def smooth_likelihood(obj,x, y): n = obj.options.get("n_kde", 100) factor = obj.options.get("factor_kde", 2.0) kde = KDE([x,y], factor=factor) x_range = (x.min(), x.max()) y_range = (y.min(), y.max()) (x_axis, y_axis), like = kde.grid_evaluate(n, [x_range, y_range]) return n, x_axis, y_axis, like
def smooth_likelihood(self, x, y): n = self.options.get("n_kde", 100) factor = self.options.get("factor_kde", 2.0) kde = KDE([x,y], factor=factor) x_range = (x.min(), x.max()) y_range = (y.min(), y.max()) (x_axis, y_axis), like = kde.grid_evaluate(n, [x_range, y_range]) return n, x_axis, y_axis, like
def smooth_likelihood(obj,x, y): n = 100 factor = 2.0 kde = KDE([x,y], factor=factor) x_range = (x.min(), x.max()) y_range = (y.min(), y.max()) (x_axis, y_axis), like = kde.grid_evaluate(n, [x_range, y_range]) return n, x_axis, y_axis, like