Python matrix.Matrix类代码示例

 def matrix_to_multi_dim_timeseries_test(self):
     """Test to create a Timeseries from a Matrix."""
     rows = 5
     cols = 3
     data = [
                 [2.4, 4.5, 6.1],
                 [3.6, 3.2, 9.4],
                 [5.6, 3.2, 8.7],
                 [4.3, 7.1, 3.3],
                 [7.2, 9.6, 0.3]
             ]
     mtrx = Matrix(cols, rows)
     mtrx.initialize(data, True)
     ts = mtrx.to_multi_dim_timeseries()
     tsData = [
                 [0, [2.4, 4.5, 6.1]],
                 [1, [3.6, 3.2, 9.4]],
                 [2, [5.6, 3.2, 8.7]],
                 [3, [4.3, 7.1, 3.3]],
                 [4, [7.2, 9.6, 0.3]]
             ]
     exTs = MDTS.from_twodim_list(tsData, dimensions=3)
     # expecting that TimeSeries.from_twodom_list() works properly
     self.assertEqual(ts, exTs)
     # Changing entries of the timeseries, should not affect matrix
     row = 3
     ts[row] = [row, 4, 3, 1]
     for col in xrange(cols):
         self.assertEqual(mtrx.get_value(col, row), data[row][col])

    def gauss_jordan_switch_column_test(self):
        """Test the gauss jordan algorithm if the first values is zero.

        This test checks, if the lines are switched correctly.
        """
        rows = 3
        cols = 6
        data = [
                    [0, 2, 0, 1, 0, 0],
                    [2, 3, 0, 0, 1, 0],
                    [3, 4, 1, 0, 0, 1]
                ]
        mtrx = Matrix(cols, rows)
        mtrx.initialize(data, rowBased=True)

        exRes = [
                    [1.0, 0.0, 0.0],
                    [0.0, 1.0, 0.0],
                    [0.0, 0.0, 1.0],
                    [-0.75, 0.5, 0.25],
                    [0.5, 0.0, -1.5],
                    [0.0, 0.0, 1.0]
        ]

        res = mtrx.gauss_jordan()
        self.assertEqual(res.matrix, exRes)

    def householder_test(self):
        """Test the householder transformation to get a matrix in bidiagonalization."""
        # set up test
        c = [
                [4, 3, 0],
                [2, 1, 2],
                [4, 4, 0]
        ]
        matrix = Matrix(3, 3)
        matrix.initialize(c, rowBased=True)

        # execute householder transformation
        u, bidiag, v = matrix.householder()

        # expect, that multiplication works correctly.
        res = u * bidiag * v
        # res should be equal with c (except some rounding errors)
        for row in range(res.get_height()):
            for col in range(res.get_width()):
                self.assertAlmostEqual(res.get_value(col, row), c[row][col], PRECISION)
        # bidiag matrix should have 0 values below the diagonal
        self.assertAlmostEqual(bidiag.get_value(0, 1), 0, PRECISION)
        self.assertAlmostEqual(bidiag.get_value(0, 2), 0, PRECISION)
        self.assertAlmostEqual(bidiag.get_value(1, 2), 0, PRECISION)

        self.assertAlmostEqual(bidiag.get_value(2, 0), 0, PRECISION)

    def mul_type_error_test(self):
        """Test for TypeError, if Matrix is multiplied with a String

        or a String is multiplied with a Matrix."""
        rows  = 3
        cols  = 2
        data  = [
                    [1, -2, 3],
                    [-4, 5, 6]
                ]
        mtrx = Matrix(cols, rows)
        mtrx.initialize(data, rowBased=False)
        try:
            mtrx * "Test"
        except TypeError:
            pass
        else:
            raise AssertionError  # pragma: no cover

        try:
            "Test" * mtrx
        except TypeError:
            pass
        else:
            raise AssertionError  # pragma: no cover

 def svd_unitary_test(self):
     """Test if matrices u and v are unitary."""
     a = [[22., 10.,   2.,   3.,  7.],
          [14.,  7.,  10.,   0.,  8.],
          [-1., 13., - 1., -11.,  3.],
          [-3., -2.,  13.,  -2.,  4.],
          [ 9.,  8.,   1.,  -2.,  4.],
          [ 9.,  1.,  -7.,   5., -1.],
          [ 2., -6.,   6.,   5.,  1.],
          [ 4.,  5.,   0.,  -2.,  2.]]
     matrix = Matrix(5, 8)
     matrix.initialize(a, rowBased=True)
     u, diag, v = matrix.svd()
     # u and v should be unitary matrices. Matrixmultiplication withs its
     # transformation should be the identity Matrix.
     res = u.transform() * u
     res1 = v * v.transform()
     for row in range(res.get_height()):
         for col in range(res.get_width()):
             if row == col:
                 # value should be 1 at diagonal
                 self.assertAlmostEqual(res.get_value(col, row), 1, PRECISION)
                 self.assertAlmostEqual(res1.get_value(col, row), 1, PRECISION)
             else:
                 # value should be 0 otherwise.
                 self.assertAlmostEqual(res.get_value(col, row), 0, PRECISION)
                 self.assertAlmostEqual(res1.get_value(col, row), 0, PRECISION)

    def flatten_test(self):
        a = Matrix(2, 2, [2, 2, 2, 2])
        b = Matrix(2, 2, [a, a, a, a])

        result = Matrix(4, 4, [2]*16)
        calculated = b.flatten()
        self.assertTrue(calculated, result)

    def pseudoinverse_with_more_columns_test(self):
        """Test to calculate the pseudoinverse of a Matrix with more columns than rows."""
        rows = 2
        cols = 4
        data = [
                    [-11,  2, -5.0, 7.0],
                    [  2, -4,  3.4, 5.4]
                ]
        mtrx = Matrix(cols, rows)
        mtrx.initialize(data, rowBased=True)
        # Expected result calculated with scipy
        exRes = [
                    [-0.0541328,   0.02473614],
                    [ 0.00705413, -0.06480734],
                    [-0.02269591,  0.05255596],
                    [ 0.03956448,  0.09492743]
                ]

        res = mtrx.pseudoinverse()
        # Pseudoinverse of a m x n Matrix has to be a n x m Matrix
        self.assertEqual(res.get_width(), rows)
        self.assertEqual(res.get_height(), cols)
        for row in range(cols):
            for col in range(rows):
                self.assertAlmostEqual(exRes[row][col], res.get_value(col, row))

    def set_string_precision_error_value_test(self):
        """Test for :py:exc:`ValueError` when trying to set the precision to a negative value."""
        size = 2
        data = [[1, 2], [3, 4]]
        mtrx = Matrix(size, size)
        mtrx.initialize(data, rowBased=True)

        self.assertRaises(ValueError, mtrx.set_string_precision, -2)

 def init_test(self):
     """Test the initialization of a matrix."""
     rows = random.randint(1, 1000)
     cols = random.randint(1, 1000)
     matrix = Matrix(cols, rows)
     if not matrix.get_height() == rows:
         raise AssertionError  # pragma: no cover
     if not matrix.get_width() == cols:
         raise AssertionError  # pragma: no cover

 def get_matrix_from_list_test(self):
     """Test to create a Matrix from a one dimensional list."""
     rows = 2
     cols = 3
     mtrx = Matrix(cols, rows)
     data = [1, 2, 3, 4, 5, 6]
     exRes = [[1, 2, 3], [4, 5, 6]]
     newMtrx = mtrx.get_matrix_from_list(rows, cols, data, rowBased=True)
     self.assertEqual(newMtrx.get_array(rowBased=True), exRes)

    def blockwise_with_zero_expansion_test(self):
        a = Matrix(4, 4, [1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0, 0, 0, 0, 0])
        b = Matrix(4, 4, [1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0, 0, 0, 0, 0])

        calculated = a.matrix_multiplication_blockwise(b, 2)
        result = Matrix(4, 4,
            [30.000, 36.000, 42.000, 0, 66.000, 81.000, 96.000, 0,
                102.000, 126.000, 150.000, 0, 0, 0, 0, 0]
        )
        self.assertEqual(result, calculated)

    def matrix_string_representation_with_precision_test(self):
        """Test if the precision is set correctly and used when printing a Matrix.
        """
        size = 2
        data = [
                    [1.0123343,    -2.012341234123],
                    [3.04674567566, 4.012341234120]
                ]
        mtrx = Matrix(size, size)
        mtrx.initialize(data, rowBased=True)
        mtrx.set_string_precision(4)
        rep = mtrx.__str__()
        # should print the number with 4 digits after decimal point
        self.assertTrue(rep.find(" 3.0467 ") >= 0)
        self.assertTrue(rep.find(" -2.0123") >= 0)

        # but should not print the full number
        self.assertFalse(rep.find(" 3.04674567566") >= 0)
        self.assertFalse(rep.find(" -2.012341234123 ") >= 0)

        # change precision
        mtrx.set_string_precision(2)
        rep = mtrx.__str__()
        print mtrx
        # should print the number with 2 digits after decimal point
        # numbers should be rounded
        self.assertTrue(rep.find(" 3.05 ") >= 0)
        self.assertTrue(rep.find(" -2.01") >= 0)

    def blockwise_multiplication_test(self):
        data = range(1, 33)
        a = Matrix(4, 2, data[:8])
        b = Matrix(6, 4, data[8:])

        result = [  210.000, 220.000, 230.000, 240.000, 250.000, 260.000,
                    498.000, 524.000, 550.000, 576.000, 602.000, 628.000]
        resultMatrix = Matrix(6, 2, result)

        calculated = a.matrix_multiplication_blockwise(b, 2)
        self.assertEqual(resultMatrix, calculated)

 def matrix_string_representation_test(self):
     """Test the String representation of a Matrix instance."""
     matrix = Matrix(2, 2)
     a = [[1, 2], [-3, 4]]
     matrix.initialize(a, rowBased=True)
     rep = matrix.__str__()
     self.assertTrue(rep.find("  1.0") >= 0)
     self.assertTrue(rep.find(" -3.0") >= 0)
     # only one space before a negative number
     self.assertFalse(rep.find("  -3.0") >= 0)
     self.assertTrue(rep.find("Matrix") >= 0)

 def invers_value_error_test(self):
     """Test if a :py:exc:`ValueError` is raised if Matrix is not regular"""
     rows = 2
     cols = 3
     data = [
                 [1, 2, 3],
                 [4, 5, 6]
             ]
     matrix = Matrix(cols, rows)
     matrix.initialize(data, rowBased=True)
     self.assertRaises(ValueError, matrix.invers)