2014-11-09 19:55:24 +00:00
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import fit
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import spread
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tests = [
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[fit.simple_estimator, [1], [1]],
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[fit.simple_estimator, [1], [1.2]],
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[fit.minimax_estimator, [1], [1.2]],
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[fit.diff_estimator, [1, 0, 0.001], [1.2, 10, 1]],
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[fit.diff_estimator, [1, 0, 0.001, 0], [1.2, 10, 1, 5]],
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[fit.ndiff_estimator, [1, 0, 0, 0.001], [1.2, 10, 1, 1]],
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2014-12-04 15:32:27 +00:00
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[fit.tx_diff_estimator, [1, 0, 0.001], [1.2, 10, 1]],
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[fit.tx_diff_estimator, [1, 0, 0.001, 0, 0], [1.2, 10, 1, 6, 2]],
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[fit.minimax_fee_estimator, [1, 3], [1.2, 60]],
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2014-11-09 19:55:24 +00:00
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]
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vals = [fit.optimize(t, mi, ma, rate=0.4, rounds=12000, tries=10)
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for t, mi, ma in tests]
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estimators = [t[0](*v) for t, v in zip(tests, vals)]
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scores = [fit.evaluate_estimates(e) for e in estimators]
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for t, v, e, s in zip(tests, vals, estimators, scores):
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print v, s
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adjestimators = [[e/est[0] for e in est] for est in estimators]
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adjprices = [[p/e*est[0] for e, p in
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zip(est, fit.prices)] for est in estimators]
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output = [['Price'] + fit.prices]
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for i in range(len(tests)):
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output.append(['Estimator %d' % (i+1)] + adjestimators[i])
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output.append(['Adjusted price %d' % (i+1)] + adjprices[i])
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spread.save('o.csv', zip(*output))
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