def setup(self): self.mn = probscale._minimal_norm() self.known__A = 0.1400122886866665 self.input = np.array([ 0.331, 0.742, 0.067, 0.826, 0.357, 0.089, 0.754, 0.342, 0.762, 0.658, 0.239, 0.910, ]) self.known_erf = np.array([ 0.36029027, 0.70598131, 0.07548843, 0.75724986, 0.38635283, 0.10016122, 0.71371964, 0.37137355, 0.71880142, 0.64791492, 0.26463458, 0.80188283, ]) self.known_ppf = np.array([ -0.43715354, 0.6495236 , -1.49851307, 0.93847570, -0.36648929, -1.34693863, 0.68713129, -0.40701088, 0.71275076, 0.40701088, -0.70952297, 1.34075503, ]) self.known_cdf = np.array([ 0.62967776, 0.77095633, 0.52670915, 0.79559795, 0.63945410, 0.53545904, 0.77457539, 0.63382455, 0.77697000, 0.74473093, 0.59444721, 0.81858875 ])
def setup(self): self.mn = probscale._minimal_norm() self.known__A = 0.1400122886866665 self.input = np.array([ 0.331, 0.742, 0.067, 0.826, 0.357, 0.089, 0.754, 0.342, 0.762, 0.658, 0.239, 0.910, ]) self.known_erf = np.array([ 0.36029027, 0.70598131, 0.07548843, 0.75724986, 0.38635283, 0.10016122, 0.71371964, 0.37137355, 0.71880142, 0.64791492, 0.26463458, 0.80188283, ]) self.known_ppf = np.array([ -0.43715354, 0.6495236, -1.49851307, 0.93847570, -0.36648929, -1.34693863, 0.68713129, -0.40701088, 0.71275076, 0.40701088, -0.70952297, 1.34075503, ]) self.known_cdf = np.array([ 0.62967776, 0.77095633, 0.52670915, 0.79559795, 0.63945410, 0.53545904, 0.77457539, 0.63382455, 0.77697000, 0.74473093, 0.59444721, 0.81858875 ])
def mn(): return _minimal_norm()