_eigvals_stack = vstack(all_eigvals)
    eigval_sum = numpy_sum(all_eigvals_stack, axis=0)
    eigval_average = eigval_sum / float(len(all_eigvals))
    return matrix_average, matrix_low, matrix_high, eigval_average, m_names