def test_should_have_nan_entries_if_specified(self):
		# for one column
		columns_list = [0]
		optargs = [dict(missing_data=1.0)] # every entry will be missing NaN
		X = sdg.predictive_columns(self.M_c, self.X_L, self.X_D, columns_list,
			optional_settings=optargs)

		assert numpy.all(numpy.isnan(X))
		
		# for two columns
		columns_list = [0,1]
		optargs = [dict(missing_data=1.0)]%2 
		X = sdg.predictive_columns(self.M_c, self.X_L, self.X_D, columns_list,
			optional_settings=optargs)

		assert numpy.all(numpy.isnan(X))

		# for one of two columns (no dict means no missing data)
		columns_list = [0,1]
		optargs = [dict(missing_data=1.0), None]
		X = sdg.predictive_columns(self.M_c, self.X_L, self.X_D, columns_list,
			optional_settings=optargs)

		assert numpy.all(numpy.isnan(X[:,0]))
		assert not numpy.any(numpy.isnan(X[:,1]))

		# for one of two columns. Missing data specified 0 for second column
		columns_list = [0,1]
		optargs = [dict(missing_data=1.0), dict(missing_data=0.0)]
		X = sdg.predictive_columns(self.M_c, self.X_L, self.X_D, columns_list,
			optional_settings=optargs)

		assert numpy.all(numpy.isnan(X[:,0]))
		assert not numpy.any(numpy.isnan(X[:,1]))
dataset/ETHPy150Open probcomp/crosscat/src/tests/unit_tests/test_synthetic_data_generator.py/TestPredictiveColumns.test_should_have_nan_entries_if_specified/operator `*` -> `%`