def run(n, N, pmin, pmax):
'''This function executes n iterations of the digital marketplace
auctioning game.
Keyword args:
n -- number of iterations
N -- number of bidders
pmin -- minimum price/cost of the service
pmax -- maximum price/cost of the service
'''
# Run the simulation for n times
#intersections = [simulate(N, pmin, pmax) for i in range(n)]
intersections = []
no_intersection = 0
for i in range(n):
sim = simulate(N, pmin, pmax)
while(sim == 0.0):
no_intersection += 1
sim = simulate(N, pmin, pmax)
intersections.append(sim)
# Compute the mean and std of the distribution
avg_intersect = np.mean(intersections)
std_intersect = np.std(intersections)
med_intersect = np.median(intersections)
# Compute the probability of an intersection occurring
prob_intersect = 1 - no_intersection/(len(intersections)+no_intersection)
print("Probability of an intersection occurring: {0}".format(prob_intersect))
# Plot the time series
plt.figure()
plt.plot(np.linspace(1, n, n), intersections, 'b*')
plt.xlabel(r"Iterations, $n$")
plt.ylabel(r"Intersections, $w$")
plt.grid()
plt.savefig("time_series_N_{0}.pdf".format(N))
# Plot the histogram
fig = plt.figure()
ax = fig.add_subplot(111)
h = 2*(mstats.idealfourths(intersections)[1]-mstats.idealfourths(intersections)[0])*n**(-1/3)
ax.hist(intersections, (max(intersections)-min(intersections))/h, normed=1, facecolor='green')
plt.xlabel(r"Intersections, $w$")
plt.ylabel(r"Empirical PDF")
plt.text(0.65, 1.12, \
"Mean: {0}\nMedian: {1}\nStd: {2}".format(avg_intersect, med_intersect, std_intersect), \
fontsize=13, \
horizontalalignment='left', \
verticalalignment='top', \
transform=ax.transAxes)
plt.grid()
plt.savefig("hist_N_{0}.pdf".format(N))
# Plot the empirical CDF
plt.figure()
ecdf = sm.tools.ECDF(intersections)
x_axis = np.linspace(min(intersections), max(intersections))
plt.step(x_axis, ecdf(x_axis))
plt.xlabel(r"Intersections, $w$")
plt.ylabel(r"Empirical CDF")
plt.grid()
plt.savefig("ecdf_N_{0}.pdf".format(N))
def test_idealfourths():
# Tests ideal-fourths
test = np.arange(100)
assert_almost_equal(np.asarray(ms.idealfourths(test)),
[24.416667,74.583333],6)
test_2D = test.repeat(3).reshape(-1,3)
assert_almost_equal(ms.idealfourths(test_2D, axis=0),
[[24.416667,24.416667,24.416667],
[74.583333,74.583333,74.583333]],6)
assert_almost_equal(ms.idealfourths(test_2D, axis=1),
test.repeat(2).reshape(-1,2))
test = [0, 0]
_result = ms.idealfourths(test)
assert_(np.isnan(_result).all())
def test_idealfourths(self):
"Tests ideal-fourths"
test = np.arange(100)
assert_almost_equal(np.asarray(ms.idealfourths(test)),
[24.416667,74.583333],6)
test_2D = test.repeat(3).reshape(-1,3)
assert_almost_equal(ms.idealfourths(test_2D, axis=0),
[[24.416667,24.416667,24.416667],
[74.583333,74.583333,74.583333]],6)
assert_almost_equal(ms.idealfourths(test_2D, axis=1),
test.repeat(2).reshape(-1,2))
test = [0,0]
_result = ms.idealfourths(test)
assert_(np.isnan(_result).all())
def IQR(x):
import scipy.stats.mstats as st
quarts = st.idealfourths(x)
val = quarts[1] - quarts[0]
return val
def IQR(x):
import scipy.stats.mstats as st
quarts = st.idealfourths(x)
val = quarts[1]-quarts[0]
return val
