try:
bids = np.array(ast.literal_eval(data_in['bids']))
costs = np.array([ast.literal_eval(data_in['costs_{}'.format(i)]) for i in range(n)])
except KeyError:
bs = [ast.literal_eval(data_in['b_lower']), ast.literal_eval(data_in['b_upper'])]
css = [ast.literal_eval(data_in['cs_{}'.format(i)]) for i in range(n)]
# Verify sufficiency
cdfs = []
for bounds in zip(lower_extremities, upper_extremities):
cdfs.append(ss.uniform(loc=bounds[0], scale=bounds[1]-bounds[0]))
try:
step = len(bids) // 35
s_costs, s_bids = verify_sufficiency(costs, bids, b_upper, cdfs, step=step)
except NameError:
# Define inverse bid function
def cost_func(l, cs, x):
return l + sum([c*(x-bs[0])**i for c,i in zip(cs, range(1,len(cs)+1))])
# Compute bids and costs
bids = np.linspace(bs[0], bs[1], 10000)
cost_funcs = [partial(cost_func, l, cs) for l,cs in zip(lower_extremities, css)]
costs = np.array([[f(b) for b in bids] for f in cost_funcs])
step = len(bids) // 50
s_costs, s_bids = verify_sufficiency(costs, bids, b_upper, cdfs, step=step)
# Plot
except KeyError:
bs = [ast.literal_eval(data_in['b_lower']), support[1]]
css = [ast.literal_eval(data_in['cs_{}'.format(i)]) for i in range(n)]
# Verify sufficiency
cdfs = []
for p in params:
loc = p['location']
scale = p['scale']
a = (support[0] - loc) / scale
b = (support[1] - loc) / scale
cdfs.append(ss.truncnorm(a, b, loc=loc, scale=scale))
try:
step = len(bids) // 35
s_costs, s_bids = verify_sufficiency(costs, bids, support[1], cdfs, step=step)
except NameError:
# Define inverse bid function
def cost_func(cs, x):
return bs[0] + sum([c*(x-bs[0])**i for c,i in zip(cs, range(len(cs)))])
# Compute bids and costs
bids = np.linspace(bs[0], bs[1], 10000)
cost_funcs = [partial(cost_func, cs) for cs in css]
costs = np.array([[f(b) for b in bids] for f in cost_funcs])
step = len(bids) // 50
s_costs, s_bids = verify_sufficiency(costs, bids, support[1], cdfs, step=step)
# Plot
