Пример #1
0
 def __init__(self, *args, **kwargs):
     NumericTestCase.__init__(self, *args, **kwargs)
     # Read data from external test data file.
     # (In this case, produced by numpy and Python 2.5.)
     location = self.get_data_location("support/test_data.zip")
     # Now read the data from that file.
     zf = zipfile.ZipFile(location, "r")
     self.data = pickle.loads(zf.read("data.pkl"))
     self.expected = pickle.loads(zf.read("results.pkl"))
     zf.close()
Пример #2
0
 def __init__(self, *args, **kwargs):
     NumericTestCase.__init__(self, *args, **kwargs)
     self.func = stats.pvariance
     # Standard test data.
     self.data = [4.0, 7.0, 13.0, 16.0]
     self.expected = 22.5  # Exact population variance of self.data.
     # Test data for exact (uniform distribution) test:
     self.uniform_data = range(10000)
     self.uniform_expected = (10000**2 - 1)/12
     # Expected result calculated by HP-48GX:
     self.hp_expected = 88349.2408884
     # Scaling factor when you duplicate each data point:
     self.scale = 1.0
Пример #3
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 def __init__(self, *args, **kwargs):
     NumericTestCase.__init__(self, *args, **kwargs)
     self.xdata = [
         1 / 64,
         1 / 32,
         1 / 16,
         1 / 8,
         1 / 4,
         1 / 2,
         1,
         3 / 2,
         5 / 2,
         7 / 2,
         9 / 2,
         11 / 2,
         13 / 2,
         15 / 2,
         17 / 2,
         19 / 2,
     ]
     self.ydata = [
         1 / 4,
         1 / 2,
         3 / 2,
         1,
         1 / 2,
         3 / 2,
         1,
         5 / 4,
         5 / 2,
         7 / 4,
         9 / 4,
         11 / 4,
         11 / 4,
         7 / 4,
         13 / 4,
         17 / 4,
     ]
     assert len(self.xdata) == len(self.ydata) == 16
Пример #4
0
 def __init__(self, *args, **kwargs):
     NumericTestCase.__init__(self, *args, **kwargs)
     self.func = stats.multivar.corr