57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
import numpy as np
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import numpy.testing as npt
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from numpy.testing import assert_raises
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from statsmodels.distributions import StepFunction, monotone_fn_inverter
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from statsmodels.distributions import ECDFDiscrete
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class TestDistributions:
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def test_StepFunction(self):
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x = np.arange(20)
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y = np.arange(20)
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f = StepFunction(x, y)
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vals = f(np.array([[3.2, 4.5], [24, -3.1], [3.0, 4.0]]))
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npt.assert_almost_equal(vals, [[3, 4], [19, 0], [2, 3]])
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def test_StepFunctionBadShape(self):
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x = np.arange(20)
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y = np.arange(21)
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assert_raises(ValueError, StepFunction, x, y)
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x = np.zeros((2, 2))
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y = np.zeros((2, 2))
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assert_raises(ValueError, StepFunction, x, y)
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def test_StepFunctionValueSideRight(self):
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x = np.arange(20)
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y = np.arange(20)
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f = StepFunction(x, y, side='right')
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vals = f(np.array([[3.2, 4.5], [24, -3.1], [3.0, 4.0]]))
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npt.assert_almost_equal(vals, [[3, 4], [19, 0], [3, 4]])
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def test_StepFunctionRepeatedValues(self):
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x = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5]
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y = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
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f = StepFunction(x, y)
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npt.assert_almost_equal(f([1, 2, 3, 4, 5]), [0, 7, 10, 13, 14])
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f2 = StepFunction(x, y, side='right')
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npt.assert_almost_equal(f2([1, 2, 3, 4, 5]), [7, 10, 13, 14, 15])
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def test_monotone_fn_inverter(self):
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x = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
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fn = lambda x : 1./x # noqa
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y = fn(np.array(x))
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f = monotone_fn_inverter(fn, x)
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npt.assert_array_equal(f.y, x[::-1])
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npt.assert_array_equal(f.x, y[::-1])
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def test_ecdf_discrete(self):
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x = [3, 3, 1, 4]
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e = ECDFDiscrete(x)
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npt.assert_array_equal(e.x, [-np.inf, 1, 3, 4])
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npt.assert_array_equal(e.y, [0, 0.25, 0.75, 1])
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e1 = ECDFDiscrete([3.5, 3.5, 1.5, 1, 4])
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e2 = ECDFDiscrete([3.5, 1.5, 1, 4], freq_weights=[2, 1, 1, 1])
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npt.assert_array_equal(e1.x, e2.x)
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npt.assert_array_equal(e1.y, e2.y)
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