1 | #!/usr/bin/env python |
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2 | # |
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3 | # Author: Alta Fang (altafang @caltech and alta @princeton) |
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4 | # Author: Mike McKerns (mmckerns @caltech and @uqfoundation) |
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5 | # Copyright (c) 1997-2016 California Institute of Technology. |
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6 | # License: 3-clause BSD. The full license text is available at: |
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7 | # - http://mmckerns.github.io/project/mystic/browser/mystic/LICENSE |
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8 | """Test Mystic's performance on some benchmark problems, with Mystic's settings |
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9 | adjusted to achieve the best results. |
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10 | """ |
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11 | from mystic.monitors import Monitor |
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12 | from mystic.math import almostEqual |
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13 | from mystic.tools import random_seed |
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14 | random_seed(123) |
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15 | import time |
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16 | |
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17 | def test_rosenbrock(): |
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18 | """Test the 2-dimensional Rosenbrock function. |
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19 | |
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20 | Testing 2-D Rosenbrock: |
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21 | Expected: x=[1., 1.] and f=0 |
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22 | |
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23 | Using DifferentialEvolutionSolver: |
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24 | Solution: [ 1.00000037 1.0000007 ] |
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25 | f value: 2.29478683682e-13 |
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26 | Iterations: 99 |
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27 | Function evaluations: 3996 |
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28 | Time elapsed: 0.582273006439 seconds |
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29 | |
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30 | Using DifferentialEvolutionSolver2: |
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31 | Solution: [ 0.99999999 0.99999999] |
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32 | f value: 3.84824937598e-15 |
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33 | Iterations: 100 |
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34 | Function evaluations: 4040 |
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35 | Time elapsed: 0.577210903168 seconds |
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36 | |
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37 | Using NelderMeadSimplexSolver: |
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38 | Solution: [ 0.99999921 1.00000171] |
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39 | f value: 1.08732211477e-09 |
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40 | Iterations: 70 |
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41 | Function evaluations: 130 |
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42 | Time elapsed: 0.0190329551697 seconds |
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43 | |
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44 | Using PowellDirectionalSolver: |
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45 | Solution: [ 1. 1.] |
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46 | f value: 0.0 |
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47 | Iterations: 28 |
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48 | Function evaluations: 859 |
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49 | Time elapsed: 0.113857030869 seconds |
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50 | """ |
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51 | |
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52 | print "Testing 2-D Rosenbrock:" |
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53 | print "Expected: x=[1., 1.] and f=0" |
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54 | from mystic.models import rosen as costfunc |
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55 | ndim = 2 |
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56 | lb = [-5.]*ndim |
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57 | ub = [5.]*ndim |
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58 | x0 = [2., 3.] |
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59 | maxiter = 10000 |
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60 | |
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61 | # DifferentialEvolutionSolver |
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62 | print "\nUsing DifferentialEvolutionSolver:" |
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63 | npop = 40 |
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64 | from mystic.solvers import DifferentialEvolutionSolver |
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65 | from mystic.termination import ChangeOverGeneration as COG |
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66 | from mystic.strategy import Rand1Bin |
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67 | esow = Monitor() |
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68 | ssow = Monitor() |
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69 | solver = DifferentialEvolutionSolver(ndim, npop) |
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70 | solver.SetInitialPoints(x0) |
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71 | solver.SetStrictRanges(lb, ub) |
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72 | solver.SetEvaluationLimits(generations=maxiter) |
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73 | solver.SetEvaluationMonitor(esow) |
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74 | solver.SetGenerationMonitor(ssow) |
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75 | term = COG(1e-10) |
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76 | time1 = time.time() # Is this an ok way of timing? |
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77 | solver.Solve(costfunc, term, strategy=Rand1Bin) |
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78 | sol = solver.Solution() |
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79 | time_elapsed = time.time() - time1 |
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80 | fx = solver.bestEnergy |
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81 | print "Solution: ", sol |
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82 | print "f value: ", fx |
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83 | print "Iterations: ", solver.generations |
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84 | print "Function evaluations: ", len(esow.x) |
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85 | print "Time elapsed: ", time_elapsed, " seconds" |
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86 | assert almostEqual(fx, 2.29478683682e-13, tol=3e-3) |
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87 | |
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88 | # DifferentialEvolutionSolver2 |
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89 | print "\nUsing DifferentialEvolutionSolver2:" |
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90 | npop = 40 |
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91 | from mystic.solvers import DifferentialEvolutionSolver2 |
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92 | from mystic.termination import ChangeOverGeneration as COG |
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93 | from mystic.strategy import Rand1Bin |
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94 | esow = Monitor() |
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95 | ssow = Monitor() |
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96 | solver = DifferentialEvolutionSolver2(ndim, npop) |
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97 | solver.SetInitialPoints(x0) |
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98 | solver.SetStrictRanges(lb, ub) |
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99 | solver.SetEvaluationLimits(generations=maxiter) |
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100 | solver.SetEvaluationMonitor(esow) |
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101 | solver.SetGenerationMonitor(ssow) |
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102 | term = COG(1e-10) |
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103 | time1 = time.time() # Is this an ok way of timing? |
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104 | solver.Solve(costfunc, term, strategy=Rand1Bin) |
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105 | sol = solver.Solution() |
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106 | time_elapsed = time.time() - time1 |
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107 | fx = solver.bestEnergy |
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108 | print "Solution: ", sol |
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109 | print "f value: ", fx |
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110 | print "Iterations: ", solver.generations |
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111 | print "Function evaluations: ", len(esow.x) |
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112 | print "Time elapsed: ", time_elapsed, " seconds" |
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113 | assert almostEqual(fx, 3.84824937598e-15, tol=3e-3) |
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114 | |
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115 | # NelderMeadSimplexSolver |
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116 | print "\nUsing NelderMeadSimplexSolver:" |
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117 | from mystic.solvers import NelderMeadSimplexSolver |
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118 | from mystic.termination import CandidateRelativeTolerance as CRT |
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119 | esow = Monitor() |
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120 | ssow = Monitor() |
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121 | solver = NelderMeadSimplexSolver(ndim) |
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122 | solver.SetInitialPoints(x0) |
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123 | solver.SetStrictRanges(lb, ub) |
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124 | solver.SetEvaluationLimits(generations=maxiter) |
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125 | solver.SetEvaluationMonitor(esow) |
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126 | solver.SetGenerationMonitor(ssow) |
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127 | term = CRT() |
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128 | time1 = time.time() # Is this an ok way of timing? |
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129 | solver.Solve(costfunc, term) |
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130 | sol = solver.Solution() |
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131 | time_elapsed = time.time() - time1 |
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132 | fx = solver.bestEnergy |
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133 | print "Solution: ", sol |
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134 | print "f value: ", fx |
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135 | print "Iterations: ", solver.generations |
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136 | print "Function evaluations: ", len(esow.x) |
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137 | print "Time elapsed: ", time_elapsed, " seconds" |
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138 | assert almostEqual(fx, 1.08732211477e-09, tol=3e-3) |
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139 | |
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140 | # PowellDirectionalSolver |
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141 | print "\nUsing PowellDirectionalSolver:" |
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142 | from mystic.solvers import PowellDirectionalSolver |
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143 | from mystic.termination import NormalizedChangeOverGeneration as NCOG |
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144 | esow = Monitor() |
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145 | ssow = Monitor() |
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146 | solver = PowellDirectionalSolver(ndim) |
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147 | solver.SetInitialPoints(x0) |
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148 | solver.SetStrictRanges(lb, ub) |
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149 | solver.SetEvaluationLimits(generations=maxiter) |
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150 | solver.SetEvaluationMonitor(esow) |
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151 | solver.SetGenerationMonitor(ssow) |
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152 | term = NCOG(1e-10) |
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153 | time1 = time.time() # Is this an ok way of timing? |
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154 | solver.Solve(costfunc, term) |
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155 | sol = solver.Solution() |
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156 | time_elapsed = time.time() - time1 |
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157 | fx = solver.bestEnergy |
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158 | print "Solution: ", sol |
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159 | print "f value: ", fx |
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160 | print "Iterations: ", solver.generations |
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161 | print "Function evaluations: ", len(esow.x) |
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162 | print "Time elapsed: ", time_elapsed, " seconds" |
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163 | assert almostEqual(fx, 0.0, tol=3e-3) |
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164 | |
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165 | def test_griewangk(): |
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166 | """Test Griewangk's function, which has many local minima. |
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167 | |
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168 | Testing Griewangk: |
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169 | Expected: x=[0.]*10 and f=0 |
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170 | |
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171 | Using DifferentialEvolutionSolver: |
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172 | Solution: [ 8.87516194e-09 7.26058147e-09 1.02076001e-08 1.54219038e-08 |
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173 | -1.54328461e-08 2.34589663e-08 2.02809360e-08 -1.36385836e-08 |
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174 | 1.38670373e-08 1.59668900e-08] |
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175 | f value: 0.0 |
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176 | Iterations: 4120 |
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177 | Function evaluations: 205669 |
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178 | Time elapsed: 34.4936850071 seconds |
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179 | |
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180 | Using DifferentialEvolutionSolver2: |
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181 | Solution: [ -2.02709316e-09 3.22017968e-09 1.55275472e-08 5.26739541e-09 |
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182 | -2.18490470e-08 3.73725584e-09 -1.02315312e-09 1.24680355e-08 |
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183 | -9.47898116e-09 2.22243557e-08] |
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184 | f value: 0.0 |
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185 | Iterations: 4011 |
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186 | Function evaluations: 200215 |
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187 | Time elapsed: 32.8412370682 seconds |
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188 | """ |
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189 | |
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190 | print "Testing Griewangk:" |
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191 | print "Expected: x=[0.]*10 and f=0" |
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192 | from mystic.models import griewangk as costfunc |
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193 | ndim = 10 |
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194 | lb = [-400.]*ndim |
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195 | ub = [400.]*ndim |
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196 | maxiter = 10000 |
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197 | seed = 123 # Re-seed for each solver to have them all start at same x0 |
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198 | |
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199 | # DifferentialEvolutionSolver |
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200 | print "\nUsing DifferentialEvolutionSolver:" |
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201 | npop = 50 |
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202 | random_seed(seed) |
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203 | from mystic.solvers import DifferentialEvolutionSolver |
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204 | from mystic.termination import ChangeOverGeneration as COG |
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205 | from mystic.termination import CandidateRelativeTolerance as CRT |
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206 | from mystic.termination import VTR |
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207 | from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp |
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208 | esow = Monitor() |
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209 | ssow = Monitor() |
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210 | solver = DifferentialEvolutionSolver(ndim, npop) |
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211 | solver.SetRandomInitialPoints(lb, ub) |
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212 | solver.SetStrictRanges(lb, ub) |
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213 | solver.SetEvaluationLimits(generations=maxiter) |
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214 | solver.SetEvaluationMonitor(esow) |
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215 | solver.SetGenerationMonitor(ssow) |
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216 | solver.enable_signal_handler() |
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217 | #term = COG(1e-10) |
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218 | #term = CRT() |
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219 | term = VTR(0.) |
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220 | time1 = time.time() # Is this an ok way of timing? |
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221 | solver.Solve(costfunc, term, strategy=Rand1Exp, \ |
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222 | CrossProbability=0.3, ScalingFactor=1.0) |
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223 | sol = solver.Solution() |
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224 | time_elapsed = time.time() - time1 |
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225 | fx = solver.bestEnergy |
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226 | print "Solution: ", sol |
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227 | print "f value: ", fx |
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228 | print "Iterations: ", solver.generations |
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229 | print "Function evaluations: ", len(esow.x) |
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230 | print "Time elapsed: ", time_elapsed, " seconds" |
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231 | assert almostEqual(fx, 0.0, tol=3e-3) |
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232 | |
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233 | # DifferentialEvolutionSolver2 |
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234 | print "\nUsing DifferentialEvolutionSolver2:" |
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235 | npop = 50 |
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236 | random_seed(seed) |
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237 | from mystic.solvers import DifferentialEvolutionSolver2 |
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238 | from mystic.termination import ChangeOverGeneration as COG |
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239 | from mystic.termination import CandidateRelativeTolerance as CRT |
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240 | from mystic.termination import VTR |
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241 | from mystic.strategy import Rand1Bin, Best1Bin, Rand1Exp |
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242 | esow = Monitor() |
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243 | ssow = Monitor() |
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244 | solver = DifferentialEvolutionSolver2(ndim, npop) |
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245 | solver.SetRandomInitialPoints(lb, ub) |
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246 | solver.SetStrictRanges(lb, ub) |
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247 | solver.SetEvaluationLimits(generations=maxiter) |
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248 | solver.SetEvaluationMonitor(esow) |
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249 | solver.SetGenerationMonitor(ssow) |
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250 | #term = COG(1e-10) |
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251 | #term = CRT() |
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252 | term = VTR(0.) |
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253 | time1 = time.time() # Is this an ok way of timing? |
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254 | solver.Solve(costfunc, term, strategy=Rand1Exp, \ |
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255 | CrossProbability=0.3, ScalingFactor=1.0) |
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256 | sol = solver.Solution() |
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257 | time_elapsed = time.time() - time1 |
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258 | fx = solver.bestEnergy |
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259 | print "Solution: ", sol |
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260 | print "f value: ", fx |
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261 | print "Iterations: ", solver.generations |
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262 | print "Function evaluations: ", len(esow.x) |
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263 | print "Time elapsed: ", time_elapsed, " seconds" |
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264 | assert almostEqual(fx, 0.0, tol=3e-3) |
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265 | |
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266 | if __name__ == '__main__': |
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267 | test_rosenbrock() |
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268 | #test_griewangk() |
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