def inverse_transform_sampling_1d(var, ab, ii, u_samples):
name = var.dist.name
if is_bounded_discrete_variable(var):
xk, pk = get_probability_masses(var)
if type(var.dist) == float_rv_discrete and name != 'discrete_chebyshev':
lb, ub = xk.min(), xk.max()
xk = (xk-lb)/(ub-lb)*2-1
return float_rv_discrete_inverse_transform_sampling_1d(
xk, pk, ab, ii, u_samples)
elif name in _continuous_distns._distn_names:
return continuous_induced_measure_ppf(var, ab, ii, u_samples)
else:
msg = 'induced sampling not yet implemented for var type %s' % name
raise Exception(msg)
return samples
def get_recursion_coefficients_from_variable(var, num_coefs, opts):
"""
Generate polynomial recursion coefficients by inspecting a random variable.
"""
var_name, _, shapes = get_distribution_info(var)
if var_name == "continuous_monomial":
return None
loc, scale = transform_scale_parameters(var)
if var_name == "rv_function_indpndt_vars":
shapes["loc"] = loc
shapes["scale"] = scale
return get_function_independent_vars_recursion_coefficients(
shapes, num_coefs)
if var_name == "rv_product_indpndt_vars":
shapes["loc"] = loc
shapes["scale"] = scale
return get_product_independent_vars_recursion_coefficients(
shapes, num_coefs)
if (var_name in askey_variable_names
and opts.get("numeric", False) is False):
return get_askey_recursion_coefficients_from_variable(var, num_coefs)
orthonormality_tol = opts.get("orthonormality_tol", 1e-8)
truncated_probability_tol = opts.get("truncated_probability_tol", 0)
if (not is_continuous_variable(var)):
if hasattr(shapes, "xk"):
xk, pk = shapes["xk"], shapes["pk"]
else:
xk, pk = get_probability_masses(var, truncated_probability_tol)
xk = (xk - loc) / scale
return get_numerically_generated_recursion_coefficients_from_samples(
xk, pk, num_coefs, orthonormality_tol, truncated_probability_tol)
# integration performed in canonical domain so need to map back to
# domain of pdf
lb, ub = var.interval(1)
# Get version var.pdf without error checking which runs much faster
pdf = get_pdf(var)
def canonical_pdf(x):
# print(x, lb, ub, x*scale+loc)
# print(var.pdf(x*scale+loc)*scale)
# assert np.all(x*scale+loc >= lb) and np.all(x*scale+loc <= ub)
return pdf(x * scale + loc) * scale
# return var.pdf(x*scale+loc)*scale
if (is_bounded_continuous_variable(var)
or is_bounded_discrete_variable(var)):
can_lb, can_ub = -1, 1
elif is_continuous_variable(var):
can_lb = (lb - loc) / scale
can_ub = (ub - loc) / scale
return predictor_corrector_known_pdf(num_coefs, can_lb, can_ub,
canonical_pdf, opts)