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1 """This module contains code from
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2 Think Python by Allen B. Downey
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3 http://thinkpython.com
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4
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5 Copyright 2012 Allen B. Downey
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6 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
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7
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8 This is 3.6 exercise 6:
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9
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10 "Test the performance of LinearMap, BetterMap and HashMap; see if you can
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11 characterize their order of growth. You can download my map implementations
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12 from thinkcomplex.com/Map.py, and the code I used in this section from
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13 thinkcomplex.com/listsum.py.
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14
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15 You will have to find a range of n that is big enough to show asymptotic
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16 behavior, but small enough to run quickly."
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17
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18 """
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19 import random
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20 import timeit
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21
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22 import matplotlib.pyplot as pyplot
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23
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24 from ch3ex4 import LinearMap, BetterMap, HashMap
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25
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26
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27 MAP = None
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28 VALUES = None
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29
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30 def test_map():
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31
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32 MAP.get(random.choice(VALUES))
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33
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34
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35 def test_func():
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36
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37 elapsed = timeit.timeit('test_map()',
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38 setup='from __main__ import test_map', number=100)
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39 return elapsed
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40
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41
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42 def test(map_class):
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43 """Tests the given function with a range of values for n.
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44
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45 Returns a list of ns and a list of run times.
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46 """
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47 global MAP, VALUES
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48
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49 klass = eval(map_class)
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50
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51 d = {'LinearMap': 100000, 'BetterMap': 100000, 'HashMap': 100000}
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52 factor = d[map_class]
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53
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54 # test (f) over a range of values for (n)
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55 ns = []
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56 ts = []
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57 for i in range(2, 25):
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58 n = factor * i
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59
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60 MAP = klass()
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61 VALUES = range(n)
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62 random.shuffle(VALUES)
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63 for v in VALUES:
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64 MAP.add(v, v)
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65
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66 t = test_func()
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67
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68 print n, t
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69 ns.append(n)
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70 ts.append(t)
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71
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72 return ns, ts
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73
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74
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75 def plot(ns, ts, label, color='blue', exp=1.0):
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76 """Plots data and a fitted curve.
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77
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78 ns: sequence of n (problem size)
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79 ts: sequence of t (run time)
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80 label: string label for the data curve
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81 color: string color for the data curve
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82 exp: exponent (slope) for the fitted curve
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83 """
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84 tfit = fit(ns, ts, exp)
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85 pyplot.plot(ns, tfit, color='0.7', linewidth=2, linestyle='dashed')
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86 pyplot.plot(ns, ts, label=label, color=color, linewidth=3)
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87
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88
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89 def fit(ns, ts, exp=1.0, index=-1):
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90 """Fits a curve with the given exponent.
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91
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92 Use the given index as a reference point, and scale all other
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93 points accordingly.
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94 """
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95 nref = ns[index]
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96 tref = ts[index]
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97
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98 tfit = []
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99 for n in ns:
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100 ratio = float(n) / nref
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101 t = ratio**exp * tref
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102 tfit.append(t)
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103
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104 return tfit
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105
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106
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107 def save(root, exts=['eps', 'pdf']):
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108 """Saves the current figure in the given formats.
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109
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110 root: string filename root
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111 exts: list of string extensions (specifying output formats).
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112 """
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113 for ext in exts:
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114 filename = '%s.%s' % (root, ext)
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115 print 'Writing', filename
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116 pyplot.savefig(filename)
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117
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118
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119 def make_fig(funcs, scale='log', exp=1.0, filename=None):
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120 pyplot.clf()
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121 pyplot.xscale(scale)
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122 pyplot.yscale(scale)
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123 pyplot.title('')
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124 pyplot.xlabel('n')
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125 pyplot.ylabel('run time (s)')
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126
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127 colors = ['blue', 'orange', 'green']
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128 for func, color in zip(funcs, colors):
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129 data = test(func)
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130 plot(*data, label=func, color=color, exp=exp)
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131
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132 pyplot.legend(loc=4)
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133
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134 if filename:
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135 save(filename)
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136 else:
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137 pyplot.show()
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138
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139
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140 def main(script):
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141 make_fig(['LinearMap', 'BetterMap', 'HashMap'], scale='log', exp=1.0)
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142
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143
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144 if __name__ == '__main__':
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145 import sys
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146 main(*sys.argv)
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147
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