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()
def mn():
return _minimal_norm()
