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Statistics inv_cdf sync with corresponding random module normal distributions (#95265)
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@ -1221,8 +1221,6 @@ def inv_cdf(self, p):
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"""
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if p <= 0.0 or p >= 1.0:
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raise StatisticsError('p must be in the range 0.0 < p < 1.0')
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if self._sigma <= 0.0:
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raise StatisticsError('cdf() not defined when sigma at or below zero')
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return _normal_dist_inv_cdf(p, self._mu, self._sigma)
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def quantiles(self, n=4):
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@ -2801,9 +2801,10 @@ def test_inv_cdf(self):
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iq.inv_cdf(1.0) # p is one
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with self.assertRaises(self.module.StatisticsError):
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iq.inv_cdf(1.1) # p over one
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with self.assertRaises(self.module.StatisticsError):
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iq = NormalDist(100, 0) # sigma is zero
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iq.inv_cdf(0.5)
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# Supported case:
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iq = NormalDist(100, 0) # sigma is zero
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self.assertEqual(iq.inv_cdf(0.5), 100)
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# Special values
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self.assertTrue(math.isnan(Z.inv_cdf(float('NaN'))))
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@ -31,7 +31,7 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
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/*[clinic end generated code: output=02fd19ddaab36602 input=24715a74be15296a]*/
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{
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double q, num, den, r, x;
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if (p <= 0.0 || p >= 1.0 || sigma <= 0.0) {
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if (p <= 0.0 || p >= 1.0) {
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goto error;
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}
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