1 | #!/usr/bin/env python |
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2 | |
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3 | import exceptions |
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4 | class TestError(exceptions.Exception): pass |
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5 | import unittest |
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6 | |
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7 | |
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8 | from Numeric import dot, allclose, array, transpose, arange, ones, Float |
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9 | from anuga.utilities.cg_solve import * |
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10 | from anuga.utilities.cg_solve import _conjugate_gradient |
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11 | from anuga.utilities.sparse import Sparse, Sparse_CSR |
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12 | |
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13 | |
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14 | class Test_CG_Solve(unittest.TestCase): |
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15 | |
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16 | def test_sparse_solve(self): |
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17 | """Solve Small Sparse Matrix""" |
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18 | |
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19 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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20 | [-1.0, 2.0, -1.0, 0.0], |
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21 | [0.0, -1.0, 2.0, -1.0], |
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22 | [0.0,0.0, -1.0, 2.0]] |
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23 | |
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24 | A = Sparse(A) |
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25 | |
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26 | xe = [0.0, 1.0, 2.0, 3.0] |
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27 | b = A*xe |
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28 | x = [0.0, 0.0, 0.0, 0.0] |
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29 | |
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30 | x = conjugate_gradient(A,b,x,iprint=0) |
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31 | |
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32 | assert allclose(x,xe) |
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33 | |
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34 | def test_max_iter(self): |
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35 | """Test max iteration Small Sparse Matrix""" |
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36 | |
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37 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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38 | [-1.0, 2.0, -1.0, 0.0], |
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39 | [0.0, -1.0, 2.0, -1.0], |
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40 | [0.0,0.0, -1.0, 2.0]] |
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41 | |
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42 | A = Sparse(A) |
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43 | |
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44 | xe = [0.0, 1.0, 2.0, 3.0] |
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45 | b = A*xe |
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46 | x = [0.0, 0.0, 0.0, 0.0] |
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47 | |
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48 | try: |
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49 | x = conjugate_gradient(A,b,x,iprint=0,imax=2) |
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50 | except ConvergenceError: |
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51 | pass |
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52 | else: |
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53 | msg = 'Should have raised exception' |
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54 | raise TestError, msg |
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55 | |
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56 | |
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57 | def test_solve_large(self): |
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58 | """Standard 1d laplacian """ |
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59 | |
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60 | n = 50 |
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61 | A = Sparse(n,n) |
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62 | |
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63 | for i in arange(0,n): |
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64 | A[i,i] = 1.0 |
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65 | if i > 0 : |
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66 | A[i,i-1] = -0.5 |
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67 | if i < n-1 : |
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68 | A[i,i+1] = -0.5 |
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69 | |
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70 | xe = ones( (n,), Float) |
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71 | |
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72 | b = A*xe |
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73 | x = conjugate_gradient(A,b,b,tol=1.0e-5,iprint=1) |
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74 | |
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75 | assert allclose(x,xe) |
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76 | |
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77 | def test_solve_large_2d(self): |
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78 | """Standard 2d laplacian""" |
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79 | |
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80 | n = 20 |
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81 | m = 10 |
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82 | |
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83 | A = Sparse(m*n, m*n) |
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84 | |
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85 | for i in arange(0,n): |
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86 | for j in arange(0,m): |
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87 | I = j+m*i |
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88 | A[I,I] = 4.0 |
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89 | if i > 0 : |
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90 | A[I,I-m] = -1.0 |
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91 | if i < n-1 : |
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92 | A[I,I+m] = -1.0 |
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93 | if j > 0 : |
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94 | A[I,I-1] = -1.0 |
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95 | if j < m-1 : |
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96 | A[I,I+1] = -1.0 |
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97 | |
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98 | xe = ones( (n*m,), Float) |
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99 | |
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100 | b = A*xe |
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101 | x = conjugate_gradient(A,b,b,iprint=0) |
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102 | |
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103 | assert allclose(x,xe) |
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104 | |
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105 | def test_solve_large_2d_csr_matrix(self): |
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106 | """Standard 2d laplacian with csr format |
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107 | """ |
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108 | |
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109 | n = 100 |
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110 | m = 100 |
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111 | |
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112 | A = Sparse(m*n, m*n) |
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113 | |
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114 | for i in arange(0,n): |
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115 | for j in arange(0,m): |
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116 | I = j+m*i |
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117 | A[I,I] = 4.0 |
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118 | if i > 0 : |
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119 | A[I,I-m] = -1.0 |
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120 | if i < n-1 : |
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121 | A[I,I+m] = -1.0 |
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122 | if j > 0 : |
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123 | A[I,I-1] = -1.0 |
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124 | if j < m-1 : |
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125 | A[I,I+1] = -1.0 |
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126 | |
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127 | xe = ones( (n*m,), Float) |
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128 | |
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129 | # Convert to csr format |
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130 | #print 'start covert' |
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131 | A = Sparse_CSR(A) |
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132 | #print 'finish covert' |
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133 | b = A*xe |
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134 | x = conjugate_gradient(A,b,b,iprint=20) |
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135 | |
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136 | assert allclose(x,xe) |
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137 | |
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138 | |
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139 | def test_solve_large_2d_with_default_guess(self): |
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140 | """Standard 2d laplacian using default first guess""" |
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141 | |
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142 | n = 20 |
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143 | m = 10 |
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144 | |
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145 | A = Sparse(m*n, m*n) |
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146 | |
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147 | for i in arange(0,n): |
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148 | for j in arange(0,m): |
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149 | I = j+m*i |
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150 | A[I,I] = 4.0 |
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151 | if i > 0 : |
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152 | A[I,I-m] = -1.0 |
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153 | if i < n-1 : |
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154 | A[I,I+m] = -1.0 |
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155 | if j > 0 : |
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156 | A[I,I-1] = -1.0 |
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157 | if j < m-1 : |
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158 | A[I,I+1] = -1.0 |
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159 | |
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160 | xe = ones( (n*m,), Float) |
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161 | |
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162 | b = A*xe |
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163 | x = conjugate_gradient(A,b) |
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164 | |
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165 | assert allclose(x,xe) |
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166 | |
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167 | |
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168 | def test_vector_shape_error(self): |
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169 | """Raise VectorShapeError""" |
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170 | |
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171 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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172 | [-1.0, 2.0, -1.0, 0.0], |
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173 | [0.0, -1.0, 2.0, -1.0], |
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174 | [0.0,0.0, -1.0, 2.0]] |
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175 | |
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176 | A = Sparse(A) |
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177 | |
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178 | xe = [[0.0,2.0], [1.0,3.0], [2.0,4.0], [3.0,2.0]] |
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179 | |
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180 | try: |
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181 | x = _conjugate_gradient(A,xe,xe,iprint=0) |
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182 | except VectorShapeError: |
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183 | pass |
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184 | else: |
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185 | msg = 'Should have raised exception' |
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186 | raise TestError, msg |
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187 | |
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188 | |
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189 | def test_sparse_solve_matrix(self): |
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190 | """Solve Small Sparse Matrix""" |
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191 | |
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192 | A = [[2.0, -1.0, 0.0, 0.0 ], |
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193 | [-1.0, 2.0, -1.0, 0.0], |
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194 | [0.0, -1.0, 2.0, -1.0], |
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195 | [0.0,0.0, -1.0, 2.0]] |
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196 | |
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197 | A = Sparse(A) |
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198 | |
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199 | xe = [[0.0, 0.0],[1.0, 1.0],[2.0 ,2.0],[3.0, 3.0]] |
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200 | b = A*xe |
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201 | x = [[0.0, 0.0],[0.0, 0.0],[0.0 ,0.0],[0.0, 0.0]] |
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202 | x = conjugate_gradient(A,b,x,iprint=0) |
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203 | |
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204 | assert allclose(x,xe) |
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205 | |
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206 | #------------------------------------------------------------- |
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207 | if __name__ == "__main__": |
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208 | suite = unittest.makeSuite(Test_CG_Solve,'test') |
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209 | #runner = unittest.TextTestRunner(verbosity=2) |
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210 | runner = unittest.TextTestRunner(verbosity=1) |
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211 | runner.run(suite) |
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212 | |
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