def calc_mode_manual(self, loc=0.0, scale=1.0): shape = np.array(self.df).shape dfmax = np.array(self.df * np.ones((1, )), dtype='i') dfmax = (dfmax.flatten() - 2).tolist() for id in range(len(dfmax)): dfmax[id] = np.max([dfmax[id], 0]) return np.reshape(dfmax, shape) + make_int(np.around(loc))
def __init__(self, **params): self.type = ProbabilityDistributionTypes.CHISQUARE self.scipy_name = "chi" self.numpy_name = ProbabilityDistributionTypes.CHISQUARE self.constraint_string = "int(df) > 0" self.df = make_int(params.get("df", 1)) self.__update_params__(df=self.df)
def __init__(self, **params): self.type = ProbabilityDistributionTypes.BINOMIAL self.scipy_name = "binom" self.numpy_name = ProbabilityDistributionTypes.BINOMIAL self.constraint_string = "n > 0 and 0 < p < 1" self.n = make_int(params.get("n", 1)) self.p = make_float(params.get("p", 0.5)) self.__update_params__(n=self.n, p=self.p)
def calc_mode_manual(self, loc=0.0, scale=1.0): loc = make_int(np.round(loc)) p = np.array(self.p) shape = p.shape mode = np.ones(np.array(p * np.ones((1,))).shape, dtype="i") + loc mode[np.where(p < 0.5)[0]] = 1 + loc p05 = p == 0.5 if np.any(p05): self.logger.warning("The mode of bernoulli distribution for p=0.5 consists of two values (0.0 + loc and 1.0 + loc)!") mode = mode.astype('O') mode[np.where(p05)[0]] = [[0.0 + loc, 1.0 + loc]] return mode
def calc_mean_manual(self, loc=0.0, scale=1.0): return self.df + make_int(np.around(loc))
def update_params(self, loc=0.0, scale=1.0, use="scipy", **params): self.__update_params__(loc, scale, use, df=make_int(params.get("df", self.df)))
def calc_mode_manual(self, loc=0.0, scale=1.0): return make_int(np.round((self.n + 1) * self.p + loc) - 1)
def calc_median_manual(self, loc=0.0, scale=1.0): return make_int(np.round(self.calc_mean_manual(loc=loc)))
def update_params(self, loc=0.0, scale=1.0, use="scipy", **params): self.__update_params__(loc, scale, use, n=make_int(params.get("n", self.n)), p=make_float(params.get("p", self.p)))
def calc_mode_manual(self, loc=0.0, scale=1.0): return [ make_int(np.round(self.lamda + loc)) - 1, make_int(np.round(self.lamda + loc)) ]